The Lighthill Debate on AI from 1973: An Introduction and Transcript

Interesting excerpt on the origin of the term, "artificial intelligence":

    Professor Sir James Lighthill: [...] Now, what are the arguments for not calling this computer science, as I did in my talk and in my report, and calling it artificial intelligence? It's because one wants to make some sort of analogy. One wants to bring in what one can gain by a study of how the brains of living creatures operate. This is the only possible reason for calling it artificial intelligence instead.

    Professor John McCarthy: Let's see. Excuse me. I invented the term artificial intelligence. I invented it because we had to do something when we were trying to get money for a summer study in 1956, and I had a previous bad experience. The previous bad experience concerns occurred in 1952, when Claude Shannon and I decided to collect a batch of studies, which we hoped would contribute to launching this field. And Shannon thought that artificial intelligence was too flashy a term and might attract unfavorable notice, and so we agreed to call it automata studies. I was terribly disappointed when the papers we received were about automata, and very few of them had anything to do with the goal that at least I was interested in. I decided not to fly any false flags anymore, but to say that this is a study aimed at the long-term goal of achieving human-level intelligence. Since that time, many people have quarreled with the term, but have ended up using it. Newell and Simon, the group at Carnegie Mellon University, tried to use complex information processing, which is certainly a very neutral term, but the trouble was that it didn't identify their field, because everyone would say, well, my information is complex. I don't see what's special about you.

I had seen the video of this debate years ago, but decided to revisit it recently in light of all the new developments in the field. I thought others might enjoy it too, especially people who had never heard of it before, but that many would prefer to read it instead of watching. So I created a full transcript with proper formatting.

I also included some thoughts in the intro and a section at the end that attempts to review the accuracy of the different speakers' arguments and claims since the debate took place 50 years ago. Hopefully it can spark an interesting debate here on the current state of the field and we can learn some lessons from the past!

From the perspective of the 1970s many of these problems would have appeared insanely hard to solve to the point of impossibility.

Consider the idea of building insane torch rockets like those in The Expanse or Avatar. We’d need something like small compact fusion reactors or antimatter manufacturing at scale, not to mention enormous advances in materials and superconductors and such.

That looks impossible today and we know the shape of the problem. In 1973 the gap between computers of the time and those of today was similar to the gap between a chemical rocket and a relativistic antimatter blowtorch, but on top of that nobody really knew what approaches to AI might even bear fruit. We had way more unknown unknowns between us and HAL 9000 than we have between us and a starship.

It took many doubling of compute power, the accumulation of petabytes of training data, and thousands and thousands of researchers not just exploring the math but also tinkering (“graduate student descent” as it’s known in machine learning).

Definitely forgivable to think this might not be achievable in 1973.

James Lighthill is sometimes criticized for commenting on a field in which he was not expert, so it adds context to look at why he was solicited for comment. At that time, fluid dynamics was one of the most prestigious technical fields in British academia, being very difficult, mathematical, and successful. Lighthill was the superstar of the field, so he was an obvious choice when the Science Research Council went looking for someone with status and technical chops to give a disinterested analysis of AI research. It is rather like how Richard Feynman was drafted to investigate the Challenger disaster.

TFA says ...Lighthill was no fool. And yet, he was very confidently and persuasively wrong about the potential for AI... I disagree, his report (which is short and readable) raises issues that are still pertinent. We currently have debate over whether GPT represents true intelligence and Lighthill's comments foreshadow those of current skeptics.

The author of TFA is clearly not a skeptic and accepts the views of John McCarthy. I have studied the debate between McCarthy and Hubert Dreyfus over the potential of AI, and I personally think that Dreyfus was right, current hype over ChatGPT notwuthstanding.

Related:

Review of “Artificial Intelligence: A General Survey” (1993) - https://news.ycombinator.com/item?id=21700906 - Dec 2019 (9 comments)

John McCarthy (and others) vs. Lighthill on AI in 1973 - https://news.ycombinator.com/item?id=856843 - Oct 2009 (1 comment)

I mean, he was right … for what we knew at that time. He predicted correctly that the only way to achieve a general intelligence it would require to mimic the extremely complex neural networks in our brain that the hardware of the time was very far away from achieving. He could not predict that things would move so fast on the hardware side (nobody could have)that made this somewhat possible. We are atill I would argue a bit out in having the appropriate computer power to make this a reality still, but it now is much more obvious that it is possible if we continue on this path