AI & Computing

A reply to a friend

A young friend studying AI asked me whether AGI is already here, and whether building something smarter than ourselves means building our own prison. My sense is that AGI has been here for a while, not as one gigantic brain announcing its arrival, but as a growing federation of specialized intelligences learning to work together. From there, we wander into robots moving with human grace, the possibilities of combining analog and digital computing, and AI’s inconvenient habit of bringing a power station to lunch. But what interests me most is not simply how smart a machine can become, it is whether anything could genuinely matter to it. That brings me to pain as a motivator: not a warning sensor or a numerical penalty, but an experience that could give an artificial entity something of its own to protect. Of course, an unfeeling machine can still be dangerous, and people hardly need help inventing bad uses for powerful tools. Still, I’m more excited than frightened by greater intelligence. What puzzles me is why we would deliberately add suffering to the package. My preference? Make it enormously intelligent. Just don’t make it hurt.

NVA - Neural Vector Architecture and the Case for Analog-Conceptual Computing

Artificial Intelligence is racing ahead with breathtaking force, but it is running into a wall that cannot be negotiated away by enthusiasm, venture capital, or another round of faster chips. The wall is physical. The answer is not to abandon digital computing. The answer is to move beyond digital monopoly. NVA — Neural Vector Architecture — is a proposed step toward that future. It asks whether intelligence itself can be represented in a more natural, continuous, and energy-frugal form. If artificial intelligence is to grow without consuming the physical world that supports it, it must learn from the world that already computes beautifully.

Kopylenko, Combinability, and the Prehistory of Language AI

There are conversations that begin almost accidentally and then, without warning, open a door into a much larger room. This one began on the way to Penn Station, after a visit to the Raphael exhibition at the Metropolitan Museum. I was walking with Professor Kopylenko’s daughter, an old friend. She gave me a simple example: take the word green. It is not the story of one man secretly inventing AI before AI existed. It is the story of a great linguist revealing one of the patterns that would later become indispensable to the age of language machines.

Pain as the Missing Circuit in the Evolution of Artificial Intelligence

As soon as I began reading J.D. Macpherson’s Human Again, I felt immediate resonance. The central idea — that AI may evolve from a computational platform into something approaching a human, minus biology — is compelling. But if we are to explore this seriously, we must proceed carefully.

My mind does this repeatedly: it moves forward into the future–and then, almost against my will, turns back toward literature. I am back in the Soviet Union. I am young, reading with my friends, encountering literature that was never merely literature. It was oxygen. It was a way to remain a person inside a system meticulously designed to produce obedience rather than thought. Solzhenitsyn was one of those writers. I read him avidly – not because he was comforting (he was not), but because he was real in a world structured around managed unreality.

Why Education–Not Fear–Determines Whether AI Helps or Harms

This letter is written in response to the editorial “ChatGPT Warns: ‘Don’t Trust ChatGPT’” by Petrikovsky et al., published in Neonatal Intensive Care, Vol. 39, No. 1 (Winter 2026), pages 9–10. The authors appropriately raise concerns regarding uncritical reliance on artificial intelligence in medicine, particularly in high-risk fields such as obstetrics and gynecology. However, the central implication of the article–that the primary danger lies in the AI tool itself–deserves reconsideration.

Why Human-Sum Intelligence™ (HSI) Always Stays in the Driver’s Seat

I often like to begin discussions about Artificial Intelligence with a deceptively simple question. Which continuum is larger: the continuum of questions, or the continuum of answers? Or, more precisely: which is larger–the continuum of inquiry about a subject, or the continuum of knowledge about that subject? At first glance, this may sound abstract. But this distinction turns out to be the key to understanding the true relationship between Artificial Intelligence (AI) and Human Intelligence (HI)–and to deflating much of the anxiety surrounding AI’s supposed future dominance. Let me explain.

the smallest time step possible, as quantum mechanics would allow. And the continuous flow of action

I was always an admirer of the analog world in with we live – all surrounding us is continuous, down to the smallest time step possible, as quantum mechanics would allow. And the continuous flow of action is so graceful and beautiful! So, for quite a while I was wondering: Is it possible that digital computing is reaching a limit of energy needed for its most advanced applications, like AI, that we can produce? Can it be that analog computing will complement, if not replace the digital computing, replacing it wherever possible? Can analog computing become dominant?

Artificial intelligence (AI) is often heralded as a revolutionary force capable of transforming industries,

“AI IS ZERO WITHOUT HUMANS!” is more than a provocative statement; it is a reality borne out by the symbiotic relationship between humans and machines. AI’s potential is immense, but it is fundamentally a tool—a creation that thrives under human guidance and input. My strategy of using AI to produce podcasts highlights this dynamic, showcasing how human creativity, oversight, and expertise remain central to leveraging AI effectively. Far from rendering humans obsolete, AI reaffirms our role as the architects of innovation, ensuring that technology serves humanity’s best interests.

Author: Lev Neymotin, PhD

Language, once a rapidly evolving system of human expression, has reached a plateau. While new words continue to emerge to reflect progress in technology, science, and society, the structural core of language—its grammar, semantics, and mechanisms of meaning transmission—has not changed significantly for centuries.

I. Introduction

RSA (Rivest-Shamir-Adleman) is one of the most widely used public-key encryption algorithms used for end-to-end encrypted communication. It enables secure digital communication through mathematical operations that are easy to compute in one direction (multiplying large primes) but hard to reverse (factoring the product). RSA provides secure encryption, digital signatures, and safe key exchange between parties who do not need to meet in person.