Simon Birrell

"We’re in a period of dizzying progress in “AI” on all fronts: no day goes by without startling advances in computer vision, robotic control and natural language processing. Above all, Large Language Models (LLMs) have turned our assumptions on their head about what is possible for a computer to achieve. Humanoid robots give us the impression that artificial human companions will soon be flooding our factories and homes to help us in work and in play. It’s impossible to keep up with the deluge of new papers. Artificial General Intelligence (AGI) is hurtling towards us. The future is almost here."

"It’s hard not to be optimistic. It’s also hard not to be cynical, particularly if you’ve been ground down by the reality of as hard a discipline as robotics. Are LLMs really intelligent? Are they on a path to sentience, and what does that even mean? Are humanoid robots practical? If “artificial intelligence” feels nebulous at times, “artificial general intelligence” is even more so. I lurch back and forth between optimism, as I see stunning advances in what we can do and cynicism, as I see overblown claims for where we are."

"It seems to me that what we have right now is the ability to build new, amazing, unprecedented tools to accomplish particular tasks: AI vision (“segment this image on a pixel by pixel basis”), reinforcement learning (“learn a policy to pick up widgets using trial and error”) and LLMs (“use your massive general knowledge to generate new text”). We do not have a coherent vision for putting these marvellous tools together into something with the flexibility and tenacity of a lifeform. We have many single-purpose parts. We do not have an architecture. This lack of a coherent framework for complete real-world agents is the first core problem in AGI today."

"The second core problem is that, because of its success and ubiquity, we tend to see digital computing as the only substrate for building these new creatures. We miss the importance of hardware, of embodiment; the shape of a fish fin outweighs any amount of clever code when swimming. We miss the power of low-level analogue electronic circuitry and it’s equivalent in animal reflexes and spinal cords. We miss the role of the environment, dismissing it as a nuisance or simply as one more annoying problem to be solved, until it resurfaces in bizarre research where artificial octopus tentacles in a fish tank appear to solve mathematical problems. We’re overspecialised, and each discipline (fluid mechanics, physiology, ethology, mechanical engineering…) only rarely peers over the wall into its neighbours’ garden. Where are the unifying principles?"

"Third, where is the intentionality in our new tools? Each application is built to achieve a single goal: to predict how proteins are folded, to generate text, to analyse an image. Reinforcement learning, the most explicitly goal-driven framework we have is still generally tied to a single goal. It “wants” to learn to pick up a vegetable with a robot hand, it’s not trying to ensure the survival of its agent."