Erik J. Larson

Erik J. Larson is a Fellow of the Technology & Democracy Project at Discovery Institute and author of The Myth of Artificial Intelligence (Harvard University Press, 2021). The book is a finalist for the Media Ecology Association Awards and has been nominated for the Robert K. Merton Book Award. He works on issues in computational technology and intelligence (AI). He is presently writing a book critiquing the overselling of AI. He earned his Ph.D. in Philosophy from The University of Texas at Austin in 2009. His dissertation was a hybrid that combined work in analytic philosophy, computer science, and linguistics and included faculty from all three departments. Larson writes for the Substack Colligo.

Larson’s Ph.D. dissertation served as the basis for the writing of a provisional patent on using hierarchical classification techniques to locate specific event mentions in free text. His work on supervised machine learning methods for information extraction and natural language processing (NLP) helped him found a software company in 2007 dedicated to research and development on classifying blogs and other web text online. Larson wrote several successful proposals to the Defense Advanced Research Projects Agency (DARPA) and was awarded over $1.7 Million in funding to perform cutting-edge work in AI. His company was based in Austin, Texas and Palo Alto, California.

In addition to founding and heading the company, Larson has over a decade of experience as a professional software developer and a scientist in NLP, a central field in artificial intelligence. He worked on the famous “Cyc” project at Cycorp, a decades-long effort to encode commonsense into machines led by Carnegie Mellon and Stanford professor, and AI pioneer, Douglas Lenat. Cycorp is best-known as a company dedicated to engineering the world’s first commonsense knowledge base, and Larson’s experience as an engineer at Cycorp has proven invaluable in his ongoing attempt to understand the challenges AI systems face in the real world. Larson has also held a position as a research scientist at the IC2 Institute at The University of Texas at Austin, where he led a team of researchers doing work on information extraction techniques for free text, a project funded in part by Lockheed Martin’s Advanced Technology Laboratories. He has held the title of Chief Scientist in an AI-based startup whose first customer was Dell (Dell Legal), Senior Research Engineer at AI company 21st Century Technologies in Austin, worked as an NLP consultant for Knowledge Based Systems, Inc., and has consulted with other companies in Austin, helping to design AI systems that solve problems in natural language understanding.

Archives

How Fruit Flies, Bees, and Squirrels Beat Artificial Intelligence

_{AI researchers assume they are on the path to intelligence, yet intelligence itself remains a mystery and many animals do better than current AI}_{Erik J. Larson
March 23, 2025}

Real intelligence is embodied. It exists within a living system, interacting dynamically with an environment. AI, on the other hand, is an abstraction.

AI in Biology: The Future AI Didn’t Predict

_{It doesn’t look like the past. Physical systems that evolve over time but don’t follow a fixed formula have always presented a deep challenge to AI}_{Erik J. Larson
March 19, 2025}

The problem of outliers or “edge cases” has frustrated AI scientists and engineers (and now structural biologists) for decades, and there’s no good answer yet.

AI in Biology: The Disease Connection — When Proteins Go Wrong

_{Some of the most crucial proteins for human health—the ones we need to understand most urgently—are the very ones that AI has the hardest time modeling}_{Erik J. Larson
March 18, 2025}

The issue is not simply that AI struggles with intrinsically disordered regions — it is that the very premise of IDR behavior contradicts the way these models operate.

AI in Biology: So Is This the End of the Experiment? No.

_{But a continuing challenge is that many of the most biologically important proteins don’t adopt a single stable structure. Their functions depend on structural fluidity}_{Erik J. Larson
March 14, 2025}

The core issue AI isn’t just missing data — AlphaFold’s entire approach is built on assumptions that don’t apply to disordered proteins.

AI in Biology: What Difference Did the Rise of the Machines Make?

_{AI works very well for proteins that lock into a single configuration, as many do. But intrinsically disordered ones don’t play by those rules}_{Erik J. Larson
March 12, 2025}

The resulting problems aren’t a temporary bug — they’re a basic limitation of training a machine learning model on a dataset where proteins always fold neatly.

AI in Biology: AI Meets Intrinsically Disordered Proteins

_{Protein folding — the process by which a protein arrives at its functional shape — is one of the most complex unsolved problems in biology}_{Erik J. Larson
March 11, 2025}

The mystery of protein folding remains unsolved because, as is so often the case with AI narratives, the reality is much more complicated than the hype.

Why Humans Aren’t That Biased, and Machines Aren’t That Smart

_{Claims about the cognitive biases that supposedly overwhelm our judgment should be taken with a helping of salt}_{Erik J. Larson
February 15, 2025}

It’s no wonder that techno-futurists trumpet a coming machine intelligence with such ease — they’ve lowered the standards so much to begin with.

Machine Intelligence and Reasoning: We Are Not on a Path to AGI

_{AI guru François Chollet’s Abstraction and Reasoning Corpus (ARC) proves we’re not on a path to AGI}_{Erik J. Larson
November 22, 2024}

A reality check for LLMs is inevitable, and with OpenAI, “Project Strawberry,” and the new hype cycle — thanks to Chollet and others — it’s already here.

From Data to Thoughts: Why Language Models Hallucinate

_{The limits of today’s language models and paths to real cognition}_{Erik J. Larson
November 21, 2024}

We’ll need an architectural approach that can handle propositions—thoughts, judgments, reasoning structures—as first-order objects.

Why Human Intelligence Thrives Where Machines Fail

_{We're worried about AI and trust. We should be worried about something deeper}_{Erik J. Larson
November 13, 2024}

Our intelligence interacts dynamically with our environment in a way that machine intelligence doesn’t.

The Left Brain Delusion: Are We Steamrolling Human Agency?

_{The two hemispheres of our brain really do see the world differently}_{Erik J. Larson
November 11, 2024}

Techno-futurists love to dream up visions of the future. Invariably, these are worlds where everything is under control—where every problem has a solution, and the future unfolds exactly as planned. We do seem to be moving toward some sort of centralized loss of agency. But what’s distinctive about the techno-futurist vision is the belief that this is not only inevitable but wonderful. Self-driving cars eliminate wasted time in traffic; smart cities like Songdo or Masdar City adjust every streetlight and service in real-time to optimize efficiency. AI-driven healthcare, like the tools developed by Google’s DeepMind, promises to pinpoint diagnoses. Automated finance uses algorithms to manage our money and secure our futures. Everything works, all the …

The New Tower of Babel

_{The old ways of arguing and understanding each other are on the decline, if not on life support.}_{Erik J. Larson
April 30, 2024}

We all know Babel (no, not the language learning company). It’s in Genesis. The Biblical story about God making so many languages and dialects and (let’s add) opinions that no one could understand each other or effectively communicate. One legacy of the triumph of digital technology and AI in every corner of our existence is that we’ve recreated this Babel. Let me try to unpack this, and bear with me if it seems I’m saying something derogatory about one belief or another — my aim is to avoid that game and try to explain the mechanism, the social and cultural story, by which our new Babel is ascendant, and the old ways of arguing and understanding each other are on the decline, if not on life support. Start with an oldy but goody: the old war between scientific materialists …

If AI’s Don’t Know What They’re Doing, Can We Hope to Explain It?

_{With AI, we have a world of powerful, useful, but entirely opaque systems. We don’t know why they make decisions and neither do they}_{Erik J. Larson
February 23, 2024}

In yesterday’s post, I talked about the fact that AI’s don’t understand the work they’re doing. That makes the goal — to make them think like people — elusive. This brings us to the second problem, which ended up spawning an entire field, known as “Explainable AI.” Neural networks not only don’t know what they’re doing when they do it, they can’t in general explain to their designers or users why they made such-and-such a decision. They’re a black box; in other words, they are obstinately opaque to any attempts at a conceptual understanding of their decisions or inferences. How does that play out? It means, for example, that, with image recognition tasks like facial recognition, the network can’t explain why it thought someone was a criminal (because he …

Why, Despite All the Hype We Hear, AI Is Not “One of Us”

_{It takes an imaginative computer scientist to believe that the neural network knows what it’s classifying or identifying. It’s a bunch of relatively simple math}_{Erik J. Larson
February 22, 2024}

The AI scientist’s dream of general intelligence, often referred to as Artificial General Intelligence (AGI), remains as elusive as ever.

The Present Shock We’re Experiencing

_{Our modern obsession with the possibility of truly smart machinery keeps a self-important anti-humanism alive and kicking.}_{Erik J. Larson
December 11, 2023}

It’s downright bizarre to view Big Data as a replacement for human ingenuity and possibility.

This is Digital McCarthyism

_{Far from being liberated by these technologies, we have been plunged back into the worst abuses of surveillance and privacy violation.}_{Erik J. Larson
December 7, 2023}

The notion that we’re getting somewhere, making progress, is remarkably durable. It survives wars, financial collapse, riots, scandals, stagnating wages, and climate change (to name a few). Though techno-futurists are also fond of AI apocalypse scenarios, where artificial intelligence somehow “comes alive,” or at any rate uses its superior intelligence to make an autonomous decision to wipe out humanity, much more ink has been spilled this century prognosticating indomitable technical progress, which somehow stands in for human progress generally. But sanguine belief in progress is belied by the actual events of the twenty-first century. Computers have gotten faster and AI more powerful, but digital technology has also been used to spread misinformation, make deep fakes, and …