7 ms·

I really like the idea of ARC. But to me the problems seem like they require a lot of spatial world knowledge, more than they require abstract reasoning. Shapes

by lacker 2y ago

I really like the idea of ARC. But to me the problems seem like they require a lot of spatial world knowledge, more than they require abstract reasoning. Shapes overlapping each other, containing each other, slicing up and reassembling pieces, denoising regular geometric shapes, you can call them "core knowledge" but to me it seems like they are more like "things that are intuitive to human visual processing". Would an intelligent but blind human be able to solve these problems? I'm worried that we will need more than 800 examples to solve these problems, not because the abstract reasoning is so difficult, but because the problems require spatial knowledge that we intelligent humans learn with far more than 800 training examples.

nickpsecurity 2y ago
To parent: the spatial reasoning and blind person were great counterexamples. It still might be OK despite the blind exceptions if it showed general reasoning. To OP: I like your project goal. I think you should look at prior, reasoning engines that tried to build common sense. Cyc and OpenMind are examples. You also might find use for the list of AGI goals in Section 2 of this paper: https://arxiv.org/pdf/2308.04445 https://arxiv.org/pdf/2308.04445 When studying intros of brain function, I also noted many regions tie into the hippocampus which might do both sense-neutral storage of concepts and make inner models (or approximations) of external world. The former helps tie concepts together through various senses. The latter helps in planning when we are imagining possibilities to evaluate and iterate on them. Seems like AGI should have these hippocampus-like traits and those in the Cyc paper. One could test if an architecture could do such things in theory or on a small scale. It shouldn’t tie into just one type of sensory input either. At least two with the ability to act on what only exists in one or what is in both. Edit: Children also have an enormous amount of unsupervised training on visual and spatial data. They get reinforcement through play and supervised training by parents. A realistic benchmark might similarly require GB of prettaining.
- HarHarVeryFunny 2y ago
  CYC was an expert system, which is arguably what LLMs are. A similar vintage GOFAI project that might do better on these, with a suitable visual front end, is SOAR - a general purpose problem solver.
  - nickpsecurity 2y ago
    LLM’s aren't expert systems. A hallmark of expert systems is they encoded human-readable, human-checked knowledge with explainable reasoning. It was usually done as if-then rules. Others with logic programming. Forward and backward chaining for rules. Usually had specialist knowledge for one, use case. LLM’s are unsupervised, use probabilities with unpredictable results, and don’t explain every step of their thinking. They’re the opposite. You might argue Cyc was. It was also more complex than any expert system I had ever seen. We just called stuff like that a reasoning engine or just Cyc to avoid confusion.
    - HarHarVeryFunny 2y ago
      An expert system is just a system based on repeated application of declarative rules. CYC was certainly an expert system - the ultimate scaling experiment of expert systems. I believe CYC also had a variety of inference/reasoning engines in addition to it's set of rules. The rules (some prefer to call it a world model) in an LLM are deduced, via gradient descent, from the training samples, but are still there. The transformations effected by each layer of a transformer are exactly those it has learnt - the rules it is applying. As with CYC people seem to be hoping that some external scaffolding (better inference engine(s)) will rescue LLMs from just being a set of rules to something more general and capable, but I tend to agree with Chollet that this active inference (reasoning) is actually the hard part.
Lerc 2y ago
I don't think the intent is to learn the entire problem domain from the examples, but the specific rule that is being applied. There may (almost certainly will be) additional knowledge encoded in the solver to cover the spacial concepts etc. The distinction with the AGI-ARC test is the disparity between human and AI performance, and that it focuses on puzzles that are easier for humans. It would be interesting to see a finetuned LLM just try and express the rule for each puzzle as english. It could have full knowledge of what ARC-AGI is and how the tests operate, but the proof of the pudding is simply how it does on the test set.
CooCooCaCha 2y ago
“Would an intelligent but blind human be able to solve these problems?” This is the wrong way to think about it IMO. Spatial relationships are just another type of logical relationship and we should expect AGI to be able to analyze relationships and generate algorithms on the fly to solve problems. Just because humans can be biased in various ways doesn’t mean these biases are inherent to all intelligences.
- janalsncm 2y ago
  Part of the concern might be that visual reasoning problems are overrepresented in ARC in the space of all abstract reasoning problems. It’s similar to how chess problems are technically reasoning problems but they are not representative of general reasoning.
  - CooCooCaCha 2y ago
    ARC is meant to test fundamental algorithms. It's entirely ok to train a model specifically for this task. Part of the beauty of ARC is that it's resistant to memorization.
- crazygringo 2y ago
  > Spatial relationships are just another type of logical relationship and we should expect AGI to be able to analyze relationships and generate algorithms on the fly to solve problems. Not really. By that reasoning, 5-dimensional spatial reasoning is "just another type of logical relationship" and yet humans mostly can't do that at all. It's clear that we have incredibly specialized capabilities for dealing with two- and three-dimensional spatiality that don't have much of anything to do with general logical intelligence at all.
  - CooCooCaCha 2y ago
    Yes really. Problem solving on the fly doesn't mean the algorithm can instantly learn anything. Reality is HEAVILY biased towards two and three spatial dimensions so our brains have hours and hours of training on that dataset. But, with time, humans can learn to be good at all sorts of things. It's important that we try to think from the perspective of an algorithm, not a human. And it's also important that we don't jump to extremes. It seems like you interpreted "solving problems on the fly" to mean "instantly being an expert on a completely different and novel domain". What it does mean is flexibility, resilience to novel situations, and being able to adapt over time.
HarHarVeryFunny 2y ago
I just did the first 5 of the "public eval set" without having looked at the "public training set", and found them easy enough. If we're defining AGI as at least human level, then the AGI should also be able to do these without seeing any more examples. I don't think there's any rules about what knowledge/experience you build into your solution.
- mewpmewp2 2y ago
  AGI should obviously be able to do them. But AI being able to do those 100 percent wouldn't be evidence of AGI however. It is a very narrow domain.
  - bubblyworld 2y ago
    Why not? If the only thing that can solve problem X is AGI (e.g. humans), and something else comes along that solves it, then rationally that should be evidence that the something else is AGI right? Unless you have strong prior beliefs (like "computers can't be AGI") or something else that's problem specific ("these problems can be solved by these techniques which don't count as AGI"). So I guess that's my real question.
    - lucianbr 2y ago
      That makes no sense at all. Any problem is initially only solvable by humans, until some technology is developed to solve it. Calculating a logarithm was at some point only doable by humans, and then digital computers came along. This would be in your view evidence that digital computers are AGI!? As in, an 8086 with some math code is AGI. We've had it for decades now, only nobody noticed :)
      - bubblyworld 2y ago
        It's just Bayes theorem - there are basically two variables that control how strong the evidence is: * How likely you think AGI is in general. * How solvable you think the problem is, independently of what's solving it. In the cases you've brought up that latter probability is very high, which means that they are extremely weak evidence that computers are AGI. So we agree! In this case the latter probability seems to be quite low - attempts to solve it with computers have largely failed so far!
modeless 2y ago
> to me it seems like they are more like "things that are intuitive to human visual processing". Yann LeCun argues that humans are not general intelligence and that such a thing doesn't really exist. Intelligence can only be measured in specific domains. To the extent that this test represents a domain where humans greatly outperform AI, it's a useful test. We need more tests like that, because AIs are acing all of our regular tests despite being obviously less capable than humans in many domains. > the problems require spatial knowledge that we intelligent humans learn with far more than 800 training examples. Pretraining on unlimited amounts of data is fair game. Generalizing from readily available data to the test tasks is exactly what humans are doing. > Would an intelligent but blind human be able to solve these problems? I'm confident that they would, given a translation of the colors to tactile sensation. Blind humans still understand spatial relationships.
andoando 2y ago
I would argue that spatial reasoning encompasses all reasoning. All the things you mentioned have a direct analogue to abstract models and logic we employ and are engrained deeply into language. For example, shapes containing eachother: There are two countries both which lay claim to the same territory. There is a set X that contains Y and there is a set Z that contains Y. In the case that the common overlap is 3D and one in on top of the other, we can extend this to there is a set X that contains -Y and a set Z that contains Y, and just as you can only see one on top and not both depending on where you stand, we can apply the same property here and say set X and Z cannot both exist, and therefore if set X is on then -Y and if set Z then Y. If you pay attention to the language you use youll start to realize how much of it uses spatial relationships to describe completely abstract things. For example, one can speak of disintigrating hegonomic economies. i.e turning things built on top of eachother into nothing, to where it came We are after all, reasoning about things which happen in time and space. And spatial != visual. Even if you were blind youd have to reason spatially, because again any set of facts are facts in space-time. What does it take to understand history? People in space, living at various distances from each other, producing goods from various locations of the earth using physical processes, and physically exchanging them. To understand battles you have to understand how armies are arranged physically, how moving supplies works, weather conditions, how weapons and their physical forms affect what they can physically do, etc. Hell LLMs, the largest advancement we had in artificial intelligence do what exactly? Encode tokens into multi dimensional space.
- parentheses 2y ago
  Spatial reasoning is easily isomorphic to many kinds of reasoning - just not all of them. Spatial reasoning in this case also limits the AI to 2 dimensions. I concede that with more dimensions, there will be more isomorphisms. Is there a number of dimensions that captures all reasoning? I don't know..
  - dimask 2y ago
    Claims of isomorphisms are really strong claims to not be backed up with some kind of evidence.
dimask 2y ago
> Would an intelligent but blind human be able to solve these problems? Blind people can have spatial reasoning just fine. Visual =/= spatial [0]. Now, one would have to adapt the colour-based tasks to something that would be more meaningful for a blind person, I guess. [0] https://hal.science/hal-03373840/document https://hal.science/hal-03373840/document
lynx23 2y ago
If a blind individual can solve a visually oriented challenge is not really a question of their intelligence but more a question of accessibility/translation. Just because I cant see something myself doesnt really say anything about my ability to deal with abstractions.

by lacker 2y ago

nickpsecurity 2y ago

To parent: the spatial reasoning and blind person were great counterexamples. It still might be OK despite the blind exceptions if it showed general reasoning. To OP: I like your project goal. I think you should look at prior, reasoning engines that tried to build common sense. Cyc and OpenMind are examples. You also might find use for the list of AGI goals in Section 2 of this paper: https://arxiv.org/pdf/2308.04445 https://arxiv.org/pdf/2308.04445 When studying intros of brain function, I also noted many regions tie into the hippocampus which might do both sense-neutral storage of concepts and make inner models (or approximations) of external world. The former helps tie concepts together through various senses. The latter helps in planning when we are imagining possibilities to evaluate and iterate on them. Seems like AGI should have these hippocampus-like traits and those in the Cyc paper. One could test if an architecture could do such things in theory or on a small scale. It shouldn’t tie into just one type of sensory input either. At least two with the ability to act on what only exists in one or what is in both. Edit: Children also have an enormous amount of unsupervised training on visual and spatial data. They get reinforcement through play and supervised training by parents. A realistic benchmark might similarly require GB of prettaining.

HarHarVeryFunny 2y ago
CYC was an expert system, which is arguably what LLMs are. A similar vintage GOFAI project that might do better on these, with a suitable visual front end, is SOAR - a general purpose problem solver.
- nickpsecurity 2y ago
  LLM’s aren't expert systems. A hallmark of expert systems is they encoded human-readable, human-checked knowledge with explainable reasoning. It was usually done as if-then rules. Others with logic programming. Forward and backward chaining for rules. Usually had specialist knowledge for one, use case. LLM’s are unsupervised, use probabilities with unpredictable results, and don’t explain every step of their thinking. They’re the opposite. You might argue Cyc was. It was also more complex than any expert system I had ever seen. We just called stuff like that a reasoning engine or just Cyc to avoid confusion.
  - HarHarVeryFunny 2y ago
    An expert system is just a system based on repeated application of declarative rules. CYC was certainly an expert system - the ultimate scaling experiment of expert systems. I believe CYC also had a variety of inference/reasoning engines in addition to it's set of rules. The rules (some prefer to call it a world model) in an LLM are deduced, via gradient descent, from the training samples, but are still there. The transformations effected by each layer of a transformer are exactly those it has learnt - the rules it is applying. As with CYC people seem to be hoping that some external scaffolding (better inference engine(s)) will rescue LLMs from just being a set of rules to something more general and capable, but I tend to agree with Chollet that this active inference (reasoning) is actually the hard part.

Lerc 2y ago

I don't think the intent is to learn the entire problem domain from the examples, but the specific rule that is being applied. There may (almost certainly will be) additional knowledge encoded in the solver to cover the spacial concepts etc. The distinction with the AGI-ARC test is the disparity between human and AI performance, and that it focuses on puzzles that are easier for humans. It would be interesting to see a finetuned LLM just try and express the rule for each puzzle as english. It could have full knowledge of what ARC-AGI is and how the tests operate, but the proof of the pudding is simply how it does on the test set.

CooCooCaCha 2y ago

“Would an intelligent but blind human be able to solve these problems?” This is the wrong way to think about it IMO. Spatial relationships are just another type of logical relationship and we should expect AGI to be able to analyze relationships and generate algorithms on the fly to solve problems. Just because humans can be biased in various ways doesn’t mean these biases are inherent to all intelligences.

janalsncm 2y ago
Part of the concern might be that visual reasoning problems are overrepresented in ARC in the space of all abstract reasoning problems. It’s similar to how chess problems are technically reasoning problems but they are not representative of general reasoning.
- CooCooCaCha 2y ago
  ARC is meant to test fundamental algorithms. It's entirely ok to train a model specifically for this task. Part of the beauty of ARC is that it's resistant to memorization.
crazygringo 2y ago
> Spatial relationships are just another type of logical relationship and we should expect AGI to be able to analyze relationships and generate algorithms on the fly to solve problems. Not really. By that reasoning, 5-dimensional spatial reasoning is "just another type of logical relationship" and yet humans mostly can't do that at all. It's clear that we have incredibly specialized capabilities for dealing with two- and three-dimensional spatiality that don't have much of anything to do with general logical intelligence at all.
- CooCooCaCha 2y ago
  Yes really. Problem solving on the fly doesn't mean the algorithm can instantly learn anything. Reality is HEAVILY biased towards two and three spatial dimensions so our brains have hours and hours of training on that dataset. But, with time, humans can learn to be good at all sorts of things. It's important that we try to think from the perspective of an algorithm, not a human. And it's also important that we don't jump to extremes. It seems like you interpreted "solving problems on the fly" to mean "instantly being an expert on a completely different and novel domain". What it does mean is flexibility, resilience to novel situations, and being able to adapt over time.

HarHarVeryFunny 2y ago

I just did the first 5 of the "public eval set" without having looked at the "public training set", and found them easy enough. If we're defining AGI as at least human level, then the AGI should also be able to do these without seeing any more examples. I don't think there's any rules about what knowledge/experience you build into your solution.

mewpmewp2 2y ago
AGI should obviously be able to do them. But AI being able to do those 100 percent wouldn't be evidence of AGI however. It is a very narrow domain.
- bubblyworld 2y ago
  Why not? If the only thing that can solve problem X is AGI (e.g. humans), and something else comes along that solves it, then rationally that should be evidence that the something else is AGI right? Unless you have strong prior beliefs (like "computers can't be AGI") or something else that's problem specific ("these problems can be solved by these techniques which don't count as AGI"). So I guess that's my real question.
  - lucianbr 2y ago
    That makes no sense at all. Any problem is initially only solvable by humans, until some technology is developed to solve it. Calculating a logarithm was at some point only doable by humans, and then digital computers came along. This would be in your view evidence that digital computers are AGI!? As in, an 8086 with some math code is AGI. We've had it for decades now, only nobody noticed :)
    - bubblyworld 2y ago
      It's just Bayes theorem - there are basically two variables that control how strong the evidence is: * How likely you think AGI is in general. * How solvable you think the problem is, independently of what's solving it. In the cases you've brought up that latter probability is very high, which means that they are extremely weak evidence that computers are AGI. So we agree! In this case the latter probability seems to be quite low - attempts to solve it with computers have largely failed so far!

modeless 2y ago

> to me it seems like they are more like "things that are intuitive to human visual processing". Yann LeCun argues that humans are not general intelligence and that such a thing doesn't really exist. Intelligence can only be measured in specific domains. To the extent that this test represents a domain where humans greatly outperform AI, it's a useful test. We need more tests like that, because AIs are acing all of our regular tests despite being obviously less capable than humans in many domains. > the problems require spatial knowledge that we intelligent humans learn with far more than 800 training examples. Pretraining on unlimited amounts of data is fair game. Generalizing from readily available data to the test tasks is exactly what humans are doing. > Would an intelligent but blind human be able to solve these problems? I'm confident that they would, given a translation of the colors to tactile sensation. Blind humans still understand spatial relationships.

andoando 2y ago

I would argue that spatial reasoning encompasses all reasoning. All the things you mentioned have a direct analogue to abstract models and logic we employ and are engrained deeply into language. For example, shapes containing eachother: There are two countries both which lay claim to the same territory. There is a set X that contains Y and there is a set Z that contains Y. In the case that the common overlap is 3D and one in on top of the other, we can extend this to there is a set X that contains -Y and a set Z that contains Y, and just as you can only see one on top and not both depending on where you stand, we can apply the same property here and say set X and Z cannot both exist, and therefore if set X is on then -Y and if set Z then Y. If you pay attention to the language you use youll start to realize how much of it uses spatial relationships to describe completely abstract things. For example, one can speak of disintigrating hegonomic economies. i.e turning things built on top of eachother into nothing, to where it came We are after all, reasoning about things which happen in time and space. And spatial != visual. Even if you were blind youd have to reason spatially, because again any set of facts are facts in space-time. What does it take to understand history? People in space, living at various distances from each other, producing goods from various locations of the earth using physical processes, and physically exchanging them. To understand battles you have to understand how armies are arranged physically, how moving supplies works, weather conditions, how weapons and their physical forms affect what they can physically do, etc. Hell LLMs, the largest advancement we had in artificial intelligence do what exactly? Encode tokens into multi dimensional space.

parentheses 2y ago
Spatial reasoning is easily isomorphic to many kinds of reasoning - just not all of them. Spatial reasoning in this case also limits the AI to 2 dimensions. I concede that with more dimensions, there will be more isomorphisms. Is there a number of dimensions that captures all reasoning? I don't know..
- dimask 2y ago
  Claims of isomorphisms are really strong claims to not be backed up with some kind of evidence.

dimask 2y ago

> Would an intelligent but blind human be able to solve these problems? Blind people can have spatial reasoning just fine. Visual =/= spatial [0]. Now, one would have to adapt the colour-based tasks to something that would be more meaningful for a blind person, I guess. [0] https://hal.science/hal-03373840/document https://hal.science/hal-03373840/document

lynx23 2y ago

If a blind individual can solve a visually oriented challenge is not really a question of their intelligence but more a question of accessibility/translation. Just because I cant see something myself doesnt really say anything about my ability to deal with abstractions.