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New Release Sometimes, the right man in the wrong uniform can make all the difference. Ethan, a lowly recruit with an oil-stained uniform and a spirit worn down by disappointment, finds his life forever changed by a twist of fate. Squinting at his reflection, he sees the sleeves of his borrowed jacket bore captain’s stripes. A grotesque emblem is embossed over the jacket's breast pocket—a roaring lion's head surrounded by a cluster of jagged broken bones—the symbol of the Special Operations Service. There is no way out. The ship is taking off because they think an elite SOS captain is on board to take command—him. His choices were brutally simple . . . act like the officer everyone thought he was or be found out as a fraud. One was survival, the other . . . The consequences sent a wave of panic through him. He was a mouse in a lion's skin. He had to become that lion until he found a way out of his cage. Ethan's path intersects with Kate Haliday, the leader of the dark matter project in the Cygni star system. A subtle dance of glances and half-spoken truths begins. But the threads of connection are fragile as they are tangled with the ambitions of Commander Varek, a skeptical officer. The emergence of an unknown alien race casts a long shadow that shifts the cosmic chessboard of a space fleet and a galactic empire. Their interest in dark matter and Earth's colonies weaves a layer of mystery and suspense. In this hard science fiction dance, Ethan must navigate the intricacies of love, rivalry, and alien invasion. The possibility of being unveiled darkens his every step. With each move, the line between the man he is and the officer he pretended to be . . . blurs. Once a misfit dropout, Mike now controls the fate of man versus machine In a world where the boundaries between man and machine blur, your thoughts, emotions, and yearnings are no longer private. The confluence of biotech and infotech has given birth to the Algorithm—a force that predicts your every move and has the power to shape your deepest desire. But when the Algorithm starts undermining human worth, many find themselves obsolete. Grappling with their waning relevance, they find solace in a new realm. They master the skills of a surreal virtual world that requires neither gravity nor light. As technology's grip tightens, a haunting question emerges: Does anyone hold the reins of the omnipotent Algorithm? Enter an unlikely hero—an aimless dropout who unwittingly finds himself at the nexus of power. Tall and lean, Mike has deep-set blue eyes that often reflect his internal conflicts and dilemmas. His past is riddled with disappointment and insecurity. Assuming another student’s ID in a crowded exam room, Mike's journey takes an unexpected turn when a stern figure declares, "I am Jacob Winters. Welcome to the AI career placement test. Today, we will discover which of you represents the pinnacle of human genius." Delve into Keeper of the Algorithm to discover a future where destiny is written in code and domination is the ultimate prize. For serious AI enthusiasts only!

Henry Gallant and the Great Ship AMAZON Chapter 1 An Unfortunate Turn of Events As soon as the morning watch settled in, Captain Henry Gallant walked onto the Constellation’s bridge. The Officer-of-the-Deck rose and vacated the command chair without speaking. The voyage had lasted long enough for the crew to become accustomed to his routine. Habitually, during the first minutes of the day, he examined the ship’s vital operational parameters from his bedside monitor before going into CIC for a detailed task force sitrep. Blips from the combat space patrol (CSP) were visible on the main viewer. The speakers broadcast communication traffic from distant Hawkeyes. Once he had satisfied himself that all was as it should be, he appeared on the bridge and assessed the more mundane needs for the day. The OOD handed him a list of completed tasks and those that demanded his approval. During this activity, he was lost in contemplation, and no one dared interrupt his train of thought. ​ Only after dictating his orders for the day did he relax and give a word of encouragement to the OOD. Then he disappeared below decks for his daily walkabout, where he gauged the temperament of the crew. The hour exercise through the spacecraft carrier allowed him to maintain his fitness. This ritual was the most efficient use of his time since it also allowed him to observe ongoing maintenance and repair activities. On the one hand, the number of administrative duties clamoring for his attention limited his time; on the other, keeping in sync with his ship’s pulse was vital to making good decisions. It brought a faint smile to his lips when he resolved to shift more of the clerical burden onto his XO. Margret Fletcher had a talent for paperwork and was known for her no-nonsense adherence to the regs. Even though he overloaded her of late, she had responded with her usual zeal. ​ As he passed through compartment after compartment, he dictated audio notes into his comm pin about items that needed attention. He marched along the corridors and stepped through the open hatches, ever mindful of the crew’s attention. Although immersed in his process, the crew discerned that his military instincts were on full alert. He would notice the slightest failure of attention to detail as the men and women went about their jobs. Occasionally, he heard a laugh or good-natured ribbing. That was well. A crew that could laugh while working would faithfully execute their duties. He enjoyed the sameness of each day; it reassured him that his world remained rational. It had been two days since the Constellation had poked her nose into the Ross star system. Gallant congratulated himself on making the deployment from Earth so rapidly. It had been a long and arduous two-month grind, but Task Force 34 was finally ready to relieve Task Force 31 as guardian of this system. ​ He shifted his mind back to the disturbing initial surveillance reports that had perplexed him for the last twenty-four hours. Task Force 31 was not visible, which by itself, wasn’t alarming. A planetary body might block their light, though they weren’t responding to radio signals either. Again, they might be on the other side of the star, and the speed of light wasn’t being accommodating. Another calculation percolated into his consciousness. He had sent Hawkeyes out on a sweep of the system. So far, nothing was amiss, but there was confusing radio chatter from the planets indicating that some horrific event had occurred recently. Gallant returned to the bridge in time to review the latest recon update. None of the information was reassuring. He noticed an anomaly in the data that prickled the hairs on the back of his neck. Though the statistics were mysteriously thin and precariously riddled with contaminated inconsistencies, they were coaxing him toward a disturbing conclusion. He worried his premonition might be correct and ordered the CIC to conduct an AI simulation analysis. It wasn’t long before Commander Fletcher stepped onto the bridge. ​ “Good morning, Captain,” she said. Then with a frown, she added, “I have the results.” Gallant spun in his command chair and cast a concerned eye on her. ​ She held a tablet by two fingers out in front of her as if she had found it in a vat of something vile. “Morning XO,” said Gallant, taking the device. Swiping through the screens, he absorbed the information while his heartbeat rose. He wanted to remain calm to reinforce his reputation as imperturbable. He didn’t want Fletcher or anyone else to suspect that he could lose his composure. But he was bursting to rush into CIC. He wanted to review the raw data to verify that it was accurate, but he knew that the analysts would have been meticulous in developing this report. She interrupted his concentration. “You were right, sir.” ​ “Ha—h’m,” he said, clearing his throat. He took a deep breath and forced himself to appear relaxed. ​ Fletcher shook her head and prodded, “Looks like an enormous debris field—possibly with escape pods.” ​ She pointed to the area spread deep throughout the star system’s heart, halfway between planets Bravo and Charlie. The OOD and the chief of the watch inched closer, craning their necks to get a peek at the tablet. ​ Gallant recalled the disturbing image of the original data. Understanding flooded over him. He visualized what must have taken place, and it took an enormous effort to suppress his emotions. ​ She scowled. “No sign of Task Force 31.” ​ Still, he didn’t respond. ​ She muttered, “That doesn’t necessarily mean . . .” ​ Everyone on the bridge gazed expectantly at him. ​ Like a father who returns home to find his front door smashed open, he ordered, “OOD, open a channel to all ships.” ​ A moment later, the OOD reported, “Channel open to all ships, Commodore.” ​ “To all ships, this is Commodore Gallant; set general quarters, assume formation diamond 4.4.” ​ “Aye aye, sir,” came the response from each ship. ​ The task force split into four strike forces. Captain Jackson of the Courageous led the first strike force designated 34.1. It was followed one light hour behind by 34.2 and 34.3, led by Captain Hernandez of the Indefatigable and Captain Chu of the Inflexible, respectively. They kept a light-hour separation from each other. Finally, Gallant led Constellation and Invincible in 34.4, another light hour behind the rest. The cruisers and destroyers were split amongst the strike forces. The dispersed strike forces looked like a baseball diamond with the Constellation at home plate. ​ It took several hours to complete the maneuver. Satisfied that the ships were sufficiently far apart for the majority to survive a blast from the Great Ship’s super-laser, he ordered, “Task Force change course to 030 Mark 2, all ahead full.” ​ Gallant waited anxiously on the bridge for the entire twenty-four hours it took for the task force to crawl across the Ross star system. Some telltale blips appeared on the scope interspersed within a belt of asteroids. When they were finally close enough, they saw the remains of many half-dead ships. They began picking up distress signals of countless escape pods. Officers and watch-standers on the bridge stared at the viewscreen, trying to glimpse the wreckage. ​ Gallant’s eye estimated the number of blips. They could only be the remnants of Task Force 31. It was worse than he imagined—a terrible loss of life. ​ “OOD, prepare med-techs. Send the search and rescue teams to recover the escape pod survivors.” The initial action report was sent by the senior surviving officer, Captain Raymond. It was sketchy. It couldn’t be called a ‘battle’ report since not a single ship of the task force had fired a shot. ​ After a brief visit to Constellation’s sickbay, the officer reported to Gallant’s stateroom. ​ Raymond was not quite fifty, but his balding head, sunken eyes, and beaked nose made him appear older. His long black mustache with grey flecks drooped, making him appear to frown. His uniform was in tatters, and he had several bandaged injuries that had been tended to by the ship’s surgeon. His thickset body was powerful, but he stood slumped over, pain etched across his face. ​ “That’s the scorched wreck of my ship, the Dauntless,” said Captain Raymond, pointing to the viewscreen. The broken battlecruiser, along with the crippled remnants of four cruisers and a dozen destroyers, were all that was left of Commodore Pearson’s Task Force 31. ​ “Commodore Pearson orders were to hold the system at all costs. Admiral Graves had assured him that the Great Ship would not appear. He was told that it would have to protect the Chameleon home planet in the Cygni star system against the Titans. At least that was President Neumann’s thinking after he found out that the Chameleon had only the one Great Ship left.” ​ “The United Planets has been in negotiation with the aliens for over a year,” said Gallant. “Was there no progress?” ​ There was anguish in Raymond’s voice. “None. And the Chameleon were angry.” He paused, dropping his gaze. “The governor told them to shove off, no deal was possible. After that ultimatum, things turned ugly.” ​ Gallant frowned. “Take your time and start from the beginning.” ​ Raymond’s words were clipped. “Task Force 31 had one carrier, four battlecruisers, and two cruiser-destroyer squadrons between planets Charlie and Bravo when the Great Ship appeared. They demanded that the United Planets evacuate the star system. Well, you know Pearson, no way that was happening. He sounded battle stations and ordered his ships to disperse to present a minimal target for the Chameleons.” ​ When Raymond hesitated, Gallant prompted, “What happened next?” ​ “The action was a disaster—a complete shock. The Chameleon looked at the dispersion as a threat and warned him to stand-down, withdraw, or surrender. After a few minutes, they fired.” ​ He cast his eyes down. ​ “The single blast was so devastating that it destroyed nearly all our ships. The blinding light and searing heat crippled my Dauntless and disintegrated most of the task force. The crippled remainders launched escape pods and waited for a follow-up salvo that, mercifully, never came. We hobbled out of the way. I sent a message to the governor on Charlie.” Raymond swallowed hard and furrowed his brow. “The governor’s response was to call it ‘an unfortunate turn of events.’” “I learned later that the Chameleon had threatened to make peace with the Titans if we didn’t yield the system. They must have since it gave them the freedom of action to leave their home world unprotected and deal with us.” He handed Gallant a flash drive. “This contains a plot of the action and the recordings of the communications between our ships and the governor. I’ve stuck my neck out to get this information on the record. You should collect and check the wreckage along with my observations.” ​ “I understand. Some powerful men in the admiralty will be worried. I will describe the action in a detailed report to be sent to Earth,” said Gallant. He worried about how to keep Task Force 34 from suffering the same fate as their predecessor.

Rear Admiral Henry Gallant AMAZON Chapter 1 Far Away ​ Captain Henry Gallant was still far away, but he could already make out the bright blue marble of Earth floating in the black velvet ocean of space. ​ His day was flat and dreary. Since entering the solar system, he had been unable to sleep. Instead, he found himself wandering around the bridge like a marble rattling in a jar. His mind had seemingly abandoned his body to meander on its own, leaving his empty shell to limp through his routine. He hoped tomorrow would bring something better. ​ I’ll be home soon, he thought. ​ A welcoming image of Alaina flashed into his mind, but it was instantly shattered by the memory of their last bitter argument. The quarrel had occurred the day he was deployed to the Ross star system and had haunted him throughout the mission. Now that incident loomed like a glaring threat to his homecoming. ​ As he stared at the main viewscreen of the Constellation, he listened to the bridge crew’s chatter. “The sensor sweep is clear, sir,” reported an operator. ​ Gallant was tempted to put a finger to his lips and hiss, “shh,” so he could resume his brooding silence. But that would be unfair to his crew. They were as exhausted and drained from the long demanding deployment as he was. They deserved better. ​ He plopped down into his command chair and said, “Coffee.” ​ The auto-server delivered a steaming cup to the armrest portal. After a few gulps, the coffee woke him from his zombie state. He checked the condition of his ship on a viewscreen. ​ The Constellation was among the largest machines ever built by human beings. She was the queen of the task force, and her crew appreciated her sheer size and strength. She carried them through space with breathtaking majesty, possessing power and might and stealth that established her as the quintessential pride of human ingenuity. They knew every centimeter of her from the forward viewport to the aft exhaust port. Her dull grey titanium hull didn’t glitter or sparkle, but every craggy plate on her exterior was tingling with lethal purpose. She could fly conventionally at a blistering three-tenths the speed of light between planets. And between stars, she warped at faster than the speed of light. Even now, returning from the Ross star system with her depleted starfighters, battle damage, and exhausted crew, she could face any enemy by spitting out starfighters, missiles, lasers, and plasma death. ​ After a moment, he switched the readout to scan the other ships in the task force. Without taking special notice, he considered the material state of one ship after another. Several were in a sorrowful dysfunctional condition, begging for a dockyard’s attention. He congratulated himself for having prepared a detailed refit schedule for when they reached the Moon’s shipyards. He hoped it would speed along the repair process. ​ Earth’s moon would offer the beleaguered Task Force 34, the rest and restoration it deserved after its grueling operation. The Moon was the main hub of the United Planets’ fleet activities. The Luna bases were the most elaborate of all the space facilities in the Solar System. They performed ship overhauls and refits, as well as hundreds of new constructions. Luna’s main military base was named Armstrong Luna and was the home port of the 1st Fleet, fondly called the Home Fleet. ​ Captain Julie Ann McCall caught Gallant’s eye as she rushed from the Combat Information Center onto the bridge. There was a troubled look on her face. ​ Is she anxious to get home too? ​ Was there someone special waiting for her? Or would she, once more, disappear into the recesses of the Solar Intelligence Agency? ​ After all these years, she’s still a mystery to me. ​ McCall approached him and leaned close to his face. ​ In a hushed throaty voice, she whispered, “Captain, we’ve received an action message. You must read it immediately.” ​ Her tight self-control usually obscured her emotions, but now something extraordinary appeared in her translucent blue eyes—fear! ​ He placed his thumb over his command console ID recognition pad. A few swipes over the screen, and he saw the latest action message icon flashing red. He tapped the symbol, and it opened. TOP SECRET: ULTRA - WAR WARNING Date-time stamp: 06.11.2176.12:00 Authentication code: Alpha-Gamma 1916 To: All Solar System Commands From: Solar Intelligence Agency Subject: War Warning Diplomatic peace negotiations with the Titans have broken down. Repeat: Diplomatic peace negotiations with the Titans have broken down. What this portends is unknown, but all commands are to be on the highest alert in anticipation of the resumption of hostilities. Russell Rissa Director SIA TOP SECRET: ULTRA - WAR WARNING He reread the terse communication. ​ As if emerging from a cocoon, Gallant brushed off his preoccupation over his forthcoming liberty. He considered the possibilities. Last month, he sent the sample Halo detection devices to Earth. He hoped that the SIA had analyzed the technology and distributed it to the fleet, though knowing government bureaucracy, he guessed that effort would need his prodding before the technology came into widespread use. Still, there should be time before it becomes urgent. The SIA had predicted that the Titans would need at least two years to rebuild their forces before they could become a threat again. Could he rely on that? ​ Even though he was getting closer to Earth with every passing second, the light from the inner planets was several days old. Something could have already transpired. There was one immutable lesson in war: never underestimate your opponent. ​ A shiver ran down his spine. ​ This is bad. Very bad! ​ Gone was the malaise that had haunted him earlier. Now, he emerged as a disciplined military strategist, intent on facing a major new challenge. ​ Looking expectantly, he examined McCall’s face for an assessment. ​ Shaking her head, she hesitated. “The picture is incomplete. I have little to offer.” ​ Gallant needed her to be completely open and honest with him, but he was unsure how to win that kind of support. ​ He rubbed his chin and spoke softly, “I’d like to tell you a story about a relationship I’ve had with a trusted colleague. And I’d like you to pretend that you were that colleague.” ​ McCall furrowed her brow, but a curious gleam grew in her eyes. ​ He said, “I’ve known this colleague long enough to know her character even though she has been secretive about her personal life and loyalties.” ​ McCall inhaled and visibly relaxed as she exhaled. Her eyes focused their sharp acumen on Gallant. ​ “She is bright enough to be helpful and wise enough not to be demanding,” continued Gallant. “She has offered insights into critical issues and made informed suggestions that have influenced me. She is astute and might know me better than I know myself because of the tests she has conducted. When I’ve strayed into the sensitive topic of genetic engineering, she has soothed my bumpy relationship with politicians.” ​ He hesitated. Then added, “Yet, she has responsibilities and professional constraints on her candidness. She might be reluctant to speak openly on sensitive issues, particularly to me.” ​ McCall’s face was a blank mask, revealing no trace of her inner response to his enticing words. He said, “If you can relate to this, I want you to consider that we are at a perilous moment. It is essential that you speak frankly to me about any insights you might have about this situation.” She swallowed and took a step closer to Gallant. Their faces were mere centimeters apart. ​ “Very well,” she said. “The Chameleon are a spent force. After the loss of their last Great Ship, they are defenseless. They agreed to an unconditional surrender. They might even beg for our help from the Titans. Their moral system is like ours and should not be a concern in any forthcoming action. However, the Titans have an amoral empathy with other species.” ​ He gave an encouraging nod. ​ She added, “Despite the defeat of Admiral Zzey’s fleet in Ross, the Titans remain a considerable threat. They opened peace negotiations ostensibly to seek a treaty with a neutral zone between our two empires. But we can’t trust them. They are too aggressive and self-interested to keep any peace for long. One option they might try is to eliminate the Chameleon while they have the opportunity. Another is to rebuild their fleet for a future strike against us. However, the most alarming possibility would be an immediate attack against us with everything they currently have. They might even leave their home world exposed. But that would only make sense if they could achieve an immediate and overwhelming strategic victory.” ​ Gallant grimaced as he absorbed her analysis. ​ She concluded, “This dramatic rejection of diplomacy can only mean that they are ready to reignite the war—with a vengeance. They will strike us with swift and ruthless abandon.” ​ Gallant turned his gaze toward the bright blue marble—still far away.

The Henry Gallant Saga COURAGE is only a word . . . until you witness it. Then . . . it is contagious. Henry Gallant is the only Natural left in Earth's genetically engineered space navy. Despite overwhelming odds and the doubts of his shipmates, Gallant refuses to back down as he uses his unique abilities to fight for victory at the farthest reaches of the Solar System. Follow Gallant as he finds the spine to stand tall, vanquish fear, and rain violence upon the methane-breathing enemy aliens. The nation needs a hero like Henry Gallant. He fights! For fans of Horatio Hornblower and Honor Harring ton. 1/9

Captain Henry Gallant AMAZON Chapter 1 Streak Across the Sky ​ Cold night air smacked Rob Ryan in the face as he stepped out of the Liftoff bar—a favorite haunt of pilots. He was still weaving his way through the parking terminal looking for his single-seat jet-flyer when a familiar face appeared at his elbow. ​ Grabbing his arm, his friend said, “You shouldn’t fly. Let me give you a ride.” ​ Ryan straightened to his full six-two height and shrugged off his friend’s hand. ​ “I’m fine,” he said, swiping a lock of unkempt brown hair out of his eyes. ​ “Don’t be pigheaded. There’s a difference between self-reliance and foolishness.” ​ He pushed past his friend. “Nonsense. I fly better when I’m . . . mellow.” ​ As he left his buddy behind, he noticed a young woman who had come out of the bar after him. He had spent the past hour eyeing this smokin’ hot redhead, but she had been with somebody. Now she was heading out on her own. She glanced at him and quickened her pace. ​ A thought penetrated the fog in his mind. ​ I’ll show her. ​ At his Cobra 777 jet-flyer, he zipped up his pressure suit, buckled into the cockpit, and pulled on his AI neural interface—all the while imagining a wild take-off that would wow the redhead. ​ He jockeyed his jet along the taxiway onto the runway. When the turbo launch kicked in, the black-and-chrome jet spewed a cloud of exhaust and dust across the strip. He jammed the throttle all the way in and gave a whoop of pure joy at the roar and explosive thrust of the machine. The exhilaration—a primitive, visceral feeling—increased by the second, along with his altitude and speed. His love of speed was only matched by his almost unhealthy fascination with flying machines—too fast was never fast enough. ​ For a few seconds, his mind flashed back to his very first flight. The thrill only lasted a few minutes before the mini flyer spun out and crashed. Without a word, his father picked him up and sat him back down in the seat, restarting the engine with a wink and a grin. Clearest of all was the memory of his father’s approval as he took off again and soared higher and faster than before. ​ Now he sliced through the crisp night air in a military jet that had his name engraved on the side. He ignited an extra thruster to drive the engine even hotter. Riding the rush of adrenaline, he pulled back on the stick to pull the nose up. Atmospheric flying was different than being in space, and for him, it had a sensual rhythm all its own. As he reached altitude, he pulled a tight loop and snapped the jet inverted, giving himself a bird’s-eye view of the ground below. ​ But instead of reveling in admiration as expected, he found himself fighting for control against a powerful shockwave as a Scorpion 699 jet blew past him. The blast of its fuel exhaust was nothing compared to the indignation and shame that burned his face. ​ It was the redhead. ​ Damn. She’s good. ​ His pulse raced as he became fully alert. Determined to pursue her, he angled the ship across air traffic lanes, breaking every safety regulation in the book. Instinctively his eyes scanned the horizon and the edges around him, watching for threats or other machines that might interfere with his trajectory. Pinwheeling in a high-G turn, he felt the crush of gravity against his chest, yet still, his hand on the throttle urged ever more speed from the machine. ​ He lost track of the Scorpion in the clouds, and in mere seconds she maneuvered behind him. He tried to shake her using every evasive maneuver he had learned in his fighter training but couldn’t do it. His eyes roamed the sky, watching for potential dangers. The night sky was dark, but several landmarks lit up the ground below him. Earth’s capital, Melbourne, glowed with activity to the north; a mountain range stretched across the horizon 50 km to the west, and an airport lay to the south at the edge of the ocean. As he scanned the skyline, he noticed a radio-telescope antenna. Impulsively he dove toward it, the Scorpion on his tail. ​ At the last moment, the redhead broke pursuit to avoid the antenna, but in a moment of reckless folly, Ryan crashed through the flimsy wire mesh, no more substantial to his Cobra than a wisp of cloud. “That’ll need a patch,” he chuckled. ​ But once more, the Scorpion blew by him. He watched it roar away as if he were in slow motion. As the redhead curved back toward him for another pass, he gritted his teeth in frustration. With thrusters already at max burn, he punched the afterburner to create his own shock wave and turned head-on into her path. “Damn!” he screamed as the other ship twisted away. ​ His golden rule for staying alive while flying was “never yield but always leave yourself an out.” Folly had made him reckless, and he knew his reflexes were sluggish, but he was pissed at himself for letting this pilot provoke him. ​ Recovering his reason, he leveled off and threw down the skid flaps to reach a more reasonable speed. The jet took the torque and inertia strain, and the flashing red lights on his display turned yellow and then green. Despite his irritation, he allowed himself a faint smile when his AI read the Scorpion’s registration: Lorelei Steward. ​ Good sense advised that he throttle back, but pride won out. Spotting the Scorpion silhouetted against a cloud, he jammed the throttle forward yet again. ​ Finally, behind her, his smile broadened. She wouldn’t slip away this time. She pulled her jet into a violent oblique pop, rolled inverted until the nose pointed to the ground then returned to upright. ​ He stuck with her, move for move. ​ Abruptly she angled for the nearby mountain range. He chased her, low and fast, through a pass and down into a twisting canyon, rolling and pitching in a dizzying display of aerobatic skill. He kept close on her six until they blew out of the ravine. ​ In a desperate ploy to shake him, she turned back toward Melbourne’s airspace and headed straight into a crowded flying highway. ​ Ryan was so close behind that it took a few seconds before he realized her blunder. She had turned into an oncoming traffic lane. ​ The cockpit warning lights lit up the cabin as Ryan dodged a stream of oncoming vehicles. Up ahead, Lorelei ducked under a passenger liner that swerved directly into his path. ​ Time slowed to a crawl as he foresaw his fate—he could escape by pulling up—but that would force the crowded passenger liner to dive and crash into the ground. ​ “Damn it all!” he yelled and dove—leaving the liner a clear path to safety. Through the neural interface, his AI shrieked, ​ TOO LOW! PULL UP! TOO LOW! PULL UP! ​ He used every bit of expertise he could muster to twist, turn, and wrestle his jet into a controlled descent. His vision narrowed as the lights of city and ships gave way to a line of unyielding rocks zooming toward him. In a blink, he ran out of time—and altitude. ​ BRACE FOR IMPACT! ​ The Cobra plowed a trough a hundred meters long across the desert floor. Ryan sat in the cockpit, stunned and disoriented amid the flames and wreckage until his lungs convulsed from the dense smoke. An acidic stench and the taste of jet fuel assailed his nose and throat, rousing him from his stupor. Fumbling to unbuckle the safety harness, he held his breath until he could release the hatch and climb out of his ruined machine. Shaking hands searched his body for broken bones. To his relief, he was intact . . . if he didn’t count the ringing in his ears and the blood that coursed down his face. ​ The maxim from flight school ran through his mind: “Any landing you walk away from . . .” But as he limped away, his beloved Cobra burned into a twisted mound of molten metal, its nose buried in the dusty red ground. He shook his head at the wreck. “Captain Gallant is going to have my ass.”

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.

Dark Genius AMAZON Time Off (Excerpt) ​ The next morning, Lawrence gazed up at the impressive face of Mont Blanc. The chill air penetrated even his warm clothing. He resolutely tugged on his ski gloves, slung his MIT scarf around his neck, and hefted his freshly waxed skis to his shoulder—he was all set. Boots climbing across the snow, he headed for the gondola. He could see the tiny figures of skiers already skimming down the steep slopes above, and his pulse quickened. ​ As the group shuffled toward the gondola, he nodded to several familiar faces, relieved to find neither Proust nor Maurice among them. He thought he’d seen Emma in line ahead of him and fidgeted through the whole ride, oblivious to the spectacular view that spread below him. When he reached the advanced level, he got off, pulled his goggles down, and stepped into his skis. He picked Emma out immediately, even under her goggles and sporty ski hat. “Hi,” he said with a big smile, glad they both had the morning free from meetings. “Hi,” she replied, moving to his side in one smooth fluid push. ​ Several others said, “Hello.” ​ He returned a nod and pulled his jacket tightly around him against the chill air. A veteran skier strolled past with weathered skin and disrupted hair. He wore a turned-down smirk that challenged all comers to prove their worth. These were all experienced skiers, dressed for warmth, and equipped with the best quality gear. The first pair left together, plunging down onto the black runs. Others quickly followed, separated enough to avoid interference. ​ Finally, he and Emma were the only ones on the top of the world. They felt as though they had the mountain all to themselves. ​ Lawrence breathed in the crisp Swiss mountain air. It felt different somehow—cleaner, freer, better. ​ The temperature was 5 C. ​ He said, “Wow, what a fantastic day! This is an amazing resort, and the snow looks perfect.” “Something tells me I’m going to like this place.” ​ “Me too.” ​ Emma tugged on his scarf, and with mischief in her eyes, dared him, “Race you to the bottom.” He started to ask, “What do I get if I win?” when he realized she was already ten yards ahead. Though not an expert, he was a good skier. He shoved his poles hard into the snow and leaned forward, propelling himself down the slope after her. The skis hissed smoothly on the packed powder as he pulled himself along with his poles. Picking up speed on the gradually steepening slope, he was still falling behind. Going over the first vertical drop with spine-chilling ease, he found his rhythm and felt the adrenaline rush of speed, snow, and slope. ​ Concentrating on his own maneuvering, he couldn’t watch Emma but could tell he still wasn’t gaining on her. ​ He leaned over his skis, pulled up his poles, and dropped into a tuck. Instantly his speed increased, and his skis drifted a little farther apart than good style dictated. His hips and knees swiveled left–right–left–right–left in smooth, sweeping micro-turns, shoulders barely moving. Still, Emma held her lead ahead of him. ​ A cluster of trees loomed ahead. He shifted his weight to come around, the right edges of his skis, biting hard into the slope and swung past them cleanly. He straightened up and turned to avoid several rocky obstacles. He maneuvered through a series of flags on the run, carving an extended S in the snow. He was close behind Emma now and could see her looking back at him, her face alive with pleasure. ​ He was delighted. ​ He aimed his skis straight down the slope again and felt the joy of zooming down a 45-degree drop. The thrill of speed and mastery of the terrain far outweighed any concern of potential danger. As he followed the curve of the mountain to the left, he came upon another row of flags, black and red, fluttering in the wind. The slope suddenly rose up under him, his knees compressed, and at this speed, he felt the lift as he caught air. He gave a shout of pure glee. ​ Emma was near, and she ran an S-turn through his track. ​ The slope eased a bit, and he jammed his left pole into the snow for leverage, pushing his skis down hard. The snow sprayed out from the abrupt stop and hung, crystallized, for a moment in the still air as he looked across a shoulder of the mountain. It plunged down toward a grove of trees, black in the distance. ​ Breathing hard, he glanced over his shoulder but couldn’t see Emma. A momentary concern flashed through his mind, but then he caught a glimpse of her through some trees to his left. He swung back downhill and zig-zagged through the mounds beneath the gondola cables, driving his poles in hard with each knee-pounding bump. ​ With her more direct route, Emma was ahead of him again. ​ He pushed harder, trying to catch up to her, his knees straining on each turn. ​ Without warning, his right ski caught an edge. He flailed, struggling to regain control, skidded, and fell. ​ Shaking himself off, he quickly regained his feet, gasping for breath, and wiped the snow off his face and goggles. He stamped his feet to make sure his bindings were still tight, then set off in pursuit of Emma once more. Gaining speed, he schussed across the undulating ground, his skis intertwining with Emma’s tracks. ​ A row of bright-orange warning signs made him check his speed sharply. This run had taken him dangerously close to a ravine. Behind the crossed sticks he could see where the cliff dropped and didn’t stop to think how far down it went into nothingness. He carved another hard turn, angling his skis back toward the left, and raced for the tree line. Keep forward. Get your hands in front of you. Set shoulders downslope, keep knees, and hips loose. The wind buffeted him, a pounding wall of resistance against his increasing speed. The wild schuss was nearing an end. ​ Pine and spruce trees rushed by him, blurring into an impenetrable wall. The sun glistened over the snow’s surface, a sharp stretch of rocks and ravines was marked by warning flags thrown into high relief. Dark shadows obscured the terrain, making the slopes more dangerous. He knew there were sheer drops on each flank of the run. He felt an absurd desire to kick off his skies and run. Instead, he kept his focus on the track ahead and ignored the folds in the landscape. ​ Finally, he saw an opening through the trees that had hemmed him in. He veered more left and shot through it. ​ As he straightened his course, Emma whizzed by him, so close that he felt a spray of snow. Is she really that good, or did she misjudge her position? ​ Trees pressed against the uphill side as the run curved around the mountain’s flank, their branches brittle against the white cold of the sky. Lake Geneva, now spread out in a breathtaking panorama below them. The thermometer had dropped precipitously to -3 C, and flakes of snow began to prick Lawrence’s cheek. ​ Speed seemed no longer possible against the cold resisting wind. ​ As the slope leveled out to the end of the run, he saw Emma out of the corner of his eye, only a few yards and scant seconds behind him. ​ He angled his skis to cross the finish line. ​ As his momentum slowed, he suddenly felt exhausted. ​ His head throbbed, and his muscles ached from a combination of exertion and dehydration. His joints ground and creaked. His fingers refused to release their grip on the poles. Every sense seemed to have turned against him, and he blinked hard, his breathing labored. With an effort, he pulled off his soaked gloves and unzipped his jacket, sweating heavily. Stabbing his poles into the ground, he groaned as he bent over to unlatch his skis. Luckily the bindings sprang open easily, and he straightened painfully. ​ The snow was falling faster now. He hadn’t noticed before. ​ He cradled his stiff hands to his chest like a drowning man trying to catch his breath. The bracing wind stung his cheeks, leaving a bittersweet icy red welt. He was spent. As he looked for Emma, he wondered . . . ​ Did I win?

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.

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

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.

Midshipman Henry Gallant in Space AMAZON Joining the Fleet 1 A massive solar flare roared across the sun, crackling every display console in the tiny spacecraft. “No need to worry, young man. We’re almost there,” said the aged pilot. ​ “I’m not concerned about the storm,” said newly commissioned Midshipman Henry Gallant. Eagerly, he shifted in his seat to get a better view of the massive battlecruiser Repulse that would be his home for the next two years. She was a magnificent fighting machine, a powerful beast in orbit around Jupiter. ​ The pilot maneuvered to minimize the effects of the x-ray and gamma radiation until the craft slid into the cold black shadow of the Repulse. Gallant could hardly contain his delight as the tiny ship quivered in the grip of the warship’s tractors. ​ By the time the docking hatch finally slid open, Gallant was waiting impatiently for his first glimpse inside the warship. ​ He hurried to the bridge. The officer of the watch stood next to the empty captain’s chair, surrounded by a dizzying array of displays and virtual readouts. The officer rested his hand on the panel that concealed the Artificial Intelligence (AI) tactical analyzer. ​ “Midshipman Henry Gallant, reporting aboard, sir.” Drawing his gangly seventeen-year-old figure to its full height, he gave a snappy salute. He tugged at his uniform jacket to pull the buttons into proper alignment. ​ “Welcome aboard, Mr. Gallant. I’m Lieutenant Mather.” Mather was of average height, barrel-chested with angular facial features and a stoic look. Beyond a glance, he showed little interest in the new arrival. “Give me your comm pin.” ​ Gallant handed over his pin, Mather made several quick selections on a touch screen, then swiped it past the chip reader. ​ While his ID loaded into the ship’s computer, Gallant took the opportunity to look around. The semicircular compartment, though spacious, bristled with displays, control panels, and analysis stations. ​ From his academy training, he could guess most of the functions. There were communications, radar, weapons, and astrogation, plus a few he couldn’t identify. Several of the positions were vacant operating automatically. Gallant’s fingers twitched, eager to be a part of the bridge’s efficient operation. A huge view screen dominating the compartment displayed Jupiter. An orbiting space station was visible against the vastness of the gas giant. He marveled at the spectacle. ​ “Junior officer authorization verified. The ID pin has been updated with Repulse’s access codes,” a computer’s voice announced from a nearby speaker. Its neutral, disinterested tone reminded Gallant of a rather cold and distant teacher he had had in basic math years ago. ​ ”Did you bring your gear aboard?” asked Mather. ​ “My duffle bag is at the docking port, sir.” ​ The aged pilot had helped Gallant carry his gear from the shuttlecraft onto Repulse. Then, after a cheery smile and a friendly, “Good luck,” he climbed back in his shuttle and left. Having no family of his own, Gallant had found some faint comfort in the good wishes. ​ ”I’ll have your gear sent to your quarters. But, for now, you had better see the captain,” said Mather, raising an eyebrow at Gallant. ​ “Aye aye, sir,” said Gallant. ​ Mather turned to one of the bridge’s junior officers, a young woman. She wore a single thin gold stripe on her blouse sleeve, indicating her rank as Midshipman First Class, one-year senior to Gallant. He ordered, “Midshipman Mitchel, take Mr. Gallant to the captain’s cabin.” ​ As they left the bridge, Mitchel said, “Henry Gallant . . . I remember you from the academy. I’m surprised you’re still in uniform.” ​ Gallant gritted his teeth, as he had done many times before when confronted with what he perceived as overt disapproval. He didn’t recognize her, but he couldn’t help but observe that she was an attractive brunette with a trim figure. ​ “Will you be training as a fighter pilot or missile weapons officer?” she asked. ​ “I had basic fighter training on Mars and will be taking advanced pilot training with Repulse’s Squadron 111.” ​ “I’m a qualified second-seat astrogator in 111. Most likely, we’ll wind up flying together at some point.” ​ Because her demeanor revealed nothing about whether that idea repelled or appealed to her, Gallant nodded. ​ When they reached the captain’s cabin, she said, “I’m Kelsey, by the way.” Then, as she turned to leave, she added as an afterthought, “Good luck.” ​ Gallant watched her walk away. He wondered if her remark was sincere. ​ *** Gallant stood like a statue inside the open hatch. ​ Captain Kenneth Caine was seated with his back to him, reviewing Gallant’s military record, which was displayed on a computer screen. Clean-shaven with close-cropped graying hair, Caine was solidly built with square shoulders and a craggy face. His well-tailored uniform hugged his robust frame, accentuating his military bearing. ​ From his brief time onboard, Gallant had already realized that Repulse was an orderly ship, and that Kenneth Caine was an orderly captain. Precision and discipline were expected. He was suddenly conscious that his tangled brown hair was longer than regulations allowed. ​ The cabin was sparsely furnished in a traditional, starkly military fashion. A desk in one well-lit corner held the single personal item in the room: a photo of an attractive, mature woman with a pleasant smile. The sadness in her eyes hinted at the difficult bargain she had made as the lonely wife of a dedicated space officer. ​ While the captain flipped through the personnel folder, Gallant’s gaze wandered to the compartment’s viewscreen. The solar flare had subsided, leaving gigantic colorful Jupiter filling most of the view. ​ “At ease, Mr. Gallant,” said Caine, finally turning to face the newcomer. “Welcome aboard the Repulse.” ​ Gallant relaxed his stance and said in a strong, clear voice, “Thank you, sir.” ​ Caine looked him up and down and scrunched his face before asking, “What do you know of this ship’s mission, Mr. Gallant?” ​ “As the flagship of the Jupiter Fleet, Repulse must prevent alien encroachment along the frontier, sir,” ventured Gallant. ​ “Quite right, as far as that goes. But you’ll find, Mr. Gallant, that this task is more nuanced and layered than may be apparent. As a United Planets officer, you must find shades of meaning that can affect your performance. What would you surmise is behind this frontier watch?” The captain’s brisk voice demanded a resolute answer. ​ Gallant spoke guardedly at first, but as his confidence grew, his voice gained assurance. “Well, sir, UP knows little about the aliens’ origins or intentions. They appear to have bases on the satellites of the outer planets. Clashes with their scout ships have proven troublesome, and Fleet Command wants to gather more intelligence. With so little known about alien technology, it isn’t easy to assess the best way to repel it. Still, this fleet must forestall an invasion of Earth by preventing the aliens from gaining a foothold in this sector.” ​ ”And what would you say will be essential in achieving victory in battle?” ​ Leaning forward with his hands behind him to balance out his jutting jaw, Gallant said with fierce intensity, “Surprise, sir! I assume that is why you’ve dispersed most of the fleet. So you can search the widest possible region of space for the first signs of significant alien activity.” ​ Caine examined the young man again as if seeing him for the first time. “Good. We will not be the ones surprised. We will be prepared. You can appreciate how important it is that Repulse performs well.” Then, he added, “And I will allow nothing, and no one, to interfere with our mission.” ​ “Yes, sir,” said Gallant, feeling the sting from the pointed comment. ​ “Tell me, Mr. Gallant,” said the captain, shifting in his chair to find a more comfortable position, “why did you apply to the academy?” ​ Gallant’s voice swelled with passion. “For as long as I can remember, I’ve wanted to pilot spaceships and explore the unknown, sir.” ​ ”You are undoubtedly aware that many people wanted your hide raised up the flagpole.” Caine’s eyebrow twitched. “Although your progress for two academic years at the academy was respectable, many doubt that a Natural can compete in the fleet. Today, your real qualification for advancement is your double helix.” ​ Caine continued, “Frankly, I’m astonished you have gotten this far without the advantages of genetic engineering. You’re a bit of a mystery that has yet to unfold.” ​ Gallant didn’t like being referred to as a mystery, but he had his own uncertainty about how his future might evolve. ​ Caine said, “Now that you are commissioned, you must serve a two-year deployment on Repulse. Then, if you complete all your qualifications and receive strong ranking marks, you may be recommended for promotion to ensign.” ​ He gave a weak smile and added. “Learn your duties, obey orders, and you will have nothing to fear.” Caine searched Gallant’s face. “Well, nothing to say for yourself?” ​ Gallant thrust his chin out and said, “I am prepared to do my duty to the best of my ability, sir!” ​ “It is exactly ‘the best of your ability’ that is in question, young man,” responded Caine.

Semantic Web Services AMAZON Chapter 6.0 ​ The Semantic Web ​ In this chapter, we provide an introduction to the Semantic Web and discuss its background and potential. By laying out a road map for its likely development, we describe the essential stepping stones including: knowledge representation, inference, ontology, search and search engines. We also discuss several supporting semantic layers of the Markup Language Pyramid Resource Description Framework (RDF) a nd Web Ontology Language (OWL). ​ In addition, we discuss using RDF and OWL for supporting software agents, Semantic Web Services, and semantic searches. ​ Background ​ Tim Berners-Lee invented the World Wide Web in 1989 and built the World Wide Web Consortium (W3C ) team in 1992 to develop, extend, and standardize the Web. But he didn’t stop there. He continued his research at MIT through Project Oxygen[1] and began conceptual development of the Semantic Web. The Semantic Web is intended to be a paradigm shift just as powerful as the original Web. ​ The goal of the Semantic Web is to provide a machine-readable intelligence that would come from hyperlinked vocabularies that Web authors would use to explicitly define their words and concepts. The idea allows software agents to analyze the Web on our behalf, making smart inferences that go beyond the simple linguistic analyses performed by today's search engines. ​ Why do we need such a system? Today, the data available within HTML Web pages is difficult to use on a large scale because there is no global schema. As a result, there is no system for publishing data in such a way to make it easily processed by machines. For example, just think of the data available on airplane schedules, baseball statistics, and consumer products. This information is presently available at numerous sites, but it is all in HTML format which means that using it has significant limitations. ​ The Semantic Web will bring structure and defined content to the Web, creating an environment where software agents can carry out sophisticated tasks for users. The first steps in weaving the Semantic Web on top of the existing Web are already underway. In the near future, these developments will provide new functionality as machines become better able to "understand" and process the data. ​ This presumes, however, that developers will annotate their Web data in advanced markup languages. To this point, the language-development process isn't finished. There is also ongoing debate about the logic and rules that will govern the complex syntax. The W3C is attempting to set new standards while leading a collaborative effort among scientists around the world. Berners-Lee has stated his vision that today’s Web Services in conjunction with developing the Semantic Web, should become interoperable. ​ Skeptics, however, have called the Semantic Web a Utopian vision of academia. Some doubt it will take root within the commercial community. Despite these doubts, research and development projects are burgeoning throughout the world. And even though Semantic Web technologies are still developing, they have already shown tremendous potential in the areas of semantic groupware (see Chapter 13) and semantic search (see Chapter 15). Enough so, that the future of both the Semantic Web and Semantic Web Services (see Chapter 11) appears technically attractive. ​ The Semantic Web The current Web is built on HTML, which describes how information is to be displayed and laid out on a Web page for humans to read. In effect, the Web has developed as a medium for humans without a focus on data that could be processed automatically. In addition, HTML is not capable of being directly exploited by information retrieval techniques. As a result, the Web is restricted to manual keyword searches. For example, if we want to buy a product over the Internet, we must sit at a computer and search for most popular online stores containing appropriate categories of products. We recognize that while computers are able to adeptly parse Web pages for layout and routine processing, they are unable to process the meaning of their content. XML may have enabled the exchange of data across the Web, but it says nothing about the meaning of that data. The Semantic Web will bring structure to the meaningful content of Web pages, where software agents roaming from page-to-page can readily carry out automated tasks. We can say that the Semantic Web will become the abstract representation of data on the Web. And that it will be constructed over the Resource Description Framework (RDF) (see Chapter 7) and Web Ontology Language (OWL) (see Chapter 8). These languages are being developed by the W3C, with participations from academic researchers and industrial partners. 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. Two important technologies for developing the Semantic Web are already in place: XML and RDF. XML lets everyone create their own tags. Scripts, or programs, can make use of these tags in sophisticated ways, but the script writer has to know how the page writer uses each tag. In short, XML allows users to add arbitrary structure to their documents, but says nothing about what the structure means. ​ 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. 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 everyone migrates towards RDF and ontologies, interoperability will become more flexible to new demands. Another example of applicability is that of digital asset management. Photography archives, digital music, and video are all applications that are looking to rely to a greater degree on metadata. The ability to see relationships between separate media resources as well as the composition of individual media resources is well served by increased metadata descriptions and enhanced vocabularies. The concept of metadata has been around for years and has been employed in many software applications. The push to adopt a common specification will be widely welcomed. ​ 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. ​ The objective of the Semantic Web, therefore, is to provide a language that expresses both data and rules for reasoning as a Web-based knowledge representation. ​ Adding logic to the Web means using rules to make inferences and choosing a course of action. A combination of mathematical and engineering issues complicates this task (see Chapter 9). The logic must be powerful enough to describe complex properties of objects, but not so powerful that agents can be tricked by a paradox. Intelligence Concepts The concept of Machine Intelligence (MI) is fundamental to the Semantic Web. Machine Intelligence is often referred to in conjunction with the terms Machine Learning, Computational Intelligence, Soft-Computing, and Artificial Intelligence. Although these terms are often used interchangeably, they are different branches of study. For example, Artificial Intelligence involves symbolic computation while Soft-Computing involves intensive numeric computation. We can identify the following sub-branches of Machine Intelligence that relate to the Semantic Web: Knowledge Acquisition and Representation. Agent Systems. Ontology. Although symbolic Artificial Intelligence is currently built and developed into Semantic Web data representation, there is no doubt that software tool vendors and software developers will incorporate the Soft-Computing paradigm as well. The benefit is creating adaptive software applications. This means that Soft-Computing applications may adapt to unforeseen input. Knowledge Acquisition is the extraction of knowledge from various sources, while Knowledge Representation is the expression of knowledge in computer-tractable form that is used to help software-agents perform. A Knowledge Representation language includes Language Syntax (describes configurations that can constitute sentences) and Semantics (determines the facts and meaning based upon the sentences). For the Semantic Web to function, computers must have access to structured collections of information. But, traditional knowledge-representation systems typically have been centralized, requiring everyone to share exactly the same definition of common concepts. As a result, central control is stifling, and increasing the size and scope of such a system rapidly becomes unmanageable. In an attempt to avoid problems, traditional knowledge-representation systems narrow their focus and use a limited set of rules for making inferences. These system limitations restrict the questions that can be asked reliably. XML and the RDF are important technologies for developing the Semantic Web; they provide languages that express both data and rules for reasoning about the data from a knowledge-representation system. The meaning is expressed by RDF, which encodes it in sets of triples, each triple acting as a sentence with a subject, predicate, and object. These triples can be written using XML tags. As a result, an RDF document makes assertions about specific things. Subject and object are each identified by a Universal Resource Identifier (URI), just as those used in a link on a Web page. The predicate is also identified by URIs, which enables anyone to define a new concept just by defining a URI for it somewhere on the Web. The triples of RDF form webs of information about related things. Because RDF uses URIs to encode this information in a document, the URIs ensure that concepts are not just words in a document, but are tied to a unique definition that everyone can find on the Web. Search Algorithms The basic technique of search (or state space search) refers to a broad class of methods that are encountered in many different AI applications; the technique is sometimes considered a universal problem-solving mechanism in AI. To solve a search problem, it is necessary to prescribe a set of possible or allowable states, a set of operators to change from one state to another, an initial state, a set of goal states, and additional information to help distinguish states according to their likeliness to lead to a target or goal state. The problem then becomes one of finding a sequence of operators leading from the initial state to one of the goal states. Search algorithms can range from brute force methods (which use no prior knowledge of the problem domain, and are sometimes referred to as blind searches) to knowledge-intensive heuristic searches that use knowledge to guide the search toward a more efficient path to the goal state (see Chapters 9 and 15). Search techniques include: Brute force Breadth-first Depth-first Depth-first iterative-deepening Bi-directional Heuristic Hill-climbing Best-first A* Beam Iterative-deepening-A* Brute force searches entail the systematic and complete search of the state space to identify and evaluate all possible paths from the initial state to the goal states. These searches can be breadth-first or depth-first. In a breadth-first search, each branch at each node in a search tree is evaluated, and the search works its way from the initial state to the final state considering all possibilities at each branch, a level at a time. In the depth-first search, a particular branch is followed all the way to a dead end (or to a successful goal state). Upon reaching the end of a path, the algorithm backs up and tries the next alternative path in a process called backtracking. The depth-first iterative-deepening algorithm is a variation of the depth-first technique in which the depth-first method is implemented with a gradually increasing limit on the depth. This allows a search to be completed with a reduced memory requirement, and improves the performance where the objective is to find the shortest path to the target state. The bi-directional search starts from both the initial and target states and performs a breadth-first search in both directions until a common state is found in the middle. The solution is found by combining the path from the initial state with the inverse of the path from the target state. These brute force methods are useful for relatively simple problems, but as the complexity of the problem rises, the number of states to be considered can become prohibitive. For this reason, heuristic approaches are more appropriate to complex search problems where prior knowledge can be used to direct the search. Heuristic approaches use knowledge of the domain to guide the choice of which nodes to expand next and thus avoid the need for a blind search of all possible states. ​ The hill-climbing approach is the simplest heuristic search; this method works by always moving in the direction of the locally steepest ascent toward the goal state. The biggest drawback of this approach is that the local maximum is not always the global maximum and the algorithm can get stuck at a local maximum thus failing to achieve the best results. To overcome this drawback, the best-first approach maintains an open list of nodes that have been identified but not expanded. If a local maximum is encountered, the algorithm moves to the next best node from the open list for expansion. This approach, however, evaluates the next best node purely on the basis of its evaluation of ascent toward the goal without regard to the distance it lies from the initial state. The A* technique goes one step further by evaluating the overall path from the initial state to the goal using the path to the present node combined with the ascent rates to the potential successor nodes. This technique tries to find the optimal path to the goal. A variation on this approach is the beam search in which the open list of nodes is limited to retain only the best nodes, and thereby reduce the memory requirement for the search. The iterative-deepening-A* approach is a further variation in which depth-first searches are completed, a branch at a time, until some threshold measure is exceeded for the branch, at which time it is truncated and the search backtracks to the most recently generated node. A classic example of an AI-search application is computer chess. Over the years, computer chess-playing software has received considerable attention, and such programs are a commercial success for home PCs. In addition, most are aware of the highly visible contest between IBM’s Deep Blue Supercomputer and the reigning World Chess Champion, Garry Kasparov in May 1997. Millions of chess and computing fans observed this event in real-time where, in a dramatic sixth game victory, Deep Blue beat Kasparov. This was the first time a computer has won a match with a current world champion under tournament conditions. Computer chess programs generally make use of standardized opening sequences, and end game databases as a knowledge base to simplify these phases of the game. For the middle game, they examine large trees and perform deep searches with pruning to eliminate branches that are evaluated as clearly inferior and to select the most highly evaluated move. We will explore semantic search in more detail in Chapter 15. Thinking The goal of the Semantic Web is to provide a machine-readable intelligence. But, whether AI programs actually think is a relatively unimportant question, because whether or not "smart" programs "think," they are already becoming useful. Consider, for example, IBM’s Deep Blue. In May 1997, IBM's Deep Blue Supercomputer played a defining match with the reigning World Chess Champion, Garry Kasparov. This was the first time a computer had won a complete match against the world’s best human chess player. For almost 50 years, researchers in the field of AI had pursued just this milestone. Playing chess has long been considered an intellectual activity, requiring skill and intelligence of a specialized form. As a result, chess attracted AI researchers. The basic mechanism of Deep Blue is that the computer decides on a chess move by assessing all possible moves and responses. It can identify up to a depth of about 14 moves and value-rank the resulting game positions using an algorithm prepared in advance by a team of grand masters. Did Deep Blue demonstrate intelligence or was it merely an example of computational brute force? Our understanding of how the mind of a brilliant player like Kasparov works is limited. But indubitably, his "thought" process was something very different than Deep Blue’s. Arguably, Kasparov’s brain works through the operation of each of its billions of neurons carrying out hundreds of tiny operations per second, none of which, in isolation, demonstrates intelligence. One approach to AI is to implement methods using ideas of computer science and logic algebras. The algebra would establish the rules between functional relationships and sets of data structures. A fundamental set of instructions would allow operations including sequencing, branching and recursion within an accepted hierarchy. The preference of computer science has been to develop hierarchies that resolve recursive looping through logical methods. One of the great computer science controversies of the past five decades has been the role of GOTO-like statements. This has risen again in the context of Hyperlinking. Hyperlinking, like GOTO statements, can lead to unresolved conflict loops (see Chapter 12). Nevertheless, logic structures have always appealed to AI researchers as a natural entry point to demonstrate machine intelligence. An alternative to logic methods is to use introspection methods, which observe and mimic human brains and behavior. In particular, pattern recognition seems intimately related to a sequence of unique images with a special linkage relationship. While Introspection, or heuristics, is an unreliable way of determining how humans think, when they work, Introspective methods can form effective and useful AI. The success of Deep Blue and chess programming is important because it employs both logic and introspection AI methods. When the opinion is expressed that human grandmasters do not examine 200,000,000 move sequences per second, we should ask, “How do they know?'' The answer is usually that human grandmasters are not aware of searching this number of positions, or that they are aware of searching a smaller number of sequences. But then again, as individuals, we are generally unaware of what actually does go on in our minds. Much of the mental computation done by a chess player is invisible to both the player and to outside observers. Patterns in the position suggest what lines of play to look at, and the pattern recognition processes in the human mind seem to be invisible to that mind. However, the parts of the move tree that are examined are consciously accessible. Suppose most of the chess player’s skill actually comes from an ability to compare the current position against images of 10,000 positions already studied. (There is some evidence that this is at least partly true.) We would call selecting the best position (or image) among the 10,000, insightful. Still, if the unconscious human version yields intelligent results, and the explicit algorithmic Deep Blue version yields essentially the same results, then couldn’t the computer and its programming be called intelligent too? For now, the Web consists primarily of huge number of data nodes (containing texts, pictures, movies, sounds). The data nodes are connected through hyperlinks to form `hyper-networks' can collectively represent complex ideas and concepts above the level of the individual data. However, the Web does not currently perform many sophisticated tasks with this data. The Web merely stores and retrieves information even after considering some of the “intelligent applications” in use today (including intelligent agents, EIP, and Web Services). So far, the Web does not have some of the vital ingredients it needs, such as a global database scheme, a global error-correcting feedback mechanism, a logic layer protocol, or universally accepted knowledge bases with inference engines. As a result, we may say that the Web continues to grow and evolve, but it does not learn. If the jury is still out on defining the Web as intelligent, (and may be for some time) we can still consider ways to change the Web to give it the capabilities to improve and become more useful (see Chapter 9). Knowledge Representation and Inference An important element of AI is the principle that intelligent behavior can be achieved through processing of symbol structures representing increments of knowledge. This has given rise to the development of knowledge-representation languages that permit the representation and manipulation of knowledge to deduce new facts. Thus, knowledge-representation languages must have a well-defined syntax and semantics system, while supporting inference. First let’s define the fundamental terms “data,” “information,” and “knowledge.” An item of data is a fundamental element of an application. Data can be represented by population and labels. Information is an explicit association between data things. Associations are often functional in that they represent a function relating one set of things to another set of things. A rule is an explicit functional association from a set of information things to a resultant information thing. So, in this sense, a rule is knowledge. Knowledge-based systems contain knowledge as well as information and data. The information and data can be modeled and implemented in a database. Knowledge-engineering methodologies address design and maintenance knowledge, as well as the data and information. 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 database language is a knowledge language. Three well-established techniques have been used for knowledge representation and inference: frames and semantic networks, logic based approaches, and rule based systems. Frames and semantic networks also referred to as slot and filler structures, 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 from those of the higher level class. 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. Because of limitations, frames and semantic networks are generally limited to representation and inference of relatively simple systems. Logic-based approaches use logical formulas to represent more complex relationships among objects and attributes. Such approaches have well-defined syntax, semantics and proof theory. When knowledge is represented with logic formulas, the formal power of a logical theorem proof can be applied to derive new knowledge. 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. They allow the representation of knowledge using sets of IF-THEN or other condition action rules. This approach is more procedural and less formal in its logic and as a result, reasoning can be controlled in a forward or backward chaining interpreter. 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. Resource Description Framework (RDF) The Semantic Web is built on syntaxes which use the Universal Resource Identifier (URI) to represent data in triples-based structures using Resource Description Framework (RDF) (see Chapter 7). A URI is a Web identifier, such as "http:" or "ftp:.” The syntax of URIs is governed by the IETF, publishers of the general URI specification the W3C maintains a list of URI schemes . In an RDF document, assertions are made about particular things having properties with certain values. This structure turns out to be a natural way to describe the vast majority of the data processed by machines. Subject, predicate, and object are each identified by a URI. The RDF triplets form webs of information about related things. Because RDF uses URIs to encode this information in a document the URIs ensure that concepts are not just words in a document, but are tied to a unique definition. All the triples result in a directed graph whose nodes and arcs are all labeled with qualified URIs. The RDF model is very simple and uniform. The only vocabulary is URIs which allow the use of the same URI as a node and as an arc label. This makes self-reference and reification possible, just as in natural languages. This is appreciable in a user-oriented context (like the Web), but is difficult to cope with in knowledge-based systems and inference engines. ​ Once information is in RDF form, data becomes easier to process. We illustrate an RDF document in Example 6-1. This piece of RDF basically says that a book has the title "e-Video: Producing Internet Video," and was written by "H. Peter Alesso." Example 6-1 ​ Listing 6-1 Sample RDF /XML H. Peter Alesso e-Video: Producing Internet Video The benefit of RDF is that the information maps directly and unambiguously to a decentralized model that differentiates the semantics of the application from any additional syntax. In addition, XML Schema restricts the syntax of XML applications and using it in conjunction with RDF may be useful for creating some datatypes. The goal of RDF is to define a mechanism for describing resources that makes no assumptions about a particular application domain, nor defines the semantics of any application. RDF models may be used to address and reuse components (software engineering), to handle problems of schema evolution (database), and to represent knowledge (Artificial Intelligence). However, modeling metadata in a completely domain independent fashion is difficult to handle. How successful RDF will be in automating activities over the Web is an open question. However, if RDF could provide a standardized framework for most major Web sites and applications, it could bring significant improvements in automating Web-related activities and services (see Chapter 11). If some of the major sites on the Web incorporate semantic modeling through RDF, it could provide more sophisticated searching capabilities over these sites (see Chapter 15). We will return to a detailed presentation of RDF in Chapter 7. RDF Schema ​ The first "layer" of the Semantic Web is the simple data-typing model called a schema. A schema is simply a document that defines another document. It is a master checklist or grammar definition. The RDF Schema was designed to be a simple data-typing model for RDF. Using RDF Schema, we can say that "Desktop" is a type of "Computer," and that "Computer" is a sub class of “Machine”. We can also create properties and classes, as well as, creating ranges and domains for properties. ​ All of the terms for RDF Schema start with namespace http://www.w3.org/2000/01/rdf-schema# . The three most important RDF concepts are "Resource" (rdfs:Resource), "Class" (rdfs:Class), and "Property" (rdf:Property). These are all "classes," in that terms may belong to these classes. For example, all terms in RDF are types of resource. To declare that something is a "type" of something else, we just use the rdf:type property: rdfs:Resource rdf:type rdfs:Class . rdfs:Class rdf:type rdfs:Class . rdf:Property rdf:type rdfs:Class . rdf:type rdf:type rdf:Property . ​ This means "Resource is a type of Class, Class is a type of Class, Property is a type of Class, and type is a type of Property." ​ We will return to a detailed presentation of RDF Schema in Chapter 7. Ontology A program that wants to compare information across two databases has to know that two terms are being used to mean the same thing. Ideally, the program must have a way to discover common meanings for whatever databases it encounters. A solution to this problem is provided by the Semantic Web in the form of collections of information called ontologies. Artificial-intelligence and Web researchers use the term ontology for a document that defines the relations among terms. A typical ontology for the Web includes a taxonomy with a set of inference rules. Ontology and Taxonomy We can express an Ontology as: Ontology = < taxonomy, inference rules> And we can express a taxonomy as: Taxonomy = < {classes}, {relations}> The taxonomy defines classes of objects and relations among them. For example, an address may be defined as a type of location, and city codes may be defined to apply only to locations, and so on. Classes, subclasses, and relations among entities are important tools. We can express a large number of relations among entities by assigning properties to classes and allowing subclasses to inherit such properties. Inference rules in ontologies supply further power. An ontology may express the rule "If a city code is associated with a state code, and an address uses that city code, then that address has the associated state code." A program could then readily deduce, for instance, that an MIT address, being in Cambridge, must be in Massachusetts, which is in the U.S., and therefore should be formatted to U.S. standards. The computer doesn't actually "understand" this, but it can manipulate the terms in a meaningful way. The real power of the Semantic Web will be realized when people create many programs that collect Web content from diverse sources, process the information and exchange the results. The effectiveness of software agents will increase exponentially as more machine-readable Web content and automated services become available. The Semantic Web promotes this synergy — even agents that were not expressly designed to work together can transfer semantic data. The Semantic Web will provide the foundations and the framework to make such technologies more feasible. Web Ontology Language (OWL) In 2003, the W3C began final unification of the disparate ontology efforts into a standardizing ontology called the Web Ontology Language (OWL). OWL is a vocabulary extension of RDF. OWL is currently evolving into the semantic markup language for publishing and sharing ontologies on the World Wide Web. OWL facilitates greater machine readability of Web content than that supported by XML, RDF, and RDFS by providing additional vocabulary along with formal semantics. ​ OWL comes in several flavors as three increasingly-expressive sublanguages: OWL Lite, OWL DL, and OWL Full. By offering three flavors, OWL hopes to attract a broad following. ​ We will return to detailed presentation of OWL in Chapter 8. ​ Inference ​ A rule may describe a conclusion that one draws from a premise. A rule can be a statement processed by an engine or a machine that can make an inference from a given generic rule. The principle of "inference" derives new knowledge from knowledge that we already know. In a mathematical sense, querying is a form of inference and inference is one of the supporting principles of the Semantic Web. ​ For two applications to talk together and process XML data, they require that the two parties must first agree on a common syntax for their documents. After reengineering their documents with new syntax, the exchange can happen. However, using the RDF/XML model, two parties may communicate with different syntax using the concept of equivalencies. For example, in RDF/XML we could say “car” and specify that it is equivalent to “automobile.” ​ We can see how the system could scale. Merging databases becomes recording in RDF that "car" in one database is equivalent to "automobile" in a second database. ​ Indeed, this is already possible with Semantic Web tools, such as a Python program called "Closed World Machine” or CWM. Unfortunately, great levels of inference can only be provided using "First Order Predicate Logic," FOPL languages, and OWL is not entirely a FOPL language. ​ First-order Logic (FOL) is defined as a general-purpose representation language that is based on an ontological commitment to the existence of objects and relations. FOL makes it easy to state facts about categories, either by relating objects to the categories or by quantifying. ​ For FOPL languages, a predicate is a feature of the language which can make a statement about something, or to attribute a property to that thing. ​ Unlike propositional logics, in which specific propositional operators are identified and treated, predicate logic uses arbitrary names for predicates and relations which have no specific meaning until the logic is applied. Though predicates are one of the features which distinguish first-order predicate logic from propositional logic, these are really the extra structure necessary to permit the study of quantifiers. The two important features of natural languages whose logic is captured in the predicate calculus are the terms "every" and "some" and their synonyms. Analogues in formal logic are referred to as the universal and existential quantifiers. These features of language refer to one or more individuals or things, which are not propositions and therefore force some kind of analysis of the structure of "atomic" propositions. ​ The simplest logic is classical or boolean, first-order logic. The "classical" or "boolean" signifies that propositions are either true or false. ​ First-order logic permits reasoning about the propositional and also about quantification ("all" or "some"). An elementary example of the inference is as follows: ​ A ll men are mortal. John is a man. ​ ​ The conclusion: ​ John is mortal. ​ Application of inference rules provides powerful logical deductions. With ontology pages on the Web, solutions to terminology problems begin to emerge. The definitions of terms and vocabularies or XML codes used on a Web page can be defined by pointers from a page to an ontology. Different ontologies need to provide equivalence relations (defining the same meaning for all vocabularies), otherwise there would be a conflict and confusion. Software Agents Many automated Web Services already exist without semantics, but other programs, such as agents have no way to locate one that will perform a specific function. This process, called service discovery, can happen only when there is a common language to describe a service in a way that lets other agents understand both the function offered and the way to take advantage of it. Services and agents can advertise their function by depositing descriptions in directories similar to the Yellow Pages. There are some low-level, service-discovery schemes which are currently available. The Semantic Web is more flexible by comparison. The consumer and producer agents can reach a shared understanding by exchanging ontologies which provide the vocabulary needed for discussion. Agents can even bootstrap new reasoning capabilities when they discover new ontologies. Semantics also make it easier to take advantage of a service that only partially matches a request. An intelligent agent is a computer system that is situated in some environment, that is capable of autonomous action and learning in its environment in order to meet its design objectives. Intelligent agents can have the following characteristics: reactivity — they perceive their environment, and respond, pro-active — they exhibit goal-directed behavior and social — they interact with other agents. Real-time intelligent agent technology offers a powerful Web tool. Agents are able to act without the intervention of humans or other systems: they have control both over their own internal state and over their behavior. In complexity domains, agents must be prepared for the possibility of failure. This situation is called non-deterministic. Normally, an agent will have a repertoire of actions available to it. This set of possible actions represents the agent’s capability to modify its environments. Similarly, the action "purchase a house" will fail if insufficient funds are available to do so. Actions therefore have pre-conditions associated with them, which define the possible situations in which they can be applied. The key problem facing an agent is that of deciding which of its actions it should perform to satisfy its design objectives. Agent architectures are really software architectures for decision-making systems that are embedded in an environment. The complexity of the decision-making process can be affected by a number of different environmental properties, such as: Accessible vs inaccessible. Deterministic vs non- deterministic. Episodic vs non-episodic. Static vs dynamic. Discrete vs continuous. The most complex general class of environment is inaccessible, non-deterministic, non-episodic, dynamic, and continuous. ​ Trust and Proof ​ The next step in the architecture of the Semantic Web is trust and proof. If one person says that x is blue, and another says that x is not blue, will the Semantic Web face logical contradiction? ​ The answer is no, because applications on the Semantic Web generally depend upon context, and applications in the future will contain proof-checking mechanisms and digital signatures. Semantic Web Capabilities and Limitations The Semantic Web promises to make Web content machine understandable, allowing agents and applications to access a variety of heterogeneous resources, processing and integrating the content, and producing added value for the user. The Semantic Web aims to provide an extra machine understandable layer, which will considerably simplify programming and maintenance effort for knowledge-based Web Services. Current technology at research centers allow many of the functionalities the Semantic Web promises: software agents accessing and integrating content from distributed heterogeneous Web resources. However, these applications are really ad-hoc solutions using wrapper technology. A wrapper is a program that accesses an existing Website and extracts the needed information. Wrappers are screen scrapers in the sense that they parse the HTML source of a page, using heuristics to localize and extract the relevant information. Not surprisingly, wrappers have high construction and maintenance costs since much testing is needed to guarantee robust extraction and each time the Website changes, the wrapper has to be updated accordingly. ​ The main power of Semantic Web languages is that anyone can create one, simply by publishing RDF triplets with URIs. We have already seen that RDF Schema and OWL are very powerful languages. ​ One of the main challenges the Semantic Web community faces for the construction of innovative and knowledge-based Web Services is to reduce the programming effort while keeping the Web preparation task as small as possible. The Semantic Web’s success or failure will be determined by solving the following: • The availability of content. • Ontology availability, development, and evolution. • Scalability – Semantic Web content, storage, and search are scalable. • Multilinguality – information in several languages. • Visualization – Intuitive visualization of Semantic Web content. • Stability of Semantic Web languages. Conclusion ​ In this chapter, we provided an introduction to the Semantic Web and discussed its background and potential. By laying out a roadmap for its likely development, we described the essential stepping stones including: knowledge representation, inference, ontology, and search. We also discussed several supporting semantic layers of the Markup Language Pyramid Resource Description Framework (RDF) and Web Ontology Language (OWL). ​ In addition, we discussed using RDF and OWL for supporting software agents, Semantic Web Services, and semantic search. [1] MIT's Project Oxygen is developing technologies to enable pervasive, human-centered computing and information-technology services. Oxygen's user technologies include speech and vision technologies to enable communication with Oxygen as if interacting directly with another person, saving much time and effort. Automaton, individualized knowledge access, and collaboration technologies will be used to perform a wide variety of automated, cutting-edge tasks.