31–40 of 66 posts
Earlier quoted context omitted.
Assume free-associations = hallucinations. Assume hallucinations are exactly what makes LLMs useful and your question can be rephrased as "Can you list some examples where LLMs were useful to you?"
Hallucinations are lies. So not the same thing.
I've found where LLMs can be useful in this context is around free-associations. Because they don't really "know" about things, they regularly grasp at straws or misconstrue intended meaning. This, along with the volume of language (let's not call it knowledge) result in the LLMs occasionally bringing in a new element which can be useful.
Can you list some examples where free-associations from LLM were useful to you?
I assume free but not random association could be a comparable support for ideation in research.
A group of PhD students at Stanford recently wanted to take AI/ML research ideas generated by LLMs like this and have teams of engineers execute on them at a hackathon. We were getting things prepared at AGI House SF to host the hackathon with them when we learned that the study did not pass ethical review . I think automating science is an important research direction nonetheless.
That’s pretty wild. What was the reason behind failing ethics review?
Imagine you've already invested time going to this event and want to win the prize/credit but to do so you have to implement a plugin that makes webpages grayscale because of a random idea generator. Maybe some people would find that interesting but others would see it as wasting their time.
Earlier quoted context omitted.
If we keep extrapolating eventually GPT will be omniscient. I really can't think of any reason why that wouldn't be the case, given the exponential curve we find ourselves on.
I think they're being factitious?
Earlier quoted context omitted.
That’s pretty wild. What was the reason behind failing ethics review?
I'm generally a proponent of AI and LLM but to me the decision was the right one. You are tasking people with implementing an idea generated by an algorithmic model with (I'm guessing) zero oversight that might have very little training that teaches it the importance of coming up with ideas worth implementing. Some may be more useful than others so it won't be fair from an accomplishment or motivation point of view.…
A group of PhD students at Stanford recently wanted to take AI/ML research ideas generated by LLMs like this and have teams of engineers execute on them at a hackathon. We were getting things prepared at AGI House SF to host the hackathon with them when we learned that the study did not pass ethical review . I think automating science is an important research direction nonetheless.
I don't think LLMs are the right approach for this. Coordinated science would basically be a search problem where we verify different facts using experiments and use what we learn to determine what experiment to do next.
The paper "Evolution through Large Models" shows the way. Just use LLMs as genetic mutation operators. Evolutionary methods are great at search, LLMs are great at intuition but get stuck on their own, they combine well. https://arxiv.org/abs/2206.08896
The interplay between LLMs and Evolutionary Algorithms, despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM's further enhancement under black box settings, empowering LLM with flexible global search capacities.
Since chatGPT was first released hundreds of millions of people have been using it for assistance, and the model outputs influenced their actions, maybe even supported scientists to make new discoveries. The LLM text is filtered through people and ends up as real world consequences and discoveries that are reported in text, and get in the next training set closing the loop.
Trillions of AI tokens per month do this slow feedback game. AI speeds up the circulation of useful information and ideas in human society, and AI feedback gets filtered by the contact with people and the real world.
I've found where LLMs can be useful in this context is around free-associations. Because they don't really "know" about things, they regularly grasp at straws or misconstrue intended meaning. This, along with the volume of language (let's not call it knowledge) result in the LLMs occasionally bringing in a new element which can be useful.
Can you list some examples where free-associations from LLM were useful to you?
Unfortunately, ChatGPT doesn't have a good search interface, so I can't search through older chats, but I know when I was looking at re-naming our company, it didn't come up with our new name, but it lead me down a path which did lead to our name.
I was trying to understand a patent, and we were looking at the algorithm which was being used. ChatGPT misunderstood how the algorithm worked, but pointed to it's knowledge of a similar algorithm which worked differently, but was better suited to our purposes.
Calling this "free-association" may be taking some liberty. Many people would consider these errors, or hallucinations, but in some ways, they do look very similar to what many would call free-association IMO.
Earlier quoted context omitted.
Assume free-associations = hallucinations. Assume hallucinations are exactly what makes LLMs useful and your question can be rephrased as "Can you list some examples where LLMs were useful to you?"
Hallucinations are lies. So not the same thing.
Earlier quoted context omitted.
Can you list some examples where free-associations from LLM were useful to you?
Assume free-associations = hallucinations. Assume hallucinations are exactly what makes LLMs useful and your question can be rephrased as "Can you list some examples where LLMs were useful to you?"
Earlier quoted context omitted.
That’s pretty wild. What was the reason behind failing ethics review?
I'm generally a proponent of AI and LLM but to me the decision was the right one. You are tasking people with implementing an idea generated by an algorithmic model with (I'm guessing) zero oversight that might have very little training that teaches it the importance of coming up with ideas worth implementing. Some may be more useful than others so it won't be fair from an accomplishment or motivation point of view.…