I still remember all the GPT-2 based startup idea generators that spits out pseudo-feasible startups.
I recently followed Karpathy’s GPT-from-scratch tutorial and was fascinated with how clearly you could see the models improving.
With no training, the model spits out uniformly random text. With a bit of training, the model starts generating gibberish. With further training, the model starts recognizing simple character patterns, like putting a consonant after a vowel. Then it learns syllables, and then words, and then sentences. With enough training (and data and parameters, of course) you eventually yield a model like GPT-4 that can write better code than many programmers.
It’s not always that clear cut, but you can clearly observe it moving up the chain of abstraction as the training loss decreases.
What happens when you go even bigger than GPT-4? We have every reason to believe that the models will be able to think more abstractly.
Your “never gonna work” comment flies in the face of exponential curve we find ourselves on.
Of course, of course. Because god forbid anyone be able to reproduce your suggestion. Funnily enough I tried the same and have the exact opposite experience.
LLMs are already super-human at some highly abstract creative tasks, including research.
There are numerous examples of LLMs solving problems that couldn't be found in the training data. They can also be improved by using reasoning methods like truth tables or causal language. See Orca from Microsoft for example.
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.
I assume free but not random association could be a comparable support for ideation in research.
Hybrid LLM+ approaches are beginning to improve efficiency of ranking candidates and even proposing tests and soon I hope—higher order non-linear interactions among DNA variants.
I think automating science is an important research direction nonetheless.
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.
I would personally let this pass ethics if someone read all the generated ideas, and took personal responsibility for them passing the basic ethics rules, or got them through the ethics committee if required, exactly the same as they would their own ideas.
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.
Go to any meeting and state the obvious fact that "any idiot can have an idea. Making it happen is the tough part" then watch how the decision makers react
They have an automated robotics powered research lab
