Where is the edge that cuts?
I have to admit, I feel like a fraud. I will never know enough about machine learning or AI or data pipelines or physics or software development or chaos theory to validate myself as a “real physicist” or “real machine learning researcher” or a real “data scientist”. I will never know how much Ilya Sutskever knows about LLMs. I will never know what Steven Strogatz knows about chaos. I will not know what François Renard knows about friction and fracturing. I am not them. I think, as a more junior scientist I always believed that I should learn what they know. But actually now that I have thought about this for some time I disagree.
My job as a scientist is to understand the fundamentals of my field in the ways that current and historical scientists have established it. This is an ongoing process where I must continually update my knowledge and understanding of what is fundamentally assumed. As I do this I must form my own thoughts and opinions about what the cutting edge should be. This is the goal of science, to establish topics about which we collectively do not understand. My job as a scientist is to be comfortable with the idea that I don’t understand things because if I did, it wouldn’t be the cutting edge.
Across all of my research I have essentially taken some idea established elsewhere and applied it to some idea space that hadn’t seen it yet. I always was very critical of myself thinking I had no original thoughts. I would read about databases or gradient boosting or whatever and then decide this is what’s missing in the science standing in front of me. The more technologies I could learn, the more problems I could solve.
But now I think there are more skills to master as a scientist and software engineer than this. Finding the cutting edge is really finding the place where only opinion exists. And I think that today that has become increasingly difficult. I think that is for a variety of reasons but one reason important to me is that it seems like there are too many ideas to consider at the same time. This is where I think an effective AI assistant could help you investigate the cutting edge. This is also why hallucinations are such a problem. What if they simply make up molecules? Or they just connect two ideas for nonsensical reasons? But assuming that an AI assistant could be built and verified to discuss science and technology with, I do find perplexity.ai to be a great product. I am able to discuss and get cited answers and find myself examining those answers more than what the AI writes itself. And thus far I find it more like a recommendation engine. Like how Netflix tells you what next to watch. You don’t agree with everything Netflix recommends even though it usually knows you just want to watch The Office one more time. And that’s just fine for me. Because it also allows me to form my own opinion about what the cutting edge is, where the cutting edge is best described as what I don’t understand and what needs to be done next.
But the great fear becomes, what if a hallucination leads me astray? What if that compounds into a project in some way that is destructive. But what if the opposite happens? You follow a line of reasoning due to an ai hallucination that leads you to discover something you would have not discovered? This all leads me to the idea that all things are models. That all models can be tested and broken and that is the other goal of science. Building models of the natural world. Hallucinations don’t matter that much if they violate a model assumption. And so your model has edges that are fuzzy and the cutting edge is trying to establish what will make things less fuzzy. And this is all very abstract but our goal is to make things less abstract by building models. This is why I directed a short documentary about scientific models a few years ago.
Defining the cutting edge of LLMs, or “state of the art” (SOTA), has lately been a numbers game. In the beginning, it was quite obvious if an LLM failed at something. It just spout out nonsense. But as the nonsense has become more believable, and thus labelled a hallucination, metrics have been developed to define what SOTA is for LLMs. These metrics are essentially designed to be the final exams of various domains such as physics, chemistry, and economics. The better LLMs perform the more likely they are to be described as “phd level” while still not understanding basic mathematics such as the commutative property of integer addition. This all reminds me of reading conversations from the 1990s about what physics conceptual surveys were really measuring.
Yann LeCun has said himself he thinks LLMs are obsolete and if you are a PhD student you should work on something else. To him, one of the fathers of modern AI, the cutting edge is somewhere else. Ultimately the cutting edge seems to be wherever you decide the cutting edge is. That the cutting edge is somewhat different for everyone. For example, I’m sure there are people who are still excited about LLMs! In my opinion, it seems like one of the best ways to determine what the cutting edge is for you is to evaluate whether you feel accomplished at work, do you feel like you have an impact, do you feel like your work is growing you as a scientist or engineer, is that work challenging to you, and do you feel engaged by the community around you? If you have all these things at work, you are likely at your cutting edge.
Anyways, maybe it’s time for a second documentary.