Search Results

excerpt from computer science technology book Connections. Connections AMAZON Chapter 1 Connecting Information “The ultimate search engine would understand exactly what you mean and give back exactly what you want.” said Larry Page[1]. We live in the information age. As society has progressed into the post-indu strial era, access to knowledge and information has become the cornerstone of modern living. With the advent of the World Wide Web, vast amounts of information have suddenly become available to people throughout the world. And searching the Web has become an essential capability whether you are sitting at your desktop PC or wandering the corporate halls with your wireless PDA. As a result, there is no better place to start our discussion of connecting information than with the world’s greatest search engine ─ Google. Google has become a global household name ─ millions use it daily in a hundred languages to conduct over half of all online searches. As a result, Google connects people to relevant information. By providing free access to information, Google offers a seductive gratification to whoever seeks it. To power its searches Google, uses patented, custom-designed programs and hundreds of thousands of computers to provide the greatest computing power of any enterprise. Searching for information is now called ‘googling’ which men, women, and children can perform over computers and cell phones. And thanks to small targeted advertisements that searchers can click for information, Google has become a financial success. In this chapter, we follow the hero’s journey of Google founders Larry Page and Sergey Brin as they invent their Googleware technology for efficient connection to information, then go on to become masters in pursuit of their holy grail ─ ‘perfect search.’ The Google Story Google was founded by two Ph.D. computer science students at Stanford University in California ─ Larry Page and Sergey Brin. When Page and Brin began their hero’s journey, they didn’t know exactly where they were headed. It is widely known that, at first, Page and Brin didn’t hit it off. When they met in 1995, 24 year-old Page was a new graduate of the University of Michigan visiting Stanford University to consider entering graduate school; Brin, at age 23, was a Stanford graduate student who was assigned to host Page’s visit. At first, the two seemed to differ on just about every subject they discussed. They each had strong opinions and divergent viewpoints, and their relationship seemed destined to be contentious. Larry Page was born in 1973 in Lansing, Michigan. Both of his parents were computer scientists. His father was a university professor and a leader in the field of artificial intelligence, while his mother was a teacher of computer programming. As a result of his upbringing in this talented and technology-oriented family, Page seemed destined for success in the computer industry in one way or another. After graduating from high school, Page studied computer engineering at the University of Michigan where he earned his Bachelor of Science degree. Following his undergraduate studies, he decided to pursue graduate work in computer engineering at Stanford University. He intended to build a career in academia or the computer science profession, building on a Ph.D. degree. Meanwhile, Sergey Brin was also born in 1973, in Moscow, Russia, the son of a Russian mathematician and economist. His entire family fled the Soviet Union in 1979 under the threat of growing anti-Semitism, and began their new life as immigrants in the United States. Brin displayed a great interest in computers from an early age. As a youth, he was influenced by the rapid popularization of personal computers, and was very much a child of the microprocessor age. He too was brought up to be familiar with mathematics and computer technology, and as a young child, in the first grade he turned in a computer printout for a school project. Later, at the age of nine, he was given a Commodore 64 computer as a birthday gift from his father. Brin entered the University of Maryland at College Park where he studied mathematics and computer science. He completed his studies at the University of Maryland in 1993 having completed his Bachelor of Science degree. Following his undergraduate studies, he was given a National Science Foundation fellowship to pursue graduate studies in computer science at Stanford University. Not only did he exhibit early talent and interest in mathematics and computer science, he also became acutely interested in data management and networking as the Internet was becoming an increasing force in American society. While at Stanford, he pursued research and prepared publications in the areas of data-mining and pattern extraction. He also wrote software to convert scientific papers written in TeX, a cross-platform text processing language, into HyperText Markup Language (HTML), the multimedia language of the World Wide Web. Brin successfully completed his Masters degree at Stanford. Like Page, Brin’s intent was to continue in his graduate studies to earn a Ph.D. which he also viewed as a great opportunity to establish an outstanding academic or professional career in computer science. The hero’s journey for Page and Brin began as they heard the call ─ to develop a unique approach for retrieving relevant information from the voluminous data on the World Wide Web. Page remembered, “When we first met each other, we each thought the other was obnoxious. Then we hit it off and became really good friends.... I got this crazy idea that I was going to download the entire Web onto my computer. I told my advisor it would only take a week... So I started to download the Web, and Sergey started helping me because he was interested in data mining and making sense of the information.”[2] Although Page initially thought the downloading of the Web would be a short term project, taking a week or so to accomplish, he quickly found that the scope of what he wanted to do was much greater than his original estimate. Once he started his downloading project, he enlisted Brin to join the effort. While working together the two became inspired and wrote the seminal paper entitled The Anatomy of a Large-Scale Hypertextual Web Search Engine[3]. It explained their efficient ranking algorithm, ‘PageRank.’ Brin said about the experience, “The research behind Google began in 1995. The first prototype was actually called BackRub. A couple of years later, we had a search engine that worked considerably better than the others available did at the time.”[4] This prototype listed the results of a Web search according to a quantitative measure of the popularity of the pages. By January 1996, the system was able to analyze the ‘back links’ pointing to a given website and from this quantify the popularity of the site. Within the next few years, the prototype system had been converted into progressively improved versions, and these were substantially more effective than any other search engine then available. As the buzz about their project spread, more and more people began to use it. Soon they were reporting that there were 10,000 searches per day at Stanford using their system. With this growing use and popularity of their search system, they began to realize that they were maxing out their search ability due to the limited number of computers they had at their disposal. They would need more hardware to continue their remarkable expansion and enable more search activity. As Page said, “This is about how many searches we can do, and we need more computers. Our whole history has been like that. We always need more computers.”[5] In many ways, the research project at Stanford was a low budget operation. Because of a chronic shortage of cash, the pair are said to have monitored the Stanford computer science department’s loading docks for newly arrived computers to ‘borrow.’ In spite of this, within a short span of time, the reputation of the BackRub system had grown dramatically and their new search technology began to be broadly noticed. They named their successor search engine ‘Google,’ in a whimsical analogy to the mathematical term ‘Googol,’ which is the immensely large number 1 followed by 100 zeros. The transition from the earlier Backrub technology to the much more sophisticated Google was slow. But the Google system began with an index of 25 million pages and the capability to handle 10,000 search queries every day, even when it was in its initial stage of introduction. The Google search engine grew quickly as it was continuously improved. The effectiveness and relevance of the Google searches, its scope of coverage, speed and reliability, and its clean user interface all contributed to a rapid increase in the popularity of the search engine. At this time, Google was still a student research project, and both Page and Brin were still intent on completing their respective doctoral programs at Stanford. As a result, they initially refused to ‘answer the call’ and continued to devote themselves their academic pursuit of the technology of search. Through all this, Brin maintained an eclectic collection of interests and activities. He continued with his graduate research interests at Stanford and he collaborated with his fellow Ph.D. students and professors on other projects such as automatic detection. At the same time, he also pursued a variety of outside interests, including sailing and trapeze. Brin’s father had stressed the importance for him to complete his Ph.D. He said, “I expected him to get his Ph.D. and to become somebody, maybe a professor.” In response to his father’s question as to whether he was taking any advanced courses one semester, Brin replied, “Yes, advanced swimming.”[6] While Brin and Page continued on as graduate students, they began to realize the importance of what they had succeeded in developing. The two aspiring entrepreneurs decided to try and license the Google technology to existing Internet companies. But they found themselves unsuccessful in stimulating the interest of the major enterprises. They were forced to face the crucial decision of continuing on at Stanford or striking out on their own. With their realization that they were onto something that was important and perhaps even groundbreaking, they decided to make the move. Thus our two heroes had reached their point of departure and they crossed over from the academic into the business world. As they committed to this new direction, they realized they would need to postpone their educational aspirations, prepare plans for their business concept, develop a working demo of their commercial search product, and seek funding sponsorship from outside investors. Having made this decision, they managed to interest Sun Microsystems founder Andy Bechtolsheim in their idea. As Brin recalls, "We met him very early one morning on the porch of a Stanford faculty member's home in Palo Alto. We gave him a quick demo. He had to run off somewhere, so he said, 'Instead of us discussing all the details, why don't I just write you a check?' It was made out to Google Inc. and was for $100,000."[7] The check remained in Page's desk un-cashed for several weeks while he and Brin set up a corporation and sought additional money from family and friends ─ almost $1 million in total. Having started the new company, lined up investor funding, and possessing a superb product, they realized ultimate success would require a good balance of perspiration as well as inspiration. Nevertheless, at this point Google appeared to be well on the road to success. Page and Brin have been on a roll every since, armed with the great confidence that they had both a superior product and an excellent vision for global information collection, storage, and retrieval. In addition, they believed that coordination and optimization of the entire hardware/software system was important, and so they developed their own Googleware technology by combining their custom software with appropriately integrated custom hardware, thereby fully leveraging their ingenious concept. Google Inc. opened its doors as a business entity in September 1998, operating out of modest facilities in a Menlo Park, California garage. As Page and Brin initiated their journey, they faced many challenges and along the way. They matured in their understanding with the help of mentors they encountered such as Yahoo!’s Dave Filo. Filo not only encouraged the two in the development of their search technology, but also made business suggestions for their project. Following the company startup, interest in Google grew rapidly. Red Hat, a Linux company, signed on as their first commercial customer. They were particularly interested in Google because they realized the importance of search technology and its ability to run on open source systems such as Linux. In addition, the press began to take notice of this new commercial venture and articles began to appear in the media highlighting the Google product that offered relevant search results. The late 1990s saw a spectacular growth in development of the technology industry, and Silicon Valley was awash with investor funding. The timing was right for Google, and in 1999, they sought and received a second round of funding, obtaining $25 million from Silicon Valley venture capital firms. The additional funding enabled them to expand their operations and move into new facilities they called the ‘Googleplex,’ Google's current headquarters in Mountain View, California. Although at the time they occupied only a small portion of the new two-story building, they had clearly come a long way from a university research project to a full-fledged technology company with a rapid growth trajectory and a product that was in high demand. Google was also in the process of developing a unique company culture. They operated in an informal atmosphere that facilitated both collegiality and an easy exchange of ideas. Google staffers enjoyed this rewarding atmosphere while they continued to make many incremental improvements to their search engine technology. For example, in an effort to expand the utility of their keyword-targeted advertising to small businesses, they rolled out the ‘AdWords’ system, a software package that represents a self-service advertisement development capability. Google took a major step forward when, in 2000, it was selected by Yahoo to replace Inktomi as their provider of supplementary search results. Because of the superiority of Google over other search engine capabilities, licenses were obtained by many other companies, including the Internet services powerhouse America Online (AOL), Netscape, Freeserve, and eventually Microsoft Network (MSN). In fact, although Microsoft has pursued its own search technology, Bill Gates once commented on search-engine technology development by saying that “Google kicked our butts.”[8] By the end of 2000, Google was handling more than 100 million searches each day. Shortly thereafter Google began to deliver new innovations and establish new partnerships to enter the burgeoning field of mobile wireless computing. By expanding into this field, Google continued to pursue its strategy of putting search into the hands of as many users as possible. As the global use of Google grew, the patterns contained within the records of search queries provided new information about what was on the minds of the global community of Internet users. Google was able to analyze the global traffic in Internet searching and identify patterns, trends, and surprises – a process they called ‘Google Zeitgeist.’ In 2004, Yahoo decided to compete directly with Google and discontinued its reliance on the Google search technology. Nevertheless, Google continued to expand, increasing its market share and dominance of the Web search market through the deployment of regional versions of its software, incorporating language capabilities beyond English. As a result, Google continued to expand as a global Internet force. Also in 2004, Google offered its stock to investors through an Initial Public Offering (IPO). This entrance to public trading of Google stock created not only a big stir in the financial markets, but also great wealth for the two founding entrepreneurs. Page and Brin immediately joined the billionaire’s club as they entered the exclusive ranks of the wealthiest people in the world. Following the IPO, Google began to challenge Microsoft in its role as the leading provider of computer services. They issued a series of new products, including the email service Gmail, the impressive map and satellite image product Google Earth, Google Talk to compete in the growing Voice of the Internet (VoIP) market, and products aimed at leveraging their ambitious project to make the content of thousands of books searchable online, Google Base and Google Book Search. In addition to these new ventures, they have continued to innovate in their core field of search by introducing new features for searching images, news articles, shopping services (Froogle), and other local search options. It is clear that Google has become an essential tool for connecting people and information in support of the developing Information Revolution. Having established itself at the epicenter of the Web, Google is widely regarded as the ‘place to be’ for the best and brightest programming talent in the industry. It is fair to say that, since the introduction of the printing press, no other entity or event has had more impact on public access to information than Google. In fact, Google has endeavored to accumulate a good part of all human knowledge from the vast amount of information stored on the Web. The effective transformation of Google into an engine for what Page calls a ‘perfect search’ would basically give people everywhere the right answers to their questions and the ability to understand everything in the world. Page and Brin could not have achieved their technological success without having a clear vision of the future of the Internet. Page recently commented in an interview that he believes that in the future "information access and communications will become truly ubiquitous,” meaning that “anyone in the world will have access to any kind of information they want or be able to communicate with anyone else instantly and for very little cost.” In fact, this vision of the future is not far from where we are now.[9] Page also noted that the real power of the Internet is the ability to serve people all over the globe with access to information that represents empowerment of individuals. The ability to facilitate the improved lives and productivity of billions of human beings throughout the world is an awesome potential outcome. And the ability to support the information needs of people from different cultures and languages is an unusual challenge. Page stated in an interview that “even language is becoming less of a barrier. There's pretty good automatic translation out there. I've been using it quite a bit as Google becomes more globalized. It doesn't translate documents exactly, but it does a pretty good job and it's getting better every day.”[10] Even with translation and global reach, however, there remain significant challenges to connecting the people of the world through advanced information technology. One of the challenges is the potential for governmental restrictions on the access to information. Encryption technology, for example, inhibits the power of governments to monitor or control such information access. However, a 1998 survey of encryption policy found that several countries, including Belarus, China, Israel, Pakistan, Russia, and Singapore, maintained strong domestic controls while several other countries were considering the adoption of such controls.[11] The phrase ‘Don't be evil’ has been attributed to Google as its catch phrase or motto. Google's present CEO Eric Schmidt commented, in response to questions about the meaning of this motto, that "evil is whatever Sergey says is evil." Brin, on the other hand, said in an interview with Playboy Magazine, “As for ‘Don’t be evil,’ we have tried to define precisely what it means to be a force for good — always do the right, ethical thing. Ultimately ‘Don’t be evil’ seems the easiest way to express it.” And Page also commented on the phrase, saying “Apparently people like it better than ‘Be good.’”[12] Page and Brin maintain lofty ambitions for the future of information technology, and they communicated those ambitions in an unprecedented seven-page letter to Wall Street entitled An Owner's Manual' for Google's Shareholders, written to detail Google's intentions as a public company. They explained their vision that “Searching and organizing all the world’s information is an unusually important task that should be carried out by a company that is trustworthy and interested in the public good.”[13] In response to questions about how Google will be used in the future, Brin said “Your mind is tremendously efficient at weighing an enormous amount of information. We want to make smarter search engines that do a lot of the work for us. The smarter we can make the search engine, the better. Where will it lead? Who knows? But it’s credible to imagine a leap as great as that from hunting through library stacks to a Google session, when we leap from today’s search engines to having the entirety of the world’s information as just one of our thoughts.”[14] At this junction, Page and Brin find themselves in a state of great personal wealth and great accomplishment, having created a technology and company that is profoundly affecting human culture and society. The two computer scientists have traveled far in their hero’s journey to carry out their vision of global search, having developed skills and capabilities for themselves as well as for Google and the Googleware technology. As they succeeded, their search technology became a key milestone in the development of the Information Revolution. Their journey is not over, however. Before continuing their story, let’s digress into the historical context. The Information Revolution Over past millennia, the world has witnessed two global revolutions: the Agricultural Revolution and the Industrial Revolution. During the Agricultural Revolution, a hunter-gather could acquire the resources from an area of 100 acres to produce an adequate food supply, whereas a single farmer needed only one acre of land to produce the equivalent amount of food. It was this 100-fold improvement in land management that fueled the agricultural revolution. It not only enabled far more efficient food production, but also provided food resources well above the needs of subsistence, resulting in a new era built on trade. Where a single farmer and his horse had worked a farm, during the Industrial Revolution workers were able to use a single steam engine that produced 100 times the horsepower of this farmer-horse team. As a result, the Industrial Revolution placed a 100-fold increase of mechanical power into the hands of the laborer. It resulted in the falling cost of labor and this fueled the unprecedented acceleration in economic growth that ensued. Over the millennia, man has accumulated great knowledge, produced a treasury of cultural literature and developed a wealth of technology advances, much of which has been recorded in written form. By the mid-twentieth century, the quantity of accessible useful information had grown explosively, requiring new methods of information management; and this can be said to have triggered the Information Revolution. As computer technology offered great improvements in information management technology, it also provided substantial reductions in the cost of information access. It did more than allow people to receive information. Individuals could buy, sell and even create their own information. Cheap, plentiful, easily accessible information has become as powerful an economic dynamic as land and energy had for the two prior revolutions. The falling cost of information has, in part, reflected the dramatic improvement in price-performance of microprocessors, which appears to be on a pattern of doubling every eighteen months. While the computer has been contributing to information productivity since the 1950’s, the resulting global economic productivity gains were initially slow to be realized. Until the late 1990’s, networks were rigid and closed, and time to implement changes in the telecommunication industry were measured in decades. Since then, the Web has become the ‘grim reaper’ of information inefficiency. For the first time, ordinary people had real power over information production and dissemination. As the cost of information dropped, the microprocessor in effect gave ordinary people control over information about consumer products. Today, we are beginning to see dramatic change as service workers experience the productivity gains from rapid communications and automated business and knowledge transactions. A service worker can now complete knowledge transactions 100 times faster using intelligent software and near ubiquitous computing in comparison to a clerk using written records. As a result, the Information Revolution is placing a 100-fold increase in transaction speed into the hands of the service worker. Therefore, the Information Revolution is based on the falling cost of information-based transactions which in turn fuels economic growth. In considering these three major revolutions in human society, a defining feature of each has been the requirement for more knowledgeable and more highly skilled workers. The Information Revolution signals that this will be a major priority for its continued growth. Clearly, the Web will play a central role in the efficient performance of the Information Revolution because it offers a powerful communication medium that is itself becoming ever more useful through intelligent applications. Over the past 50 years, the Internet/World Wide Web has grown into the global Information Superhighway. And just as roads connected the traders of the Agricultural Revolution and railroads connected the producers and consumers of the Industrial Revolution, the Web is now connecting information to people in the Information Revolution. The Information Revolutions enables service workers today to complete knowledge transactions many times faster through intelligent software using photons over the Internet, in comparison to clerks using electrons over wired circuits just a few decades ago. But perhaps the most essential ingredient in the Web’s continued success has been search technology such as Google, which has provided real efficiency in connecting to relevant information and completing vital transactions. Now Google transforms data and information into useful knowledge energizing the Information Revolution. Defining Information Google started with Page’s and Brin’s quest to mine data and make sense of the voluminous information on the Web. But what differentiates information from knowledge and how do companies like Google manipulate it on the Web to nourish the Information Revolution? First let’s be clear about what we mean by the fundamental terms ‘data,’ ‘information,’ ‘knowledge,’ and ‘understanding.’ An item of data is a fundamental element of information, the processed data that has some independent usefulness. And right now data is the main thing you can find directly on the Web in its current state. Data can be considered the raw material of information. Symbols and numbers are forms of data. Data can be organized within a database to form structured information. While spreadsheets are ‘number crunchers,’ databases are the ‘information crunchers.’ Databases are highly effective in managing and manipulating structured data.[15] Consider, for example, a directory or phone book which contains elements of information (i.e., names, addresses and phone numbers) about telephone customers in a particular area. In such a directory, each customer’s information is laid out in the same pattern. The phone book is basically a table which contains a record for each customer. Each customer’s record includes his name, address, and phone number. But you can’t directly search such a database on the Web. This is because there is no ‘schema’ defining the structure of data on the Web. Thus, what looks like information to the human being who is looking at the directory (taking with him his background knowledge and experience as a context) in reality is data because it lacks this schema. On the other hand, information explicitly associates one set of things to another. A telephone book full of data becomes information when we associate the data to persons we know or wish to communicate with. For example, suppose we found data entries in a telephone book for four different persons named Jones, but all of them were living within one block of each other. The fact that there are four bits of data about persons with the same name in approximately the same location is interesting information. Knowledge, on the other hand, can be considered to be a meaningful collection of useful information. We can construct information from data. And we can construct knowledge from information. Finally, we can achieve understanding from the knowledge we have gathered. Understanding lies at the highest level. It is the process by which we can take existing knowledge and synthesize new knowledge. Once we have understanding, we can pursue useful actions because we can synthesize new knowledge or information from what is previously known. Again, knowledge and understanding are currently elusive on the Web. Future Semantic Web architectures seek to redress this limit. To continue our telephone example, suppose we developed a genealogy tree for the Jones and found the four Jones who lived near each other were actually brothers. This would give us additional knowledge about the Jones in addition to information about their addresses. If we then interviewed the brothers and found that their father had bought each brother a house in his neighborhood when they married, we would finally understand quite a bit about them. We could continue the interviews to find out about their future plans for their off-spring – thus producing more new knowledge. If we could manipulate data, information, knowledge, and understanding by combining a search engine, such as Google, with a reasoning engine, we could create a logic machine. Such an effort would be central to the development of Artificial Intelligence (AI) on the Web. AI systems seek to create understanding through their ability to integrate information and synthesize new knowledge from previously stored information and knowledge. An important element of AI is the principle that intelligent behavior can be achieved through processing of symbolic structures representing increments of knowledge. This has produced knowledge-representation languages that allow the representation and manipulation of knowledge to deduce new facts from the existing knowledge. The World Wide Web has become the greatest repository of information on virtually every topic. Its biggest problem, however, is the classic problem of finding a needle in a haystack. Given the vast stores of information on the Web, finding exactly what you’re looking for can be a major challenge. This is where search engines, like Google, come in ─ and where we can look for the greatest future innovations to come when we combine AI and search. Larry Page and Sergey Brin found that the existing search technology looked at information on the Web in simple ways. They decided that to deliver better results, they would have to go beyond simply looking, to looking good. Looking Good Commercial search engines are based upon one of two forms of Web search technologies: human directed search and automated search. Human directed search is search in which the human performs an integral part of the process. In this form of search engine technology, a database is prepared of keywords, concepts, and references that can be useful to the human operator. Searches that are keyword based are easy to conduct but they have the disadvantage of providing large volumes of irrelevant or meaningless results. The basic idea in its simplest form is to count the number of words in the search query that match words in the keyword index, and rank the Web page accordingly. Although more sophisticated approaches also take into account the location of the keywords, the improved performance may not be substantial. As an example, it is known that keywords used in the title tags of Web pages tend to be more significant than words that occur in the web page, but not in the title tag; however, the level of improvement may be modest. Another approach is to use hierarchies of topics to assist in human-directed search. The disadvantage of this approach is that the topic hierarchies must be independently created and are therefore expensive to create and maintain. The alternative approach is automated search; this approach is the path taken by Google. It uses software agents, called Web crawlers (also called spiders, robots, bots, or agents) to automatically follow hypertext links from one site to another on the Web until they accumulate vast amounts of information about the Web pages and their interconnections. From this, a complex index can be prepared to store the relevant information. Such automated search methods accumulate information automatically and allow for continuing updates. However, even though these processes may be highly sophisticated and automatic, the information they produce is represented as links to words, and not as meaningful concepts. Current automated search engines must maintain huge databases of Web page references. There are two implementations of such search engines: individual search engines and meta-searchers. Individual search engines (such as Google) accumulate their own databases of information about Web pages and their interconnections and store them in such a way as to be searchable. Meta-searchers, on the other hand, access multiple individual engines simultaneously, searching their databases. In the use of key words in search engines, there are two language-based phenomena that can significantly impact effectiveness and therefore must be taken into account. The first of these is polysemy, the fact that single words frequently have multiple meanings; and the second is synonymy, the fact that multiple words can have the same meaning or refer to the same concept. In addition, there are several characteristics required to improve a search engine’s performance. It is important to consider useful searches as distinct from fruitless ones. To be useful, there are three necessary criteria: (1) maximize the relevant information, (2) minimize irrelevant information, and (3) make the ranking meaningful, with the most highly relevant results first. The first criterion is called recall. The desire to obtain relevant results is very important, and the fact is that, without effective recall, we may be swamped with less relevant information and may, in fact, leave out the most important and relevant results. It is essential to reduce the rate of false negatives ─ important and relevant results that are not displayed ─ to a level that is as low as possible. The second criterion, minimizing irrelevant information, is also very important to ensure that relevant results are not swamped; this criterion is called precision. If the level of precision is too low, the useful results will be highly diluted by the uninteresting results, and the user will be burdened by the task of sifting through all of the results to find the needle in the haystack. High precision means a very low rate of false positives, irrelevant results that are highly ranked and displayed at the top of our search result. Since there is always a tradeoff between reducing the risk of missing relevant results and reducing the level of irrelevant results, the third criterion, ranking, is very important. Ranking is most effective when it matches our information needs in terms of our perception of what is most relevant in our results. The challenge for a software system is to be able to accurately match the expectations of a human user since the degree of relevance of a search contains several subjective factors such as the immediate needs of the user and the context of the search. Many of the desired characteristics for advanced search, therefore, match well with the research directions in artificial intelligence and pattern recognition. By obtaining an awareness of individual preferences, for example, a search engine could more effectively take them into account in improving the effectiveness of search. Recognizing ranking algorithms were the weak point in competing search technology Page and Brin introduced their own new ranking algorithm ─ PageRanking. Google Connects Information Just as the name Google is derived from the esoteric mathematical term ‘googol,’ in the future, the direction of Google will focus on developing the esoteric ‘perfect search engine,’ defined by Page as something that "understands exactly what you mean and gives you back exactly what you want." In the past, Google has applied great innovation to try and overcome the limitations of prior search approaches; PageRank was conceived by Google to overcome some of the key limitations.[16] Page and Brin recognized that providing the fastest, most accurate search results would require a new approach to server systems. While most search engines used a small number of large servers that often slowed down under peak use, Google went the other direction by using large numbers of linked PCs to find search results in response to queries. The approach turned out to be effective in that it produced much faster response times and greater scalability while minimizing costs. Others have followed Google’s lead in this innovation while Google has continued its efforts to make their systems more efficient. Google takes a parallel processing approach to its search technology by conducting a series of calculations on multiple processors. This has provided Google with critical timing advantage, permitting their search algorithms to be very fast. While other search engines rely heavily on the simple approach of counting the occurrences of keywords, Google’s PageRank approach considers the entire link structure of the Web to help in the determination of Web page importance. By then performing a hypertext matching assessment to narrow the search results for the particular search being conducted, Google achieves superior performance. In a sense, they combine insight into Web page importance with query-specific attributes to rank pages and deliver the most relevant results at the top of the search results. The PageRank algorithm analyzes the importance of the Web pages it considers by solving an exceptionally complex set of equations with a huge number of variables and terms. By considering links between Web pages as ‘votes’ from one page to another, PageRank can assign a measure of a page’s importance by counting its votes. It also takes into account the importance of each page that supplies a vote, and by appropriately weighting these votes, further improves the quality of the search. In addition, PageRank considers the Web page content, but unlike other search engines that restrict such consideration to the text content, Google consider the full contents of the page. In a sense, Google attempts to use the collective intelligence of the Web, a topic for further discussion later in this book, in its effort to improve the relevance of its search results. Finally, because the search algorithms used by Google are automated, Google has earned a reputation for objectivity and lack of bias in its results. Throughout their exciting years establishing and growing Google as a company, Page and Brin realized that continued innovation was essential. They undertook to find new innovative services that would enhance access to Web information with added thought and not a little perspiration. Page said that he respected the idea of having “a healthy disregard for the impossible.”[17] In February 2002, the Google Search Appliance, a plug-and-play application for search, was introduced. In short order, this product was dispersed throughout the world populating company networks, university systems, and the entire Web. The popular Google Search Appliance is referred to as ‘Google in a box.’ In another initiative, Google News was introduced in September of 2002. This free news service, which allows automatic selection and arrangement of news headlines and pictures, features real time updating and tailoring allowing users to browse the news with scan and search capabilities. Continuing Google's emphasis on innovation, the Google search service for products, Froogle, was launched in December of 2002. Froogle allows users to Search millions of commercial websites to find product and pricing information. It enables users to identify and link to a variety of sources for specific products, providing images, specifications and pricing information for the items being sought. Google's innovations have also impacted the publishing business with both search and advertising features. Google purchased Pyra Labs in 2003, and thus became the host of Blogger, a leading service for the sharing of thoughts and opinions through online journals, or blogs (weblogs). Finally, Google Maps became a dynamic online mapping feature, and Google Earth a highly popular mapping and satellite imagery resource. Using these innovative applications, users can find information about particular locations, get directions, and display both maps as well as satellite images of a desired address. With each new capability, Google expands our access to more information and moves us closer to Page’s Holy Grail: ‘perfect search.’ At this junction, Page and Brin have finally completed their hero’s journey. They have become the Masters of Search; committed to improving access to information and lifting the bonds of ignorance from millions around the world. Pattern of Discovery Larry Page and Sergey Brin were trying to solve the problem of easy, quick access to all Web information, and ultimately to all human knowledge. In order to index existing Web information and provide rapid relevant search results, their challenge was to sort through billions of pages of material efficiently and explicitly find the right responses. They were confident that their vision for developing a global information collection, storage, and retrieval system would succeed if they could base it on a unique and efficient ranking algorithm. The process of inspiration for Page and Brin became fulfilled when they completed their seminal paper entitled The Anatomy of a Large-Scale Hypertextual Web Search Engine which explained their efficient ranking algorithm, PageRank. In developing a breakthrough ranking algorithm based upon the ideas of publication ranking, Page and Brin experienced a moment of inspiration. But they didn’t stop there. They also believed that optimization was vitally important and so they developed their own Googleware technology consisting of combining custom software with custom hardware thereby reflecting the founder’s genius. They built the world’s most powerful computational enterprise, and they have been on a roll every since. Page stressed that inspiration still required perspiration and that Google appeared destined for rapid growth and expansion. In building the customized computer Googleware infrastructure for PageRank, they were demonstrating the 1% Inspiration and 99% Perspiration pattern. The result was Google, the dominant search engine connecting people to all of the World Wide Web’s information. Forecasts for Connecting Information For many of us it seems that an uncertain future looms ahead like a massive opaque block of granite. But just as Michelangelo suggested that he took a block of stone and chip away the non-essential pieces to produce David, we can chip away the improbable to uncover the possible. By examining inventors and their process of discovery, we are able to visualize the tapestry of our past to help unveil patterns that can serve as our guide posts on our path forward. Page and Brin invented an essential search technology, but their contributions to information processing were evolutionary in nature – built on inspiration and perspiration. One forecast for connecting information is that we can expect a continued pattern of inspired innovation as we go forward in the expansion of search and related technology. Discoveries requiring inspiration and perspiration: In considering the future for connecting information, we expect that improved ranking algorithms will ensure Google’s continued dominance for some time to come. Extrapolating from Google’s success, we can expect a series of inspired innovations building upon its enterprise computer system, such as offering additional knowledge related services. Future Google services could include: expanding into multimedia areas such as television, movies, and music using Google TV and Google Mobile. Viewers would have all the history of TV to choose from. And Google would offer advertisers targeted search. Google Mobile could deliver the same service and products to cell phone technology. By 2020, Google could digitize and indexed every book, movie, TV show, and song ever produced; making it available conveniently. In addition, Google could dominate the Internet as a hub site. The ubiquitous GoogleNet, would dominate wireless access and cell-phone. As for Google browser, Gbrowser, it could replace operating systems. However, our vision also concludes connecting information through developing more intelligent search capabilities. A new Web architecture such as Tim Berners-Lee’s Semantic Web, would add knowledge representation and logic to the markup languages of the Web. Semantics on the Web would offer extraordinary leaps in Web search capabilities. Since Google has cornered online advertising, they have made it progressively more precision-targeted and inexpensive. But Google also has 150,000 servers with nearly unlimited storage space and massive processing power. Beyond simply inspired discoveries, Google or other search engine powers could find innovations based upon new principles yet to be proven, as suggested in the following. Discoveries requiring new proof of principle: Technology futurists such as Ray Kurzweil have suggested that Strong AI (software programs that exhibit true intelligence) could emerge from developing web-based systems such as that of Google. Strong AI could perform data mining at a whole new level. This type of innovation would require a Proof of Principle. Some have suggested that Google’s purpose in converting books into electronic form is not to provide for humans to read them, but rather to provide a form that could be accessible by software, with AI as the consumer. One of the great areas of innovation resulting from Google’s initiatives is its ability to search the Human Genome. Such technology could lead to a personal DNA search capability within the next decade. This could result in the identification of medical prescriptions that are specific to you; and you would know exactly what kinds of side-effects to expect from a given drug. And consider what might happen if we had ‘perfect search?’ Think about the capability to ask any question and get the perfect answer – an answer with real context. The answer could incorporate all of the world’s knowledge using text, video, or audio. And it would reflect every nuance of meaning. Most importantly, it would be tailored to your own particular context. That’s the stated goal of IBM, Microsoft, Google and others. Such a capability would offer its greatest benefits when knowledge is easily gathered. Soon search will move away from the PC-centric operations to the Web connected to many small devices such as mobile phones and PDAs. The most insignificant object with a chip and the ability to connect will be network-aware and searchable. And search needs to solve access to deep databases of knowledge, such as the University of California’s library system. While there are several hundred thousand books online, there are 100 million more that are not. ‘Perfect search’ will find all this information and connect us to the world’s knowledge, but this is the beginning of decision making, not the end. Search and artificial intelligence seem destined to get together. In the coming chapters, we will be exploring all the different technologies involved in connecting information and we will be exploring how the prospects for ‘perfect search’ could turn into ‘ubiquitous intelligence.’ First, ubiquitous computing populates the world with devices using microchips everywhere. Then the ubiquitous Web connects and controls these devices on a global scale. The ubiquitous Web is a pervasive Web infrastructure allows all physical objects access by URIs, providing information and services that enrich users’ experiences in their physical context just as the Web does in cyberspace. The final step comes when artificial intelligence reaches the capability of managing and regulating devices seamlessly and invisibly within the environment – achieving ubiquitous intelligence. Ubiquitous intelligence is the final step of Larry Page’s ‘perfect search’ and the future of the Information Revolution. References: [1] Prather, M., “Ga-Ga for Google,” Entrepreneur Magazine , April 2002 . [2] Vise, D. A., and Malseed, M., The Google Story, Delacourt Press, New York, NY, 2005 [3] Brin, S., and Page, L., The Anatomy of a Large-Scale Hypertextual Web Search Engine, Computer Science Department, Stanford University, Stanford, 1996 [4] Brin S., and Page, L., “The Future of the Internet,” Speech to the Commonwealth Club, March 21, 2001, [5] Vise, D. A., and Malseed, M., The Google Story, Delacourt Press, New York, NY, 2005 [6] Vise, D. A., and Malseed, M., The Google Story, Delacourt Press, New York, NY, 2005 [7] Technology Review, interview entitled “Search Us, Says Google,” 1/11/2002 [8] Kevin Kelleher, “Google vs. Gates,” Wired, Issue 12.03, March 2004. [9] Brin S., and Page, L., “The Future of the Internet,” Speech to the Commonwealth Club, March 21, 2001, [10] Ibid [11] Cryptography and Liberty 1998, An International Survey of Encryption Policy, February 1998, from http://www.gilc.org/crypto/crypto-survey.html [12] Playboy Magazine Interview, “Google Guys,” Playboy Magazine, September 2004 [13] From Google's Letter to Prospective Shareholders http://www.thestreet.com/_yahoo/markets/marketfeatures/10157519_6.htm l [14] Playboy Magazine Interview, “Google Guys,” Playboy Magazine, September 2004 [16] Quotes from http://www.google.com/corporate/tech.html [17] Vise, D. A., and Malseed, M., The Google Story, Delacourt Press, New York, NY, 2005

Excerpt of fourth book in the Henry Gallant Saga, Commander Gallant. Commander Henry Gallant AMAZON Chapter 1 Methane Planet As the warp bubble collapsed, the Warrior popped out on the edge of the Gliese-581star system. The Warrior was Captain Henry Gallant’s first command, the culmination of everything he’d worked for since entering the academy. With a rocket-shaped hull over one hundred meters long, she boasted stealth technology, a sub-light antimatter engine, and an FTL dark-matter drive. Gallant gawked. “What an awesome sight.” The busy bridge crew stole their eyes away from their instrument panels long enough to gaze in amazement at the Titan civilization. The many ships traveling between planets were remarkable, but the energy readings of the densely populated planets were off the charts. From his command chair, Gallant focused on the home of his alien enemy. The star was an M-class red dwarf—smaller, cooler, and less massive than Sol, at about twenty light-years from Earth. “Sir,” said the astrogator, “we’re three light-days from the sun. Five planets are visible.” “It’s like the solar system,” marveled Midshipman Stedman, an eager but green officer. His slight build and round, boyish face often seemed to get lost amid the bustle of the more experienced crew. “If you don’t notice that the sun is ruby instead of amber and that there are only five planets,” chided Chief Howard. The oldest member of the crew, he was a seasoned veteran with a slight potbelly. He wore his immaculate uniform with pride. Every ribbon, insignia, and star on his left breast had a long and glorious story. He was only too glad to retell the stories—with appropriate embellishments—over a whiskey, preferably Jack Daniels. The astrogator said, “Only two planets are within the liquid methane zone. But some of the asteroids and moons may have been methane-formed.” “Wow, the system is full of mining colonies, military bases, and communications satellites. The spaceship traffic is amazing. There must be many thousands of ships,” said Stedman. The astrogator reported, “Scans on the second and third planets show billions of beings. The second planet has the greatest energy density. I’ll bet that’s their planet of origin.” “Quite likely,” acknowledged Gallant, his curiosity aroused. “An imposing presence, Skipper,” said Roberts. Young and garrulous, Roberts had steady nerves and sound professional judgment. He was of average height with brown hair, a lean, smooth face, and a sturdy body. Gallant had come to trust him as a stalwart friend—something one only discovers during a crisis. That moment had come several months earlier when Roberts put his career and his life on the line for Gallant. “It has one large moon,” added Chief Howard. Gliese-Beta was a majestic ringed planet wrapped in a dense hydrocarbon nitrogen-rich atmosphere. It was opaque to blue light but transparent to red and infrared. The red dwarf's infrared warmed the planet and made it habitable for methane lifeforms. Gliese-Gamma was similar. The astrogator continued, “The next two planets are gas giants with several moons. Gliese-Delta is composed of hydrogen and helium with volcanic methane moons, much like our Neptune. Gliese-Epsilon is a low-mass planet with a climate model like a runaway greenhouse effect—analogous to our Venus.” “That’s interesting,” said Roberts. Encouraged, the astrogator concluded his report, “The system includes a disk-shaped asteroid field.” The Warrior used its radars and telescopes to plot the planets’ orbits. The CIC team computed the course of nearby contacts. The Warrior’s emission spectrum was controlled in stealth mode. “What’s your assessment of their military strength?” asked Gallant. The CIC team listed the large and small warships, followed by planetary defenses. They added an estimate of the traffic flow. It was a long list. OOD said, “Here is the compiled reported, sir.” “Very well,” acknowledged Gallant as he scanned the tablet. The sub-light engines drove the ship onward into the heart of the system. Calculating their flight path, the astrogator reported, “We’ll reach the asteroids in about forty-eight hours, sir.” Gallant tapped the screen to call up the AI settings for plot control and touched the destination. He ordered a deep-space probe sent toward the largest asteroids. “It’ll take several hours to start transmitting, sir.” Gallant said, “Once we get a base established, we can go deeper into their system. I’m interested in seeing how their home planet differs from our solar system. We need to learn about their society and leadership structure.” Roberts asked, “Skipper, we’ve always called them Titans, but what do they call themselves?” Gallant said, “I can’t replicate their name in our language. As autistic savants, their communication is different from our speech. We’ll continue to call them Titans.” The CIC team reported, “Our initial assessment shows that Gliese-Beta has a diverse topology and climate. It’s ecologically rich with many species. Extensive methane oceans and landmasses have abundant soil and temperature conditions. It can support a wide variety of methane-breathing lifeforms.” Roberts said, “It’s so different from our water-rich Earth.” “Earth is mostly water,” said Gallant. “The oceans provide us with fish to eat, water vapor to fill our skies with clouds, rain to nurture our crops, and water for us to drink. Our metabolism and food cycle are water-based, and we ourselves are 97 percent water. For us, water is life.” “How does this methane world sustain the Titans?” asked Roberts Gallant said, “The temperature variations provide methane in all three phases: gas, liquid, and solid. Methane rivers freeze at high latitudes to form polar sheets. The methane cycle is a complex molecular soup. It is formed from reactions when the ultraviolet radiation from the sun strikes the methane. Their methane life forms are comparable to our oxygen-based life cycle. And just as methane is a poison to us, oxygen is toxic to them.” Roberts asked, “Are they autistic savants because of the methane-based chemistry?” “That’s one of the things we’re here to learn. Our first task is to establish a base,” said Gallant. “From that hideout, the Warrior can recharge her stealth battery and remain safe between operations.” Maintaining stealth mode, the Warrior approached the outer edge of the asteroid belt. She conducted a spiral search to map the interrelated defenses. The crew looked for an asteroid large enough to hide the ship. Gallant and the XO combed through the CIC data to check for potential locations. “How about here, sir?” asked one of the analysts, pointing to a cluster near the outer perimeter of the field. The asteroid belt included many asymmetrical rocky bodies. Three smaller clusters skirted the outer edge. Some asteroids were more than one kilometer wide. “Yes, that might do,” said Gallant. “It’s large enough to block radar detection and shield us from view while we’re recharging. We’ll call this base Alamo." Gallant ordered a two-man team to construct a relay station on the asteroid. He left one of the Warrior’s remote-controlled drones on the surface along with a supply depot. Once Alamo was established, he settled the Warrior into orbit behind the rocks to recharge her stealth batteries. The next day, they reconnoitered the fifth planet and discovered a communication junction box. Moving deeper into Titan territory, they caught a bird’s-eye view of the alien’s home planet. They saw several orbiting shipyards and space stations. The Warrior collected information about the Titan fleet and civilization. The bridge crew was surprised at the incredible infrastructure the aliens had developed. Operating in such a populated environment was a challenge, but the cloaking technology allowed the Warrior to remain undetected. What followed were busy days as the Warrior peered into the inner workings of the Titan system. The crew compiled detailed lists of warships and their deposition, as well as their refueling and patrolling pattern. They learned shipping traffic patterns, monitored the industrial capacity, and accumulated population statistics. There were over twenty billion inhabitants. The Titans had built their main military headquarters on the third planet. It had a layered defense with satellites, minefields, and overlapping fortresses. A display showed fluctuating energy emissions for their industry. After two weeks of collecting information, Roberts approached Gallant’s command chair. He asked, “Captain, can you give us your game plan going forward?” Gallant recalled Admiral Collingsworth’s orders detailing their hazardous mission. All of which required his stealth ship and crew to be at peak operational and battle readiness. He said, “Yes. It’s time to fill you in. We’ve collected a lot of info, but scouting isn’t our sole mission. I intend to do more. Much more.” The bridge crew leaned closer, eager to drink in every tidbit of juicy news. He said, “We are finally ready to engage in asymmetric warfare. We will penetrate the Titan communication network to learn about their military deployment.” He paused for dramatic effect as everyone drew in a deep breath. “And we will raid commercial shipping to throw their civilian population into turmoil.” A buzz of excitement filled the bridge. “That’s a tall order, Skipper,” said Roberts. “Yes, it is.” Gallant asked, “Are you up for it?” “Can do, sir!” said Roberts. “Can do, sir!” roared the bridge crew. Commander Julie Ann McCall stepped out of CIC and onto the bridge. She walked straight to Gallant and grabbed his arm. She said, “I must speak to you immediately.” McCall was not a line officer. She was a product of genetic engineering who had inherited tendencies of the most diabolical kind, which made her a talented Solar Intelligence Agency (SIA) operative. Her considerable skills in manipulation and deception had fostered her brilliant career. What she lacked in kindness and empathy, she more than made up for in intellect, guile, and allure. She was astonishingly efficient at analyzing an opponent’s flaws. Some who had felt her cold-blooded sting labeled her a sociopath who would do anything to achieve her goals. Gallant’s long and checkered relationship with her remained a riddle to him. Now he gazed into her blazing eyes, and then he looked down at her hand on his arm. She pulled her hand away but repeated, “I need to speak with you privately.” The commotion on the bridge died down and the two senior officers became the focus of attention. Gallant rose from his command chair, and said, “Commander, please come with me.” All eyes on the bridge followed the pair as they left.

Excerpt from the second book in the Henry Gallant Saga, Lieutenant Henry Gallant. Lieutenant Henry Gallant 1 RUN AMAZON Gallant ran—gasping for breath, heart pounding—the echo of his footsteps reverberated behind him. He hoped to reach the bridge, but hope is a fragile thing. Peering over his shoulder into the dark, he tripped on a protruding jagged beam, one of the ship’s many battle scars. As he crashed to the deck, the final glow of emergency lights sputtered out, leaving only the pitch black of power failure—his failure. He lay still and listened to the ship’s cries of pain; the incessant wheezing of atmosphere bleeding from the many tiny hull fissures, the repetitious groaning of metal from straining structures, and the crackling of electrical wires sparking against panels. Thoughts flashed past him. How long will the oxygen last? He was reluctant to guess. Where are they? The clamor of dogged footsteps drew closer even as he rasped for another breath. Trembling from exhaustion, he clawed at the bulkhead to pull himself up. His hemorrhaging leg made even standing brutally painful. Nevertheless, he ran. The bulkhead panels and compartment hatches were indistinguishable in the dimness. Vague phantoms lurked nearby even while his eyes adjusted to whatever glowing plasma blast embers flickered from the hull. As he twisted around a corner, he crashed his shoulder into a bulkhead. The impact knocked him back and spun him around. Reaching out with a bloody hand, he grasped the hatch handle leading into the Operation’s compartment. Going through the hatch, he pulled it shut behind him. He started to run, then awkwardly fought his own momentum, and stopped. Stupid! Stupid! Going back to the hatch, he hit the security locking mechanism. It wouldn’t stop a plasma blast, but it might slow them down, he thought. At least this compartment is airtight. Finally, able to take a deep breath, he tried to clear his head of bombarding sensations. He should’ve been in battle armor, but he’d stayed too long in engineering trying to maintain power while the hull had been breached and the ship boarded. Now his uniform was scorched, revealing the plasma burns of seared flesh from his left shoulder down across his back to his right thigh. He had no idea where the rest of the crew was; many were probably dead. His comm pin was mute, and the ship’s AI wasn’t responding. He had only a handgun, but, so far, he didn’t think they were tracking him specifically, merely penetrating into the ship to gain control. Gallant tried to run once more, but his legs were unwilling. Leaning against the bulkhead, like a dead weight, he slid slowly down to the deck. Unable to go farther, he sat dripping blood and trembling as the potent grip of shock grabbed hold. The harrowing pain of his burnt flesh swept over him. Hope and fear alike abandoned him, leaving only an undeniable truth; without immediate medical treatment, he wouldn’t survive. I’m done. Closing his eyes, he fought against the pain and the black vertigo of despair. He took a deep breath and called upon the last of his inner resolve and resilience . . . No! I won’t give up. Exhaling and opening his eyes, he caught sight of a nearly invisible luminescent glow of a Red Cross symbol, offering him a glimmer of hope. He stretched his arm toward the cabinet. “Argh.” He heard a cry of agony and only belatedly realized it had escaped his own lips as he strained to pull away twisted metal from the door to a medical cabinet. Reaching inside, he grabbed a damaged medi-pack. Painstakingly he used the meager emergency provisions to stop the bleeding and to infuse blood plasma. His limited mobility prevented him from reaching awkward areas, but he managed to insert an analgesic hypodermic into his raw, blistered flesh. Next, he crudely bandaged his suffering body. He relaxed momentarily as the medication coursed through his veins, working to stifle the worst effects of shock and blood loss. His parched throat demanded . . . Water. He looked at more cabinets but was unable to make out their markings in the dark. Stretching his fingers, he opened the nearest one, groping for something familiar inside. No. He opened the next. No. And another. Yes. Finally, he snatched a half-buried survival kit. Greedily he drank and even managed to take a few bites of an energy bar. A surge of adrenaline helped him shift his position to sit more comfortably as his mind came into sharper focus. As he examined his surroundings in the faint light, he spotted an interface station. He was about to reach up and patch into the ship’s AI to get an update on the ship’s defensive posture when he was disturbed by the dismal clangor of footsteps. He held his breath. Are they coming this way?

H. Peter Alesso wrote a self portrait to reveal his history and experiences that helped him on his writing journey. My Story I love words, but that wasn't always the case. I grew up with a talent for numbers, leading me to follow a different path. I went to Annapolis and MIT and became a nuclear physicist at Lawrence Livermore National Laboratory. Only after retiring was my desire to tell stories reawakened. In recent years, I have immersed myself in the world of words, drawing on my scientific knowledge and personal experience to shape my writing. As a scientist, I explored physics and technology, which enabled me to create informative and insightful books, sharing my knowledge with readers who sought to expand their understanding in these areas—contributing to their intellectual growth while satisfying my own passion. But it was my time as a naval officer, that genuinely ignited my imagination and propelled me into science fiction. After graduating from the United States Naval Academy and serving on nuclear submarines during both hot and cold wars, I witnessed firsthand the complexities and challenges of military operations that seamen face daily. This allowed me a unique perspective, which I channeled into creating Henry Gallant and a 22nd-century world where a space officer fought against invading aliens. Through this narrative, I explored the depths of human resilience, the mysteries of space, and the intricacies of military conflict. My stories let me share the highlights of my journey with you. I hope you enjoy the ride. 1/9 Contact First name* Last name Email* Write a message Submit

Excerpt from the sixth book in the Henry Gallant Saga, Commodore Henry Gallant. Commodore Henry Gallant AMAZON Chapter 1 Unidentified Flying Object Lieutenant Rob Ryan was bored. He hated the mundane tasks of being a squadron leader. He liked ‘fast’—the faster, the better. But that wasn’t happening today as he cruised over Earth in his Viper. He was stuck with the tedious job of training his new wingman, Glenn Holman, in strafing maneuvers against the Antarctic target range. As he executed a simple wingover in his starfighter, he wa s about to comment on the poor performance of his novice companion when out of nowhere, the world changed, shifting with shocking suddenness. That’s not possible! He instinctively flung an arm across his face to ward off the seemingly endless wall of steel that had materialized in front of him. I must be hallucinating! Heart-throbbing fear gripped him. But there is something delicious about fear. It starts with bitter panic and grows into sour excitement—until, at last, comes sweet courage. Ryan pulled his arm down, tightened his grip on the thruster, and yelled, “Hard to port! Max thrust! Flip gyros!” Over the next several seconds, he concentrated on avoiding a collision with the mountain of metal. In the first second, he felt the chest-crushing weight of 14 g’s as his Viper began the pivot. In the next second, he fought down the blackness of his vision, narrowing into a tunnel as 20 g’s tested the limitations of his pressure suit. By the third second, he felt as if he was being squashed like a ripe tomato—right before he blacked out. Several seconds later, he came to, blinking against the glare of the sun. Even as he aimed his ship toward it, he heard Holman gasp, “I can’t . . . make it . . .” Almost immediately, Ryan saw the brilliant red-white explosion of Holman’s Viper as it went splat against the steel wall. He sighed with relief when he saw an escape pod spiral toward Earth. *** The July blizzard howled across the high plateau of the Amundsen–Scott South Pole Antarctic Station, leaving a record snowfall of crystalline ice in its wake, and blustering so hard that the Earth defense sensor arrays were blanketed under the full fury of the whiteout. So powerful was the blizzard that sharp flecks of ice pierced the multilayered protective gear of the technician sent to investigate some minor static interference. As the man crawled toward the besieged sensors, his hands lost feeling despite the well-insulated flex-gloves. A large scavenger Skuas bird dive-bombed him, causing him to grab hold of the lifeline tether to keep from falling off the sheer rock cliff. “Damn!” “What’s wrong?” Against the howling of the wind, he could barely hear the question. During the six-month southern hemisphere ‘night,’ the wind blew at 160 km/h, and the temperature dropped to minus 89 °C. Despite the harsh conditions, the dry atmosphere and extended darkness made the station the Earth’s best location for astronomical observations. It had every conceivable type of sensor from microwave telescopes to neutrino detectors. The sensors were so accurate and dependable that the people of Earth rested reassured of their absolute safety. The man gripped the taut cable as he spoke into the mic, “Why do I always get the crap jobs?” “Just do it. And better hurry. Something big is brewing.” Inside the station’s geodesic dome, a sensor operator screamed, “Contact! Contact over Melbourne. It’s massive!” The duty officer came over to the operator’s station. “What’s the problem?” The operator pointed, his finger trembling in shock at the image that filled his screen. Flabbergasted, the duty officer asked, “Where did that come from? No unidentified contacts have been reported!” “It just popped up out of nowhere.” “That’s impossible.” “I’m telling you. Everything was normal, nothing but standard traffic patterns, and then WHAM! There it was.” “Have you run a diagnostic on your equipment?” “Look at the other sensors. They all show the same thing. We have a man outside checking some minor glitches, but nothing that would explain this.” “It isn’t a colossal malfunction? Do you think this is a bona fide contact?” “Yes, sir!” In the stunned silence, the senior chief operator said, “Designate contact as Tango 101, in geosynchronous orbit over Melbourne.” Still unable to grasp the situation, the duty officer asked again, “Why didn’t you spot this earlier?” “I’m telling you; it wasn’t there before. It came out of nowhere. As if it dropped out of cloak.” The dark eyes of the duty officer met the senior chief’s gaze. “That’s impossible. Even in a blizzard, our active sensors can penetrate any cloaking device within a million kilometers of Earth.” As he shook his head, the chief’s white hair fell across his grizzled face, but his eyes stayed steady. “Until now.” The officer asked, “What type of craft is it?” “Nothing in our databases even comes close. Visual images are starting to come in now. Man, it’s the strangest thing I’ve ever seen.” The officer’s eyes bugged out. “Oh, my Gawd! That’s incredible. It’s enormous. What the hell is it?” His hand smacked the red alert button, and his voice echoed over the base-wide intercom. “Activate planet defenses. Scramble standby fighters.” A second later, he said into the emergency radio, “Put me through to Admiral Devens, immediately.” When Admiral Devens responded, the duty officer said, “We have an unidentified flying object over the capital.” “Notify all missile and laser batteries to target the contact, but hold fire until further notice,” said the admiral, unruffled. “Have fighter command scramble all fighters and intercept the UFO.” *** “Fighter command, this is Lieutenant Ryan flying Constellation’s Viper 607. I have Tango 101 in sight.” Like a minnow swimming next to a blue whale, Ryan flew alongside the alien craft examining its features. He said, “Tango 101 is a monster ship that looks like a giant squid. It has an ellipsoid body thirty kilometers in diameter with protruding spikes seventy kilometers long. This Great Ship is beyond the combined resources of all the planets.” “Is it broadcasting?” asked the command center. “Negative, according to my sensors. It has not responded to radio communications, and I can detect no emissions at all, hostile or otherwise.” “Shadow it, but do not engage.” *** Twelve hours later, President Kent addressed the nation. “My fellow citizens, what you have heard is true. We have detected an alien vessel over Earth, but there is no immediate cause for alarm. Planetary defenses are on full alert. Our space fleet and fighters have surrounded the unknown spaceship. We do not know who these beings are, but they are not our Titan enemy. And though victory against that enemy may still seem a long way off, we are prepared to face any challenge they set against us. This new arrival has so far taken no hostile action, and our hope is that they will prove to be a benefactor rather than an adversary. “So, we must be patient until our visitor decides to speak. Until then, I am certain that you will all remain as brave and resolute as our proud space navy that stands guard protecting us at this moment.” Over the next several hours, news stations maintained uninterrupted coverage around the world. Opinions were divided over accepting the president’s optimism. Some listened to the vitriolic counterargument made by presidential candidate Gerome Neumann. He advised swift and total annihilation of the aliens who had violated Earth’s space. When it seemed that the tension couldn’t get any greater, an astounding event occurred. A shuttlecraft departed the Great Ship and landed at the Melbourne spaceport.

Excerpts from my books and projects to encourage engagement. Excerpts Writing Porfolio The Henry Gallant Saga Midshipman Henry Gallant in Space Lieutenant Henry Gallant Henry Gallant and the Warrior Commander Gallant Captain Henry Gallant Commandor Henry Gallant Henry Gallant and the Great Ship Rear Admiral Henry Gallant Midshipman Henry Gallant at the Academy Dramatic Novels Youngblood Dark Genius Captain Hawkins Short Stories All Androids Lie Computer Books Connections Thinking on the Web The Semantic Web The Intelligent Wireless Web E-Video Computer Apps Graphic Novels Screenp lay

An excerpt from the non-fiction technology book Thinking on the Web. Thinking on the Web AMAZON Chapter 2 Gödel: What is Decidable? In the last chapter, we suggested that small wireless devices connected to an intelligent Web could produce ubiquitous computing and empower the Information Revolution. In the future, Semantic Web architecture is designed to add some intelligence to the Web through machine processing capabilities. For the Semantic Web to succeed the expressive power of the logic added to its mark-up languages must be balanced against the resulting computational complexit y. Therefore, it is important to evaluate both the expressive characteristics of logic languages, as well as, their inherit limitations. In fact, some options for Web logic include solutions that may not be solvable through rational argument. In particular, the work of Kurt Gödel identified the concept of undecidability where the truth or falsity of some statements may not be determined. In this chapter, we review some of the basic principles of logic and related them to the suitability for Web applications. First, we review the basic concept of logic, and discuss various characteristics and limitations of logic analysis. We introduce First Order Logics (FOL) and its subsets, such as Descriptive Logic and Horn Logic which offer attractive characteristics for Web applications. These languages set the parameters for how expressive Web markup languages can become. Second, we investigate how logic conflicts and limitations in computer programming and Artificial Intelligence (AI) have been handled in closed environments to date. We consider how errors in logic contribute to significant ‘bugs’ that lead to crashed computer programs. Third, we review how Web architecture is used to partition the delivery of business logic from the user interface. The Web architecture keeps the logic restricted to executable code residing on the server and delivers user-interface presentations residing within the markup languages traveling over the Web. The Semantic Web changes this partitioned arrangement. Finally, we discuss the implications of using logic in markup languages on the Semantic Web. Philosophical and Mathematical Logic Aristotle described man as a “rational animal” and established the study of logic beginning with the process of codifying syllogisms. A syllogism is a kind of argument in which there are three propositions, two of them premises, one a conclusion. Aristotle was the first to create a logic system which allowed predicates and subjects to be represented by letters or symbols. His logic form allowed one to substitute for subjects and predicates with letters (variables). For example: If A is predicated of all B, and B is predicated of all C, then A is predicated of all C. By predicated, Aristotle means B belongs to A, or all B's are A's. For instance, we can substitute subjects and predicates into this syllogism to get: If all humans (B's) are mortal (A), and all Greeks (C's) are humans (B's), then all Greeks (C's) are mortal (A). Today, Aristotle's system is mostly seen as of historical value. Subsequently, other philosophers and mathematicians such as Leibniz developed methods to represent logic and reasoning as a series of mechanical and symbolic tasks. They were followed by logicians who developed mechanical rules to carry out logical deductions. In logic, as in grammar, a subject is what we make an assertion about, and a predicate is what we assert about the subject. Today, logic is considered to be the primary reasoning mechanism for solving problems. Logic allows us to sets up systems and criteria for distinguishing acceptable arguments from unacceptable arguments. The structure of arguments is based upon formal relations between the newly produced assertions and the previous ones. Through argument we can then express inferences. Inferences are the processes where new assertions may be produced from existing ones. When relationships are independent of the assertions themselves we call them ‘formal’. Through these processes, logic provides a mechanism for the extension of knowledge. As a result, logic provides prescriptions for reasoning by machines, as well as, by people. Traditionally, logic has been studied as a branch of philosophy. However, since the mid-1800’s logic has been commonly studied as a branch of mathematics and more recently as a branch of computer science. The scope of logic can therefore be extended to include reasoning using probability and causality. In addition, logic includes the study of structures of fallacious arguments and paradoxes. By logic then, we mean the study and application of the principles of reasoning, and the relationships between statements, concepts or propositions. Logic incorporates both the methods of reasoning and the validity of the results. In common language, we refer to logic in several ways; logic can be considered as a framework or system of reasoning, a particular mode or process of reasoning, or the guiding principles of a field or discipline. We also use the term "logical" to describe a reasoned approach to solve a problem or get to a decision, as opposed to the alternative "emotional" approaches to react or respond to a situation. As logic has developed, its scope has splintered int o many distinctive branches. These distinctions serve to formalize different forms of logic as a science. The distinctions between the various branches of logic lead to their limitations and expressive capabilities which are central issues to designing the Semantic Web languages. The following sections identify some of the more important distinctions. Deductive and Inductive Reasoning Originally, logic consisted only of deductive reasoning which was concerned with a premise and a resultant deduction. However, it is important to note that inductive reasoning – the study of deriving a reliable generalization from observations – has also been included in the study of logic. Correspondingly, we must distinguish between deductive validity and inductive validity. The notion of deductive validity can be rigorously stated for systems of formal logic in terms of the well-understood notions of semantics. An inference is deductively valid if and only if there is no possible situation in which all the premises are true and the conclusion false. Inductive validity on the other hand requires us to define a reliable generalization of some set of observations. The task of providing this definition may be approached in various ways, some of which use mathematical models of probability. Paradox A paradox is an apparently true statement that seems to lead to a contradiction or to a situation that defies intuition. Typically, either the statements in question do not really imply the contradiction; or the puzzling result is not really a contradiction; or the premises themselves are not all really true (or, cannot all be true together). The recognition of ambiguities, equivocations, and unstated assumptions underlying known paradoxes has often led to significant advances in science, philosophy and mathematics. Formal and Informal Logic Formal logic (sometimes called ‘symbolic logic’) attempts to capture the nature of logical truth and inference in formal systems. This consists of a formal language, a set of rules of derivation (often called ‘rules of inference’), and sometimes a set of axioms. The formal language consists of a set of discrete symbols, a syntax (i.e., the rules for the construction of a statement), and a semantics (i.e., the relationship between symbols or groups of symbols and their meanings). Expressions in formal logic are often called ‘formulas.’ The rules of derivation and potential axioms then operate with the language to specify a set of theorems, which are formulas that are either basic axioms or true statements that are derivable using the axioms and rules of derivation. In the case of formal logic systems, the theorems are often interpretable as expressing logical truths (called tautologies). Formal logic encompasses a wide variety of logic systems. For instance, propositional logic and predicate logic are kinds of formal logic, as well as temporal logic, modal logic, Hoare logic and the calculus of constructions. Higher-order logics are logical systems based on a hierarchy of types. For example, Hoare logic is a formal system developed by the British computer scientist C. A. R. Hoare. The purpose of the system is to provide a set of logical rules in order to reason about the correctness of computer programs with the rigor of mathematical logic. The purpose of such a system is to provide a set of logical rules by which to reason about the correctness of computer programs with the rigor of mathematical logic. The central feature of Hoare logic is the Hoare triple. A triple describes how the execution of a piece of code changes the state of the computation. A Hoare triple is of the form: {P} C {Q} where P and Q are assertions and C is a command. P is called the precondition and Q the post-condition. Assertions are formulas in predicate logic. An interpretation of such a triple is: Whenever P holds of the state before the execution of C, then Q will hold afterwards. Alternatively, informal logic is the study of logic that is used in natural language arguments. Informal logic is complicated by the fact that it may be very hard to extract the formal logical structure embedded in an argument. Informal logic is also more difficult because the semantics of natural language assertions is much more complicated than the semantics of formal logical systems. Mathematical Logic Mathematical logic really refers to two distinct areas of research: the first is the application of the techniques of formal logic to mathematics and mathematical reasoning, and the second, the application of mathematical techniques to the representation and analysis of formal logic. The boldest attempt to apply logic to mathematics was pioneered by philosopher-logician Bertrand Russell. His idea was that mathematical theories were logical tautologies, and his program was to show this by means to a reduction of mathematics to logic. The various attempts to carry this out met with a series of failures, such as Russell's Paradox, and the defeat of Hilbert's Program by Gödel's incompleteness theorems (which we shall describe shortly). Russell's paradox represents either of two interrelated logical contradictions. The first is a contradiction arising in the logic of sets or classes. Some sets can be members of themselves, while others can not. The set of all sets is itself a set, and so it seems to be a member of itself. The null or empty set, however, must not be a member of itself. However, suppose that we can form a set of all sets that, like the null set, are not included in themselves. The paradox arises from asking the question of whether this set is a member of itself. It is, if and only if, it is not! The second form is a contradiction involving properties. Some properties seem to apply to themselves, while others do not. The property of being a property is itself a property, while the property of being a table is not, itself, a table. Hilbert's Program was developed in the early 1920s, by German mathematician David Hilbert. It called for a formalization of all of mathematics in axiomatic form, together with a proof that this axiomatization of mathematics is consistent. The consistency proof itself was to be carried out using only what Hilbert called ‘finitary’ methods. The special epistemological character of this type of reasoning yielded the required justification of classical mathematics. It was also a great influence on Kurt Gödel, whose work on the incompleteness theorems was motivated by Hilbert's Program. In spite of the fact that Gödel's work is generally taken to prove that Hilbert's Program cannot be carried out, Hilbert's Program has nevertheless continued to be influential in the philosophy of mathematics, and work on Revitalized Hilbert Programs has been central to the development of proof theory. Both the statement of Hilbert's Program and its refutation by Gödel depended upon their work establishing the second area of mathematical logic, the application of mathematics to logic in the form of proof theory. Despite the negative nature of Gödel's incompleteness theorems, a result in model theory can be understood as showing how close logics came to being true: every rigorously defined mathematical theory can be exactly captured by a First-Order Logical (FOL) theory. Thus it is apparent that the two areas of mathematical logic are complementary. Logic is extensively applied in the fields of artificial intelligence and computer science. These fields provide a rich source of problems in formal logic. In the 1950s and 60s, researchers predicted that when human knowledge could be expressed using logic with mathematical notation, it would be possible to create a machine that reasons, or produces artificial intelligence. This turned out to be more difficult than expected because of the complexity of human reasoning. In logic programming, a program consists of a set of axioms and rules. In symbolic logic and mathematical logic, proofs by humans can be computer-assisted. Using automated theorem proving, machines can find and check proofs, as well as work with proofs too lengthy to be written out by hand. However, the computation complexity of carrying out automated theorem proving is a serious limitation. It is a limitation that we will find in subsequent chapters significantly impacts the Semantic Web. Decidability In the 1930s, the mathematical logician, Kurt Gödel shook the world of mathematics when he established that, in certain important mathematical domains, there are problems that cannot be solved or propositions that cannot be proved, or disproved, and are therefore undecidable. Whether a certain statement of first order logic is provable as a theorem is one example; and whether a polynomial equation in several variables has integer solutions is another. While humans solve problems in these domains all the time, it is not certain that arbitrary problems in these domains can always be solved. This is relevant for artificial intelligence since it is important to establish the boundaries for a problem’s solution. Kurt Gödel Kurt Gödel (shown Figure 2-1) was born April 28, 1906 in Brünn, Austria-Hungary (now Brno, Czech Republic). He had rheumatic fever when he was six years old and his health became a chronic concern over his lifetime. Kurt entered the University of Vienna in 1923 where he was influenced by the lectures of Wilhelm Furtwängler. Furtwängler was an outstanding mathematician and teacher, but in addition he was paralyzed from the neck down, and this forced him to lecture from a wheel chair with an assistant to write on the board. This made a big impression on Gödel who was very conscious of his own health. As an undergraduate Gödel studied Russell's book Introduction to Mathematical Philosophy. He completed his doctoral dissertation under Hans Hahn in 1929. His thesis proved the completeness of the first order functional calculus. He subsequently became a member of the faculty of the University of Vienna, where he belonged to the school of logical positivism until 1938. Gödel is best known for his 1931 proof of the "Incompleteness Theorems." He proved fundamental results about axiomatic systems showing that in any axiomatic mathematical system there are propositions that cannot be proved or disproved within the axioms of the system. In particular, the consistency of the axioms cannot be proved. This ended a hundred years of attempts to establish axioms and axiom-based logic systems which would put the whole of mathematics on this basis. One major attempt had been by Bertrand Russell with Principia Mathematica (1910-13). Another was Hilbert's formalism which was dealt a severe blow by Gödel's results. The theorem did not destroy the fundamental idea of formalism, but it did demonstrate that any system would have to be more comprehensive than that envisaged by Hilbert. One consequence of Gödel's results implied that a computer can never be programmed to answer all mathematical questions. In 1935, Gödel proved important results on the consistency of the axiom of choice with the other axioms of set theory. He visited Göttingen in the summer of 1938, lecturing there on his set theory research and returned to Vienna to marry Adele Porkert in 1938. After settling in the United States, Gödel again produced work of the greatest importance. His “Consistency of the axiom of choice and of the generalized continuum-hypothesis with the axioms of set theory” (1940) is a classic of modern mathematics. In this he proved that if an axiomatic system of set theory of the type proposed by Russell and Whitehead in Principia Mathematica is consistent, then it will remain so when the axiom of choice and the generalized continuum-hypothesis are added to the system. This did not prove that these axioms were independent of the other axioms of set theory, but when this was finally established by Cohen in 1963 he used the ideas of Gödel. Gödel held a chair at Princeton from 1953 until his death in 1978. Propositional Logic Propositional logic (or calculus) is a branch of symbolic logic dealing with propositions as units and with the combinations and connectives that relate them. It can be defined as the branch of symbolic logic that deals with the relationships formed between propositions by connectives such as compounds and connectives shown below: Symbols Statement Connectives p q "either p is true, or q is true, or both" disjunction p · q "both p and q are true" conjunction p q "if p is true, then q is true" implication p q "p and q are either both true or both false" equivalence A ‘truth table’ is a complete list of the possible truth values of a statement. We use "T" to mean "true", and "F" to mean "false" (or "1" and "0" respectively). Truth tables are adequate to test validity, tautology, contradiction, contingency, consistency, and equivalence. This is important because truth tables are a mechanical application of the rules. Propositional calculus is a formal system for deduction whose atomic formulas are propositional variables. In propositional calculus, the language consists of propositional variables (or placeholders) and sentential operators (or connectives). A well-formed formula is any atomic formula or a formula built up from sentential operators. First-Order Logic (FOL) First-Order Logic (FOL), also known as first-order predicate calculus, is a systematic approach to logic based on the formulation of quantifiable statements such as "there exists an x such that..." or "for any x, it is the case that...”. A first-order logic theory is a logical system that can be derived from a set of axioms as an extension of first-order logic. FOL is distinguished from higher order logic in that the values "x" in the FOL statements are individual values and not properties. Even with this restriction, first-order logic is capable of formalizing all of set theory and most of mathematics. Its restriction to quantification of individual properties makes it difficult to use for the purposes of topology, but it is the classical logical theory underlying mathematics. The branch of mathematics called Model Theory is primarily concerned with connections between first order properties and first order structures. First order languages are by their nature very restrictive and as a result many questions can not be discussed using them. On the other hand first-order logics have precise grammars. Predicate calculus is quantificational and based on atomic formulas that are propositional functions and modal logic. In Predicate calculus, as in grammar, a subject is what we make an assertion about, and a predicate is what we assert about the subject. Automated Inference for FOL Automated inference using first-order logic is harder than using Propositional Logic because variables can take on potentially an infinite number of possible values from their domain. Hence there are potentially an infinite number of ways to apply the Universal-Elimination rule of inference. Godel's Completeness Theorem says that FOL is only semi-decidable. That is, if a sentence is true given a set of axioms, there is a procedure that will determine this. However, if the sentence is false, then there is no guarantee that a procedure will ever determine this. In other words, the procedure may never halt in this case. As a result, the Truth Table method of inference is not complete for FOL because the truth table size may be infinite. Natural deduction is complete for FOL, but is not practical for automated inference because the ‘branching factor’ in the search process is too large. This is the result of the necessity to try every inference rule in every possible way using the set of known sentences. Let us consider the rule of inference known as Modus Ponens (MP). Modus Ponens is a rule of inference pertaining to the IF/THEN operator. Modus Ponens states that if the antecedent of a conditional is true, then the consequent must also be true: (MP) Given the statements p and if p then q, infer q. The Generalized Modus Ponens (GMP) is not complete for FOL. However, Generalized Modus Ponens is complete for Knowledge Bases (KBs) containing only Horn clauses. An other very important logic that we shall discuss in detail in chapter 8 is Horn logic. A Horn clause is a sentence of the form: (Ax) (P1(x) ^ P2(x) ^ ... ^ Pn(x)) => Q(x) where there are 0 or more Pi's, and the Pi's and Q are positive (i.e., un-negated) literals. Horn clauses represent a subset of the set of sentences representable in FOL. For example: P(a) v Q(a) is a sentence in FOL, but is not a Horn clause. Natural deduction using GMP is complete for KBs containing only Horn clauses. Proofs start with the given axioms/premises in KB, deriving new sentences using GMP until the goal/query sentence is derived. This defines a forward chaining inference procedure because it moves "forward" from the KB to the goal. For example: KB = All cats like fish, cats eat everything they like, and Molly is a cat. In first-order logic then, (1) KB = (Ax) cat(x) => likes(x, Fish) (2) (Ax)(Ay) (cat(x) ^ likes(x,y)) => eats(x,y) (3) cat(Molly) Query: Does Molly eat fish? Proof: Use GMP with (1) and (3) to derive: (4) likes(Molly, Fish) Use GMP with (3), (4) and (2) to derive: eats(Molly, Fish) Conclusion: Yes, Molly eats fish. Description Logic Description Logics (DLs) allow specifying a terminological hierarchy using a restricted set of first-order formulas. DLs have nice computational properties (they are often decidable and tractable), but the inference services are restricted to classification and subsumption. That means, given formulae describing classes, the classifier associated with certain description logic will place them inside a hierarchy. Given an instance description, the classifier will determine the most specific classes to which the instance belongs. From a modeling point of view, Description Logics correspond to Predicate Logic statements with three variables suggesting that modeling is syntactically bound. Descriptive Logic is one possibility for Inference Engines for the Semantic Web. Another possibility is based on Horn-logic, which is another subset of First-Order Predicate logic (see Figure 2-2). In addition, Descriptive Logic and rule systems (e.g., Horn Logic) are somewhat orthogonal which means that they overlap, but one does not subsume the other. In other words, there are capabilities in Horn logic that are complementary to those available for Descriptive Logic. Both Descriptive Logic and Horn Logic are critical branches of logic that highlight essential limitations and expressive powers which are central issues to designing the Semantic Web languages. We will discuss them further in chapter 8. Using Full First-Order Logic (FFOL) for specifying axioms requires a full-fledged automated theorem prover. However, FOL is semi-decidable and doing inferencing becomes computationally untractable for large amounts of data and axioms. This means, than in an environment like the Web, FFOL programs will not scale to handle huge amounts of knowledge. Besides full first theorem proving would mean maintaining consistency throughout the Web, which is impossible. Description Logic fragment of FOL. FOL includes expressiveness beyond the overlap, notably: positive disjunctions; existentials; and entailment of non-ground and non-atomic conclusions. Horn FOL is another fragment of FOL. Horn Logic Program (LP) is a slight weakening of Horn FOL. "Weakening" here means that the conclusions from a given set of Horn premises that are entailed according to the Horn LP formalism are a subset of the conclusions entailed (from that same set of premises) according to the Horn FOL formalism. However, the set of ground atomic conclusions is the same in the Horn LP as in the Horn FOL. For most practical purposes (e.g., relational database query answering), Horn LP is thus essentially similar in its power to the Horn FOL. Horn LP is a fragment of both FOL and nonmonotonic LP. This discussion may seem esoteric, but it is precisely these types of issues that will decide both the design of the Semantic Web as well as is likelihood to succeed. Higher Order Logic Higher Order Logics (HOL's) provide greater expressive power than FOL, but they are even more difficult computationally. For example, in HOL's, one can have true statements that are not provable (see discussion of Gödel’s Incompleteness Theorem). There are two aspects of this issue: higher-order syntax and higher-order semantics. If a higher-order semantics is not needed (and this is often the case), a second-order logic can often be translated into a first-order logic. In first-order semantics, variables can only range over domains of individuals or over the names of predicates and functions, but not over sets as such. In higher-order syntax, variables are allowed to appear in places where normally predicate or function symbols appear. Predicate calculus is the primary example of logic where syntax and semantics are both first-order. There are logics that have higher-order syntax but first-order semantics. Under a higher-order semantics, an equation between predicate (or function) symbols, is true, if and only if logics with a higher-order semantics and higher-order syntax are statements expressing trust about other statements. To state it another way, higher-order logic is distinguished from first-order logic in several ways. The first is the scope of quantifiers; in first-order logic, it is forbidden to quantify over predicates. The second way in which higher-order logic differs from first-order logic is in the constructions that are allowed in the underlying type theory. A higher-order predicate is a predicate that takes one or more other predicates as arguments. In general, a higher-order predicate of order n takes one or more (n − 1)th-order predicates as arguments (where n > 1). Recursion theory Recursion is the process a procedure goes through when one of the steps of the procedure involves rerunning a complete set of identical steps. In mathematics and computer science, recursion is a particular way of specifying a class of objects with the help of a reference to other objects of the class: a recursive definition defines objects in terms of the already defined objects of the class. A recursive process is one in which objects are defined in terms of other objects of the same type. Using a recurrence relation, an entire class of objects can be built up from a few initial values and a small number of rules. The Fibonacci numbers (i.e., the infinite sequence of numbers starting 0, 1, 1, 2, 3, 5, 8, 13, …, where the next number in the sequence is defined a s the sum of the previous two numbers) is a commonly known recursive set. The following is a recursive definition of person's ancestors: One's parents are one's ancestors (base case). The parents of any ancestor are also ancestors of the person under consideration (recursion step). Therefore, your ancestors include: your parents, and your parents' parents (grandparents), and your grandparents' parents, and everyone else you get by successively adding ancestors. It is convenient to think that a recursive definition defines objects in terms of "previously defined" member of the class. While recursive definitions are useful and widespread in mathematics, care must be taken to avoid self-recursion, in which an object is defined in terms of itself, leading to an infinite nesting (see Figure 1-1: “The Print Gallery” by M.C. Escher is a visual illustration of self-recursion). Knowledge Representation Let’s define what we mean by the fundamental terms “data,” “information,” “knowledge,” and "understanding." An item of data is a fundamental element of an application. Data can be represented by populations and labels. Data is raw; it exists and has no significance beyond its existence. It can exist in any form, usable or not. It does not have meaning by itself. Information on the other hand is an explicit association between items of data. Associations represent a function relating one set of things to another set of things. Information can be considered to be data that has been given meaning by way of relational connections. This "meaning" can be useful, but does not have to be. A relational database creates information from the data stored within it. Knowledge can be considered to be an appropriate collection of information, such that it is useful. Knowledge-based systems contain knowledge as well as information and data. A rule is an explicit functional association from a set of information things to a specific information thing. As a result, a rule is knowledge. We can construct information from data and knowledge from information and finally produce understanding from knowledge. Understanding lies at the highest level. Understanding is an interpolative and probabilistic process that is cognitive and analytical. It is the process by which one can take existing knowledge and synthesize new knowledge. One who has understanding can pursue useful actions because he can synthesize new knowledge or information from what is previously known (and understood). Understanding can build upon currently held information, knowledge, and understanding itself. AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge. An important element of AI is the principle that intelligent behavior can be achieved through processing of symbolic structures representing increments of knowledge. This has produced knowledge-representation languages that allow the representation and manipulation of knowledge to deduce new facts from the existing knowledge. The knowledge-representation language must have a well-defined syntax and semantics system while supporting inference. Three techniques have been popular to express knowledge representation and inference: (1) Logic-based approaches, (2) Rule-based systems, and (3) Frames and semantic networks. Logic-based approaches use logical formulas to represent complex relationships. They require a well-defined syntax, semantics, and proof theory. The formal power of a logical theorem proof can be applied to knowledge to derive new knowledge. Logic is used as the formalism for programming languages and databases. It can also be used as a formalism to implement knowledge methodology. Any formalism that admits a declarative semantics and can be interpreted both as a programming language and a database language is a knowledge language. However, the approach is inflexible and requires great precision in stating the logical relationships. In some cases, common sense inferences and conclusions cannot be derived, and the approach may be inefficient, especially when dealing with issues that result in large combinations of objects or concepts. Rule-based approaches are more flexible and allow the representation of knowledge using sets of IF-THEN or other conditional rules. This approach is more procedural and less formal in its logic. As a result, reasoning can be controlled through a forward or backward chaining interpreter. Frames and semantic networks capture declarative information about related objects and concepts where there is a clear class hierarchy and where the principle of inheritance can be used to infer the characteristics of members of a subclass. The two forms of reasoning in this technique are matching (i.e., identification of objects having common properties), and property inheritance in which properties are inferred for a subclass. Frames and semantic networks are limited to representation and inference of relatively simple systems. In each of these approaches, the knowledge-representation component (i.e., problem-specific rules and facts) is separate from the problem-solving and inference procedures. For the Semantic Web to function, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. AI researchers have studied such systems and produced today’s Knowledge Representation (KR). KR is currently in a state comparable to that of hypertext before the advent of the Web. Knowledge representation contains the seeds of important applications, but to fully realize its potential, it must be linked into a comprehensive global system. Computational Logic Programming a computer involves creating a sequence of logical instructions that the computer will use to perform a wide variety of tasks. While it is possible to create programs directly in machine language, it is uncommon for programmers to work at this level because of the abstract nature of the instructions. It is better to write programs in a simple text file using a high-level programming language which can later be compiled into executable code. The ‘logic model’ for programming is a basic element that communicates the logic behind a program. A logic model can be a graphic representation of a program illustrating the logical relationships between program elements and the flow of calculation, data manipulation or decisions as the program executes its steps. Logic models typically use diagrams, flow sheets, or some other type of visual schematic to convey relationships between programmatic inputs, processes, and outcomes. Logic models attempt to show the links in a chain of reasoning about relationships to the desired goal. The desired goal is usually shown as the last link in the model. A logic program may consist of a set of axioms and a goal statement. The logic form can be a set of ‘IF-THEN’ statements. The rules of inference are applied to determine whether the axioms are sufficient to ensure the truth of the goal statement. The execution of a logic program corresponds to the construction of a proof of the goal statement from the axioms. In the logic programming model the programmer is responsible for specifying the basic logical relationships and does not specify the manner in which the inference rules are applied. Thus Logic + Control = Algorithms The operational semantics of logic programs correspond to logical inference. The declarative semantics of logic programs are derived from the term model. The denotation of semantics in logic programs are defined in terms of a function which assigns meaning to the program. There is a close relation between the axiomatic semantics of imperative programs and logic programs. The control portion of the equation is provided by an inference engine whose role is to derive theorems based on the set of axioms provided by the programmer. The inference engine uses the operations of resolution and unification to construct proofs. Faulty logic models occur when the essential problem has not been clearly stated or defined. Program developers work carefully to construct logic models to avoid logic conflicts, recursive loops, and paradoxes within their computer programs. As a result, programming logic should lead to executable code without paradox or conflict, if it is flawlessly produced. Nevertheless we know that ‘bugs’ or programming errors do occur, some of which are directly or indirectly a result of logic conflicts. As programs have grown in size from thousands of line of code to millions of lines, the problems of ‘bugs’ and logic conflicts have also grown. Today programs such as operating systems can have over 25 million lines of codes and considered to have hundreds of thousands of ‘bugs’ most of which are seldom encountered during routine program usage. Confining logic issues to beta-testing on local servers allows programmers reasonable control of conflict resolution. Now consider applying many lines of application code logic to the Semantic Web were it may access many information nodes. The magnitude of the potential conflicts could be somewhat daunting. Artificial Intelligence John McCarthy of MIT contributed the term ‘Artificial Intelligence’ (AI) and by the late 1950s, there were many researchers in AI working on programming computers. Eventually, AI expanded into such fields as philosophy, psychology and biology. AI is sometimes described in two ways: strong AI and weak AI. Strong AI asserts that computers can be made to think on a level equal to humans. Weak AI simply holds that some ‘thinking-like’ features can be added to computers to make them more useful tools. Examples of Weak AI abound: expert systems, drive-by-wire cars, smart browsers, and speech recognition software. These weak AI components may, when combined, begin to approach the expectations of strong AI. AI includes the study of computers that can perform cognitive tasks including: understanding natural language statements, recognizing visual patterns or scenes, diagnosing diseases or illnesses, solving mathematical problems, performing financial analyses, learning new procedures for problem solving, and playing complex games, like chess. We will provide a more detailed discussion on Artificial Intelligence on the Web and what is meant by machine intelligence in Chapter 3. Web Architecture and Business Logic So far we have explored the basic elements, characteristics, and limitations of logic and suggested that errors in logic contribute to many significant ‘bugs’ that lead to crashed computer programs. Next we will review how Web architecture is used to partition the delivery of business logic from the user interface. The Web architecture keeps the logic restricted to executable code residing on the server and delivering user interface presentations residing within the markup languages traveling along the Internet. This simple arrangement of segregating the complexity of logic to the executable programs residing on servers has minimized processing difficulties over the Web itself. Today, markup languages are not equipped with logic connectives. So all complex logic and detailed calculations must be carried out by specially compiled programs residing on Web servers where they are accessed by server page frameworks. The result is highly efficient application programs on the server must communicate very inefficiently with other proprietary applications using XML in simple ASCII text. In addition, there is difficulty in interoperable programming which greatly inhibits automation of Web Services. Browsers such as Internet Explorer and Netscape Navigator view Web pages written in HyperText Markup Language (HTML). The HTML program can be written to a simple text file that is recognized by the browser and it can call embedded script programming. In addition, HTML can include compiler directives that call server pages with access to proprietary compiled programming. As a result, simple-text HTML is empowered with important capabilities to call complex business logic programming residing on servers both in the frameworks of Microsoft’s .NET and Sun’s J2EE. These frameworks support Web Services and form a vital part of today’s Web. When a request comes into the Web server, the Web server simply passes the request to the program best able to handle it. The Web server doesn't provide any functionality beyond simply providing an environment in which the server-side program can execute and pass back the generated responses. The server-side program provides functions as transaction processing, database connectivity, and messaging. Business logic is concerned with logic about: how we model real world business objects - such as accounts, loans, travel; how these objects are stored; how these objects interact with each other - e.g. a bank account must have an owner and a bank holder's portfolio is the sum of his accounts; and who can access and update these objects. As an example, consider an online store that provides real-time pricing and availability information. The site will provide a form for you to choose a product. When you submit your query, the site performs a lookup and returns the results embedded within an HTML page. The site may implement this functionality in numerous ways. The Web server delegates the response generation to a script, however, the business logic for the pricing lookup is included from an application server. With that change, instead of the script knowing how to look up the data and formulate a response, the script can simply call the application server's lookup service. The script can then use the service's result when the script generates its HTML response. The application server serves the business logic for looking up a product's pricing information. That functionality doesn't say anything about display or how the client must use the information. Instead, the client and application server send data back and forth. When a client calls the application server's lookup service, the service simply looks up the information and returns it to the client. By separating the pricing logic from the HTML response-generating code, the pricing logic becomes reusable between applications. A second client, such as a cash register, could also call the same service as a clerk checking out a customer. Recently, eXtensible Markup Language (XML) Web Services use an XML payload to a Web server. The Web server can then process the data and respond much as application servers have in the past. XML has become the standard for data transfer of all types of applications. XML provides a data model that is supported by most data-handling tools and vendors. Structuring data as XML allows hierarchical, graph-based representations of the data to be presented to tools, which opens up a host of possibilities. The task of creating and deploying Web Services automatically requires interoperable standards. The most advanced vision for the next generation of Web Services is the development of Web Services over Semantic Web Architecture. The Semantic Web Now let’s consider using logic within markup languages on the Semantic Web. This means empowering the Web’s expressive capability, but at the expense of reducing Web performance. The current Web is built on HTML and XML, which describes how information is to be displayed and laid out on a Web page for humans to read. In addition, HTML is not capable of being directly exploited by information retrieval techniques. XML may have enabled the exchange of data across the Web, but it says nothing about the meaning of that data. In effect, the Web has developed as a medium for humans without a focus on data that could be processed automatically. As a result, computers are unable to automatically process the meaning of Web content. For machines to perform useful automatic reasoning tasks on these documents, the language machines use must go beyond the basic semantics of XML Schema. They will require an ontology language, logic connectives, and rule systems. By introducing these elements the Semantic Web is intended to be a paradigm shift just as powerful as the original Web. The Semantic Web will bring meaning to the content of Web pages, where software agents roaming from page-to-page can carry out automated tasks. The Semantic Web will be constructed over the Resource Description Framework (RDF) and Web Ontology Language (OWL). In addition, it will implement logic inference and rule systems. These languages are being developed by the W3C. Data can be defined and linked using RDF and OWL so that there is more effective discovery, automation, integration, and reuse across different applications. These languages are conceptually richer than HTML and allow representation of the meaning and structure of content (interrelationships between concepts). This makes Web content understandable by software agents, opening the way to a whole new generation of technologies for information processing, retrieval, and analysis. If a developer publishes data in XML on the Web, it doesn’t require much more effort to take the extra step and publish the data in RDF. By creating ontologies to describe data, intelligent applications won’t have to spend time translating various XML schemas. An ontology defines the terms used to describe and represent an area of knowledge. Although XML Schema is sufficient for exchanging data between parties who have agreed to the definitions beforehand, their lack of semantics prevents machines from reliably performing this task with new XML vocabularies. In addition, the ontology of RDF and RDF Schema (RDFS) is very limited (see Chapter 5). RDF is roughly limited to binary ground predicates and RDF Schema is roughly limited to a subclass hierarchy and a property hierarchy with domain and range definitions. Adding an Ontology language will permit the development of explicit, formal conceptualizations of models (see Chapter 6). The main requirements of an onotology language include: a well-defined syntax, a formal semantics, convenience of expression, n efficient reasoning support system, and sufficient expressive power. Since the W3C has established that the Semantic Web would require much more expressive power than using RDF and RDF Schema would offer, the W3C has defined Web Ontology Language (called OWL). The layered architecture of the Semantic Web would suggest that one way to develop the necessary ontology language is to extend RDF Schema by using the RDF meaning of classes and properties and adding primitives to support richer expressiveness. However, simply extending RDF Schema would fail to achieve the best combination of expressive power and efficient reasoning. The layered architecture of the Semantic Web promotes the downward compatibility and reuse of software is only achieved with OWL Full (see Chapter 6), but at the expense of computational intractability. RDF and OWL (DL and Lite, see Chapter 6) are specializations of predicate logic. They provide a syntax that fits well with Web languages. They also define reasonable subsets of logic that offer a trade-off between expressive power and computational complexity. Semantic Web research has developed from the traditions of Artificial Intelligence (AI) and ontology languages. Currently, the most important ontology languages on the Web are XML, XML Schema, RDF, RDF Schema, and OWL. Agents are pieces of software that work autonomously and proactively. In most cases agent will simply collect and organize information. Agents on the Semantic Web will receive some tasks to perform and seek information from Web resources, while communicating with other Web agents, in order to fulfill its task. Semantic Web agents will utilize metadata, ontologies, and logic to carry out its tasks. In a closed environment, Semantic Web specifications have already been used to accomplish many tasks, such as data interoperability for business-to-business (B2B) transactions. Many companies have expended resources to translate their internal data syntax for their partners. As the world migrates towards RDF and ontologies, interoperability will become more flexible to new demands. An inference is a process of using rules to manipulate knowledge to produce new knowledge. Adding logic to the Web means using rules to make inferences and choosing a course of action. The logic must be powerful enough to describe complex properties of objects, but not so powerful that agents can be tricked by a paradox. A combination of mathematical and engineering issues complicates this task. We will provide a more detailed presentation on paradoxes on the Web and what is solvable on the Web in the next few chapters. Inference Engines for the Semantic Web Inference engines process the knowledge available in the Semantic Web by deducing new knowledge from already specified knowledge. Higher Order Logic (HOL) based inference engines have to greatest expressive power among all known logics such as the characterization of transitive closure. However, higher order logics don't have nice computational properties. There are true statements, which are unprovable (Gödel’s Incompleteness Theorem). Full First Order Logic (FFOL) based inference engines for specifying axioms requires a full-fledged automated theorem prover. FOL is semi-decidable and doing inferencing is computationally not tractable for large amounts of data and axioms. This means, than in an environment like the Web, HOL and FFOL programs would not scale up for handling huge amounts of knowledge. Besides full first theorem proving would mean to maintain consistency throughout the web, which is impossible. Predicate calculus is the primary example of logic where syntax and semantics are both first-order. From a modeling point of view, Description Logics correspond to Predicate Logic statements with three variables suggesting that modeling is syntactically bound and is a good candidate language for Web logic. Other possibilities for inference engines for the Semantic Web are languages based on Horn-logic, which is another fragment of First-Order Predicate logic (see Figure 2-2). In addition, Descriptive Logic and rule systems (e.g., Horn Logic) have different capabilities. Both Descriptive Logic and Horn Logic are critical branches of logic that highlight essential limitations and expressive powers which are central issues to designing the Semantic Web languages. We will discuss them further in chapters, 6, 7, 8 and 9. Conclusion For the Semantic Web to provide machine processing capabilities, the logic expressive power of mark-up languages must be balanced against the resulting computational complexity of reasoning. In this chapter, we examined both the expressive characteristics of logic languages, as well as, their inherit limitations. First Order Logics (FOL) fragments such as Descriptive Logic and Horn Logic offer attractive characteristics for Web applications and set the parameters for how expressive Web markup languages can become. We also reviewed the concept of Artificial Intelligence (AI) and how logic is applied in computer programming. After exploring the basic elements, characteristics, and limitations of logic and suggesting that errors in logic contribute to many significant ‘bugs’ that lead to crashed computer programs, we reviewed how Web architecture is used to partition the delivery of business logic from the user interface. The Web architecture keeps the logic restricted to executable code residing on the server and delivering user interface presentations residing within the markup languages traveling along the Internet. Finally, we discussed the implications of using logic within markup languages on the Web through the development of the Semantic Web. Our conclusions from this chapter include: Logic is the foundation of knowledge representation which can be applied to AI in general and the World Wide Web specially. Logic can provide a high-level language for expressing knowledge and has high expressive power. Logic has a well-understood formal semantics for assigning unambiguous meaning to logic statements. In addition, we saw that proof systems exist that can automatically derive statements syntactically from premises. Predicate logic uniquely offers a sound and complete proof system while higher-order logics do not. By tracking the proof to reach its consequence the logic can provide explanations for the answers. Currently, complex logic and detailed calculations must be carried out by specially compiled programs residing on Web servers where they are accessed by server page frameworks. The result is highly efficient application programs on the server must communicate very inefficiently with other proprietary applications using XML in simple ASCII text. In addition, this difficulty for interoperable programs greatly inhibits automation of Web Services. The Semantic Web offers a way to use logic in the form of Descriptive Logic or Horn Logic on the Web. Exercises 2-1. Explain how logic for complex business calculations is currently carried out through .NET and J2EE application servers. 2-2. Explain the difference between FOL and HOL. 2-3. Why is it necessary to consider less powerful expressive languages for the Semantic Web? 2-4. Why is undeciability a concern on the Web? Website http://escherdroste.math.leidenuniv.nl/ offers visualize the mathematical structure behind Escher's Print Gallery using the Escher and the Droste effect. This mathematical structure answers some questions about Escher's picture, such as: "what's in the blurry white hole in the middle?" This project is an initiative of Hendrik Lenstra of the Universiteit Leiden and the University of California at Berkeley. Bart de Smit of the Universiteit Leiden runs the project. Interlude #2: Truth and Beauty As John passed with a sour look on his face, Mary looked up from her text book and asked, “Didn’t you enjoy the soccer game?” “How can you even ask that when we lost?” asked John gloomily. “I think the team performed beautifully, despite the score” said Mary. This instantly frustrated John and he said, "Do you know Mary that sometimes I find it disarming the way you express objects in terms of beauty. I find that simply accepting something on the basis of its beauty can lead to false conclusions?" Mary reflected upon this before offering a gambit of her own, "Well John, do you know that sometimes I find that relying on objective truth alone can lead to unattractive conclusions." John became flustered and reflected his dismay by demanding, "Give me an example." Without hesitation, Mary said, "Perhaps you will recall that in the late 1920's, mathematicians were quite certain that every well-posed mathematical question had to have a definite answer ─ either true or false. For example, suppose they claimed that every even number was the sum of two prime numbers,” referring to Goldbach's Conjecture which she had just been studying in her text book. Mary continued, “Mathematicians would seek the truth or falsity of the claim by examining a chain of logical reasoning that would lead in a finite number of steps to prove if the claim were either true or false." "So mathematicians thought at the time," said John. "Even today most people still do." "Indeed," said Mary. "But in 1931, logician Kurt Gödel proved that the mathematicians were wrong. He showed that every sufficiently expressive logical system must contain at least one statement that can be neither proved nor disproved following the logical rules of that system. Gödel proved that not every mathematical question has to have a yes or no answer. Even a simple question about numbers may be undecidable. In fact, Gödel proved that t here exist questions that while being undecidable by the rules of logical system can be seen to be actually true if we jump outside that system. But they cannot be proven to be true.” “Thank you for that clear explanation,” said John. “But isn’t such a fact simply a translation into mathematic terms of the famous Liar’s Paradox: ‘This statement is false.’” “Well, I think it's a little more complicated than that,” said Mary. “But Gödel did identify the problem of self-reference that occurs in the Liar’s Paradox. Nevertheless, Gödel’s theorem contradicted the thinking of most of the great mathematicians of his time. The result is that one can not be as certain as the mathematician had desired. See what I mean, Gödel may have found an important truth, but it was – well to be frank – rather disappointingly unattractive," concluded Mary. "On the contrary,” countered John, “from my perspective it was the beauty of the well-posed mathematical question offered by the mathematicians that was proven to be false. Mary replied, “I’ll have to think about that.”

Excerpt of short story collection book, All Androids Lie. All Androids Lie AMAZON THE GAME Kateryna said, “Hold still, Dear,” as she wiped the dirty smudge off the corner of Maria’s mouth. Maria asked, “Why is everyone so excited?” Kateryna said, “They’re scared of the loud noise.” “What is it?” “Fireworks. See the bright flashes exploding in the night sky,” said the girl’s mother. Maria nodded. “It’s the start of The Game,” lied her mother. “I told you all about it. Don’t you remember?” Maria shook her head, puzzled. “Everyone in the city plays, and there are terrific prizes.” Kateryna added, “What a pity you’re only four. You can’t play. I’m sooo sorry. You might have been great.” “What’s the game?” “It’s a big, big game of tag. Everyone in the city will run to escape. If you’re tagged, you lose. Everyone wants to win. It’s too bad you can’t play.” “Why can’t I play?” Kateryna said, “You’re only four. You’d get tired, cry, and make a fuss.” “I won’t. I won’t make a fuss.” “You would have been good at this game. The prizes are spectacular. Including that new doll, Laura, that you wanted so badly.” “If I win, I will get Laura?” “Yes, and lots more.” Outside, people were running and shouting. “There are candies, treats, games, and other toys for the winners. But you could never win. You would cry and quit.” “No, Mommy. I’ll be good. I want to play and win the prizes.” “I’m so sorry, Dear. The game is long and hard, and I don’t think you’re strong enough.” “Oh, Mommy, I really, really want to. I promise to be good.” Maria looked as if she was ready to throw a tantrum. “None of that, or you will lose immediately,” scolded her mother. “Please?” she asked with the most adoring smile. “Well, I don’t know,” said her mother. “There are many people who can tag you, and you must run away from all of them.” “I will. Please?” Kateryna looked appreciatively at her fair-haired daughter. The prekindergarten teacher told her that Maria was her star pupil because she was so advanced with her numbers and letters. She loved her toy piano and played well with the other children. Kateryna could see herself in the child, not just in the likeness of her face and features but in spirit and desire. Normally, a good-natured and happy-go-lucky sort of woman, she felt she could rise to any challenge. And now, she faced her fiercest test. “If I let you play, there can be no quitting. Do you agree? Pinky Swear?” “Yes! Yes! Pinky Swear,” said Maria jumping up and down. Static from the radio crackled behind them. The news announcer said, “This city has been a center for trade and manufacturing for key businesses along the Black Sea coast. But now its magnificent architecture and unique decor are being wiped off the face of the earth.” With steely determination, Kateryna suppressed her fears and shut the radio off. As the explosions drew near, she calmly said, “Let’s get ready! “Keep these documents safe,” Kateryna said, tucking the papers into Maria’s coat pocket. “They are the game tickets with your name. The rules of the game are strict. And you must reach the winning flag without being tagged. You must stay close to me and don’t talk to people. Do you understand?” “Yes.” “Whenever I say run, you run. Or else, the bad men will tag you.” Maria nodded. She put a scarf around Maria’s neck and buttoned up her coat. Then she pulled up the collar before being satisfied that she would be warm. “My gloves,” squeaked Maria. “Here they are.” As they left their apartment building and stepped out onto the street, they saw people leaving their houses in panic. “Are all these people playing the game?” “Yes. See how much fun they’re having. I told you it was a popular game. You must be tough to play. Are you tough?” “Yes. Mommy.” “Are you?” her mother asked with a raised brow. The skinny four-year-old put her hands on her hips, stood like a superhero with her chest out, and shouted, “I’m tough, and I mean it!” Fairly bursting with laughter, Kateryna said, “Okay, then. Let’s go,” Kateryna gripped the girl’s hand firmly and said, “This way.” As they hurried, there were loud explosions throughout the city. When they reached the train station, shells were bursting high above. “Gosh! Everything is happening so fast.” “Be patient, Dear.” They managed to squeeze onto a packed rail car, but the train was slow and made many erratic stops as if it were engaged in a game of dodgeball. Soon Maria complained, “The people are scary.” Kateryna touched the girl’s cheek and said, “Be brave. We’re on a great adventure. You must be bold.” But after two hours, Maria scowled and said, “I’m cold.” As Kateryna rearranged the girl’s scarf and coat, deep frown lines bit into her face threatening to become a permanent mask. She removed the girl’s gloves and rubbed the tiny hands. Then she planted a kiss on Maria’s rosy cheek. Maria pouted, “I’m hungry.” “Maria, you’re a troublesome thing.” Kateryna took a package out of her pocket and unwrapped a Kanapky sandwich for her. The girl took several bites and then looked disinterested in the rest. She sulked, “I’m thirsty.” “I don’t have any water,” said her exasperated mother. “But if you’re going to be a nasty girl, we will have to quit the game and go home immediately.” “Mommmm,” whined Maria. Nearby, a very old, cantankerous-looking woman, rumpled and wrinkled as a walnut, said, “Here, I have an extra.” She handed Maria a small water bottle. “Thank you. That’s generous of you,” said Kateryna with relief. After another hour, Maria pressed her face against the window, peering into the night as February’s frost crept along the windowpane, forming the jagged lines of an ice blossom. Suddenly, the train bounced and rocked. Pieces of steel and glass flew about. People screamed in pain. A bit of shrapnel cracked the skull of a nearby man. It made the sound of a champagne cork popping. THUNK! “Mommy, that man is bleeding.” “Shhh. It was an accident. He will be taken care of. We must keep moving.” They fled the train and the bombardment area. Kateryna gripped her daughter’s hand tightly and pulled her along as quickly as possible. When they reached a military checkpoint, a soldier told them it was safer to travel on the back roads. “He’s dressed like Daddy. Is Daddy playing too?” “Yes, Darling,” said Kateryna, holding back a tear. “I’m afraid he is.” “I’m scared, Mommy.” Gathering her courage, Kateryna said, “Don’t be frightened, Maria. Remember, it’s only a game. And we’re going to win. Just don’t let them tag you, okay.” “Huh ha.” In the early morning hours, the rosy glow of the sun kissed the horizon just as they reached the top of a hill. “Can we rest, Mommy? I’m tired.” “Not yet. See that bunker across the field? That’s the finish line. When we get there, we’ll win the prize.” “Oh good,” said Maria, perking up, but she could barely move. Kateryna picked her up and carried her. But after going only a hundred yards, Maria exclaimed, “Huh, oh. Mommy are those the bad men?” pointing to men with guns chasing them. Kateryna looked over her shoulder and said, “Yes, Maria. They are very bad men. Evil does not sleep; it waits for a chance to catch you. So, we must hurry.” She put Maria down and said, “See that bunker ahead. That’s the finish line. That’s where you turn in your ticket. Hold it fast to your chest.” Then she leaned closer and whispered, “I love you, Dearest,” though the sentiment seemed more like goodbye. “I love you too, Mommy,” said Maria clutching her ticket. The child’s words wrapped around Kateryna like a thick warm blanket. She yelled, “Run, Maria, run!” The noise from the blasts was terrific and the flashes of the overhead lights cast eerie shadows on their path. Cold breath steamed from their mouths as they huffed and puffed. Gripped by the full force of her worst fears, Kateryna yelled, “Run, Maria! Don’t look back! Run!” Maria ran with all the might and passion a four-year-old could muster. Finally, when she reached the bunker, a giant armor-clad soldier pulled her to safety. Maria jumped up and down and shouted over the din, “Did we win, Mommy? Did we win?” Then, suddenly, and loudly, Maria let out a cry that tore through the night. She sobbed unrelentingly, even as she stuttered out several snot-thick breaths. In the open field, just a dozen yards from the bunker, her mother lay face-down, sprawled out like a discarded rag doll.

excerpt of the technology book The Intelligent Wireless Web. The Intelligent Wireless Web AMAZON Chapter 10.0 Progress in Developing the Intelligent Wireless Web In this chapter, we take the components developed in earlier chapters and lay out a plausible framework for building the Intelligent Wireless Web including our evaluation of the compatibility, integration and synergy issues facing the five merging technology areas that will build it: User Interface – from click to speech Personal Space – from tangled wires to multifunction wireless devices Networks – from wired infrastructure to integrated wired/wireless Protocols – from IP to Mobile IP Web Architecture – from dumb and static to intelligent and dynamic. Finally, we present strategic planning guidelines and the conclusions you could reach as a result of this book. We began this book by describing what we meant by the “Intelligent Wireless Web and presenting an overview of the framework for plausibly constructing it. Our concept of an Intelligent Wireless Web weaves together several important concepts related to intelligence (the ability to learn), “wirelessness” (mobility and convenience) and its advances in telecommunications and information technology that together promised to deliver increasingly capable information services to mobile users anytime and anywhere. We suggested putting these concepts together to form the “Intelligent Wireless Web.” We stated that it was certainly possible to develop intelligent applications for the Internet without media (audio/video) Web features or wireless capability. But, it was our suggestion that Web media, such as, audio could lead to improved user interfaces using speech and that small wireless devices widely distributed could lead to easier access to large portions of the worlds population. The end result could be, not just an intelligent Internet but a widely available, easily accessible, user friendly, Intelligent Wireless Web. Fundamentally, our vision for an Intelligent Wireless Web is very simple - it is a network that provides anytime, anywhere access through efficient user interfaces to applications that learn. Notwithstanding the difficulty of defining intelligence (in humans or machines), we recognized that terms such as “artificial intelligence”, “intelligent agents”, “smart machines” and the like, refer to the performance of functions that mimic those associated with human intelligence. The full range of information services is the next logical step along with the introduction of a variety of different portable user devices (e.g., pagers, PDAs, web-enabled cell phones, small portable computers) that have wireless connectivity. The results will be wireless technology as an extension of the present evolutionary trend in information technology. In addition, Artificial Intelligence and intelligent software applications will also make their way onto the wireless Web and that a performance Index or measure should be developed to evaluate the progress of Web smarts. In the following sections, we will bring together the components of the Intelligent Wireless Web and how it is being constructed. But building it will be a broad and far-reaching task involving more technology integration and synthesis than revolutionary inventions. Future Wireless Communication Process Ideally, the future wireless communication process should start with a user interface based on speech recognition where we merely talk to a personal mobile device that recognizes our identity, words and commands. The personal mobile device would connect seamlessly to embedded and fixed devices in the immediate environment. The message would be relayed to a server residing on a network with the necessary processing power and software to analyzed the contents of the message. The server could then draw necessary supplemental knowledge and services from around the world through the Internet. Finally, the synthesized messages would be delivered to the appropriate parties in their own language on their own personal mobile device. To build this ideal future wireless communication process we must connect the following inherent technologies of communications along with their essential components: Connecting People to Devices – the user interface. Currently we rely on the mouse, keyboard and video display; speech recognition and understanding deployed for mobile devices is a key component for the future. Connecting Devices to Devices. Currently hard-wired connections between devices limit mobility and constrain the design of networks. In the future, the merging of wired and wireless communication infrastructure require the establishment of wireless protocols and standards for the connection between devices; future smart applications require the development and improvement of intelligence services. Also needed is a method to measure the performance and/or intelligence of the Internet so that we can assess advancements. Connecting Devices to People. To deliver useful information to the globally mobile user, future systems require advances in speech synthesis and language translation. So lets start connecting the necessary technologies to fulfill the vision of an Intelligent Wireless Web. The physical components and software necessary to construct and implement the Intelligent Wireless Web require compatibility, integration and synergy of five merging technology areas: < >User Interface – to transition from the mouse click to speech as the primary method of communication between people and devices;Personal Space – to transition from connection of devices by tangled wires to multifunction wireless devices;Networks – to transition from a mostly wired infrastructure to an integrated wired/wireless system of interconnections;Protocols – transition from the original Internet protocol (IP) to the new Mobile IP; andWeb Architecture – to transition from dumb and static applications to new applications that are intelligent, dynamic and constantly learning. FIGURE 10-1 Building the Intelligent Wireless Web User Interface – from Click to Speech We have evaluated communication between humans and their machines and found the problem of how to obtain speech recognition functionality in a handheld or embedded device to be challenging; however efforts currently underway look favorable for solutions in the relatively near term. While we may expect speech interfaces to permeate society steadily, we anticipate that successful traditional interfaces, such as, mouse and touch screen, will continue to be in operation for a very long time. Particularly, for such high power applications as selecting events on detailed graphical representations. Certainly, it is not a difficult problem for a handheld device (such as a cell phone) to perform limited speech recognition activities (such as voice activated dialing). But since the demands for speech functionality increase with the greater complexity of the speech recognition tasks, it becomes more and more difficult to provide these capabilities on a small mobile wireless device with limited capabilities. Therefore, the problem becomes one of distributing the capability for speech recognition and understanding between the local wireless device and remote processing resources to which it is connected. This problem is being currently addressed in far-reaching research at several places, but most notably at the MIT AI Laboratory and at Microsoft Research. The Microsoft effort is directed at technology projects supporting and leading to the vision of a fully speech enabled computer. The Microsoft concept Dr. Who, uses continuous speech recognition and spoken language understanding. Dr. Who is designed to power a voice-based pocket PC with a web browser, email, and cellular telephone capabilities. The highly promising initiative know as, Project Oxygen, is ongoing at MIT’s AI Laboratory. This visionary effort is developing a comprehensive system to achieve the objective of anytime anywhere computing. In this concept, a user carries a wireless interface device that is continuously connected to a network of computing devices in a manner similar to the way cell phone communications maintain continuous connection to a communications network. The local device is speech enabled, and much of the speech recognition capability is embedded in the remote system of high-capability computers. Systems for conversational interface are also being developed that are capable of communicating in several languages. These systems can answer queries in real-time with a distributed architecture that can retrieve data from several different domains of knowledge to answer a query. Such systems have five main functions: speech recognition, language understanding, information retrieval, language generation and speech synthesis. Speech recognition may be an ideal interface for the handheld devices being developed as part of the Oxygen project, but the Oxygen project will need far more advanced speech-recognition systems than are currently available to achieve its ultimate objective of enabling interactive conversation with full understanding. Figure 10-2 identifies the main requirements for an effective speech-based user interface and identifies the current status of each. To meet the needs of the Intelligent Wireless Web, the ultimate desired result is that speech recognition, understanding, translation and synthesis become practical for routine use on handheld, wearable and embedded devices. USER INTERFACE – from click to speech REQUIREMENTS STATUS Speech Recognition Speech Understanding Text to Speech Translation Speech Synthesis Speech Synthesis Markup Language Advanced Continuing Advanced Continuing Continuing Lagging Speech recognition, understanding, translation and synthesis become practical for use on handheld, wearable and embedded devices. RESULTS FIGURE 10-2 Building the User Interface Personal Space – from Wired to Wireless We imagined living our life within the confines of our own Personal Space - without wires, but with devices to “connect” us wherever we travel. Implementation of a Wireless Personal Area Network (WPAN), composed of the personal devices around us as well as our immediate environment is one solution. In the office, devices improve work productivity by enabling access to data, text, and images relating to performing our jobs and by providing for analysis, access to software applications and communications as needed. Creating a WPAN of our immediately available devices will enable a future where a lifetime of knowledge may be accessed through gateways worn on the body or placed within the immediate environment (including our home, auto, office, school, library, ect). WPAN will also allow devices to work together and share each other's information and services. For example, a web page can be called up on a small screen, and can then be wirelessly sent to a printer for full size printing. A mobile WPAN can even be created in a vehicle via interface devices such as wireless headsets, microphones and speakers for communications. As envisioned, WPAN will allow the user to customize his or her communications capabilities permitting everyday devices to become smart, tetherless devices that spontaneously communicate whenever they are in close proximity. Figure 10-3 summarizes the requirements and their status for this element of the Intelligent Wireless Web; the objective is the achieve the ability for handheld, wearable, and embedded devices to connect easily without wires and share software applications, as needed, producing office, home and mobile Wireless Personal Area Networks. PERSONAL SPACE – from wired to wireless REQUIREMENTS STATUS Advanced Continuing Lagging Lagging Adaptable wireless devices Wireless protocol Wireless small screen applications “Nomadic” or mobile software for devices Handheld, wearable, and embedded devices connect easily without wires and share software applications, as needed, producing office, home and mobile Wireless Personal Area Networks. RESULTS FIGURE 10 - 3 Building the Your Personal Space Networks – from Wired to Integrated Wired/Wireless The earliest computers were stand-alone, unconnected machines. During the 1980’s, mergers, takeovers and downsizing have led to a need to consolidate company data in fast, seamless, integrated database have for all corporate information. With these driving forces, Intranets and local networks began to increase in size, and this required ways to interface with each other. Over the past decade, enterprise models and architectures, as well as, their corresponding implementation in actual business practices have changed to take advantage of new technologies. The big lure to wireless is the potential for big money in implementing wireless architectures that can send information packets from people with small personal devices, such as cell phones, to the a company’s Web site and there to conduct transactions. The number of wireless subscribers is expected to grow globally from the current few million to more than 400 million by 2005. The vast system of interconnecting networks that comprise the Internet is composed of several different types of transmission media, dominated by wired media but including: < >WiredFiber opticTwisted pairs (copper)Coaxial cable < >WirelessMicrowaveInfraredLaser NETWORKS – from wired to integrated wired/wireless REQUIREMENTS STATUS Wireless LAN Wireless WAN Satellites Wired Interface Advanced Advanced Continuing Continuing Networks continue migration to optical fiber for long haul while last mile is met by both fiber, mobile wireless, and fixed wireless (LMDS & MDDS) RESULTS FIGURE 10-4 Building Integrated Networks Protocols – from IP to Mobile IP To achieve the mobility requirements of the Intelligent Wireless Web, the Wireless Appliance Protocol, WAP, provides a global standard for data-oriented services to mobile devices thereby enabling anywhere and anytime access. In so doing, access will be provided to far more end-users than can be reached by using the personal computer as a fixed end point. Figure 10-5 provides an overview of the needed changes to support the Intelligent Wireless Web. The anticipated result is to provide intelligent networking software for routing and tracking that leads to general changes in IP networking protocols toward mobile IP. Sitting on top of the entire layer infrastructure will be a new control-plan for applications that smooth routing. PROTOCOLS - from IP to Mobile IP Continuing Continuing IPv6 Mobile IP standard REQUIREMENTS STATUS Intelligent networking software for routing and tracking that leads to general changes in IP networking protocols toward mobile IP. Sitting on top of the entire layer infrastructure will be a new control-plan for applications that smooth routing. RESULTS FIGURE 10 - 5 Building the Mobile Internet Protocols Web Architecture - Dumb & Static to Intelligent & Dynamic Ideally, the wireless communication process should start with the user talking to a personal, or embedded, device that recognizes his identity, words and commands. It will connect seamlessly to the correct transmission device, drawing on whatever resources are required from around the Web. In one case, only database search sorting and retrieval might be required. Or in another case, a specialized Web Service application program might be required. In any case, the information will be evaluated, and the content of the message will be augmented with the appropriate supporting data to fill in the ‘blanks’. If there is appropriate supplementary audio, or video, it will be included for reference. Finally, the results will be delivered to the appropriate parties in their own language through their own different and varied connection devices. For the Web to learn how to conduct this type of intelligent processing requires a mechanism for the adapting and self-organizing on a hypertext network. In addition, it needs to develop Learning Algorithms that would allow it to autonomously change its structure and organize the knowledge it contains, by "learning" the ideas and preferences of its users. The World Wide Web Consortium (W3C) suggests the use of better semantic information as part of web documents, and of the use of next generation Web languages Figure 10-6 provides a summary of the semantic web architecture needed to support the Intelligent Wireless Web. Intelligent applications running directly over the Web, as well as, AI Web Services served from AI service providers will progressively increase the tasking performed with adaptive, dynamic intelligent products. In addition, a Web performance Index will provide some useful measures of Web progress. WEB ARCHITECTURE – from dumb and static to intelligent and dynamic REQUIREMENTS STATUS XMLschema RDF schema & Topic Maps Logic Layer Dynamic Languages Adaptive Applications Distributed AI AI Web Sevices Registration and Validation of Information Intelligent applications running directly over the Web, as well as, AI Web Services supported from AI service providers progressively increasing the percent of applications performed with adaptive, dynamic intelligent products. An overall increase can be expected in the total percentage of learning algorithms operating on the Web. RESULTS FIGURE 10- 6 Building AI Servers with the Semantic Strategic Planning Guidelines Strategic planning is the determination of the course of action and allocation of resources necessary to achieve selected long-term goals. But charting strategic direction for wireless communications networks in a diverse and competitive landscape is complicated by an economy that has introduced dynamic rules for success. Both the rate of technology change and the speed at which new technologies become available have increased. The shorter product life cycles resulting from this rapid diffusion of new technologies places a competitive premium on being able to quickly introduce new goods and services into the marketplace. In order to develop guidelines for strategic planning, we must consider enterprise goals. Traditionally driven by technology, network planning has evolved and now faces new challenges. But the network planning process itself includes two "discordant" requirements: first, to optimize of the network’s long-term investment while second, optimizing of the time to market for each new product. Finding the right balance is not easy. However, opportunities for developers and service providers will exist if they can reach all mobile users by developing infrastructure to support: < >any wireless carrierany wireless network (TDMA, CDMA, etc.)any wireless device (pager, digital cell phone, PDA)any wireless applicationsany Web format (XML, HTML, etc.)any wireless technology (WAP, SMS, pager, etc.)any medium (text, audio, text-to-speech, voice recognition or video)balancing innovations in software (e.g. adaptive software, nomadic software) against innovations in hardware (e.g. chip designs), balancing proprietary standards (motivating competition) against open standards (offering universal access), and balancing local(centralized) Web innovations (e.g. Web Services) against global(distributed) Web architectural evolution (e.g. the Semantic Web).A vendor dominates a market and sets a de facto standard (for example; POTS telephony from AT&T, or PC operating systems from Microsoft).Standards organizations establish standards (for example; HTML).Vendor and market collaboration that is not clearly attributable to any one organization (for example; TCP/IP or VCR formats). FIGURE 10-7 Possible Technology Timeline Conclusion In this chapter, we presented the components developed in earlier chapters and outlined a feasible framework for building the Intelligent Wireless Web, including our evaluation of the compatibility, integration and synergy issues facing the five merging technology areas: User Interface, Personal Space, Networks, Protocols, and Web Architecture. Ten conclusions you could reach from this book about building the Intelligent Wireless Web include: - User Interface - < >Speech recognition and speech synthesis offer attractive solutions to overcome the input and output limitations of small mobile devices, if they can overcome their own limitation of memory and processing power through the right balance for the client-server relationship between the small device and nearby embedded resources. The essential components for achieving this balance are new chip designs coupled with open adaptive nomadic software. The new chips may provide hardware for small devices that is small, light weight, and consumes little power while having the ability to perform applications by downloading adaptive software as needed.- Personal Space - < >Handheld, wearable and embedded devices are upgrading many existing office and home locations making computing access more universal through Wireless Personal Area Networks.Competition between the wireless networking standards Bluetooth and IEEE 802.11b, as well as general networking software, Jini and UpnP, will continue for several years as each finds strong points to exploit before a final winner emerges. MIT’s Project OXYGEN may introduce some innovative protocol alternatives within several years. - Networks - < >Wired and wireless networks will continue to merge and improve backbone performance to greater than the 10 Tera-bps range as well as produce improved interoperability. < >Over time, there will be a migration of core networks to optical fiber simply because photons carry a lot more information more efficiently and at less expense than electrons. By 2003, ultra-long haul (> 4000 km) high bandwidth optical transport will be deployed in the US. The quest for the last mile will be met with a combination of fiber and wireless. In dense metropolitan areas free-space optical networks will provide 622Mbps of bandwidth to buildings without digging the streets. Second generation LMDS and MDDS fixed wireless will be deployed to buildings requiring less bandwidth.- Internet Protocols - < >Intelligent networking software for routing and tracking will lead to general changes in IP networking protocols to include IPv6 and mobile IP. Sitting on top of the entire layer infrastructure may be a number of new control-plane software applications that may add intelligence to the network for smooth integration of routing (layer 3) and wavelength switching. - Web Architecture - < >Intelligent agents, intelligent software application and Artificial Intelligence applications from AI Servers Providers may make their way onto the Web in greater numbers as adaptive software, dynamic programming languages and Learning Algorithms are introduced into Web Services (including both .NET and J2EE architectures).The evolution of Web Architecture may allow intelligent applications to run directly on the Web by introducing XML, RDF/Topic Maps and a Logic Layer.A Web performance Index, or measure, may be developed to evaluate the progress of Internet progress in performing intelligent tasks utilizing learning algorithms.The Intelligent Wireless Web’s significant potential for rapidly completing information transactions may become an important contribution to global worker productivity. 1 [1] Bogdanowicz, K.D., Scapolo, F., Leijten, J., and Burgelman, J-C., “Scenarios for Ambient Intelligence in 2010,” ISTAG Report, European Commission, Feb. 2001.

Excerpt of book Midshipman Henry Gallent at the Academy. Midshipman Henry Gallant at the Academy AMAZON 1 Threadbare Still a boy, not yet a man, Henry Gallant dug his stiff fingers deep into his pockets. He shivered as the bitter-cold wind clawed through his threadbare clothes . “Do you see it?” asked the elderly woman beside him, pulling her shawl tight around her. The overhead streetlamp offered little illumination as they squinted down the dark, winding dirt road. “Not yet,” said Gallant, standing on his tiptoes. The woman was a head shorter than him with a careworn face that the chill air made rosy. Her elegant features revealed that she had once been a beauty, and while time had weathered her, she had aged gracefully. Gallant stomped his feet impatiently while his mind was already racing, considering the prospects for his future. She asked, “Will you visit me when you get liberty?” “Of course, Grandmother,” he said, but he had no idea when that might be. “You know I’ve always tried to do my best, ever since . . .,” Gallant took a deep breath and wrapped his arms tight around his chest. “They were heroes, you know,” she said softly. “I know,” he said as the painful memory boiled up. She had told him many times about the meteor that struck the family outpost on Phobos when he was a child. His parents had only seconds to seal him in an escape pod and couldn’t save themselves. The picture his mind conjured up was of their selfless act. Since that ordeal, he had become obsessed with controlling his emotions. He had learned to set his own rules of behavior, things he would allow himself to express and things he wouldn’t. He kissed her gently on her forehead. “You gave meaning to my parents’ sacrifice by caring for me all these years.” Her work as a clerk by day and a seamstress at night had been taxing but necessary to make ends meet. She said, “You have been a blessing to me. Your freelance programming helped us manage.” She brushed back a tangled lock of brown hair from his forehead and said, “I wish I could have done more to mend your clothes.” “There’s nothing wrong with them,” he said. He stretched his arms wide as proof, but he was careful not to tear open a seam. “They’re perfect.” Anxiously, he stared down the road, wishing the bus had wings. Several minutes later, he said, “I think I see lights.” She brightened. “You’ll soon have a brand-new uniform.” While the bus approached, his grandmother continued to give him last-minute advice and encouragement, but he couldn’t concentrate on her words. As he looked into her eyes and saw her love, he could only feel guilt at leaving her alone. He planned to send her his meager midshipman’s allowance. It wouldn’t be much, but it was all he could do. It will be all right , he thought. The bus sputtered to a stop in front of them. A creaking door opened. Gallant barely had time for a quick hug and kiss before getting aboard. He carried a small bag that contained a change of underclothes and a few toiletries. He made his way to a rear window seat and waved as the bus departed. He watched her figure wave back as it faded into the shadows. The darkness seemed to swallow her like a living thing. Gallant sat next to a woman holding a small spaghetti-armed child. He remained quiet, staring straight ahead. The night was dark and cold along the remote, meandering mountain road. During the first hour of his journey, he worried about leaving his grandmother alone in their tiny mountain cabin. Although it was set in a pastoral valley with a natural spring, it lacked many modern conveniences. Besides his financial contribution over the years, he helped her by taking care of daily necessities. He cleaned the solar panels and maintained the storage batteries. Unfortunately, home delivery in rural areas had not yet taken hold, so he undertook the long jet-flyer trip to the nearest store. Now she would have to manage on her own, and her arthritis had been acting up. How will she manage without me? His emotional baggage shifted during the second hour. While he bounced around in the obsolete vehicle, self-doubt crept in. All his weaknesses, failings, and fears blossomed full form into his mind. He had never been aboard a spaceship, wasn’t a legacy, and didn’t even know a space officer. Most likely, he would be hazed, ridiculed, and driven out as undesirable within a week. His frown deepened with each passing mile, and he began to wish he had never applied for admission to the academy. Finally, he considered getting off and catching the return bus. I’m getting too good at predicting adverse outcomes, he thought. Gallant decided that untrustworthy emotions wouldn’t control him. Instead, he would let his logical mind guide him. He tried to calculate his chances of success. Then, after weighing the pros and cons, he thought, I must be bold. He straightened his spine, lifted his head, and vanquished guilt and fear. Either I make it, or I die trying! That’s all there was to it. Everything changed after that. As daylight trickled over the last hill, the road broadened into a smoothly paved highway. The sun’s resilient brightness lifted his spirits. He couldn’t wait for the adventure to begin.

New release "Fallout of War: Ukraine Year One" available on Amazon. New Releases In the tradition of Herman Wouk's sweeping historical war epics, Fallout of War follows Lieutenant Commander James Fairbanks, a career naval submarine officer assigned as a military attaché to the American embassy in Kyiv in late 2021. Fairbanks arrives in Ukraine with his wife, Lucy, a State Department analyst, just as tensions with Russia reach a critical juncture. A thoughtful, disciplined officer known for his strategic acumen and unvarnished assessments, Fairbanks quickly becomes immersed in the complex political and military landscape of Eastern Europe. Fairbanks tours the Chernobyl exclusion zone, where he meets Ukrainian special forces conducting training exercises amid the haunting ruins of the 1986 disaster. These encounters with hardened Ukrainian soldiers, many of whom fought in the Donbas since 2014, give Fairbanks his first understanding of Ukrainian determination and the existential nature of their struggle. Through a series of diplomatic functions and intelligence briefings, he develops relationships with key Ukrainian officials and eventually meets President Volodymyr Zelensky, whose evolution from entertainer to wartime leader forms one of the novel's central character studies. On the Cusp of Superintelligence captures pivotal moment when the race to artificial general intelligence transformed from a research project to an engineering sprint. On December 20, 2024, OpenAI quietly released a three-minute video that marked the moment when artificial general intelligence shifted from "someday" to "soon.” Their o3 model had achieved 87.5% on the ARC-AGI benchmark, a test specifically designed to resist pattern-matching shortcuts and measure genuine reasoning. Just months earlier, the best AI systems struggled to break 32%. The average human scores 85%. It reveals how multiple paths to AGI are converging simultaneously, each backed by billion-dollar labs with fundamentally different theories of intelligence. OpenAI bet everything on scaling—that intelligence emerges from processing enough information with enough parameters. Their progression from GPT-3's 175 billion parameters to o3's breakthrough validated their conviction that the path to AGI is a straight highway that needs to be extended far enough. Meanwhile, DeepMind, led by neuroscientist Demis Hassabis, pursued a portfolio strategy combining hierarchical reasoning, self-improving Gödel machines, multi-agent systems, embodied intelligence, and scientific discovery. Their synthesis approach suggests that superintelligence might require not choosing between paradigms but orchestrating them into unified systems. Anthropic took a different path, prioritizing safety through Constitutional AI, building alignment into the architecture rather than adding it afterward. Their Claude models demonstrated that capability and safety need not be mutually exclusive. In 2019, Giuseppe Carleo and his team pioneered the application of machine learning to quantum physics. This book is an introduction to how AI revolutionizes quantum field theory (QFT) , from scalar fields to complex gauge theories describing quarks and gluons. The narrative unfolds in three acts. First, readers discover the mathematical kinship between neural networks and quantum fields—the renormalization group maps onto information flow through neural layers, while gauge symmetry provides blueprints for AI architectures. The second act examines how AI addresses each type of quantum field. For scalar fields, neural networks identify exotic phases that traditional methods miss. For fermions, architectures like FermiNet achieve chemical accuracy while sidestepping computational barriers. For gauge fields, flow-based models conquer critical slowing down that has limited simulations for decades. Key breakthroughs include MIT's gauge-equivariant flows, which reduce autocorrelation times by a factor of 100, DeepMind's solution to 30-electron molecules, and the discovery by transformers that million-term scattering amplitudes can be expressed as a single equation. The final act envisions AI not just calculating but creating physics systems like MELVIN, designing quantum experiments that no human has imagined. Language models solve bootstrap equations. Neural networks propose routes to grand unification. The book culminates in a convergence of quantum computers and classical AI—a partnership that could crack QFT's deepest mysteries. By teaching AI nature's symmetries, we're creating systems that reveal patterns invisible to human analysis—AI intelligence is offering a different way of interrogating reality. Written as an introduction for physicists curious about AI and ML, as well as for AI and ML experts interested in fundamental physics, the book strikes a balance between rigor and practical implementation, offering both conceptual frameworks and tools for the quantum field theory revolution.