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-... 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... 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-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".