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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 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.

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.