It's not just statistics: GPT-4 does reason

Computers are not biological. Therefore (imho) they will never obtain, and only replicate by trained example, the phenomenological human experiences.

so it's great that it can reason better than humans on small-medium probems already well trained for, but so far Transformers are not reasoning (not doing causal graph analysis, or not even doing zero order logic), they are eerily well writing text that has the right keywords. and of course it's very powerful and probably will be useful for many applications.

I'm not quite sure what you mean that they cannot do causal graph analysis, since that was one of many different tasks provided in the various different types of reasoning studies in the paper I mentioned. In fact it may have been the best performing task. Perhaps try checking the paper again - it's quite a lot of experiments and text, so it's understandable to not ingest all of it quickly.

In addition, if you're interested in seeing further evidence of algorithmic reasoning capabilities occurring in transformers, Hattie Zhou has a good paper on that as well. https://arxiv.org/pdf/2211.09066.pdf

The story is really not shaping up to be 'stochastic parrots' if any real deep analysis is performed. The only way that I see someone could have such a conclusion is if they are not an expert in the field, and simply glance at the mechanics for a few seconds and try to ham handedly describe the system (hence the phrase: "it just predicts next token"). Of course, this is a bit harsh, and I don't mean to suggest that these systems are somehow performing similar brain-like reasoning mechanisms (whatever that may mean) etc, but stating that they cannot reason (when there is literature on the subject) because 'its just statistics' is definitely not accurate.

So what? Biology isn't magic, it's nanotech. A lot of very tiny machines. It obeys the same rules as everything else in the universe.

More than that, the theoretical foundation on which our computers are built is universal - it doesn't depend on any physical material. We've made analog computers using water flowing between buckets. We've made digital computers from pebbles falling down and bouncing around what's effectively a vertical labyrinth. We've made digital computers out of gears. We've made digital computers out of pencil, paper, and a human with lots of patience.

Hell, we can make light compute by shining it at a block of plastic with funny etching. We can really make anything compute, it's not a difficult task.

We're using electricity, silicon wafers and nanoscale lithography because it's a process that's been best for us in practice, not because it's somehow special. We can absolutely make a digital computer out of anything biological. Grow cells into structures implementing logic gates for chemical signals? Easy. Make a CPU by wiring literal neurons and nerve cells extracted from some poor animal? Sure, it can be done. At which point, would you say, such a computing tapestry made of living things gain the capability of "phenomenological experiences"? Or, conversely, what makes human brains fundamentally different from a computer we'd build out of nerve cells?

Is human intelligence anything more than a statistical model? Our entire biology is a massive gradient descent optimization system. Our brains are no different. The establishment of connectivity and potential and resistance, etc etc, it’s statistical in behavior all the way down. Our way of learning is what these models are built around, to the best of our ability. It’s not perfect but it’s a reasonable approximation.

Further it’s not soundex. I see the stochastic parrot argument too much and it’s annoying. Soundex is symbolic only. LLMs are also semantic. In fact the semantic nature is where their interesting properties emerge from. The “just a fancy Markov model” or “just a large scale soundex” misses the entire point of what they do. Yes they involve tokenizing and symbols and even conditional probability. But so does our intelligence. The neural net based attention to semantic structure is however not soundex of Markov model. It’s a genuine innovation and the properties that emerge are new.

But new doesn’t mean complete. To be complete you need to build an ensemble model integrating all the classical techniques of goal based agency, optimization, solvers, inductive/deductive reasoning systems, IR, etc etc in a feedback loop. The LLM provides an ability to reason abductively in an abstract semantic space and interpret inputs and draw conclusions classical AI is very bad at. The places where LLM fall down… well, classical AI really shine there. Why does it need to be able to do logic as well as a logical solver? We already have profoundly powerful logic systems. Why does it need to count? We already have things that count. What we did not have is what LLMs provide, and more specifically multimodal LLMs.