This will be kind of a funny post from one perspective, and a post that I probably should not write, were I to optimize for minimal eye-rolling. But I'd rather just express an opinion, so here it is.

I was part of a team that wrote a really nice (I think) paper on the theory behind modeling in neuroscience (which was largely led by the first author, Dan Levenstein) and as that might suggest I am firmly convinced that there are good philosophical discussions to be had on philosophy in neuroscience and biology. What is the role of theory? What does a theory look like? These were some of the questions that we tackled in that paper.

But then, there are some philosophical questions that are easily answered if you just know a little physics and mathematics. And some people do try to answer them. The problem in my opinion is that they often do not know the math and physics well and are often speaking to an audience that doesn't know the math and physics well either. It's like the blind following those who only have one eye that's mutilated. This can lead to some appallingly funny results, like the scandal in which some physicist wrote bullshit tied up in the language of quantum mechanics and got it through a philosophy journal's peer review process. If the referees don't know quantum mechanics but like the conclusions, why would this not happen? But I'm speaking of what I know, which is that this sometimes happens in theoretical biology as well. This more often than not can lead to years of trying to answer conundrums that are not actually conundrums because someone has fundamentally misunderstood what causality means or how something could mathematically be goal-directed without being psychic.

There's a particular paper that I want to cite that is quite good in some ways and interestingly wrong in others, and because I do like the paper and do not want to be a jackass (more on that later), I will not link anyone to this paper. Suffice it to say that it had some good ideas. Apparently biologists and the philosophers who had aided them had been questioning the validity of teleonomy for years-- this is the idea that organisms are goal-directed. Questioning this idea is no problem. It's just that some questioned the idea of goal-directedness on the grounds that it violated causality. Wouldn't you have to look into the future in order actually be goal-directed? Sounds common sense. And yet, if one were to talk to a stockbroker, they might tell you that while they can't see the future prices of stocks, they use the past as a guide to the future and make predictions regardless. There is no psychic ability here and no violation of causality. The stockbroker is certainly goal-directed in their desire to make money; they have a strategy that uses learning and memory; and this strategy does not require psychic powers. So, the concern that goal-directedness violates causality violates common sense, in my opinion. This particular paper did a nice job of pointing out (in different words for sure) that goal-directedness and causality were not at odds.

What this paper did not do well: it confused quantum mechanics and chaos; it confused homeostasis for goal-directedness, and while homeostasis is often a goal, it is not the only goal, since we sometimes need to modify our internal state in order to survive when we are making predictions about the world, finding food, mating, seeking shelter, and so on. The latter was the manuscript's main contribution to the literature.

I don't think anybody is ever going to spark a debate with the manuscript. I am pretty aggressive and I feel like that just wouldn't be "nice". There's this unstated idea in science that we should be collegial. It has, in my opinion, led to a few huge reproducibility crises, and yet I feel the pull of being in a scientific world in which I basically do not challenge papers that I think are wrong for two reasons. First, this "nice" reason: I feel a gendered pull to not be "mean" to this person who put their ideas down in a scientific paper, even though the point is that ideas are supposed to be challenged. In fact, senior researchers told me (when I was a more argumentative graduate student) that certain papers were not written by experts but were supposed to just introduce ideas, so I should just drop the idea of writing a manuscript correcting their basic information-theoretic misunderstandings. Second, a resource-based argument: if I sat around all day writing papers that correct papers that are published, I would never get anything done. And yet, that impedes the progress of science, right? So, I have taken to writing these short blog posts that who knows how many people read instead of writing paper responses to papers... but even now, I'm too afraid of being not "nice" to say the author's name!!!! How's that for a sociological problem. Or maybe, it's just me.

Picture this scenario: your back room during the summer is taken up by six undergraduates, all working on an independent project, all coding. At any given time, roughly two of the six students need to talk to you because they've gotten stuck and can't work past it by themselves. What should the students who need help do?

I know ChatGPT and other large language models might have negative effects on society, including (for example) the spread of disinformation in an authoritative manner due to their hallucinations that nobody can seem to get rid of. But ChatGPT really helps out in this particular mentoring situation because the students who need help but who can't get it until an hour from now don't have to just sit there and wait for me to be free-- they can use ChatGPT to help them write code.

That being said, there are ways in which this works and ways in which this doesn't. So I just wanted to share my experience with how to use ChatGPT successfully and how it sometimes fails as a mentorship tool if not used properly. Honestly, I should probably pretty this idea up and publish in an education journal but I'm too exhausted, so here we go.

One of the students who regularly used ChatGPT was coding in a language that I didn't remember well. She already knew other languages but needed this particular language to code an application for a really cool potential psychotherapy intervention that we're trying. To learn this language and how to use this language to code applications, she took a course at the same time that she coded up her application at the same time that she used ChatGPT when she couldn't figure out the right piece of code. As far as I can tell, this worked really, really well. Because she took the coding course (that was free) at the same time that she used ChatGPT, she could check that ChatGPT wasn't spitting out nonsense, and could prompt ChatGPT to change what it was spitting out if it was almost but not quite right. Because she was coding the project at the same time that she was taking the coding course and using ChatGPT, she could keep focused on exactly what she needed to learn for that summer. (Keep in mind that the summer research assistantship is only 2 months long for us!) And so, in the end-- though I have to ask this student for her impression of the summer-- I would say that she needed my input for more general user design questions and less for the questions that I couldn't answer without taking the course with her and Googling a lot. ChatGPT was value-added.

Another student came in with less coding experience and less math under his belt and was doing a reinforcement learning project. He used ChatGPT a lot to help him out, from the start to the finish, even on conceptual questions. It is important to note that because he started out not knowing how to code, ChatGPT was used as a crutch rather than as a tool. When ChatGPT is used as a crutch rather than as a tool, research projects don't work out so well. First of all, it turns out, unsurprisingly, that even if a student, like this student, is smart, eager, and really just enthusiastic beyond belief, it is a bad idea to give them a project where you assert to yourself that you can teach them enough probability theory, multivariable calculus, and coding to understand Markov Decision Processes and policy gradient methods in 2 months. Just, no. By the end of the summer, this poor student was getting scared to come to work because he did not feel like he was going to be able to solve the problem that I gave him that day. Second of all, even if I made mistakes with this mentorship, I learned a valuable lesson about what happens when you don't really understand the theory behind a research project and ask ChatGPT for help-- you hit a wall, and quickly. This student often had slightly wrong prompts (which ChatGPT sort of auto-corrected) and then got answers that didn't quite work and he didn't know what prompts to try next. So by the end of the summer, I was drilling him on the theory so that he could do prompt engineering a bit better. That was successful, although scary for him, he said. But basically, if you or your students are using Large Language Models to do research and don't really understand the theory behind the project and don't really know the coding language if there is coding involved, the Large Language Model is not going to be able to do the project for you.

And then finally, my own efforts to use ChatGPT were funny. I just thought it would be interesting to see if ChatGPT could come up with anything novel. I know people are working on this actively, but at the time that I tried it, either ChatGPT or Bard (I can't remember which) could not write anything that was not boilerplate. The exact prompt that I used was a question on a research project finding the information acquired by the environment during evolution. (My student and I had just roughly written a paper on that, which would not be in the corpus.) What it came up with was some not-very-interesting text on how there was a calculation of the information acquired by evolution and how that could lead to better models, which doesn't even really make any sense-- you use a model to calculate the information acquired unless you happen to have some really nice experimental data. And if you calculate the information acquired, regardless, you will not get a better model. Later, I thought it might be interesting to try and see if Bard could teach. I was trying to figure out how to teach thermodynamics concepts in a week, which is a ridiculous ask, but that's introductory physics for life sciences for you, and what it spit out was just subpar and dry. Nothing of active learning, and no real sense of how long it would take to really teach thermodynamics concepts so that they would be understood. (Three laws of thermodynamics in one hour-long class is not a good idea.) Anyway, it was a long time ago, but I'm interested in seeing if the new Large Language Models like Gemini can actually show some signs of creativity that could be helpful enough that I could use them as tools in research or in teaching.