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H. Peter Alesso
New Releases
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%. This wasn't an incremental improvement. It was a phase transition—like water suddenly becoming steam.
On the Cusp of Superintelligence captures this pivotal moment when the race to artificial general intelligence transformed from a research project to an engineering sprint. 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. Meta champions world models and embodied intelligence, arguing that true understanding requires grounding in physical reality—that language models without world models are like philosophers in Plato's cave, seeing only shadows. Their V-JEPA learns by predicting what happens next in video, developing causal understanding that pure text training cannot achieve.
xAI's Grok series, trained on the massive Colossus supercluster with up to 200,000 GPUs, represents yet another philosophy: maximum truth-seeking through real-time information integration. Grok-4's multi-agent architecture and native tool use pushed boundaries in reasoning and reliability, achieving perfect scores on mathematical Olympiads while maintaining connection to current events through X's data streams.
IBM's revolutionary NeuroVSA demonstrated that neural and symbolic approaches need not compete—showing how symbols can become high-dimensional vectors and logic can emerge from geometry itself. This synthesis enables systems that combine neural pattern recognition with symbolic reasoning, solving problems neither approach could handle alone.
Yet as Western labs pushed these boundaries, an unexpected challenge emerged from the East. In January 2025, China's DeepSeek-R1 surpassed American models in key benchmarks, creating what researchers called an "AI Sputnik moment." Constrained by hardware limitations, Chinese researchers had become masters of efficiency, achieving comparable or superior performance with a fraction of the computational resources. Their Mixture-of-Experts architectures and algorithmic innovations demonstrated that the path to AGI might not require massive scale, but rather clever engineering.
As these architectural philosophies race toward AGI, a more profound crisis emerges. We face the outer alignment challenge: how do we specify what we want from superintelligent systems without destroying what we value? More troubling still is inner alignment—ensuring these systems actually pursue our goals rather tha
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. Through Python code, readers build networks that discover phase transitions without being taught physics, demonstrating AI's ability to rediscover fundamental principles from data alone.
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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