View: https://www.youtube.com/live/CyyL0yDhr7I
The Royal Society posted on YouTube on 18 Feb 2026 Michael Faraday Prize Lecture delivered by 2025 winner Professor Michael John Wooldridge, titled "This Is Not the AI We Were Promised". Rather than being polemical or very opinionated it's a very level-headed appraisal of where we are with all this as seen from a largely academic, technological vantage point but eminently understandable and logical and thus, dare I say, to be recommended. So rather than reading on, do watch it; it is much more entertaining and effective than my undoubtedly lacking summary and musings below.
I introduced myself and was introduced to AI precepts at a certain point and while I'm immeasurably less qualified than Wooldridge on the subject I could hardly disagree with anything he presents in the lecture and remarkably, am not surprised by it either. Remarkably because since all the seemingly rapid developments, economical and social change at their core largely aren't based on some accelerating, self-perpetuating, singularity headed advances they're often made out to be. Rather AI LLMs remain, as Wooldridge puts it, "remarkable but weird".
Remarkable since GPT3's breakthrough and emergent capabilities such as advancing to solve math olympiad problems, weird since LLMs nonetheless have no meaningful innate understanding. Popularly AI is often presented as an algorithm given a problem, computing a solution and presenting it; this is indeed how many services work but AI doesn't satisfy the soundness and completeness criteria of algorithms but at its core is still statistical, probabilistic, matching patterns to data. Realistic LLMs have only become possible (and due to the economic, societal, environmental, etc. effects I use the word "possible" with reservation) due to immense scale. The stats for GPT3 alone are 175 Bn parameters, 500 Bn words and 3*10^23 FLOPs (Floating-Point Operations per Second).
Natural language does encode logic, algorithms, compositional structure, problem solving traces. State of the art is "chain of thought reasoning", breaking problems into smaller chunks to iterate and explore the solution space. One can indeed experiment with prompt engineering, "let's think through this step by step" will yield different results from "can you do better". This can have the appearance of more algorithmic intelligence but is still very distinct from human intelligence, let alone sentience. Yet we're biased to see these in AI LLMs. Wooldridge posits that AIs have practically passed the Turing test but technically not so much. LLMs remain inconsistent, have no distinction between knowledge/facts/beliefs, hallucinate, are unaware of their limitations, reason only approximately, have very limited abilities to revise beliefs and are disembodied from the World.
Wooldridge holds that LLMs in fact evidence that human intelligence is more complex than thought. Per his account, understanding what LLMs are is the central AI science problem of our time, while understanding how LLMs can be safely and productively used is the key AI engineering challenge of our time.
