Not necessarily. Here's a rough example (it's not what's going on here, just a representative idea):
There are words/tokens that are heavily correlated to the prompt (a yes or a no, for example), and then there are others that are going to be less so (adjectives with a lot of synonyms for example).
Given a text, you can identify what the "load bearing" and auxiliary words/chunks are. Then, looking only at the auxiliary words/chunks, you should, in principle, be able to determine what other wordings could have gone there instead. From this, you can, very roughly, recreate the token probability distribution that was in effect when those tokens were generated. With the probability distribution in hand for enough chunks of text, you can start inferring properties about the RNG process that was used to sample from those distributions.
But then, this notion of "load bearing" vs "auxiliary" can be expressed directly in the probability distributions. A load bearing token just has a very high probability, and thus any RNG bias that may have been in effect will likely be swallowed in the distribution. So the parts of the text that are highly dependant on the prompt will naturally not be contributing much information about he RNG in the first place.
Humanoid robots can be dropped in wherever human labor is currently used.
That makes them extremely easy to sell to industry. This unlocks, in principle, a large chunk of the potential untapped industrial automation market that still relies on human labor because the ROI of redesigning production lines didn't make sense.
These humanoid robots would cost $10-100k to make and would require costly maintenance for them to do the job people in poor countries do for a dollar. I don't say we won't come to it eventually, but not in near future
More realistically it seems that llms in several years could help dramatically decrease costs of automation and make it available for more industries
I don't think it's the dream, I think it's the cynical market-driven logic.
Automation has been rolling out for a long while now, and most of the low-hanging fruit of things that can easily be redesigned in a cost-effective way has been dealt with already. The role of these robots is to address all of the stuff that would have been automated by now if they could have.
I almost entirely agree with this. However, after coding quite a few large projects with LLM assistance, I can say with certainty that there is some form of "art" at play with regards to structuring requests for LLMs.
However, this "art" is not so much about how to present a given request to the LLM, but rather guestimating what the scope of the next chunk of work should be to balance getting as much out of the model as possible while avoiding the machine going off the rails.
Obviously, this is a moving target and different models perform differently for various chunk/scope of work. I look at my successful sessions with LLMs and I'm not sure I'd be able to articulate a clear set of rules to apply here. You just... gradually build a intuition for how much you can throw at the LLM at once.
That being said, I'm pretty convinced at this point that this is a property of the coding assistants as they exist today, and what "working well with LLM assistance" means will keep on changing.
There are words/tokens that are heavily correlated to the prompt (a yes or a no, for example), and then there are others that are going to be less so (adjectives with a lot of synonyms for example).
Given a text, you can identify what the "load bearing" and auxiliary words/chunks are. Then, looking only at the auxiliary words/chunks, you should, in principle, be able to determine what other wordings could have gone there instead. From this, you can, very roughly, recreate the token probability distribution that was in effect when those tokens were generated. With the probability distribution in hand for enough chunks of text, you can start inferring properties about the RNG process that was used to sample from those distributions.
But then, this notion of "load bearing" vs "auxiliary" can be expressed directly in the probability distributions. A load bearing token just has a very high probability, and thus any RNG bias that may have been in effect will likely be swallowed in the distribution. So the parts of the text that are highly dependant on the prompt will naturally not be contributing much information about he RNG in the first place.