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_...
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.
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?
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.
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
Perfect responses are more Likely indicative of a machine than a person.
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:
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?
As a trivial example, a century ago "can do arithmetic" was a signifier of being smart, yet if the entire human population today were all as fast as the current world record holder and on the same team, we would be defeated by one Raspberry Pi.
Easy to measure, but also very limited sense of "smart".
A Pi can also run Stockfish, so in that also-limited sense of "smart", it still beats humans. And chess inspires the wider use of Elo ratings in AI arenas, which means we can usefully assign scores to different AI that all beat the best humans.
For now, it's possible to point to things humans are (collectively) able to do better than AI — I originally wrote "very, very easy" rather than "possible", but then remembered noticing that whenever anyone actually tries to do so here on Hacker News, they're out of date already and there's is an AI which can do that thing superhumanly well (either that or they overstate what humans can do, e.g. claiming we can beat the halting problem); actual research papers with experiments generally do better when it comes to listing AI failure modes, including when the research comes from an AI lab showing off their new AI.
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)
that's what the exponential lift off people want right
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.
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.
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.
