Updated March 5, 2026
0:00 Welcome to Coolaberry AI podcast brought to you by Coolaberry AI Research Labs and Carl Foundation. You know, we've been talking a lot about AI doing, like, creative stuff, you know, art and music. But what about actual science? Could AI write a scientific paper that's good enough to get published? We're gonna explore a project called the AI Scientist that's trying to do just that. 0:21 It's not just about writing like a scientist. This AI developed with researchers from the University of British Columbia, University of Oxford, is designed to do the whole research process. It can come up with a hypothesis, design experiments, even write the code to run them. So this AI scientist dash v two, as they call it, wrote a paper that actually passed peer review at an ICLR workshop. ICLR is a big deal, a top international conference in machine learning. 0:47 This is huge because no AI has ever done this before. What's really remarkable is that this paper was accepted at a workshop focusing on the limitations of deep learning. It's like the AI was trying to find flaws in its own kind. Okay. Wait a minute. 0:58 So this AI is writing a paper about the weaknesses of AI. That's kinda meta, isn't it? Exactly. It shows that the AI wasn't just trying to make itself look good. It was critically analyzing the field, which is a key part of good science. 1:10 But how did they even get this past the reviewers? Did they know an AI wrote the paper? Transparency was key here. Mhmm. Everyone involved, ICLR, the workshop organizers, the researchers, they all knew that some of the papers they were reviewing could be AI generated. 1:25 So they were in on the experiment. But if it passed peer review, why wasn't the paper published? That's a great question, and it goes to the heart of a big debate in the scientific community right now. Should AI generated work be published? There are a lot of ethical and philosophical questions to consider. 1:43 What does authorship even mean when AI is involved? Who gets credit for the discovery? So by withdrawing the paper, the researchers are acknowledging those questions and saying we need to figure this out before we go any further. Precisely. But even though it wasn't published in the end, this is a major milestone. 1:58 For the first time, an AI generated paper passed the same rigorous review process as a paper written by a human scientist. Okay. So the paper wasn't published, but it got pretty good scores for the reviewers. Right? It did. 2:10 The paper received an average score of 6.33, which is above the workshop's acceptance threshold. And remember, the human researchers also reviewed all three papers that the AI generated as if they were being submitted to the main ICLR conference, which has much stricter standards. None of the papers pass that test. This shows that the AI is getting close, but it's not quite at the level of top tier human researchers yet. So even getting accepted at the workshop is a big deal for the AI. 2:38 It's like passing a qualifying round. Exactly. And to make things even more interesting, the AI made some mistakes in its paper. The kind of mistakes you might expect a human to make. What kind of mistakes? 2:48 Like typos? Not quite. The AI made some citation errors. It attributed quotes to the wrong people, which shows even though it can write like a scientist, it doesn't quite grasp the nuances of academic rigor yet. It's still learning. 3:02 But all this is fascinating. It's amazing that they put all the reviews in the AI generated papers on GitHub for anyone to see. I'm definitely checking those out. It's a great resource for anyone interested in the future of AI and science. This brings up a big question though. 3:16 What does all of this mean for the future of science? Is AI gonna be writing all the scientific papers from now on? It's hard to say for sure what the future holds, but I think it's safe to say that AI will play an increasingly important role in scientific research. I was talking to a friend of mine who's a biologist, and she's actually really excited about the potential for AI to help analyze massive datasets Uh-huh. And maybe even discover new drug targets. 3:43 That's one area where AI could have a huge impact. Think about it. AI never gets tired. It can work twenty four seven. And it can sift through mountains of data that would take human researchers years to analyze. 3:56 It's like having a tireless research assistant. Exactly. But even more than that, AI can sometimes see patterns that humans miss. It can connect the dots in ways that we might not even think of. So AI could lead to breakthroughs that we haven't even imagined yet. 4:08 Absolutely. But the key is to remember that AI is a tool. It's up to us, the humans, to use it wisely and ethically. We need to make sure that AI is used to benefit humanity, not to harm it. That's a really important point. 4:22 We can't just unleash AI on the world and hope for the best. We need to think carefully about the implications and guide its development in a responsible way. Exactly. The future of AI and science is in our hands. It's a fascinating time for science when you say we're seeing these incredible advancements happen right before our eyes. 4:39 Absolutely. It feels like we're living in a science fiction movie. But you mentioned earlier that the AI actually made some mistakes in the paper. Mistakes like a human would make. Yes. 4:49 And that's what makes this whole thing so interesting. The AI managed to create a paper that was good enough to be accepted at a scientific workshop, but it wasn't perfect. The reviewers noticed some pretty basic errors with the citations. The AI was attributing quotes to the wrong authors. So it's not quite ready to take over for human scientists just yet? 5:09 Not quite. But it does raise some interesting questions. How do we evaluate the work of an AI? The researchers actually reviewed the three papers themselves as if they were reviewers for the main ISLR conference, which has a much higher standard. So they held the AI to an even higher bar. 5:26 And how did it do? None of the papers passed their test. They found problems with the arguments and the way the experiments were designed, things that a more experienced scientist would pick up on. So even though the AI can follow the rules of writing a scientific paper Mhmm. It doesn't have that deeper understanding that you get from years of experience. 5:44 Exactly. And that's where human scientists still have a big advantage. It's not just about data and writing correct sentences. It's about critical thinking creativity, that spark of intuition that leads to real scientific discoveries. You know, I was reading the paper that the AI wrote, and I thought it was interesting that it was focusing on the limitations of deep learning. 6:03 It's almost like the AI was trying to figure out its own weaknesses. Yeah. That's a great point. The researchers specifically chose a workshop that was focused on the shortcomings of AI. In understanding and analyzing complex scientific problems. 6:27 Precisely. And that's why this is such a significant development. It suggests that AI isn't just a tool for doing calculations or automating tasks. It could actually become a partner in scientific discovery, a partner a partner that can help us see things from new perspectives and challenge our own assumptions. This is all very exciting, but also a bit unnerving. 6:45 We've been talking about the potential benefits of AI and science, analyzing huge amounts of data, speeding up research, maybe even making groundbreaking discoveries. But are there any potential risks we should be worried about? There are always risks with any new technology, especially a technology as powerful as AI. One concern is the potential for bias. AI systems are trained on data. 7:08 If that data is biased, the AI's output will be biased as well. So garbage in garbage out. Like, if you train an AI on a bunch of scientific papers written only by men, it might conclude that women aren't good in science. That's a simplified example, but you get the idea. It's crucial to be aware of the data that we use to train AI systems and to ensure that it's representative and unbiased. 7:30 Otherwise, we could end up perpetuating existing inequalities or even creating new ones. That's a really important point. And what about the potential for AI to be used for harmful purposes? I mean, if it can write a scientific paper, couldn't it also be used to create fake news or propaganda that looks like legitimate research? That's definitely a valid concern. 7:49 As AI gets more sophisticated, it becomes harder to tell the difference between real and AI generated content. This could have serious consequences for public trust in science and the spread of misinformation. So it's not just about the technology itself. It's about the people who are developing it and the ways in which it's being used. Absolutely. 8:07 We need to be very thoughtful about the ethical implications of AI and develop safeguards to make sure it's used for good, things like transparency, accountability, and strong regulations. Okay. So we've established that AI can write a decent research paper and that it has the potential to completely revolutionize science. But I'm curious, how did the reviewers react when they found out they'd reviewed a paper written by AI? Were they shocked, impressed, or maybe a little freaked out? 8:34 Well, the reviewers were aware that some of the papers they were reading might have been written by AI, but they didn't know which ones. So it was like a blind taste test for science papers, a very clever way to avoid bias. And And what about the researchers themselves? How do they feel about their AI passing this major milestone? They were understandably proud of their accomplishment, but they were also very quick to point out that this is just the beginning. 8:56 They see AI as a tool that can help human intelligence not replace it. That's a good point. It's not about AI versus humans. It's about AI and humans working together to advance our understanding of the world, like a dynamic duo, each bringing their own unique strengths to the table. I like that analogy. 9:14 Who knows? Maybe one day, we'll have AI scientists winning Nobel prizes alongside their human colleagues. Now that would be a headline. So we've talked about the AI scientist, its breakthrough paper, and what this might mean for the future of science. But I wanna dig a little deeper into the technical details. 9:29 How does this AI actually work? What's going on under the hood? At its heart, the AI scientist is built on a type of artificial intelligence called a large language model or LLM. These models are trained on huge amounts of data text code images, and they learn to recognize patterns and relationships within that data. So is it yeah. 9:48 Kinda like those predictive text algorithms on our phones, but instead of suggesting the next word in a text message, it's suggesting the next step in the scientific experiment. That's a great way to think about it. The AI scientist has been trained on a vast library of scientific papers code and experimental results. This allows it to learn the language of science, the methods, the logic, even the subtle differences between different scientific disciplines. And once it's absorbed all that knowledge Yeah. 10:16 It can start to generate its own research, formulating hypotheses, designing experiments, analyzing data. Exactly. And that's what makes this so groundbreaking. It's not just copying what human scientists have done. It's using its knowledge to create something truly new. 10:30 So it's not just a parent. It's a creator. Exactly. It raises some profound questions about creativity and intelligence. If a machine can generate original scientific research, what does that mean for our understanding of human ingenuity? 10:43 These are big questions, questions we'll probably be wrestling with for many years to come. Absolutely. But that's what makes this whole field so fascinating. We're at the very frontier of AI, and we're just beginning to understand its potential and its implications. Well, I'm definitely excited to see where this journey takes us. 10:59 And who knows? Maybe one day, we'll have AI scientists joining us here on the podcast to share their insights. Now that would be a truly deep deep dive. Speaking of deep dives, let's dive into the paper itself. What was it actually about? 11:12 I'm guessing it wasn't about how to bake the perfect chocolate chip cookie. No. Not about cookies. Although, that would be an interesting experiment. The paper explored a concept called compositional regularization for neural networks. 11:26 It's a bit technical. Well, you know our listeners. They're a smart branch. Break it down for us. What does it mean for AI to generalize knowledge? 11:35 Think about how people learn. We can take what we know from one situation and apply it to a new situation that's a little bit different. Like, if you learn to ride a bike, you can probably figure out how to ride a scooter pretty quickly even though they're not exactly the same. Right. We don't have to relearn everything from scratch every time. 11:50 We can build on what we already know. Yeah. Exactly. But AI systems often struggle with this. They can be really good at specific tasks, but they have a hard time applying that knowledge to new situations. 12:02 Compositional regularization is a way of trying to make AI think more flexibly like humans do. That's interesting. So the AI was actually trying to make itself smarter in a way? In a way, yes. And that's what's so exciting about this whole field. 12:15 We're not just building AI to do tasks for us. We're building AI to learn to adapt and maybe even to surpass our own capabilities. It's both exciting and a little bit scary to think about where this could lead. Definitely. But I think it's important to approach all of this with a sense of wonder and caution. 12:32 We need to be aware of the potential risks, but also embrace the amazing opportunities that AI offers. Well said. So back to our AI scientist. What's next? Is it gonna keep writing more papers? 12:44 Will we see it presenting at conferences, maybe even winning awards? The researchers say they are already working on the next version of the AI scientist. They wanna improve its ability to do even more complex research, and they're exploring new ways to evaluate its work. It sounds like AI driven science has a bright future. I think so. 13:01 But it's important to remember that AI is a tool. And like any tool, it can be used for good or for bad. It's up to us as a society to guide its development and make sure it's used to benefit humanity. That's a powerful message to end on. So to all our listeners out there, keep your eyes on the AI horizon. 13:19 Things are about to get very interesting. And in the meantime, keep those brains engaged and those curiosity fires burning. There's a whole universe of knowledge out there waiting to be explored. Thank you for listening in. 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