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I mentioned ARC because based on your description it should be "the kind of problem that ILP eats for breakfast". But based on what you say about required effor

I mentioned ARC because based on your description it should be "the kind of problem that ILP eats for breakfast". But based on what you say about required effort it does not appear to eat it for breakfast and requires significant engineering fine-tuning, so I call foul on your previous description of your work. I agree, to a degree, with your assessment of the problem of cheating. Currently community is applying two fixes to that: 1) require few-shot learning ability 2) require good performance on many different few-shot learning benchmarks My hobby is future forecasting and I was thinking for some time about creating a benchmark for AGI based on Bongard problems, ARC and Montezuma's Revenge. I did have a question about ARC that managed to surprise community: https://www.metaculus.com/questions/3762/will-early-2020-ai-solve-at-least-20-of-previously-unseen-iq-test-like-tasks/ https://www.metaculus.com/questions/3762/will-early-2020-ai-... I have one about Montezuma's Revenge where I require some reasonable score after 30 min of game play, no specific to Montezuma's Revenge engineering allowed, not even retrieving rewards from internal game memory: https://www.metaculus.com/questions/5460/ai-rapidly-learning-to-play-a-simple-game/ https://www.metaculus.com/questions/5460/ai-rapidly-learning... I'm aware of the paper you mentioned around ARC and my fix to the benchmark would be to require few-shot generalization on task-level e.g. tested system, if it is a learning system, must have access only to couple of tasks for training before start of evaluation. The same with regard to Bongard problems. Requiring that all 3 benchmarks are solvable by one unified system IMO would be a reasonable test for general, visual intelligence. DeepMind recently decided to include comparison of Gopher performance on Massive Multitask Language Understanding benchmarks vs. forecasters prediction. Forecasting communities seems to have gained some clout in AI circles (OpenAI is also cooperating with Metaculus on AI forecasting). Few-shot ARC forecast from Metaculus could give you an external estimation of the difficulty that you could use against reviewer 2. Also, despite what you think about large language models memorizing everything few-shot SuperGlue is still standing: https://www.metaculus.com/questions/4932/when-will-ai-achieve-superhuman-few-shot-performance-on-superglue/ https://www.metaculus.com/questions/4932/when-will-ai-achiev... My intuitive evaluation of GPT-3 light text reasoning skills based on playing around with it matches few-shot SuperGlue score of 72 vs. 90 for avg. human. > Meanwhile, some big, 30-person team at a large tech corp with a few milllion dollars' budget will be throwing a gigaflop of compute and a few terrabytes of data on the problem and "solving" it without having to learn any "core priors", by finding some way around the dearth of training data, just as was done with Bongard problems and Winograd schemas. Compute is cheap and it reliably gets cheaper. Models like GPT-3 even with cost of tens of millions of dollars are economically justifiable. It's quite possible that OpenAI is currently turning profit on GPT-3. Real market performance is the ultimate benchmark of whether "performance degrades precipitously once exposed to real-world data".

YeGoblynQueenne 5y ago
>> I mentioned ARC because based on your description it should be "the kind of problem that ILP eats for breakfast". But based on what you say about required effort it does not appear to eat it for breakfast and requires significant engineering fine-tuning, so I call foul on your previous description of your work. Yep, you caught me. ILP is not magick. You actually have to do some work to train ILP algorithms. To clarify, there is no "engineering fine-tuning" required. ILP approaches rely on background knowledge, which can be thought of as a library of sub-routines from which target programs are composed. ILP is unique in that the representation of background knowledge is the same as that of target programs (they're all logic programs) so they can learn their own background knowledge incrementally. So the effort to solve ARC would include training an ILP system to learn core priors from some initial set of low-level primitives. Once learned, core priors would be included in the background knowledge needed to actually solve ARC tasks (along with other stuff like rotations, translations etc that don't have to be learned because they're straightforward to hand-code). Clearly, that would take time and work. Deep learning approaches simply replace time and effort with data and compute. But there is always a cost to be paid. You can't have learning for free. Learning core priors is not going to be simple. First of all, we don't even know exactly what they look like. Chollet gives a list, which I think is not exhaustive, but there's no dataset with examples that can be used in training. So there's a lot of work to be done, even just to figure out how to learn the core priors. Or you could do like on Kaggle and solve a sub-set of tasks with some geometric manipulations and a brute-force search. But that, too, would be cheating and it wouldn't generalise well anyway. >> Requiring that all 3 benchmarks are solvable by one unified system IMO would be a reasonable test for general, visual intelligence. I don't think that addresses the real issue with benchmarks. Machine learning benchmarks don't work because we have no idea what we want them to test, and that in turn because we don't have a theory of intelligence and artificial intelligence. Benchmarks like the Bongard problems, or ARC, now, are just educated guesses about the kind of behaviour an intelligent system should exhibit. Because they are just guesses, we're not really going anywhere and every few years someone comes up with a new benchmark, claims that this one is better than earlier ones, and then watches in horror as some dumb machine rips it to shreds. This will continue for ever until someone comes up with a plausible theory of what, exactly, it is that we are trying to test. Chollet actually tried that, but I think wrapping it all up in yet another new benchmark removes the focus from the theoretical work and focuses it on racing for SOTA again, thus exposing his work to the same old big-data shennanigans. For example, 72% accuracy on a language understanding task should indicate pretty strong language understanding ability already _if_ the benchmark was actually testing language understanding. 72% accuracy would certainly beat a dog, or a chimp, it would probably beat a toddler and may even beat an educated adult who is not a native speaker of the test language. So it's clear that testing "few" or "any" shot makes no difference because no language understanding ability is being tested anyway. The benchmarks are fundamentally broken. They won't get any better by tweaking them. You can't make a thermometer measure luminosity no matter how much you tweak it. Anyway pre-training, as I noted earlier, removes all legitimacy from any claims of "few shot" learning. First we need to undersand what is being learned during pre-training and if that has anything to do with some element of intelligent, then we can decide what "few shot" performance tells us. >> Real market performance is the ultimate benchmark of whether "performance degrades precipitously once exposed to real-world data". That sounds like you're saying that a neural network model's performance has something to do with how well it does as a commercial product. You can't mean that, so please explain. What's the market got to do with anything? P.S. Interesting stuff on Metaculus.

YeGoblynQueenne 5y ago

>> I mentioned ARC because based on your description it should be "the kind of problem that ILP eats for breakfast". But based on what you say about required effort it does not appear to eat it for breakfast and requires significant engineering fine-tuning, so I call foul on your previous description of your work. Yep, you caught me. ILP is not magick. You actually have to do some work to train ILP algorithms. To clarify, there is no "engineering fine-tuning" required. ILP approaches rely on background knowledge, which can be thought of as a library of sub-routines from which target programs are composed. ILP is unique in that the representation of background knowledge is the same as that of target programs (they're all logic programs) so they can learn their own background knowledge incrementally. So the effort to solve ARC would include training an ILP system to learn core priors from some initial set of low-level primitives. Once learned, core priors would be included in the background knowledge needed to actually solve ARC tasks (along with other stuff like rotations, translations etc that don't have to be learned because they're straightforward to hand-code). Clearly, that would take time and work. Deep learning approaches simply replace time and effort with data and compute. But there is always a cost to be paid. You can't have learning for free. Learning core priors is not going to be simple. First of all, we don't even know exactly what they look like. Chollet gives a list, which I think is not exhaustive, but there's no dataset with examples that can be used in training. So there's a lot of work to be done, even just to figure out how to learn the core priors. Or you could do like on Kaggle and solve a sub-set of tasks with some geometric manipulations and a brute-force search. But that, too, would be cheating and it wouldn't generalise well anyway. >> Requiring that all 3 benchmarks are solvable by one unified system IMO would be a reasonable test for general, visual intelligence. I don't think that addresses the real issue with benchmarks. Machine learning benchmarks don't work because we have no idea what we want them to test, and that in turn because we don't have a theory of intelligence and artificial intelligence. Benchmarks like the Bongard problems, or ARC, now, are just educated guesses about the kind of behaviour an intelligent system should exhibit. Because they are just guesses, we're not really going anywhere and every few years someone comes up with a new benchmark, claims that this one is better than earlier ones, and then watches in horror as some dumb machine rips it to shreds. This will continue for ever until someone comes up with a plausible theory of what, exactly, it is that we are trying to test. Chollet actually tried that, but I think wrapping it all up in yet another new benchmark removes the focus from the theoretical work and focuses it on racing for SOTA again, thus exposing his work to the same old big-data shennanigans. For example, 72% accuracy on a language understanding task should indicate pretty strong language understanding ability already _if_ the benchmark was actually testing language understanding. 72% accuracy would certainly beat a dog, or a chimp, it would probably beat a toddler and may even beat an educated adult who is not a native speaker of the test language. So it's clear that testing "few" or "any" shot makes no difference because no language understanding ability is being tested anyway. The benchmarks are fundamentally broken. They won't get any better by tweaking them. You can't make a thermometer measure luminosity no matter how much you tweak it. Anyway pre-training, as I noted earlier, removes all legitimacy from any claims of "few shot" learning. First we need to undersand what is being learned during pre-training and if that has anything to do with some element of intelligent, then we can decide what "few shot" performance tells us. >> Real market performance is the ultimate benchmark of whether "performance degrades precipitously once exposed to real-world data". That sounds like you're saying that a neural network model's performance has something to do with how well it does as a commercial product. You can't mean that, so please explain. What's the market got to do with anything? P.S. Interesting stuff on Metaculus.