To elaborate @LinuxBender's argument, AGI might be just as far away today as it seemed two years ago.

ChatGPT has a lot in common with

https://en.wikipedia.org/wiki/ELIZA

in that it hijacks people's perception of others and desire for closure to make people perceive it has a personality when it really doesn't. In particular it seems to have a hypnotic ability that comes out of its "highest probability" approach to text generation and how HFRL trains it to write text that pleases people -- ChatGPT's bullshitting goes right past many people's critical thinking capability and if anything is dangerous about it, it is that.

Those people hypnotized by it inevitability think that adding more parameters and more processing power to it will make it overcome any and all limitations but that's likely to be wrong, instead it will reach some asymptote where you feed it more resources and it only improves marginally. That's the most dangerous place in technology development, it's like Uber putting in $25 billion into subsidizing half priced taxi rides and thinking the next $1 billion will make it a profitable business.

On the other hand if you looked at arXiv instead of blog postings on HN you'd see that people frequently do "zero-shot" prompting, get the same 70% accuracy that the blog posters do, then fine-tune a similar model on a modest sized training set and get 95% accuracy. The way I'd put it is that there are 13 users of transformer models listed here

https://huggingface.co/docs/transformers/quicktour

and y'all are obsessed with just one of them. With fine-tuning a model with fewer parameters and a modest amount of training data will thoroughly trash ChatGPT at specific tasks most of the time. The people who download models from huggingface and learn to use them will be putting applications in front of customers while people messing around randomly w/ GPT-N will be forever pushing bubbles around under the rug.

However, that kind of generative model can be a lot more powerful when it is combined with systems that can do the things it can't do. For instance having it "shell out" to Python or Wolfram Alpha. People are perplexed when they give it instructions which it inevitably quits following after a while, but in addition to not having the correct structure to obey programming language like instructions, the instructions will inevitably fall out of the attention window and disappear and it's no wonder it loses track. See

https://jake.mirror.xyz/sPZECVTkrbVq4DerB13Thvqq_XqsDGwTBDD3...

Now, if there was some program surrounding ChatGPT that implemented executive control (what that guy really wants) that would rewrite the prompt, keep the instructions in the window (much shorter because they only concern ChatGPT's area of competence, and move all the things ordinary software does well (scorekeeping) into ordinary software.

We will eventually see things that work like an SAT/SMT solver or theorem prover that is integrated w/ an LLM, much like the way Alpha Go integrates "old AI" heuristic search with a neural network heuristic.

Real advances in the capability of those chatbots will involve approaches that avoid the structural weaknesses of chatbots, not an appeal to Moore's Law. The asymptote of naked chatbots is already in sight I think, but the asymptote of hybrid systems is further away in both time and capability.