5 ms·

The average chess players of Bletchley Park and AI research in Britain

51 points by salonium_ 1y ago

PaulRobinson 1y ago
It's strange today to remember that playing chess well was seen as a great marker of AI, but today we consider it much less so. I thought Turing's Test would be a good barometer of AI, but in today's World of mountains of AI slop fooling more and more people, and ironically there being software that is better at solving CAPTCHAs than humans, I'm not so sure. Add into the mix that there are reports of people developing psychological disorders when exposed deeply to LLMs, I'm not sure they are good replacements for therapists (ELIZA, ah, what a thought), and they seem - even with a lot of investment in agentic workflows and getting a lot of context into GraphRAG or wiring up MCP - to be good at helping experts get a bit faster, not replace experts. And that's not software development specific - it seems to be the case across all domains of expertise. So what are we now chasing for? What's the test for AGI? It's definitely not playing games well, like we thought, or pretending to be human, or even being useful to a human. What is it, then?
- deleted 1y ago
  [deleted]
- pvg 1y ago
  playing chess well was seen as a great marker of AI Was it? Alpha-beta pruning is from 1957 they had a decent idea chess of what human-beating computer chess would be like and that it probably wasn't some pathway to Turing-test-beating AI.
- dandellion 1y ago
  How about: the ability to independently implement ways to manipulate the local environment for their own benefit or self-preservation?
- zmgsabst 1y ago
  I’d argue we have AGI, at the level of a child; now we’re debating further steps, such as adult AGI and super intelligence. But because AI is not like us, we have different results at different stages — eg, they’ve been better at arithmetic for a hundred years, games for twenty, and slowly are climbing up other domains.
  - nyrikki 1y ago
    Any discussion about AGI requires a written definition of the term to have a reasonable discussion. What we have now matches what many of the popular texts would call "Narrow AI", which is limited to specific tasks like speech recognition or playing chess, or mixtures of those. Traditionally AGI represents a more aspirational goal, machines that could theoretically perform any intellectual task a human can do. Under that definition we aren't close, and we will actually need new math to even hope to reach that goal. Obviously individuals concepts of what 'AGI' differ, as well as their motivations for choosing one. But the traditional hopeful mnomics concept of AGI is known to be unreachable without discoveries that upend what we think are hard limits today. Machines being better at arithmetic, the ties from to the limits of algorithms is actually the source of the limits. The work of Turing, Gödel, Tarski, Markov, Rice etc... is where that claim is coming from IMHO Fortunately there is a lot of practical utility without AGI, but our industries use of aspirational mnomics is almost guaranteed to disappoint the rest of the world.
    - NitpickLawyer 1y ago
      > Any discussion about AGI requires a written definition of the term to have a reasonable discussion. I agree, this is the hardest thing to pin in any discussion. What version of AGI are we even talking about? Here's a definition / explanation of AGI from the early days of DeepMind (from the movie "The thinking game"): Quote from Shane Legg: "Our mission was to build an AGI - an artificial general intelligence, and so that means that we need a system which is general - it doesn't learn to do one specific thing. That's really key part of human intelligence, learn to do many many things". Quote from Hassabis: "So, what is our mission? We summarise it as <Build the world's first general learning machine>. So we always stress the word general and learning here the key things." And the key slide (that I think cements the difference between what AGI stood for then, vs. now): AI - one task vs. AGI - many tasks at human level intelligence. ---- Now, if we go by this definition, which is pretty specific and clear, I think we've already achieved this. We already have systems that have "generally" learned stuff. And can do "many tasks" at "human level intelligence". Again, notice the emphasis on "general" and "learning". We have a learning machine, that takes in vast amounts of tokens (text, multimodal, even bytes at the end of the day) and "learns" to "do" many things. And notice it's many tasks, not all tasks. I think this is QED at this point. But, due to the old problem of "AI is everything that hasn't been done yet", and the constant goalpost moving, together with lots and lots of writing on this topic, the waters are muddier today, and lots of people argue and emphasise different things in the AGI field. > Fortunately there is a lot of practical utility without AGI Yeah, completely agree. I'm with Simon's recent article on this one. It doesn't even matter at this point if we reach AGI or not, or who's definition we use. I get a lot of value today from these systems. The debates are moot from my point.
- Scarblac 1y ago
  That can only be decided in hindsight. By the time everybody agrees that the system is clearly generally intelligent, it will have been for ages already. It will already be far more intelligent than even very smart humans. But I think general problem solving is a part of it. Coming up with its own ideas for possible solutions rather than what it generalized from a training set, and being able to try them out and iterate. In an environment it wasn't specifically designed for by humans. (not claiming most humans can do that)
  - zmgsabst 1y ago
    Are you saying most humans aren’t generally intelligent, by your definition?
    - Scarblac 1y ago
      Humans are very different from computers. In particular there are some things that computers are vastly better at (computation, memory, etc), and humans are optimized for surviving in their biological environment, not necessarily for general intelligence. I think asking of an AGI to do what humans do is asking a submarine to swim. It's not very useful. So I think that when we have useful computer AGI, it will be much better at it than humans. You already see that even with say ChatGPT -- it's not expert level, but the knowledge it does have is way way wider than any human's. If we get something that's as smart as humans, it will probably still be as widely applicable. And why even try, otherwise? We already have human intelligence.
- pyman 1y ago
  I have a similar philosophical question: My dog doesn't know what I do for a living, and he has no concept of how intelligent I am. So if we're limited by our own intelligence, how would we ever recognise or measure the intelligence of an AI that's more advanced than us? If an AI surpasses us, not just in memory or calculation but in reasoning, self-reflection, and abstraction, how would we even know?
  - officehero 1y ago
    Wittgenstein's lion
    - dale_glass 1y ago
      We could test it. We know with certainty that computers play far better chess than we do. How do we know? Play a game with the computer, and see who wins. There's no reason why we can't apply the same logic elsewhere. Set up a testable scenario, see who wins.
      - card_zero 1y ago
        Either the alleged super-intelligence affects us in some way, directly or indirectly by altering things we can detect about the world/universe, in which case we can ultimately detect it, or else it doesn't, in which case it might as well belong to a separate universe, not only in terms of our perception but objectively too. The error here is thinking that dogs understand anything.
        Retric 1y ago
        Some dogs can respond to “bring me my slippers” and go get them in another room, a concrete task that’s still difficult for robots today. With dogs it’s less a question of intelligence but communication something more intelligent AI is unlikely to have a problem with.
        card_zero 1y ago
- captainbland 1y ago
  I think with the Turing test, it's turned out to be a fuzzier line than expected. People are to various degrees learning LLM tells even as they improve. So what might have passed the Turing test in 2020 might not today. Similarly it seems to be a case that conversations with LLMs often start better than they end, even today - so an LLM might pass a short turing test but fail a very long one that goes into hundreds of rounds.
  - kranke155 1y ago
    We’ve clearly passed the Turing test I think. I can’t think of many ways I’d be able to detect an LLM reliably, if it was coded to just act as a person talking to me on discord.
    - uonr 1y ago
      The Turing test isn't dead. The true Turing test is a thought experiment, and it's not something that can be replicated in the real world. Given enough time and interaction, you can still spot a person on Discord being faked by an LLM—at the very least, something will feel off. This is even more true in a formal, knowing, adversarial setting.
- iamflimflam1 1y ago
  > It's strange today to remember that playing chess well was seen as a great marker of AI, but today we consider it much less so. It was seen as so difficult to do that research should be abandoned. Projects in category B were held to be failures. One important project, that of "programming and building a robot that would mimic human ability in a combination of eye-hand co-ordination and common-sense problem solving", was considered entirely disappointing. Similarly, chess playing programs were no better than human amateurs. Due to the combinatorial explosion, the run-time of general algorithms quickly grew impractical, requiring detailed problem-specific heuristics. The report stated that it was expected that within the next 25 years, category A would simply become applied technologies engineering, C would integrate with psychology and neurobiology, while category B would be abandoned. https://en.wikipedia.org/wiki/Lighthill_report https://en.wikipedia.org/wiki/Lighthill_report
  - rjsw 1y ago
    The linked post points out that it is a low-cost area of research and you don't need to explain the context to a reviewer.
- jltsiren 1y ago
  Tests can only show that something is not AGI. If you want to show that a system is AGI, you must wait for expert consensus. That means adding new tests and dropping old ones, as our understanding of intelligence improves. If something is truly AGI, people will eventually run out of plausible objections.
- nemomarx 1y ago
  I suppose doing useful research becomes the next target? that's what the exponential lift off people want right
- ben_w 1y ago
  > and they seem - even with a lot of investment in agentic workflows and getting a lot of context into GraphRAG or wiring up MCP - to be good at helping experts get a bit faster, not replace experts. And that's not software development specific - it seems to be the case across all domains of expertise. For now, this is a good thing: Given how generally LLMs are displacing juniors, if this was a situation where doing the same thing but harder can replace experts, it replaces approximately all of them. But: in limited domains, not the "G" of "AGI" but just specific places here and there, AI does beat human experts. Those domains are often sufficiently narrow that they don't even encompass the entire role — think "can analyse an X-ray for cancer, can't write up its findings" kind of specificity. Indeed, I can only think of two careers where even the broadest definition of AI (some kind of programmable system) has been able to essentially fully replace that occupation: 1. https://en.wikipedia.org/wiki/Jacquard_machine https://en.wikipedia.org/wiki/Jacquard_machine 2. https://en.wikipedia.org/wiki/Computer_(occupation) https://en.wikipedia.org/wiki/Computer_(occupation)
- GuB-42 1y ago
  > I thought Turing's Test would be a good barometer of AI Depends on what you consider a "Turing's Test". Fooling unsuspecting humans is relatively easy, it has been done with relatively simple software and some trickery. LLMs can do that too of course. A more convincing "Turing's Test" would be: - You have one interrogator, and two players, one human and one computer - The interrogator, after chatting with both players has to find which is which - The interrogator is an expert in the field, he knows everything there is to know when it comes to finding the computer - The human player is also an expert, he knows how to solve problems that are hard for computers to solve, he also knows what to expect from the interrogator - The interrogator and human player collaborate to find the computer - The interrogator and human player are not allowed to have shared information that the computer doesn't have (and ideally, they shouldn't know each other personally), but everything else is fair game
  - PaulRobinson 1y ago
    I feel like we could code that up right now and test that. The only problem is matching human expertise.
  - MrSkelter 1y ago
    The entire premise is wrong. Humanity isn’t defined by expertise. Most average people will reach the “I don’t know” or making-it-up stage on most subjects pretty fast. Perfect responses are more Likely indicative of a machine than a person.
- jeremyjh 1y ago
  I'm not sure that there is more to it than continuous learning. If an LLM of top-tier strength could learn from its experiences even the way a junior developer could, I'm not sure I can place an upper bound on how capable it would be at software engineering. But from what I understand this will require a completely different architecture.
codeulike 1y ago
If you look at the stuff Turing was writing in the 1950s its fascinating because he really saw the potential of what computation was going to be able to do. There was a paradigm shift in thinking about possibilities here that he grasped in the very early days. https://www.cs.ox.ac.uk/activities/ieg/e-library/sources/t_article.pdf https://www.cs.ox.ac.uk/activities/ieg/e-library/sources/t_a... It would be amazing to go and fetch Turing with a time machine and bring him to our time. Show him an iPhone, his face on the UK £50 note, and Wikipedia's list of https://en.wikipedia.org/wiki/List_of_openly_LGBTQ_heads_of_state_and_government https://en.wikipedia.org/wiki/List_of_openly_LGBTQ_heads_of_...
- barrenko 1y ago
  The Christ of computation...
marcodiego 1y ago
I remember an interview with Kasparov. He said something, I don't remember exactly... It was something like "The skills chess develops are very important for... playing chess"; as a way to say "if you're good in chess, that doesn't mean you're particularly smart or good in other areas too". As someone who played chess competitively in my childhood and teens, chess helped me a lot about concentration, problem solving and decision taking. I also learned to win and lose and to have respect for other people due to the competition. As a teacher in my adulthood, I was extremely impressed by knowing a high rated player that was very weak student, especially in logic. I now agree deeply with Kasparov about the importance of the skills chess develops.

51 points by salonium_ 1y ago

It's strange today to remember that playing chess well was seen as a great marker of AI, but today we consider it much less so. I thought Turing's Test would be a good barometer of AI, but in today's World of mountains of AI slop fooling more and more people, and ironically there being software that is better at solving CAPTCHAs than humans, I'm not so sure. Add into the mix that there are reports of people developing psychological disorders when exposed deeply to LLMs, I'm not sure they are good replacements for therapists (ELIZA, ah, what a thought), and they seem - even with a lot of investment in agentic workflows and getting a lot of context into GraphRAG or wiring up MCP - to be good at helping experts get a bit faster, not replace experts. And that's not software development specific - it seems to be the case across all domains of expertise. So what are we now chasing for? What's the test for AGI? It's definitely not playing games well, like we thought, or pretending to be human, or even being useful to a human. What is it, then?

deleted 1y ago
[deleted]
pvg 1y ago
playing chess well was seen as a great marker of AI Was it? Alpha-beta pruning is from 1957 they had a decent idea chess of what human-beating computer chess would be like and that it probably wasn't some pathway to Turing-test-beating AI.
dandellion 1y ago
How about: the ability to independently implement ways to manipulate the local environment for their own benefit or self-preservation?
zmgsabst 1y ago
I’d argue we have AGI, at the level of a child; now we’re debating further steps, such as adult AGI and super intelligence. But because AI is not like us, we have different results at different stages — eg, they’ve been better at arithmetic for a hundred years, games for twenty, and slowly are climbing up other domains.
- nyrikki 1y ago
  Any discussion about AGI requires a written definition of the term to have a reasonable discussion. What we have now matches what many of the popular texts would call "Narrow AI", which is limited to specific tasks like speech recognition or playing chess, or mixtures of those. Traditionally AGI represents a more aspirational goal, machines that could theoretically perform any intellectual task a human can do. Under that definition we aren't close, and we will actually need new math to even hope to reach that goal. Obviously individuals concepts of what 'AGI' differ, as well as their motivations for choosing one. But the traditional hopeful mnomics concept of AGI is known to be unreachable without discoveries that upend what we think are hard limits today. Machines being better at arithmetic, the ties from to the limits of algorithms is actually the source of the limits. The work of Turing, Gödel, Tarski, Markov, Rice etc... is where that claim is coming from IMHO Fortunately there is a lot of practical utility without AGI, but our industries use of aspirational mnomics is almost guaranteed to disappoint the rest of the world.
  - NitpickLawyer 1y ago
    > Any discussion about AGI requires a written definition of the term to have a reasonable discussion. I agree, this is the hardest thing to pin in any discussion. What version of AGI are we even talking about? Here's a definition / explanation of AGI from the early days of DeepMind (from the movie "The thinking game"): Quote from Shane Legg: "Our mission was to build an AGI - an artificial general intelligence, and so that means that we need a system which is general - it doesn't learn to do one specific thing. That's really key part of human intelligence, learn to do many many things". Quote from Hassabis: "So, what is our mission? We summarise it as <Build the world's first general learning machine>. So we always stress the word general and learning here the key things." And the key slide (that I think cements the difference between what AGI stood for then, vs. now): AI - one task vs. AGI - many tasks at human level intelligence. ---- Now, if we go by this definition, which is pretty specific and clear, I think we've already achieved this. We already have systems that have "generally" learned stuff. And can do "many tasks" at "human level intelligence". Again, notice the emphasis on "general" and "learning". We have a learning machine, that takes in vast amounts of tokens (text, multimodal, even bytes at the end of the day) and "learns" to "do" many things. And notice it's many tasks, not all tasks. I think this is QED at this point. But, due to the old problem of "AI is everything that hasn't been done yet", and the constant goalpost moving, together with lots and lots of writing on this topic, the waters are muddier today, and lots of people argue and emphasise different things in the AGI field. > Fortunately there is a lot of practical utility without AGI Yeah, completely agree. I'm with Simon's recent article on this one. It doesn't even matter at this point if we reach AGI or not, or who's definition we use. I get a lot of value today from these systems. The debates are moot from my point.
Scarblac 1y ago
That can only be decided in hindsight. By the time everybody agrees that the system is clearly generally intelligent, it will have been for ages already. It will already be far more intelligent than even very smart humans. But I think general problem solving is a part of it. Coming up with its own ideas for possible solutions rather than what it generalized from a training set, and being able to try them out and iterate. In an environment it wasn't specifically designed for by humans. (not claiming most humans can do that)
- zmgsabst 1y ago
  Are you saying most humans aren’t generally intelligent, by your definition?
  - Scarblac 1y ago
    Humans are very different from computers. In particular there are some things that computers are vastly better at (computation, memory, etc), and humans are optimized for surviving in their biological environment, not necessarily for general intelligence. I think asking of an AGI to do what humans do is asking a submarine to swim. It's not very useful. So I think that when we have useful computer AGI, it will be much better at it than humans. You already see that even with say ChatGPT -- it's not expert level, but the knowledge it does have is way way wider than any human's. If we get something that's as smart as humans, it will probably still be as widely applicable. And why even try, otherwise? We already have human intelligence.
pyman 1y ago
I have a similar philosophical question: My dog doesn't know what I do for a living, and he has no concept of how intelligent I am. So if we're limited by our own intelligence, how would we ever recognise or measure the intelligence of an AI that's more advanced than us? If an AI surpasses us, not just in memory or calculation but in reasoning, self-reflection, and abstraction, how would we even know?
- officehero 1y ago
  Wittgenstein's lion
  - dale_glass 1y ago
    We could test it. We know with certainty that computers play far better chess than we do. How do we know? Play a game with the computer, and see who wins. There's no reason why we can't apply the same logic elsewhere. Set up a testable scenario, see who wins.
    - card_zero 1y ago
      Either the alleged super-intelligence affects us in some way, directly or indirectly by altering things we can detect about the world/universe, in which case we can ultimately detect it, or else it doesn't, in which case it might as well belong to a separate universe, not only in terms of our perception but objectively too. The error here is thinking that dogs understand anything.
      - Retric 1y ago
        Some dogs can respond to “bring me my slippers” and go get them in another room, a concrete task that’s still difficult for robots today. With dogs it’s less a question of intelligence but communication something more intelligent AI is unlikely to have a problem with.
        card_zero 1y ago
captainbland 1y ago
I think with the Turing test, it's turned out to be a fuzzier line than expected. People are to various degrees learning LLM tells even as they improve. So what might have passed the Turing test in 2020 might not today. Similarly it seems to be a case that conversations with LLMs often start better than they end, even today - so an LLM might pass a short turing test but fail a very long one that goes into hundreds of rounds.
- kranke155 1y ago
  We’ve clearly passed the Turing test I think. I can’t think of many ways I’d be able to detect an LLM reliably, if it was coded to just act as a person talking to me on discord.
  - uonr 1y ago
    The Turing test isn't dead. The true Turing test is a thought experiment, and it's not something that can be replicated in the real world. Given enough time and interaction, you can still spot a person on Discord being faked by an LLM—at the very least, something will feel off. This is even more true in a formal, knowing, adversarial setting.
iamflimflam1 1y ago
> It's strange today to remember that playing chess well was seen as a great marker of AI, but today we consider it much less so. It was seen as so difficult to do that research should be abandoned. Projects in category B were held to be failures. One important project, that of "programming and building a robot that would mimic human ability in a combination of eye-hand co-ordination and common-sense problem solving", was considered entirely disappointing. Similarly, chess playing programs were no better than human amateurs. Due to the combinatorial explosion, the run-time of general algorithms quickly grew impractical, requiring detailed problem-specific heuristics. The report stated that it was expected that within the next 25 years, category A would simply become applied technologies engineering, C would integrate with psychology and neurobiology, while category B would be abandoned. https://en.wikipedia.org/wiki/Lighthill_report https://en.wikipedia.org/wiki/Lighthill_report
- rjsw 1y ago
  The linked post points out that it is a low-cost area of research and you don't need to explain the context to a reviewer.
jltsiren 1y ago
Tests can only show that something is not AGI. If you want to show that a system is AGI, you must wait for expert consensus. That means adding new tests and dropping old ones, as our understanding of intelligence improves. If something is truly AGI, people will eventually run out of plausible objections.
nemomarx 1y ago
I suppose doing useful research becomes the next target? that's what the exponential lift off people want right
ben_w 1y ago
> and they seem - even with a lot of investment in agentic workflows and getting a lot of context into GraphRAG or wiring up MCP - to be good at helping experts get a bit faster, not replace experts. And that's not software development specific - it seems to be the case across all domains of expertise. For now, this is a good thing: Given how generally LLMs are displacing juniors, if this was a situation where doing the same thing but harder can replace experts, it replaces approximately all of them. But: in limited domains, not the "G" of "AGI" but just specific places here and there, AI does beat human experts. Those domains are often sufficiently narrow that they don't even encompass the entire role — think "can analyse an X-ray for cancer, can't write up its findings" kind of specificity. Indeed, I can only think of two careers where even the broadest definition of AI (some kind of programmable system) has been able to essentially fully replace that occupation: 1. https://en.wikipedia.org/wiki/Jacquard_machine https://en.wikipedia.org/wiki/Jacquard_machine 2. https://en.wikipedia.org/wiki/Computer_(occupation) https://en.wikipedia.org/wiki/Computer_(occupation)
GuB-42 1y ago
> I thought Turing's Test would be a good barometer of AI Depends on what you consider a "Turing's Test". Fooling unsuspecting humans is relatively easy, it has been done with relatively simple software and some trickery. LLMs can do that too of course. A more convincing "Turing's Test" would be: - You have one interrogator, and two players, one human and one computer - The interrogator, after chatting with both players has to find which is which - The interrogator is an expert in the field, he knows everything there is to know when it comes to finding the computer - The human player is also an expert, he knows how to solve problems that are hard for computers to solve, he also knows what to expect from the interrogator - The interrogator and human player collaborate to find the computer - The interrogator and human player are not allowed to have shared information that the computer doesn't have (and ideally, they shouldn't know each other personally), but everything else is fair game
- PaulRobinson 1y ago
  I feel like we could code that up right now and test that. The only problem is matching human expertise.
- MrSkelter 1y ago
  The entire premise is wrong. Humanity isn’t defined by expertise. Most average people will reach the “I don’t know” or making-it-up stage on most subjects pretty fast. Perfect responses are more Likely indicative of a machine than a person.
jeremyjh 1y ago
I'm not sure that there is more to it than continuous learning. If an LLM of top-tier strength could learn from its experiences even the way a junior developer could, I'm not sure I can place an upper bound on how capable it would be at software engineering. But from what I understand this will require a completely different architecture.

codeulike 1y ago

If you look at the stuff Turing was writing in the 1950s its fascinating because he really saw the potential of what computation was going to be able to do. There was a paradigm shift in thinking about possibilities here that he grasped in the very early days. https://www.cs.ox.ac.uk/activities/ieg/e-library/sources/t_article.pdf https://www.cs.ox.ac.uk/activities/ieg/e-library/sources/t_a... It would be amazing to go and fetch Turing with a time machine and bring him to our time. Show him an iPhone, his face on the UK £50 note, and Wikipedia's list of https://en.wikipedia.org/wiki/List_of_openly_LGBTQ_heads_of_state_and_government https://en.wikipedia.org/wiki/List_of_openly_LGBTQ_heads_of_...

barrenko 1y ago
The Christ of computation...

marcodiego 1y ago

I remember an interview with Kasparov. He said something, I don't remember exactly... It was something like "The skills chess develops are very important for... playing chess"; as a way to say "if you're good in chess, that doesn't mean you're particularly smart or good in other areas too". As someone who played chess competitively in my childhood and teens, chess helped me a lot about concentration, problem solving and decision taking. I also learned to win and lose and to have respect for other people due to the competition. As a teacher in my adulthood, I was extremely impressed by knowing a high rated player that was very weak student, especially in logic. I now agree deeply with Kasparov about the importance of the skills chess develops.