State Space

SeedAI

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The scientific research ecosystem is its own kind of state space — full of decades-old ideas, signal mixed with noise, and breakthroughs increasingly shaped by AI. State Space sits down with leading voices in science acceleration to explore that terrain, one new lens per episode. acceleratesciencenow.substack.com

Oct 14

EP7: Why AI in Science Needs Informative Data, Not Just More Data with Peter Koo (CSHL)

Zhane Yamin: People are always going on about data these days, especially with AI. Get the data, create the data, protect the data, manufacture the data, don’t steal the data, you get the idea. People especially talk about data sets when they’re trying to train AI models on them because their predictive ability improves with more data. So everyone agrees, yes, we need more data, but more data is pretty much where the agreement ends and the trouble starts. My guest today has thought harder than most about why that happens. Peter Koo is an associate professor at Cold Spring Harbor Laboratory, where he runs a lab at the Simon Center for Quantitative Biology, building machine learning methods to decode the regulatory genome. I would say that’s a pretty relevant background for this conversation, but he also wrote one of the winning pieces on Hesero’s MetaScience Essay Competition, a piece called “Before the Next Protein Data Bank,” where he provides an argument about how a field decided what to build before it spends a fortune building the wrong thing. Excited to dive into it right from Peter’s lab in Cold Spring Harbor Laboratory. Peter, welcome to the show. Peter Koo: Thanks for having me. Zhane Yamin: It’s great to have you. So, softball, I guess, to start off with, depending on how you see it, but maybe also a very big question: why do we need data sets, and why are they helpful for machine learning or AI or these models that we like to build for science? Peter Koo: Yeah, in the sciences, and in machine learning generally, we’re trying to understand the world. We’re trying to build a predictive understanding of the world. And in some fields, like in theoretical physics, we can write down equations that describe how dynamical processes will evolve over time, and that in itself is a predictive model of the world. But we can’t build those kinds of models for everything. In other areas we need data to build statistical models, to learn patterns from the data in order to extract the rules of how the world works. So it’s a combination of the data itself, which encodes information about some aspect of the world that we’re trying to understand, and the statistical model that’s going to extract that information out from these models so that we can end up using it to predict future events or phenomena. Zhane Yamin: So in your work specifically, what does that look like? What is that data? What are you trying to build? What are you trying to predict? Peter Koo: So we are focused in the life sciences, and specifically we cover a lot of different scales of biology. The majority of my lab, we want to build models that understand how the DNA code functions. Within every cell in our body, they share the same copy of DNA, yet every cell in our body functions so differently. So we want to understand what parts of the DNA encode and control different kinds of cell types all throughout our body. And just to step back a second, the genome is about three billion bases, meaning As, Cs, Gs, and Ts, and only 1% of those code for proteins. The vast majority, it’s not really known exactly what it does. Sometimes it acts structurally to compact the genome into the nucleus, but there are large portions of it that control which genes to turn on and off. For instance, my heart doesn’t need a neurotransmitter, so I could just turn that off. Turn that gene off. The gene regulation program controls how much each protein should be expressed, and we want to understand how that code works. Zhane Yamin: So then when we’re building these models, and maybe your work as an example, why is a model only as good as the data? What role does the quality of the data play into the quality of the model or the predictions? Peter Koo: That’s a good question. The quality of the data is a very challenging question, because what we want to measure is at a very small molecular scale. If we could just see everything that’s going on, just visualize it, and see all the interactions taking place, and just take a movie of that, then we understand how the process works. But we can’t do that at this molecular scale. All we have tools to probe different aspects of this process. Maybe we can see which proteins are binding to DNA, but we can’t look at all the proteins binding to DNA. We can only look at the specific protein we’re interested in and see where that protein is bound to DNA all throughout the genome. These all provide little noisy snapshots. Also, even when we look at a protein binding to DNA, we’re not actually visualizing that. We’re indirectly measuring that, because we have to figure out biochemical processes, steps to interlock the protein with the DNA. Then we have to get rid of the cell walls and extract out the DNA bound to the protein, and we have to somehow sequence where that protein was bound. So there are a lot of processes happening. Biochemical steps where you cut up the DNA, where you remove the protein from the DNA, and then you have to sequence it. And even then you have lots of processes happening. So what you end up getting at the end, the actual raw data measurements, is going to be a very indirect and very noisy measurement of the underlying biological process that we were truly wanting to capture. We need models to be able to take in that raw data and try to go back and prune out all that noise and extract out the biological signals from this. Different data types have di fferent noise levels. They have different ways of indirectly monitoring this, and so the quality is of course different across the board. Zhane Yamin: Is it more so the job of the model to take out the patterns in that data? Or how much responsibility do we have as researchers, or the creators of these data sets, to make sure that those patterns are easily as noticeable? I guess what I’m saying is, in terms of cleaning the data, what does that mean? Peter Koo: So cleaning the data means processing it and getting it ready for machine learning models to analyze it. If you give a machine learning model too much noisy data, maybe it could learn the underlying biology on its own, but the noise level is so large that it’s often hard for it to find those patterns. And maybe in this sort of framework you would need lots more data, because the noise level is so high that you need to be able to see so many different samples of noise that the shared underlying biological signal, you can identify that. But by using human priors, humans figuring out how to clean up the noise of the data, it ends up making the data quality a lot higher, and so you don’t need as much data for a machine learning model than to learn the underlying patterns. But you still need lots of data. It’s just you don’t need as much data, and it’s more data efficient in that sense. Zhane Yamin: Would it be fair to say then that there’s active trade-off between the amount of data we need and the quality of that data? Like if you need less data... Could you theoretically just brute force it with a bunch of data that maybe is very noisy? Obviously that’s suboptimal, but is that theoretically possible, or is there sort of a roof or a ceiling or something? Peter Koo: I think when we think of data as high quality versus noisy, there are other axes that are important at play here. The task that we want these models, what patterns should they be learning from the data? If it’s a relatively simple set of rules or patterns that the models need to infer, like a protein binding to DNA might, if the binding is very, very strong for a specific subset of nucleotides, then learning that rule can be more straightforward, versus a protein that is very promiscuous and just binds loosely across the genome. And so the underlying patterns aren’t as strong, so the complexity of the data can also matter. When the underlying patterns aren’t that complex and the underlying rules aren’t so complex, then you don’t need as much data to learn those rules. As the complexity of the underlying mechanisms grows, then of course you need more data. Zhane Yamin: So one question I’ve been wondering is, are all data points created equal? And how does that factor into our conversation around convergence and the creation of data sets? I feel like before, at least in your essays or in previous writing, you mentioned this idea of mechanisms and how mechanisms inform predictive distributions. I was wondering if you could just talk more to that in general. Peter Koo: Definitely, definitely. So maybe I’ll just walk it back a step for a quick second and talk about how we typically build AI models right now in biology. Biologists typically generate data on their own, and then we train a model on that data. We try to build a statistical predictive model based on that data, and it can do certain tasks. And we never ask whether that was the right data to train the models on. I think as modelers we need to start asking what kind of data do we want our models to be trained on? That’s the right question. Not all data is created equal. I like to think of data as living in a combinatoric space. For sequences this is all possible sequence space; for images this might be all possible image spaces. It’s a huge, vast space. For DNA sequences, the space of even just very short DNA sequences of only 200 nucleotides long, that’s so tiny. Our genome is three billion nucleotides long. For 200 nucleotides, the space of all possible sequences is 4 to the L, 4 to the 200. That’s already more combinations than the number of atoms in the universe. So that’s the scale that we’re talking about. It’s just practically speaking infinite. It is a finite number, but it’s practically infinite, where we can never sample all of the data, even for short DNA sequences. So the space is vast, and when we sample data points, this is only a very sparse sampling of this vast space. Then we train a mod
Sep 30

EP6: Building a Time Machine for Science with Chris Fussell (Lila Sciences/McChrystal Group)

Zhane Yamin: My guest today isn’t a scientist, though I think you could call him an engineer of sorts. Chris Fussell is president of business operations at Lila Sciences and vice chairman of McChrystal Group. Before that, he spent 15 years as an officer on Navy SEAL teams. And suffice it to say, Chris has got some knowledge in trying to get large siloed organizations to move like fast connected networks. Luckily, or perhaps unluckily for us, the same thing could be said about science. That’s why I think it’s such an amazing opportunity to talk to him right now, because the question of whether America meets the AI for science moment might not be entirely a question of science. It might be a question of whether a sprawling enterprise, whether the agencies, labs, universities, and companies can act like they’re on the same team. And that’s the one problem Chris has a lot of experience solving. Chris, welcome to the show. Chris Fussell: Thanks, Zhane. Yeah, and I think the scientists that I work with at Lila would definitely agree that I’m not a scientist. But I appreciate it. Yeah. Zhane Yamin: No problem. So I guess I just wanted to start with an honest assessment because I think when we talk about scientific competitiveness, there are a lot of alarm bells that are rung, and they’re rung pretty frequently. And I don’t want to just ring them like everyone else. So I want to start off with one question: when you look at where the U.S. actually stands scientifically right now, how competitive are we? Chris Fussell: The entire field is at a transition point, right? So there’s a lot of unknowns in that question, right? Go back two years, where are we competitively? We’ve got, you know, but a 60-year advantage, post-World War II, right? But now as AI starts to intersect with science, which is a totally unique space inside of the AI zip code, you know, AI and science is a very specific neighborhood inside of that much broader world. Where are we competitively in that neighborhood? I feel very good about it. But it’s not one—it needs a special type of attention, both from the U.S. government and allies and from companies like Lila. And there are not a lot of companies in our space. I would say we are uniquely positioned globally. But where are we in that competition globally? I think we have an advantage and I think we are ahead of others, but I think it requires a certain level of attention and understanding from government authorities, private industry, et cetera. And if we don’t give it the proper attention, there’s a world where you get passed. I’m confident we won’t, and I’m confident we have the right pole position right now, but we can’t take our eye off of it. Zhane Yamin: So one thing I kind of want to track with that is, honestly, this idea of watching, and trying to know what we need to watch. Because I think when we’re talking about scientific competitiveness, there’s always a lot of metrics that are thrown around, like China’s taking us in this, but we’ve got this in the U.S. And so I want to get your thoughts on what sort of things we should be watching and metrics we should be taking account of when we’re talking about AI for science and AI competitiveness globally. Chris Fussell: Yeah, on the AI for science question, and again, caveating with your opening, I am not a scientist, but I spend my days with them. And so there’s, again, in this neighborhood inside of the AI discussion, once you’ve seen this, you can’t unsee it, which is: the winners of AI and science, I believe, will have to do what we are doing. And this gets to your point of measurability and how do we know how far along we are? Lila’s thesis and mission and what we are building is an AI model that’s able to connect with physical labs. We’re building many tens of thousands of square feet of labs that will be controlled by AI models, with autonomous systems, robotics, humans very much involved in the labs, which allows incredible throughput, right? And so I like to think of it as a time machine for science. We’ll be able to reach years, decades into the future and pull knowledge into today. So what will be measurable there, rather than just the speed of your model or a lot of stuff that the average person just can’t really get their heads around? We’re right on the cusp of being able to look at a lab and say—sort of like what happened in manufacturing, if you go back to, I don’t know, the post-industrial revolution all the way through the early 20th century—we changed the way we looked at output. Like, how much can this factory produce per square foot of X, and how much energy does it need? It wasn’t just a human question or a knowledge question or an artisan question. You could look at the size of a factory and say it’s going to be able to produce this much X. We’re going to be able to do that with science. Like, this lab, with this configuration and the power of this model, can produce this much new scientific knowledge. And we’ll eventually be able to measure that, I believe, down to sort of the square foot. And that’s where the measurement question, I think, becomes really interesting and different than it’s ever been in science. We’ve seen that evolution in other industries, but we’re on the brink of doing that in the scientific space. Zhane Yamin: So on that point, I want to backtrack into your background and the perspective it gives you. Because again, you’re saying you’re not a scientist, but I think that actually offers you a very unique perspective on this entire landscape. How does that inform your work with Lila Sciences and this idea of autonomous science and building out these labs of the future? What does that strategic perspective offer you in that sense? Chris Fussell: Yeah, just quickly on my background. My first career, for about 15 years from the late nineties until 2012, I guess, I was an officer in the SEAL teams. And so for your listeners, the U.S. special operations community is now, I don’t know, twice as big as it was 25 years ago, but say 70,000 people. Each branch has its own component of special operations. The SEAL teams sit inside the Navy. And there’s a long history to that that goes back to 1980 when those communities were really created. For the first 20 years or so of their history—and this is the force that I joined in the late ’90s—they were built for siloed excellence. The SEALs had to be really good at X. Army units had to be really good at Y, et cetera. And there wasn’t a world where we’d have to integrate together. Well, post-9/11, now 25 years ago, we were put into a little bit of a time machine ourselves, because we jumped onto a battlefield that was networked and digitally enabled, and our much less capable, much less well-funded adversaries were connected globally through a digital network, right? That’s obvious now, right? It’s at our fingertips every day. Twenty-five years ago that was new and interesting, and it broke the way that we had to operate. And we had to rebuild and say, okay, how do we create a truly networked set of units that weren’t built for that culturally, operationally—none of our systems integrated, from the human level to the technical level? But we have to be able to do that to defeat this adversary. And so we went through this whole rebuilding process, and then I spent years post-military thinking about that, writing about that. I teach on it, and then obviously built a consulting firm based on it with retired General Stan McChrystal, who was sort of the father of this inside of special operations. So that’s a quick background on what I think is hyper-applicable here, because if you go back just a few minutes, what we were talking about is building these highly integrated lab systems. AI doesn’t care if you’re a chemist or a life scientist, et cetera. Eventually it will just understand, like we see in large language models, except there are way more combinations in science. It’s just going to see atoms and molecules and the way that life works and the universe works. But you have to teach it through all those different verticals that we as humans have come to understand. And so the chemist needs to work with these labs and develop an understanding of the AI model of how chemistry works. Same with life sciences, same with material sciences. All the different expertise that goes into building these types of labs, from AI specialists to software engineers that can connect the AI model down into the physical labs, the labs that need to be built in a very special way that don’t exist yet, the robotics that have to go in and operate in a way that doesn’t yet exist—all of these are very cutting-edge, interdisciplinary requirements. You don’t have to squint too hard to see the parallels with bringing together a global task force of highly siloed, highly competitive, uniquely special people. Right? So all big complex problems come down to very strong-willed and thoughtful people living in their lane, and you have to connect them across if you want to truly function as a network. So there’s an evolution story there, and that’s how I ended up in this very fun, fortunate, interesting position with Lila. Zhane Yamin: So do you think, in terms of people currently working on meeting the moment in AI for science, that what you see is a lot of people trying to brute force it—like, we need more budget or we need more force, we just need to try really hard—instead of trying to rework these things? Or do you see that a balance is necessary, in terms of brute forcing it, or maybe the problem is more funding, and then maybe half of our attention span should go to rewiring, I guess? What is that balance that you see, and what do you think it should be? What’s the difference between those two, I guess? Chris Fussell: I mean, we’re a young company, so I’m
Sep 23

EP5: The Making of an AI Scientist with Sam Rodriques (Edison Scientific/Future House)

In a world where scientific discovery is often hampered by the limited number of exceptional scientists, Sam Rodriques, co-founder and CEO of Edison Scientific, sheds light on how AI can bridge this talent gap. With a background in physics and a history of pioneering work in AI for scientific research, Sam is at the forefront of a movement to augment human talent with intelligent systems. Defining AI Scientists Sam begins by addressing the common misconceptions surrounding AI scientists. He notes that while the term is frequently used, its meaning can often be unclear. For Sam, the purpose of an AI scientist is to **augment human scientific talent**. By providing access to exceptional scientific intelligence, AI scientists aim to accelerate the pace of research and discovery. “Removing talent as the bottleneck in science is going to be one of the fastest ways to accelerate progress in science today.” — Sam Rodriques The Role of AI Agents Sam explains that the AI agents developed at Edison Scientific are designed to operate within the scientific process, facilitating everything from hypothesis generation to laboratory experimentation and data analysis. By collaborating with pharmaceutical companies, these agents are already making novel discoveries that can be validated in real-world settings. Key Components of Scientific Progress According to Sam, successful scientific inquiry requires three essential components: intelligence, knowledge, and experimentation. He emphasizes that while intelligence in AI is becoming increasingly commoditized, the focus will be on ensuring that models have the knowledge and experimental capabilities necessary for groundbreaking research. Addressing the Knowledge Explosion With the vast amount of scientific literature published today, it’s becoming impractical for any single individual to keep up. Sam argues that AI scientists are uniquely positioned to manage this overwhelming influx of information. Their ability to aggregate knowledge and facilitate new discoveries can lead to significant advancements in various fields. “The proliferation of knowledge means that there’s now too much for any one person to hold in their head. AI can help manage and leverage that information effectively.” — Sam Rodriques Infrastructure and Sovereignty in AI As AI tools become more integral to scientific research, the infrastructure supporting these technologies is evolving. Sam highlights the importance of **sovereignty in AI**, where companies need to run models independently to protect their proprietary data. This shift is crucial for companies that want to leverage AI without relinquishing control over their most valuable asset—their data. The Importance of Benchmarks To evaluate the effectiveness of AI agents, Sam and his team have developed benchmarks like Labbench and Bixbench. These tools assess the performance of AI in scientific tasks, ensuring that as capabilities advance, the benchmarks do too. “You can’t tell whether or not the thing that you’re doing is correct if you don’t have a benchmark.” — Sam Rodriques Conclusion Sam’s insights into the role of AI in science reveal a future where human and artificial intelligence work together to push the boundaries of research. As the landscape of scientific inquiry evolves, the integration of AI promises to unlock new possibilities for discovery and innovation. For those interested in the intersection of technology and science, this conversation offers a compelling glimpse into what the future may hold. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit acceleratesciencenow.substack.com
Sep 16

EP4: How AI Learned to Speak Science with Rafael Gomez-Bombarelli (MIT/Lila Sciences)

Rafael Gomez-Bombarelli sits down with Zhane to discuss the intersection of AI and science, exploring how AI models are transforming research in chemistry, materials science, and beyond. He shares insights on the evolution of AI for science, the role of scalable architectures, and the future of autonomous laboratories. The Evolution of AI in Science Rafa’s career illustrates a pivotal moment in science when computational methods began merging with experimental research. He shared, “Back around 2014, 2015, the simulation community realized that what they were doing was highly scalable.” This scalability allowed researchers to harness vast amounts of simulation data, coinciding with the rise of deep learning techniques. As Rafa explained, this period marked a significant shift: “We had the ability to make large data sets and deep learning architectures that performed well on these data sets.” He emphasized how the emergence of specialized neural networks transformed the way scientists approach data and simulations. The integration of machine learning into scientific research has sparked what Rafa terms the first incarnation of AI for science. The Bitter Lesson Rafa introduced the concept of the “bitter lesson,” which posits that the most effective AI models are those that leverage large datasets rather than relying on human intuition or domain-specific knowledge. He noted, “The models that ultimately succeed are the ones that can just take the most available data into the most expressive general models.” This understanding has profound implications for how scientists approach problem-solving in their fields, suggesting a need to adapt to more scalable methods of inquiry. A New Era for Scientific Discovery So why is there such a surge of interest in AI for science right now? According to Rafa, the urgency of scientific advancement is a major driver. He articulated that “Science is a very noble, important, and sort of unambiguously good for humanity.” The need to innovate, especially in fields like medicine and technology, creates a fertile ground for the application of AI. As Rafa pointed out, science inherently generates new knowledge, making it a compelling domain for AI applications. The Role of Lila Sciences At Lila Sciences, Rafa is pushing the boundaries of what’s possible with AI in the lab. He describes a vision for creating programmable laboratories or “AI Science Factories” that integrate AI’s capabilities with experimental verification. He explained that, “The only way to have scalable verification for AI-driven science is to give the whole wheel of science to AI.” This revolutionary approach aims to automate not only data analysis but also the execution of experiments, allowing AI to verify its hypotheses in real-time. Rafa’s work serves as a bridge between language-based AI models and the nuanced, complex world of scientific inquiry. His role involves translating scientific concepts into formats that AI can understand, ensuring that the rich data of the scientific domain can be effectively utilized by machine learning models. In this exciting intersection of AI and science, we are witnessing a transformation that promises to accelerate discovery and innovation like never before. To explore more about these insights and the future of AI in scientific research, listen to the full episode. Chapters 00:00 Introduction to Rafa Gomez-Bombarelli and his background in AI and science 01:24 The merging of experimental science and simulation in Rafa’s career 02:24 The rise of data science and deep learning in materials and chemistry 03:20 The impact of neural networks and large data sets in AI for science 04:20 The difference between physics-based simulation and machine learning models 05:43 Generative models for chemistry and materials design 06:37 How language models relate to scientific data and models 09:03 Transformers and the scaling of AI architectures 10:38 Why now is a pivotal moment for AI in science Lila Sciences and the role of AI in scientific discovery The concept of AI Science Factories and autonomous labs Concrete examples of AI enabling scientific breakthroughs The potential and limitations of AI in generating new knowledge Open questions and the future role of human scientists This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit acceleratesciencenow.substack.com
Aug 26

EP3: The Case for Open Science with Prachee Avasthi (Astera Institute/Arcadia Science)

In this episode, Dr. Prachee Avasthi discusses the transformative potential of open science, challenging traditional publishing and funding models, and exploring how technology and policy can accelerate scientific progress. Chapters 00:00 Introduction to open science and its importance 01:51 What is open science and how is it defined? 04:00 The role of open access and open data in research 06:04 Challenges of traditional publishing and peer review 07:57 Funding models and their influence on research direction 10:10 The impact of AI and new technologies on science dissemination 12:09 Strategies for implementing open science practices 14:02 The distinction and relationship between for-profit and nonprofit research 15:52 Policy initiatives and organizational efforts to promote open science 18:12 The cultural shift needed for open science adoption 20:08 The future of scientific collaboration and data sharing 22:06 The societal stakes and benefits of open science 23:54 Overcoming inertia and resistance in the research community 26:01 The role of grassroots efforts and individual action 28:06 The importance of infrastructure and resources for open science 30:07 The potential of AI and machine learning to transform research 32:04 How to navigate the evolving landscape of scientific publishing 33:49 The impact of open science on innovation and societal progress 36:07 Final thoughts: embracing change for a better scientific future This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit acceleratesciencenow.substack.com
Aug 5

EP2: The Challenge of Focus & Messaging in AI for Science with Dan Turner-Evans (Institute for Progress)

In this episode, we explore the Genesis mission’s focus, funding challenges, and the importance of clear communication in AI for science. Dan Turner-Evans of the Institute for Progress shares insights from his diverse background in physics, neuroscience, and policy, emphasizing the need for strategic focus and effective messaging to advance AI initiatives in government and research. Chapters 00:00 Introduction to the Genesis mission and guest background 02:01 Dan’s diverse scientific background and policy experience 07:08 Overview of the Genesis mission and its recent expansion 11:10 The importance of focus and funding for the mission 15:11 Challenges of broad AI initiatives and strategic focus 19:52 Communication strategies for AI in science to the public and Congress 25:08 Platforms versus challenges in AI research and development 30:00 Balancing basic research with targeted AI applications 34:53 Concluding thoughts on focus, communication, and the future of AI in science This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit acceleratesciencenow.substack.com
Jul 29

EP1: How AI Can Accelerate Scientific Discovery with Josh New (SeedAI)

In this episode, Josh New, director of policy at SeedAI, shares insights on accelerating science through AI, policy challenges, and innovative models like autonomous labs and FROs. We explore how to unlock AI’s potential for scientific breakthroughs and the role of policy in fostering a more dynamic research ecosystem. Chapters 00:00 Introduction and episode overview 01:00 Guest introduction: Josh New and SeedAI 02:05 Josh’s path from tech policy to AI for science 03:23 The rise of AI in scientific research 05:14 Changing conversations in AI policy 07:43 Rapid evolution of AI and policy challenges 08:35 The impact of AI saturation on scientific workflows 12:00 Unlocking AI’s potential versus steering its direction 14:02 Decentralized science and policy intervention 17:28 Reimagining research labs with AI and automation 20:55 Funding models and data infrastructure for AI-driven science 23:07 Lessons from the Human Genome Project and big science 29:45 The ASN model as connective tissue in AI and science 34:30 Closing thoughts and call to action Episode Resources SeedAI - https://seedai.org AlphaFold - Protein structure prediction - https://deepmind.com/research/highlighted-research/alphafold Human Genome Project - https://www.genome.gov/human-genome-project ASN Coalition - Accelerate Science Now - https://acceleratesciencenow.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit acceleratesciencenow.substack.com
Jul 23

Introducing State Space

Introducing State Space In this trailer, host Zhane Yamin introduces State Space — a new show exploring the ideas shaping science acceleration, AI for science, and meta-science. Drawing on the concept of a "state space" (the mathematical universe of every possible configuration a system can take), Zhane frames the show's core question: how do we find the right variables — the right basis — to cut through the noise and make sense of a research ecosystem that's evolving faster than ever under AI? Each episode of State Space will feature a conversation with a leading voice in science acceleration, offering a new angle on how science moves faster in an AI-native era. In this episode: - What a "state space" is, and why it's a useful lens for thinking about complex systems — from a mass on a spring to a neural network to a game of chess - Why the scientific research ecosystem is itself a kind of state space, full of decades of accumulated ideas and new AI-driven breakthroughs - What to expect from the show: conversations at the intersection of science acceleration, AI for science, and meta-science Host: Zayn Yaman, Science Policy Fellow at Seed AI About the show: State Space is powered by the Accelerate Science Now Coalition, a Seed AI project. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit acceleratesciencenow.substack.com

8 Episodes

out of 5

2 Ratings

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SeedAI
Years Active

2026
Episodes

8
Rating

Clean
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State Space

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Technology

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State Space

Episodes

EP7: Why AI in Science Needs Informative Data, Not Just More Data with Peter Koo (CSHL)

EP6: Building a Time Machine for Science with Chris Fussell (Lila Sciences/McChrystal Group)

EP5: The Making of an AI Scientist with Sam Rodriques (Edison Scientific/Future House)

EP4: How AI Learned to Speak Science with Rafael Gomez-Bombarelli (MIT/Lila Sciences)

EP3: The Case for Open Science with Prachee Avasthi (Astera Institute/Arcadia Science)

EP2: The Challenge of Focus & Messaging in AI for Science with Dan Turner-Evans (Institute for Progress)

EP1: How AI Can Accelerate Scientific Discovery with Josh New (SeedAI)

Introducing State Space

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