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