Pierre-Yves Oudeyer
1 HN recommender · recommended in 6 months, 2019–2025
1:23:12 · 15K views · Mar 7, 2018
The full 1973 BBC Lighthill debate on artificial intelligence, featuring James Lighthill, Donald Michie, Richard Gregory and John McCarthy. It documents the argument that preceded the 1970s AI winter and includes early robot demos.
HN cites it as primary-source history of AI hype and skepticism, and for the vintage Freddy robot footage.
6 comments from Hacker News
Anyone interested in the History of AI can also watch the report being presented by Lighthill, and subsequently debated by John McCarthy, Donald Michie, and Richard Gregory. this video
> ... "AI can't do X" until we actually achieve X and then the goalpost is moved. This gets said often but I don't think anybody whose credible in the field actually makes statements like this. Take Chess for instance. In the IBM "Big Blue" documentary from ~2004, they quote journalists as saying "AI can't play Chess as well as humans but if it could, then AI would be solved." Why did the techniques from Big Blue not seem to go anywhere? I know for a fact that scientists _were not saying this_. The Lighthill Debates on AI specifically talk about why playing games well doesn't really prove anything. [0] I do agree that OCR has improved greatly by AI but this feels very niche. Somebody mentioned defect anomaly detection in another comment and I was not aware of this. All useful for sure. Still, this doesn't amount to anywhere near the hype that was announced earlier last decade. Moreover, the economics in AI are mostly awful despite everybody's seemingly best efforts. [1] Even if its useful in some vague sense, it's not necessarily economically useful. Amazon has ~10,000 people working on Alexa. [2] Have they turned a profit on these endeavours? I understand they can absorb the costs but its not clear to me how the economics will work out here. ML models haven't even been useful in places where statistical methods have reined supreme such as Renaissance Technologies and other hedge funds. No large companies are using neural networks in a significant capacity to my knowledge. An
To be honest, 89-92% sounds too optimistic for a real-world application. But I was talking about applications of the lego assembly model. My intuition is that lego is a special case: the pieces are made to interlock with each other and there are only a small number of ways in which two pieces can lock together. Compare this with building a prefab house, for example, or a car, where elements must be bound together with nails, mortar, soldering, or glue etc, in essentially unpredictable ways. I say that the approach described is pointless because I can't see any real-world applications of extracting assembly plans from a visualisation of an already-known assembly plan. Even less so when the object assembled is just a toy. And it's been possible to assemble toys using machine vision, given a pre-determined set of instructions, from the 1970's already: (Donald Michie presenting the Edinburgh Research Purpose General Assembly Robot, a.k.a. "Freddy"): this video More info of Freddy here [LINK] Freddy was tediously hand-programmed with a full plan but it used vision and touch to find the pieces of the toy it assembles in the video.
I'd like you to view this demo from 1974 that showcases the then current state of research in AI. Then reflect that the research presented in the link is 50 years later. this video
Ah, the AI versus advanced automation debate and the Lighthill Report (UK, 1973). [1] Report: [LINK] [2] Video of the public debate: this video