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So I'm trying to understand your argument here, but why isn't "Reasoning is a cognitive process" circular logic? AlphaGo wasn't reasoning, how? I think reasona

• mjburgess 3y ago
  > Reasoning is a cognitive process You could have a non-cognitive view of reasoning, or an embodied one. NNs are a-cognitive systems, they do not engage in reasoning of any form. Reasoning concerns inference across truth-apt propositions (eg., A->B, A thef. B). NNs have no propositions, nor are any parts truth-apt, true or false. NNs are statistical systems which select answers by weights found from optimisation. No process either in optimisation or prediction is an inferential one in the sense of cognition. I also deny that the "world" as used by frankly just philosophically incompetent ML researchers, whose gross lack of familiarity with basically anything outside pytorch, is even relevant to the sense of "world" that propositions bare a truth relation to. A world in the relevant sense isn't the state space of the training data -- this is an insane supposition which makes the claim "AI has world models" actually circular. The relevant sense of world is the cause of the training data. If the training data is about an abstract game then you collapse the distinction since the rules are the data. The famous "NNs learn WMs" paper is just this: choose a system whose data is just a restatement of the system; rather than a measure of a world. NNs do not form representational models of the cause of their measurement data because all they do is induce (ie., compress by function-fitting) across the measurement space. They model the measurements not their causes. This is only "predictive" of features of the causal origin of measurement data in rigged scenarios, and in general, fails catastrophically to be predictive. Consider running a NN across photos of the sky: it is impossible for this process to produce newton's law of gravity. The weights are just models of the pixels, and these are not distributed according to this law. Worse, in general, there is no function from the measurement space to properties of its causal origin -- so it is impossible to build representations by induction. (eg., there is no function Photo->Cat|Dog, the distributions of pixels in photos is ambiguous, and changes over time). Reasoning, as in cognition, is an (logically) inferential process which considers propositions that bare a truth relation to the world which is the causal origin of concepts which the proposition comprises (created by a biogenerative process). It is the activity of an agent with an interior subjectivity and ecological rationality. Reasoning is done by an agent about something of interest to that agent, with motivation towards a goal the agent has, in the service of the agent's preferences, etc. If reasoning is an abstract pattern, then rice falling to the ground is likewise "reasoning".
  • FrustratedMonky 3y ago
    This is kind of ignoring other NN's. You're very focused on LLM's as the example. AlphaGo learned by playing itself. And is able to anticipate multiple moves ahead. Then, that same 'engine', was able to be applied to Chess, and learned how to beat a master from scratch, by playing itself, in just a few hours. There was no lookups, or zip'ing of aggregated data. A lot of what you are postulating as cognition, humans don't do either. Humans didn't figure out gravity from photos of the sky (unless you mean tracing stars and planets, and then yes an NN probably can figure it out). Many humans go through their whole day without analyzing propositions and inferring reality and what to do next. Humans are similarly un-conscious. A lot of AI news all the time. This link is from today. A little more in the theme of 'world building models', than this current post about LLM's. https://news.ycombinator.com/item?id=39692387 https://news.ycombinator.com/item?id=39692387 There was another on also about an NN that could pass some international Geometry competition. It was based on propositions, and reasoning.
    • mjburgess 3y ago
      Yes, it is impossible for anyone to figure out gravity by induction. That's the problem with AI. The way we build explanations is largely by reasoning-by-analogy. We build physical models with our hands, to resolve ambiguities in our environment, ever more complexly -- and then, eventually, land upon the right analogy that then falls away. Prior to gravity we had crystal spheres -- reasoning by ananlogy with such things. Since machines arent in the world, as in my robot example, they can never build explantory conceptualisations of it. Chess et al. are not worlds in any relevant sense. No one cares, or doubts, that a system with a fully mathematically specific "world" can be "learnt" by a computer. The full specification of a "world" in mathematical terms doesnt require intelligence. At that point you can use the dumb strategies of alphago. Intelligence is what you do when you don't know what you're doing. The "World Model"s we're interested in are those that arent already specified to the machine. All these formal games are just outcomes spaces where every event is known a priori. As I said, the AI people arent even operating in the category of intelligence. It just the profound lack of background knowledge of any field outside pytorch.lol() that their meglomania plausible Go look at every paper in AI or ML that purports to build a model of anything: can you find a single one where the outcome space cannot be fully specified either formally (as with chess, etc.) or empirically (as with data samples)? This has nothing to do with intelligence. We do not either start from the answers, or samples of the answers. We have no answers. The resolution to this problem requires having a body: you have to move in order to think, in direct causal contact with the world beyond the capacities of clay, to think about it.