You may be mistaking reasoning ability with sheer volume of more patterns to match on. You can only reason when you have awareness within which to reason, which these systems do not.
What exactly is "reasoning"? Seem, like the ability to consider options and choose one that fits the situation best. One might even call it "prediction". I generally argue that these big brains of ours (and science too) are intended to predict the future of various hypotheticals in order to choose the path forward with the best outcome.
Another frightening thing about chatGPT is that it has no "motivation". A lot of people think that AI will need some kind of motivation in order to be useful. How much of what we do as humans is actually done on autopilot? And then there's the NPC meme. I'm not sure these concepts (reason, thought, motivation) are so well defined that we will be certain when AI attains them or not.
It's not my work.
And no, they probed this theory. The OCWCourses benchmark was new questions they generated from MIT Open Courseware that didn't exist prior to this benchmark.
> You can only reason when you have awareness within which to reason, which these systems do not.
I don't understand this objection at all.
Reasoning seems roughly the same as logical inference, and logical inference is a computational process.
Well that isn't true for any sensible definition of "idea of what words mean". If you examine embeddings produced by LLMs you find similar words in similar locations in the multi-dimensional space.
> They just statistically mash up patterns of words from massive input with no clue whatsoever what those patterns of words actually mean in the real world.
This is simply not true. As multiple papers (eg [1]) have shown these models can do tasks that require chained reasoning and give results that can only be explained by this ability.
There's even a hint of a possible explanation in that paper:
> For certain tasks, there may be natural intuitions for why emergence requires a model larger than a particular threshold scale. For instance, if a multi-step reasoning task requires l steps of sequential computation, this might require a model with a depth of at least O(l) layers.
Your understanding might have been correct for LSTMs trained before 2018 or so, and it was a reasonable model in the BERT era of early transformers. But it needs to be updated now with more recent results.
[1] https://ai.googleblog.com/2022/11/characterizing-emergent-ph...
It is akin to someone looking over the shoulder at your math homework and deriving understanding just from that.
This isn't a mistake. This is what the current science says.
What makes me curious is that chatGPT even has output that displays complete understanding and is so novel it is impossible for the output to be anything else other then actual understanding. Yet people are still running around downplaying it all.
The proof is right there. You can interact with chatGPT. The science is also there. Yet people just want to downplay it.
Perhaps it's fear of the future and what it means for humanity?