Which parts of reasoning do you think is missing? I do feel like it covers a lot of 'reasoning' ground despite its on the surface simplicity
For example, if we asked an LLM to produce an image of a "human woman photorealistic" it produces result. After that you should be able to ask it "tell me about its background" and it should be able to explain "Since user didn't specify background in the query I randomly decided to draw her standing in front of a fantasy background of Amsterdam iconic houses. Usually Amsterdam houses are 3 stories tall, attached to each other and 10 meters wide. Amsterdam houses usually have cranes on the top floor, which help to bring goods to the top floor since doors are too narrow for any object wider than 1m. The woman stands in front of the houses approximately 25 meters in front of them. She is 1,59m tall, which gives us correct perspective. It is 11:16am of August 22nd which I used to calculate correct position of the sun and align all shadows according to projected lighting conditions. The color of her skin is set at RGB:xxxxxx randomly" etc.
And it is not too much to ask LLMs for it. LLMs have access to all the information above as they read all the internet. So there is definitely a description of Amsterdam architecture, what a human body looks like or how to correctly estimate time of day based on shadows (and vise versa). The only thing missing is logic that connects all this information and which is applied correctly to generate final image.
I like to think about LLMs as a fancy genius compressing engines. They took all the information in the internet, compressed it and are able to cleverly query this information for end user. It is a tremendously valuable thing, but if intelligence emerges out of it - not sure. Digital information doesn't necessarily contain everything needed to understand how it was generated and why.
Large language models don't do that. You'd want an image model.
Or did you mean "multi-model AI system" rather than "LLM"?
You are confusing LLM:s with Generative AI.
It can language.
Humans also don’t tend to operate in a rigorously logical mode and understand that math word problems are an exception where the language may be adversarial: they’re trained for that special context in school. If you tell the LLM that social context, eg that language may be deceptive, their “mistakes” disappear.
What you’re actually measuring is the LLM defaults to assuming you misspoke trying to include relevant information rather than that you were trying to trick it — which is the social context you’d expect when trained on general chat interactions.
Establishing context in psychology is hard.
'Agents' (i.e. workflows intermingling code and calls to LLMs) are still a thing (as shown by the fact there is a post by anthropic on this subject on the front page right now) and they are very hard to build.
Consequence of that for instance: it's not possible to have a LLM explore exhaustively a topic.
I’d say, humans are also bound to promoting sessions in that way.
Consider the following use case: keeping a swimming pool water clean. I can have a long running conversation with a LLM to guide me in getting it right. However I can't have a LLM handle the problem autonomously. I'd like to have it notify me on its own "hey, it's been 2 days, any improvement? Do you mind sharing a few pictures of the pool as well as the ph/chlorine test results ?". Nothing mind-boggingly complex. Nothing that couldn't be achieved using current LLMs. But still something I'd have to implement myself and which turns out to be more complex to achieve than expected. This is the kind of improvement I'd like to see big AI companies going after rather than research-grade ultra smart AIs.
Luckily we don't know the problem exists, so in a cultural/phenomenological sense it is already cracked.
Does it include the invention of tools?
people with (high) intelligence talking and building (artificial) intelligence but never able to convincingly explain aspects of intelligence. just often talk ambiguously and circularly around it.
what are we humans getting ourselves into inventing skynet :wink.
its been an ongoing pet project to tackle reasoning, but i cant answer your question with regards to llms.
Kinda interesting that mathematicians also can't do the same for mathematics.
And yet.
i doubt your mathmatician example is equivalent.
examples that are fresh on the mind that further my point. ive heard yann lecun baffled by llms instantiation/emergence of reasoning, along with other ai researchers. eric Schmidt thinks the agentic reasoning is the current frontier and people should be focusing on that. was listening to the start of an ai machine learning interview a week ago with some cs phd asked to explain reasoning and the best he could muster up is you know it when you see it…. not to mention the guy responding to the grandparent that gave a cop out answer ( all the most respect to him).
I'm going to bet you haven't encountered the right people then. Maybe your social circle is limited to folks like the person who presented a slide about A* to a dumb-struck roomfull of Deep Learning researchers, in the last NeurIps?
but no, my take on reasoning is really a somewhat generalized reframing of the definition of reasoning (which you might find on the stanford encylopedia of philosophy) thats reframed partially in axiomatic building blocks of neural network components/terminology. im not claiming to have discovered reasoning, just redefine it in a way thats compatible and sensible to neural networks (ish).
Still somehow the question keeps coming up- "what is reasoning". I'll be honest and say that I imagine it's mainly folks who skipped CS 101 because they were busy tweaking their neural nets who go around the web like Diogenes with his lantern, howling "Reasoning! I'm looking for a definition of Reasoning! What is Reasoning!".
I have never heard the people at the top echelons of AI and Deep learning - LeCun, Schmidhuber, Bengio, Hinton, Ng, Hutter, etc etc- say things like that: "what's reasoning". The reason I suppose is that they know exactly what that is, because it was the one thing they could never do with their neural nets, that classical AI could do between sips of coffee at breakfast [3]. Those guys know exactly what their systems are missing and, to their credit, have never made no bones about that.
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[1] e.g. see my profile for a quick summary.
[2] See all of Russeel & Norvig, as a for instance.
[3] Schmidhuber's doctoral thesis was an implementation of genetic algorithms in Prolog, even.
it pertains to the source of the inference power of deductive inference. do you think all deductive reasoning originated inductively? like when some one discovers a rule or fact that seemingly has contextual predictive power, obviously that can be confirmed inductively by observations, but did that deductive reflex of the mind coagulate by inductive experiences. maybe not all deductive derivative rules but the original deductive rules.
but im getting at a few things. one of those things is neurological. how do deductive inference constructs manifest in neurons and is it really inadvertently an inductive process that that creates deductive neural functions.
other aspect of the question i guess is more philosophical. like why does deductive inference work at all, i think clues to a potential answer to that can be seen in the mechanics of generalization of antecedents predicting(or correlating with) certain generalized consequences consistently. the brain coagulates generalized coinciding concepts by reinforcement and it recognizes or differentiates inclusive instances or excluding instances of a generalization by recognition properties that seem to gatekeep identities accordingly. its hard to explain succinctly what i mean by the latter, but im planning on writing an academic paper on that.
If they did not actually, would they (and you) necessarily be able to know?
Many people claim the ability to prove a negative, but no one will post their method.