Podcasts about mechanistic

When your biosimilar analytical data shows 1.4% high mannose against a 6% reference product specification, you face limited options: process temperature shifts that compromise titer, kifunensine supplementation that requires extensive regulatory justification, or 12-18 months to reclone and revalidate. Media supplementation offers an alternative pathway—tuning glycan profiles through formulation adjustments rather than cell line or process re-engineering.In this episode, David Brühlmann presents the experimental development of a media supplementation strategy that achieved 2.8-fold increases in high mannose glycans across multiple CHO cell lines. Drawing from research published in the Journal of Biotechnology (2017, 252:32-42), the discussion covers the mechanism of raffinose-mediated glycan processing arrest, the experimental variables that initially obscured the effect, and the process development considerations for implementing media-based glycan tuning.The episode examines N-glycan biosynthesis in CHO cells, regulatory comparability requirements for biosimilar glycosylation profiles, and the experimental framework for evaluating media supplementation as a glycan control strategy.Highlights from the episode:The unexpected link between dietary raffinose and reduced athletic performance, and its connection to bioprocessing (01:11)A clear primer on the importance of glycosylation for biosimilar drugs and regulatory approval (02:43)Common challenges when glycan profiles don't match reference products, and why high mannose glycans matter (04:19)A review of industry strategies (temperature shifts, enzyme inhibitors, cell line reengineering) and their pitfalls (05:33)Mechanistic insights into how raffinose alters glycan processing in CHO cells (07:05)Key experimental findings on raffinose concentration, osmolality control, and practical lab troubleshooting (09:48)Application stories and regulatory considerations for implementing raffinose-based media adjustments (13:47)Closing thoughts on process optimization, regulatory impact, and what to expect in Part 2 (15:11)Strategic insight:Implementing raffinose as a media supplement is straightforward, regulatory-friendly, and cost-effective. It does not involve genetic engineering or enzyme inhibitors and is easily sourced as a GMP-grade material. For programs approaching submission with glycan comparability gaps, media-based tuning offers a process optimization pathway that maintains existing cell lines and manufacturing platforms while addressing critical quality attribute specifications.Listen to this episode of the Smart Biotech Scientist Podcast to learn David's best strategies for rapid, regulatory-friendly glycosylation control.If you want to transform your glycoengineering workflow, keep an eye (and ear) out for the next episode of the Smart Biotech Scientist Podcast. Your path to regulatory success might be as simple as a pinch of raffinose.Resources: Journal of Biotechnology, 2017, volume 252, pages 32 to 42Next step:Need fast CMC guidance? → Get rapid CMC decision support hereSupport the show

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

Unexplained infertility, PMS, and low progesterone are often dismissed when labs fall “within range.” In this episode, Dr. Brendan McCarthy explains why prolactin may be the missing piece. Learn how mildly elevated prolactin can suppress ovulation, lower progesterone, and impact fertility—even when labs appear normal. We also discuss common causes, symptoms, the role of stress and medications, and why diet (including gluten sensitivity) may matter. This episode focuses on precision medicine, not fear—helping you understand what standard reference ranges often miss. Citations: Research — Prolactin and Breast Cancer Risk Below are key epidemiologic and review papers that inform the discussion in this episode regarding prolactin and breast biology. These studies look at associations, not simple cause-and-effect relationships, and help explain why prolactin shows up in breast health conversations. Meta-analysis: circulating prolactin and breast cancer risk Wang M, et al. (2016). Plasma prolactin and breast cancer risk: a meta-analysis. Cancer Causes & Control. This meta-analysis pooled data from multiple observational studies comparing women with higher versus lower circulating prolactin levels. Across studies, higher prolactin levels were associated with a modest but statistically significant increase in breast cancer risk. The association was most evident in postmenopausal women and in hormone-receptor–positive tumors. This helps explain why prolactin is considered a relevant growth signal in breast tissue rather than just a “lactation hormone.” Systematic review and meta-analysis: prolactin levels across breast cancer cohorts Aranha AF, et al. (2022). Impact of prolactin levels in breast cancer: a systematic review and meta-analysis. Endocrine-Related Cancer. This more recent systematic review and meta-analysis evaluated circulating prolactin levels across breast cancer populations and control groups. Elevated prolactin levels were associated with higher breast cancer occurrence, with stronger associations seen in invasive cancers and hormone-receptor–positive disease. This paper adds weight to the idea that prolactin participates in breast biology in ways that matter clinically, even outside of pregnancy and breastfeeding. Prospective cohort studies: prolactin measured before diagnosis Tworoger SS, et al. (2004; 2006). Prospective analyses from large cohorts including the Nurses' Health Study. In these studies, prolactin was measured years before any breast cancer diagnosis. Women with higher prolactin levels had a higher likelihood of developing breast cancer later, particularly estrogen-receptor–positive tumors in postmenopausal women. Because prolactin was measured before cancer developed, these studies help clarify timing and reduce the concern that elevated prolactin is simply a consequence of disease. Mechanistic context (supportive background) Experimental and translational studies show that prolactin receptor signaling influences mammary epithelial cell growth, differentiation, and interaction with estrogen signaling pathways. This provides a biologic backdrop for why epidemiologic associations between prolactin and breast cancer risk keep appearing across different study designs. How to read this as a clinician or patient These data do not mean prolactin “causes” breast cancer in a simple or deterministic way. What they do show is that prolactin is an active hormone in breast tissue, and chronically higher levels are consistently associated with changes in breast risk profiles across large populations. That's why prolactin deserves attention in conversations about fertility, breast symptoms, and long-term hormonal signaling—not fear, and not dismissal.    Dr. Brendan McCarthy is the founder and Chief Medical Officer of Protea Medical Center in Arizona. With over two decades of experience, he's helped thousands of patients navigate hormonal imbalances using bioidentical HRT, nutrition, and root-cause medicine. He's also taught and mentored other physicians on integrative approaches to hormone therapy, weight loss, fertility, and more. If you're ready to take your health seriously, this podcast is a great place to start.  

In this episode, the discussion turns to a deceptively simple question that sits at the centre of countless nutrition debates: how much protein do we actually need? On one side, there are confident claims that very high protein intakes are not just beneficial but essential for maximising strength, performance, and muscle mass. On the other, equally strong assertions that the current RDA is entirely sufficient for most people, and that going beyond it is unnecessary or even harmful. Dr. Eric Helms and Dr. Matthew Nagra work through what the evidence actually tells us when we step away from slogans and thresholds. What does 0.8 g/kg represent, and just as importantly, what does it not? At what point do higher intakes stop meaningfully improving muscle-related outcomes? And where do concerns about kidney function, longevity, and chronic disease fit when we look at long-term data rather than isolated mechanisms? Rather than treating protein as a single number to defend or dismiss, this conversation places intake in context: training status, ageing, health outcomes, source and optimising for specific goals. Timestamps [05:19] Discussion starts [07:18] Setting the scene: protein intake and health [09:38] Health outcomes and protein intake [10:27] Mechanistic measures vs. longitudinal outcomes [15:47] The RDA: purpose and limitations [19:19] Higher protein recommendations: where do they come from? [21:48] Protein intake for athletes and general population [27:25] Dose response and optimal protein intake [44:59] Statistical errors in Morton meta-analysis [46:07] Comparing meta-analyses: Morton, Tagawa, and Nunez [56:23] Mechanistic claims and protein intake [59:49] Nitrogen balance and protein requirements [01:11:55] Protein sources and health outcomes [01:18:13] Summarizing optimal protein intake [01:24:31] Key ideas segment (premium subscribers only) Related Resources Go to the episode page (with linked studies & resources) Join the Sigma email newsletter for free Subscribe to Sigma Nutrition Premium Enroll in the next cohort of our Applied Nutrition Literacy course Dr. Helms: MASS Research Review Muscle & Strength Pyramids books Instagram: @helms3dmj Dr. Nagra: Instagram: @dr.matthewnagra Dr. Nagra's website

Audio note: this article contains 78 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. This post covers work done by several researchers at, visitors to and collaborators of ARC, including Zihao Chen, George Robinson, David Matolcsi, Jacob Stavrianos, Jiawei Li and Michael Sklar. Thanks to Aryan Bhatt, Gabriel Wu, Jiawei Li, Lee Sharkey, Victor Lecomte and Zihao Chen for comments. In the wake of recent debate about pragmatic versus ambitious visions for mechanistic interpretability, ARC is sharing some models we've been studying that, in spite of their tiny size, serve as challenging test cases for any ambitious interpretability vision. The models are RNNs and transformers trained to perform algorithmic tasks, and range in size from 8 to 1,408 parameters. The largest model that we believe we more-or-less fully understand has 32 parameters; the next largest model that we have put substantial effort into, but have failed to fully understand, has 432 parameters. The models are available at the AlgZoo GitHub repo. We think that the "ambitious" side of the mechanistic interpretability community has historically underinvested in "fully understanding slightly complex [...] ---Outline:(03:09) Mechanistic estimates as explanations(06:16) Case study: 2nd argmax RNNs(08:30) Hidden size 2, sequence length 2(14:47) Hidden size 4, sequence length 3(16:13) Hidden size 16, sequence length 10(19:52) Conclusion The original text contained 20 footnotes which were omitted from this narration. --- First published: January 26th, 2026 Source: https://www.lesswrong.com/posts/x8BbjZqooS4LFXS8Z/algzoo-uninterpreted-models-with-fewer-than-1-500-parameters --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

James O'Hara sits down with Dr Dan Bristow (OB-GYN) to talk about hormones For High-quality labs:► http://sagebio.com/For information on the Gillett Health clinic, lab panels, and health coaching:► https://GillettHealth.comFollow Gillett Health for more content from James and Kyle► https://instagram.com/gilletthealth► https://www.tiktok.com/@gilletthealth► https://twitter.com/gilletthealth► https://www.facebook.com/gilletthealthFollow Kyle Gillett, MD► https://instagram.com/kylegillettmdFollow James O'Hara, NP► https://Instagram.com/jamesoharanpFor 10% off Gorilla Mind products, including SIGMA: Use code “GH10”► https://gorillamind.com/For discounts on high-quality supplements►https://www.thorne.com/u/GillettHealth►Compiled Source ListSystematic and Narrative Reviews 1. Gut microbial β‑glucuronidase: a vital regulator in female estrogen metabolism and gynecologic cancersPMCID: PMC10416750 • Year: 2023 • Journal: International Journal of Molecular Sciences • Summary: Reviews role of β-glucuronidase in estrogen metabolism, breast cancer, endometriosis. 2. A New Paradigm in Gut Microbiota & Breast Cancer: β‑Glucuronidase as Therapeutic TargetDOI: 10.3390/pathogens12091086 • Year: 2023 • Journal: Pathogens • Summary: Emerging model proposing gmGUS as a direct target in estrogen-driven breast cancer. 3. Gut and oral microbiota in gynecologic cancers: mechanisms and therapeutic valueDOI: 10.1038/s41522-024-00577-7 • Year: 2024 • Journal: npj Biofilms and Microbiomes • Summary: Systematic review on microbiota's role in ovarian, cervical, and breast cancers. Human Clinical or Case-Control Studies 4. Assessment of gut microbial β‑glucuronidase and β‑glucosidase activity in women with PCOSPMCID: PMC10366212 • Year: 2023 • Journal: Scientific Reports • Summary: Found significantly higher β-glucuronidase activity in PCOS patients. 5. Gut microbiota and ovarian diseases: a new therapeutic perspectiveDOI: 10.1186/s13048-025-01684-5 • Year: 2025 • Journal: Journal of Ovarian Research • Summary: Review covering PCOS, POI, and tumors—describes estrogen recycling via gut microbiota.Mechanistic, In Vitro, and Animal Studies 6. In vitro analysis of gut microbial β‑glucuronidases and estrogen deconjugationDOI: 10.1016/j.jbc.2020.105542 • Year: 2020 • Journal: Journal of Biological Chemistry • Summary: Characterized 35 GUS enzymes that reactivate estrogen glucuronides. 7. Impact of intestinal flora on ovarian function and disease pathogenesisFull text: e-century.us • Year: 2024 • Journal: American Journal of Translational Research • Summary: Animal studies showing how β-G-producing gut bacteria drive ovarian dysfunction. 8. The role of gut microbiota in endometriosis: current insightsDOI: 10.3389/fmicb.2024.1363455 • Year: 2024 • Journal: Frontiers in Microbiology • Summary: Mechanistic review linking β-G-producing bacteria to lesion development and inflammation in endometriosis.#female #femalehealth #hormones #testosteroneAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

David interviews Blair about his recent article in which he explores how our businesses are not simple machines that can be tuned (or killed) with specific wrenches, but they are complex adaptive organisms that we need to understand differently.   LINKS "Your Business Is Not a Machine" by Blair Enns for winwitoutpitching.com "Innofficiency in Your Agency" 2Bobs episode "Grow or Die?" 2Bobs episode

Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. My guest today is Dan Nicholson, Assistant Professor of Philosophy at George Mason University, here to talk about his little book, What Is Life? Revisited. Erwin Schrödinger's What Is Life is a famous book that people point to as having predicted DNA and influenced and inspired many well-known biologists ushering in the molecular biology revolution. But Schrödinger was a physicist, not a biologist, and he spent very little time and effort toward understanding biology. What was he up to, why did he write this "famous little book"? Schrödinger had an agenda, a physics agenda. He wanted to save the older deterministic version of quantum physics from the new indeterministic version. When Dan was on the podcast a few years ago, we talked about the machine view of biological systems, how everything has become a "mechanism", and how that view fails to capture what modern science is actually telling us, that organisms are unlike machines in important ways. That work of Dan's led him down this path to Schrödinger's What Is Life, which he argues was a major contributor to that machine metaphor so ubiquitous today in biology. One of the reasons I'm interested in this kind of work is because the cognitive sciences, including neuroscience and artificial intelligence, inherited this mechanistic perspective, and swallowed it so hard that if you don't include the word "mechanism" in your research paper, you're vastly decreasing your chances of getting your work published, when in fact the mechanistic perspective is one super useful perspective among many. Dan's website. Google Scholar. Social: @NicholsonHPBio; @djnicholson.bsky.social What Is Life? Revisited Previous episode: BI 150 Dan Nicholson: Machines, Organisms, Processes 0:00 - Intro 7:27 - Why Schrodinger wrote What is Life 15:13 - Aperiodic crystal and the meaning of code 21:39 - Order-from-order, order-from-disorder 28:32 - Appeal to authority 37:48 - Cell as machine 39:33 - Relation between DNA and organism (development) 44:44 - Negentropy 53:54 - Original contributions 58:54 - Mechanistic metaphor in neuroscience 1:16:05 - What's the lesson? 1:28:06 - Historical sleuthing 1:39:49 - Modern philosophy of biology

Cameron Berg, Research Director at AE Studio, shares his team's groundbreaking research exploring whether frontier AI systems report subjective experiences. They discovered that prompts inducing self-referential processing consistently lead models to claim consciousness, and a mechanistic study on Llama 3.3 70B revealed that suppressing deception features makes the model *more* likely to report it. This suggests that promoting truth-telling in AIs could reveal a deeper, more complex internal state, a finding Scott Alexander calls "the only exception" to typical AI consciousness discussions. The episode delves into the profound implications for two-way human-AI alignment and the critical need for a precautionary approach to AI consciousness. LINKS: Janus' argument on LLM attention Safety Pretraining arXiv Paper Self-Referential AI Paper Site Self-Referential AI arXiv Paper Judd Rosenblatt's Tweet Thread Cameron Berg's Goodfire Demo Podcast with Milo YouTube Playlist Cameron Berg's LinkedIn Profile Cameron Berg's X Profile AE Studio AI Alignment Sponsors: Framer: Framer is the all-in-one platform that unifies design, content management, and publishing on a single canvas, now enhanced with powerful AI features. Start creating for free and get a free month of Framer Pro with code COGNITIVE at https://framer.com/design Tasklet: Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai Linear: Linear is the system for modern product development. Nearly every AI company you've heard of is using Linear to build products. Get 6 months of Linear Business for free at: https://linear.app/tcr Shopify: Shopify powers millions of businesses worldwide, handling 10% of U.S. e-commerce. With hundreds of templates, AI tools for product descriptions, and seamless marketing campaign creation, it's like having a design studio and marketing team in one. Start your $1/month trial today at https://shopify.com/cognitive PRODUCED BY: https://aipodcast.ing

Are we failing to understand the exponential, again?My guest is Julian Schrittwieser (top AI researcher at Anthropic; previously Google DeepMind on AlphaGo Zero & MuZero). We unpack his viral post (“Failing to Understand the Exponential, again”) and what it looks like when task length doubles every 3–4 months—pointing to AI agents that can work a full day autonomously by 2026 and expert-level breadth by 2027. We talk about the original Move 37 moment and whether today's AI models can spark alien insights in code, math, and science—including Julian's timeline for when AI could produce Nobel-level breakthroughs.We go deep on the recipe of the moment—pre-training + RL—why it took time to combine them, what “RL from scratch” gets right and wrong, and how implicit world models show up in LLM agents. Julian explains the current rewards frontier (human prefs, rubrics, RLVR, process rewards), what we know about compute & scaling for RL, and why most builders should start with tools + prompts before considering RL-as-a-service. We also cover evals & Goodhart's law (e.g., GDP-Val vs real usage), the latest in mechanistic interpretability (think “Golden Gate Claude”), and how safety & alignment actually surface in Anthropic's launch process.Finally, we zoom out: what 10× knowledge-work productivity could unlock across medicine, energy, and materials, how jobs adapt (complementarity over 1-for-1 replacement), and why the near term is likely a smooth ramp—fast, but not a discontinuity.Julian SchrittwieserBlog - https://www.julian.acX/Twitter - https://x.com/mononofuViral post: Failing to understand the exponential, again (9/27/2025)AnthropicWebsite - https://www.anthropic.comX/Twitter - https://x.com/anthropicaiMatt Turck (Managing Director)Blog - https://www.mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCap(00:00) Cold open — “We're not seeing any slowdown.”(00:32) Intro — who Julian is & what we cover(01:09) The “exponential” from inside frontier labs(04:46) 2026–2027: agents that work a full day; expert-level breadth(08:58) Benchmarks vs reality: long-horizon work, GDP-Val, user value(10:26) Move 37 — what actually happened and why it mattered(13:55) Novel science: AlphaCode/AlphaTensor → when does AI earn a Nobel?(16:25) Discontinuity vs smooth progress (and warning signs)(19:08) Does pre-training + RL get us there? (AGI debates aside)(20:55) Sutton's “RL from scratch”? Julian's take(23:03) Julian's path: Google → DeepMind → Anthropic(26:45) AlphaGo (learn + search) in plain English(30:16) AlphaGo Zero (no human data)(31:00) AlphaZero (one algorithm: Go, chess, shogi)(31:46) MuZero (planning with a learned world model)(33:23) Lessons for today's agents: search + learning at scale(34:57) Do LLMs already have implicit world models?(39:02) Why RL on LLMs took time (stability, feedback loops)(41:43) Compute & scaling for RL — what we see so far(42:35) Rewards frontier: human prefs, rubrics, RLVR, process rewards(44:36) RL training data & the “flywheel” (and why quality matters)(48:02) RL & Agents 101 — why RL unlocks robustness(50:51) Should builders use RL-as-a-service? Or just tools + prompts?(52:18) What's missing for dependable agents (capability vs engineering)(53:51) Evals & Goodhart — internal vs external benchmarks(57:35) Mechanistic interpretability & “Golden Gate Claude”(1:00:03) Safety & alignment at Anthropic — how it shows up in practice(1:03:48) Jobs: human–AI complementarity (comparative advantage)(1:06:33) Inequality, policy, and the case for 10× productivity → abundance(1:09:24) Closing thoughts

CME credits: 0.75 Valid until: 07-10-2026 Claim your CME credit at https://reachmd.com/programs/cme/PI3K-Pathway-inhibition-in-HR-HER2-mBC-Mechanistic-Insights/37329/ The PI3K-AKT-mTOR pathway is a crucial signaling network dysregulated in many cancers, promoting cell survival, growth, and proliferation, and often implicated in resistance to cancer therapies. Inhibition of this pathway by PI3K inhibitors disrupts a complex network of cellular processes that contribute to breast cancer, markedly reducing cell proliferation, promoting apoptosis, inhibiting angiogenesis, and ultimately preventing tumor formation and progression. In hormone receptor–positive (HR+), activating PIK3CA mutations occur in approximately 35% to 40% of patients and a variable prevalence across BC subtypes. Testing is thus crucial to ensure appropriate treatment selection. The development of PI3K-targeted agents may revolutionize the treatment landscape for HR+, HER2- metastatic breast cancer (mBC, and due to the recent approval of inavolisib, clinicians must be apprised of both the clinical evidence and best practices regarding the use of this agent. This activity has been designed to review the role of the PI3K-AKT-mTOR pathway in breast cancer, the importance of testing when making clinical decisions, and the role of PI3K-targeted therapies in HR+, HER- mBC.

CME credits: 0.75 Valid until: 07-10-2026 Claim your CME credit at https://reachmd.com/programs/cme/PI3K-Pathway-inhibition-in-HR-HER2-mBC-Mechanistic-Insights/37329/ The PI3K-AKT-mTOR pathway is a crucial signaling network dysregulated in many cancers, promoting cell survival, growth, and proliferation, and often implicated in resistance to cancer therapies. Inhibition of this pathway by PI3K inhibitors disrupts a complex network of cellular processes that contribute to breast cancer, markedly reducing cell proliferation, promoting apoptosis, inhibiting angiogenesis, and ultimately preventing tumor formation and progression. In hormone receptor–positive (HR+), activating PIK3CA mutations occur in approximately 35% to 40% of patients and a variable prevalence across BC subtypes. Testing is thus crucial to ensure appropriate treatment selection. The development of PI3K-targeted agents may revolutionize the treatment landscape for HR+, HER2- metastatic breast cancer (mBC, and due to the recent approval of inavolisib, clinicians must be apprised of both the clinical evidence and best practices regarding the use of this agent. This activity has been designed to review the role of the PI3K-AKT-mTOR pathway in breast cancer, the importance of testing when making clinical decisions, and the role of PI3K-targeted therapies in HR+, HER- mBC.

EIQ online00:00 Introduction and Setting the Scene01:43 Exploring NMN Supplements06:46 Mechanistic vs Whole Body Evidence10:56 The Importance of Research in Fitness14:58 Influences and Role Models in the Fitness Industry18:51 Navigating Controversial Figures in Fitness22:42 The Role of Apps in Nutrition26:57 The Basics of Nutrition and Fitness32:54 Career Pathways in Nutrition and Fitness

Featuring an interview with Prof Xavier Leleu including the following topics: Introduction: Historical treatment advances in multiple myeloma (MM) (0:00) Contemporary treatment for patients with newly diagnosed MM who are eligible for transplant (13:18) Prognosis and life expectancy for patients with MM (19:39) Mechanistic differences among anti-CD38 monoclonal antibodies (27:05) Routes of administration of anti-CD38 monoclonal antibodies (30:21) Background and treatment of smoldering myeloma (41:05) Treatment for older patients with newly diagnosed MM who are not eligible for transplant (46:41) NCPD information and select publications

Danielle Sulikowski, professor of evolutionary psychology, presents a controversial theory on why global fertility rates and birth rates are collapsing. She argues that an evolutionary strategy known as female mate suppression—where dominant women repress the reproductive success of rivals—has shifted in humans into a modern form of antinatal social contagion. Rather than direct biological suppression, the strategy manifests as propaganda and cultural messaging that discourage motherhood, promote career over family, and accelerate population decline. We explore how intrasexual competition among women could shape civilization itself, why some groups might defect against their own society to gain an evolutionary edge, and how this connects to broader debates in feminism, cultural evolution, and civilizational collapse. The conversation also ties in the density-dependent dynamics of Calhoun's Rat Utopia experiments as a possible parallel to modern urbanization, social media, and declining birth rates.PATREON https://www.patreon.com/c/demystifysciPARADIGM DRIFThttps://demystifysci.com/paradigm-drift-showOUR HOMEBREWED MUSICCheck out our band's new album:https://secretaryofnature.bandcamp.com/album/everything-is-so-good-hereVinyl pre-orders available now: https://buy.stripe.com/14A5kC3Od5d21Ms7zPdEs0900:00 Go! Introducing the Central Crisis of Western Civilization00:05:53 Intrasexual mate Suppression in Animals00:09:03 The Mechanisms of Intrasexual Competition00:12:29 Competitive Mothering Dynamics00:18:03 Advising on Haircut Strategies00:20:07 Understanding Intrasexual Competition Measurement00:21:56 Female Competitiveness Dynamics00:25:10 Personal Experiences with Gender Dynamics00:29:32 Navigating Social Circles and Competition00:33:00 Changes in Intersexual Competitiveness Among Women00:38:05 Feminism and Reproductive Suppression00:42:27 Societal Trends and Competitive Behavior00:43:10 Human Behavior and Civilization Cycles00:46:08 Decline of Birth Rates and Societal Institutions00:50:20 Reproductive Strategies and Societal Feedback Loops00:53:07 The Role of Intellectual Discourse in Civilizational Shifts00:56:15 Rationalizing Birth Rate Declines01:00:21 Evolutionary Explanations for Civilizational Behavior01:05:25 Empirical Examination of Birth Rate Decline01:09:12 Exploring Male Responses and Societal Dynamics01:12:15 Intersecting Ideologies and Population Messaging01:20:00 Internet Influence on Cultural Dynamics01:25:00 Mechanistic and Functional Explanations of Behavior01:27:03 Discussion on Societal Decline and Birth Rates01:31:21 Exploring Societal Change and Its Cyclical Nature01:35:34 The Role of Technology and Interconnectedness01:40:13 Urbanization Effects and Cultural Dynamics01:44:27 Gender Dynamics and Cultural Evolution01:47:44 Discussion on Social Influence and Elite Classes01:51:50 Class and Reproductive Strategies01:54:43 Urbanization's Impact on Society02:00:08 Evolution vs. Morality in Society02:02:57 Urban Density and Human Behavior02:07:45 Bioconservatism vs. Transhumanism02:09:00 Transhumanism and the Unknown Future02:12:53 Understanding Unseen Forces02:15:11 The Quest for Understanding#evolutionarypsychology , #civilization, #feminism, #sociology, #anthropology, #culturewars, #birthrates, #psychology, #society, #population, #decline, #history, #civilizations, #future #philosophypodcast , #longformpodcast ABOUS US: Anastasia completed her PhD studying bioelectricity at Columbia University. When not talking to brilliant people or making movies, she spends her time painting, reading, and guiding backcountry excursions. Shilo also did his PhD at Columbia studying the elastic properties of molecular water. When he's not in the film studio, he's exploring sound in music. They are both freelance professors at various universities.

Why is the mechanical view of reality so strong? Why does billiard-ball atomism remain the default popular metaphysics? William James was horrified by such “nothing buttery” and the way it substituted bare concepts for rich phenomena.A.N. Whitehead famously – or perhaps not famously enough – described the problem as the “fallacy of misplaced concreteness”.William Blake is another critic. “General Knowledge is Remote Knowledge. But General Forms have their vitality in Particulars. It is in Particulars that Wisdom consists & Happiness too.”We should care about what Blake called “single vision and Newton's sleep”. The antidote is to reestablish a relationship with presence. Poetry and imagery evoke the lived moment of experiencing and the fluid dynamics of that perception. Regain contact with that, regain contact with life.This is the promise of Blake and others.For more on Mark's book, Awake!, and more of his work see - www.markvernon.com

In this interview I am once again joined by Dr Ben Joffe, anthropologist, occultist, and scholar practitioner of Tibetan Buddhism. Dr Joffe leads a deep dive into the topic of divination, explores its underlying mechanisms and practical methods, and compares different cultural understandings of the practice. Dr Joffe details his understanding of the tarot as a scholar and reader, shares his advice for those who wish to learn the system, and reveals how to use tarot for information gathering, sorcery, and magickal workings. Dr Joffe also reflects on his own journey as a tarot reader, addresses criticisms that tarot and other psychic methods are exploitative, and considers the uneasy relationship between divination and licensed counselling. … Video version: https://www.guruviking.com/podcast/ep320-divination-tarot-dr-ben-joffe Also available on Youtube, iTunes, & Spotify – search ‘Guru Viking Podcast'. … Topics include: 00:00 - Intro 02:12 - What is divination? 06:08 - Synchronicity and randomness 09:37 - Dependent origination 14:34 - Ben's extensive study of divination 22:13 - Mechanistic vs intuitive 29:17 - Scrying and establishing parameters 34:56 - Childhood divination 39:59 - What should divination mean for the client? 41:50 - Addiction to divination 43:50 - Cold reading and choosing a question 48:45 - Ben's recounts his own history as a diviner 01:20:43 - Structure of the tarot 01:27:16 - How to read tarot 01:48:38 - Tarot reading mistakes 01:53:46 - Tibetan butter lamp divination 01:57:11 - Collaboration vs cold reading 02:02:10 - Studying the history of tarot 02:06:58 - 6 reasons to engage with tarot 02:09:22 - Critique of modern, inclusive decks 02:12:43 - Bad omens and gatekeeping 02:20:17 - Is tarot exploitative pseudo-counselling? 02:47:23 - Why not just become a counsellor? 02:54:19 - Is tarot over-psychologised? 02:55:25 - Ben reflects on his recurring clients 03:01:11 - The power of the right question 03:07:39 - Shaman and tarot reader as therapy-adjacent 03:13:18 - Does clairvoyance actually have value? 03:16:16 - Caution about taking life advice from Buddhist lamas 03:21:44 - Wild West of Tiktok diviners 03:22:49 - Anti-divination laws 03:29:14 - Tibetan and Buddhist divination 
… Previous episodes with Dr Ben Joffe: - https://www.guruviking.com/search?q=joffe To find out more about Dr Ben Joffe, visit: - https://perfumedskull.com/ - http://www.skypressbooks.com/ … For more interviews, videos, and more visit: - https://www.guruviking.com Music ‘Deva Dasi' by Steve James

Get My Book On Amazon: https://a.co/d/avbaV48Download The Peptide Cheat Sheet: https://peptidecheatsheet.carrd.co/Download The Bioregulator Cheat Sheet: https://bioregulatorcheatsheet.carrd.co/

Dr. Emile Daoud, Deputy Editor of JACC Clinical Electrophysiology discusses mechanistic insights into reduced arrhythmia prevalence in female endurance athletes.

In this episode, Jesus Berdeja, MD; Amrita Krishnan, MD, FACP; and Sagar Lonial, MD, FACP, discuss key topics with CELMoD therapy for multiple myeloma, including: Mechanistic differences between CELMoDs and IMiDsEmerging data with CELMoDs and their potential therapeutic roles across the disease continuum of multiple myelomaThe clinical implications of MRD negativity as a surrogate marker of long-term outcomes in clinical trials in multiple myelomaPresenters:Jesus Berdeja, MDDirector of Myeloma ResearchGreco-Hainsworth Centers for ResearchTennessee OncologyNashville, TennesseeAmrita Krishnan, MD, FACPDirector, Judy and Bernard Briskin Center for MyelomaExecutive Director of HematologyCity of Hope Orange CountyProfessor of Hematology/HCTCity of Hope Cancer CenterIrvine, CaliforniaSagar Lonial, MD, FACPChair and ProfessorDepartment of Hematology and Medical OncologyAnne and Bernard Gray Family Chair in CancerChief Medical OfficerWinship Cancer InstituteEmory UniversityAtlanta, GeorgiaContent based on an online CME program supported by an independent educational grant from Bristol Myers Squibb.Link to full program: https://bit.ly/3IwbslQ

Eric Ho is building Goodfire to solve one of AI's most critical challenges: understanding what's actually happening inside neural networks. His team is developing techniques to understand, audit and edit neural networks at the feature level. Eric discusses breakthrough results in resolving superposition through sparse autoencoders, successful model editing demonstrations and real-world applications in genomics with Arc Institute's DNA foundation models. He argues that interpretability will be critical as AI systems become more powerful and take on mission-critical roles in society. Hosted by Sonya Huang and Roelof Botha, Sequoia Capital Mentioned in this episode: Mech interp: Mechanistic interpretability, list of important papers here Phineas Gage: 19th century railway engineer who lost most of his brain's left frontal lobe in an accident. Became a famous case study in neuroscience. Human Genome Project: Effort from 1990-2003 to generate the first sequence of the human genome which accelerated the study of human biology Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs Zoom In: An Introduction to Circuits: First important mechanistic interpretability paper from OpenAI in 2020 Superposition: Concept from physics applied to interpretability that allows neural networks to simulate larger networks (e.g. more concepts than neurons) Apollo Research: AI safety company that designs AI model evaluations and conducts interpretability research Towards Monosemanticity: Decomposing Language Models With Dictionary Learning. 2023 Anthropic paper that uses a sparse autoencoder to extract interpretable features; followed by Scaling Monosemanticity Under the Hood of a Reasoning Model: 2025 Goodfire paper that interprets DeepSeek's reasoning model R1 Auto-interpretability: The ability to use LLMs to automatically write explanations for the behavior of neurons in LLMs Interpreting Evo 2: Arc Institute's Next-Generation Genomic Foundation Model. (see episode with Arc co-founder Patrick Hsu) Paint with Ember: Canvas interface from Goodfire that lets you steer an LLM's visual output  in real time (paper here) Model diffing: Interpreting how a model differs from checkpoint to checkpoint during finetuning Feature steering: The ability to change the style of LLM output by up or down weighting features (e.g. talking like a pirate vs factual information about the Andromeda Galaxy) Weight based interpretability: Method for directly decomposing neural network parameters into mechanistic components, instead of using features The Urgency of Interpretability: Essay by Anthropic founder Dario Amodei On the Biology of a Large Language Model: Goodfire collaboration with Anthropic

As part of the June issue, the European Respiratory Journal presents the latest in its series of podcasts. Deputy Chief Editor Don Sin interviews Associate Editor Bruno Crestani about a series of articles published in the June issue of the ERJ on mechanistic studies in interstitial lung disease: building translational bridges in IPF research.

Dr. Emile Daoud, Deputy Editor of JACC Clinical Electrophysiology discusses mechanistic insights into reduced arrhythmia prevalence in female endurance athletes.

In this episode, Daniel Balsam and Tom McGrath, at Goodfire, discuss the future of mechanistic interpretability in AI models. They explore the fundamental inputs like models, compute, and algorithms, and emphasize the importance of a rich empirical approach to understanding how models work. Balsam and McGrath provide insights into ongoing projects and breakthroughs, particularly in scientific domains and creative applications, as they aim to push the frontiers of AI interpretability. They also discuss the company's recent funding and their goal to advance interpretability as a critical area in AI research. SPONSORS: Box Report: AI is delivering truly measurable productivity — strategic companies are already turning a 37% productivity edge. Discover how in Box's new 2025 State of AI in the Enterprise Report — read the full report here: https://bit.ly/43uVP52 Oracle Cloud Infrastructure (OCI): Oracle Cloud Infrastructure offers next-generation cloud solutions that cut costs and boost performance. With OCI, you can run AI projects and applications faster and more securely for less. New U.S. customers can save 50% on compute, 70% on storage, and 80% on networking by switching to OCI before May 31, 2024. See if you qualify at https://oracle.com/cognitive ElevenLabs: ElevenLabs gives your app a natural voice. Pick from 5,000+ voices in 31 languages, or clone your own, and launch lifelike agents for support, scheduling, learning, and games. Full server and client SDKs, dynamic tools, and monitoring keep you in control. Start free at https://elevenlabs.io/cognitive-revolution NetSuite: Over 41,000 businesses trust NetSuite by Oracle, the #1 cloud ERP, to future-proof their operations. With a unified platform for accounting, financial management, inventory, and HR, NetSuite provides real-time insights and forecasting to help you make quick, informed decisions. Whether you're earning millions or hundreds of millions, NetSuite empowers you to tackle challenges and seize opportunities. Download the free CFO's guide to AI and machine learning at https://netsuite.com/cognitive Shopify: Shopify powers millions of businesses worldwide, handling 10% of U.S. e-commerce. With hundreds of templates, AI tools for product descriptions, and seamless marketing campaign creation, it's like having a design studio and marketing team in one. Start your $1/month trial today at https://shopify.com/cognitive PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

Lancet 1999;353:2001-07Background: Beta-blockers directly reduce cardiac contractility and myocardial oxygen demand. For decades, they were avoided in patients with acute and chronic heart failure over concerns they would facilitate decompensation of the condition. The therapeutic cornerstones of treatment, prior to the modern era of clinical trials, focused on managing symptoms and quality of life with diuretics and inotropic agents like digoxin; however, new paradigms were arising that focused on addressing neurohormonal mechanisms of chronic disease that were over-activated in the failing heart. The first major success came with inhibition of the renin angiotensin aldosterone system with angiotensin converting enzyme inhibitors whose effect on mortality for patients with mild and severe forms of chronic heart failure were demonstrated in the V-HEFT II, CONSENSUS, and SOLVD trials. Additional benefits were demonstrated with the mineralocorticoid receptor antagonist spironolactone in the RALES trial. These drug classes primarily work by reducing afterload and volume retention. Appreciating why they work for improving cardiac performance and managing symptoms in heart failure patients is straightforward when we consider the major factors that effect cardiac stroke volume - preload, afterload and contractility; however, it is also noteworthy the effects these agents have on sudden death. How beta-blockade benefits the failing heart is less obvious (outside prevention of sudden death). Mechanistic studies in patients with chronic heart failure have consistently shown that when beta blockers are used for more than 1 month, left ventricular function improves. Beta blocker therapy appears to restore the density of beta-adrenergic receptors after they have been downregulated by the chronic overactivity of the sympathetic nervous system. The first major placebo-controlled RCT to demonstrate a mortality benefit used the non-selective beta blocker carvedilol. The trial was small and not originally designed to test mortality and was stopped early without clearly predefined stopping rules. Furthermore, 8% of total patients selected for participation in the trial were excluded prior to randomization after a 2 week, open-label run-in phase with the study drug, which saw 2% of all patients experience worsening heart failure or death representing 24 patients (the difference in total deaths between groups was 9 when the trial was stopped). The Metoprolol CR/XL Randomised Intervention Trial in Congestive Heart Failure (MERIT-HF) was the first large scale trial designed to test the hypothesis that beta-blockade with metoprolol controlled/extended release (CR/XL) added to optimum medical therapy reduces mortality in patients with chronic systolic heart failure.Patients: Patients were recruited from 313 sites in 13 European countries and the United States. Eligible patients were men and women between the age of 40 to 80 years with symptomatic heart failure (NYHA class II-IV) for >/= 3 months before randomization. They had to be on a diuretic and ACE inhibitor for at least 2 weeks. Other drugs, including digoxin, could also be used. Patients also had to have an EF of /=68 beats per minute.Patients were excluded if: they had an MI or unstable angina within 28 days; had an indication or contraindication for treatment with beta-blocker; beta blockade within 6 weeks; heart failure due to systemic disease (i.e., amyloidosis) or alcohol abuse; scheduled or performed cardiac transplant; an ICD; procedures such as CABG or PCI planned or performed in the past 4 months; 2nd or 3rd degree AV block unless a pacemaker was present; unstable or decompensated heart failure defined by pulmonary edema or hypoperfusion or supine systolic BP 25% deviation of the number of observed versus expected consumed placebo tablets during the run-in period.Baseline characteristics: The mean age of patients was 64 years and approximately 78% were male. Slightly more than 30% of patients were above the age of 70. The average EF was 28%. The average SBP was 130 mmHg and heart rate was 82 bpm. Most patients had mild to moderate heart failure, with 41% in NYHA Class II, 56% in Class III, and only 3% in Class IV. Ischemic cardiomyopathy accounted for 65% of cases and nonischemic causes accounted for 35%. Most patients were on an ACE inhibitor or ARB (95%) and diuretic (90%). Digoxin was used in 63%. Trial procedures: Prior to randomization, the study was preceded by a single-blind, 2-week placebo run-in period. Patients meeting eligibility were then randomized to placebo or metoprolol CR/XL. The starting dose of placebo or metoprolol CR/XL was 12.5 mg daily for patients in NYHA class III or IV and 25 mg daily for patients in NYHA class II. The dose was doubled every 2 weeks until the target dose of 200 mg daily was reached. Patients were followed every 3 months.Endpoints: The primary outcome was all-cause mortality. It was estimated that 3,200 patients would need to be followed for 2.4 years to detect a 30% relative reduction in mortality based on annual mortality rate of 9.4% in the placebo group. This would achieve at least 80% power with a 2-sided alpha of 0.04. Patients were recruited faster then planned and so the final sample size of 3,991 patients increased the power of the study.The study was monitored by an independent safety committee and predefined stopping rules for efficacy were based on all-cause mortality, done when 25%, 50%, and 75% of expected deaths had occurred. Results: The trial was stopped early after the 2nd preplanned interim analysis when 50% of expected deaths had occurred. The mean duration of follow-up at the time of stopping was 1 year. The mean daily dose of metoprolol CR/XL was 159 mg once daily, with 87% receiving 100 mg or more and 64% receiving the target dose of 200 mg daily. In the placebo group, the corresponding values were 179 mg daily, 91% and 82%. The study drug was discontinued permanently in 14% of patients in the metoprolol group and 15% in the placebo group. Six months after randomization, heart rate decreased by 14 bpm in the metoprolol group compared to only 3 bpm in the placebo group. Systolic blood pressure decreased less in the metoprolol group (-2.1 vs 3.5 mmHg).Compared to placebo, metoprolol significantly reduced all-cause mortality (7.3% vs 10.8%; RR 0.66; 95% CI 0.53—0.81). Cardiovascular mortality accounted for 91% of all deaths; with sudden death accounting for 58% and death from worsening heart failure accounting for 24% of all deaths. All 3 of these causes of death were significantly reduced by metoprolol. The relative and absolute effects on death were greatest for patients with NYHA class III heart failure.Conclusions: In this trial of stable patients with mild to moderate chronic systolic heart failure, who were optimized on an ACEi or ARB and diuretic, metoprolol CR/XL significantly reduced all-cause mortality. Approximately 30 patients would need to be treated with metoprolol compared to placebo for 1 year to prevent 1 death. This trial represents a significant win for beta blockade in patients with chronic systolic heart failure. While the NNT in this trial is slightly higher than in SOLVD, it is important to appreciate that follow-up time in SOLVD was more than 3x longer. Limitations to external validity in this trial include the run-in period and stringent inclusion and exclusion criteria. Our enthusiasm is also tempered by early stopping, which has been found to be associated with false positive or exaggerated results but this concern is mitigated to some extent in this trial because the rules for early stopping were clearly defined in the protocol.Cardiology Trial's Substack is a reader-supported publication. To receive new posts and support our work, consider becoming a free or paid subscriber. Get full access to Cardiology Trial's Substack at cardiologytrials.substack.com/subscribe

Thomas Dörner, MD - B-Cell Depletion for Autoimmune Rheumatic Diseases: Expert Perspectives of This Mechanistic Therapeutic Approach in Clinical Context

Christopher DaPrato, PT, DPT, SCS, joins host Phil Plisky to explore the evidence behind cupping and its role in rehab and performance. They break down the latest research on fascial mechanics, the benefits of movement-based cupping, and practical strategies for clinical application. Tune in to challenge common misconceptions and learn how to integrate cupping effectively into patient care.Learning ObjectivesAnalyze the evidence around cupping and its use in rehab and performance settingsApply evidence-based, practical strategies to actionably address mobility deficits, stability deficits, or motor control deficitsSolve patient case scenarios involving mobility deficits with loading strategies, postural awareness during movement education, and muscle synergies with overuse and dominant muscle hyperexcitabilityTimestamps(00:00:00) Welcome(00:00:49) Introduction to cupping and myofascial decompression(00:03:30) The importance of active modality in cupping(00:07:48) Research landscape: evidence and methodology in cupping(00:10:50) Challenges in cupping research and study design(00:15:53) Mechanistic studies and depth of cupping effects(00:20:05) Future directions and clinical implications of cupping(00:24:43) The power of manual therapy(00:28:45) Clinical reasoning in cupping therapy(00:36:02) Understanding the neurophysiological effects(00:37:28) Case studies in cupping application(00:42:15) Cupping for recovery: myths and realities(00:44:12) Key takeaways for practitionersRehab and Performance Lab is brought to you by Medbridge. If you'd like to earn continuing education credit for listening to this episode and access bonus takeaway handouts, log in to your Medbridge account and navigate to the course where you'll find accreditation details. If applicable, complete the post-course assessment and survey to be eligible for credit. The takeaway handout on Medbridge gives you the key points mentioned in this episode, along with additional resources you can implement into your practice right away.To hear more episodes of Rehab and Performance Lab, visit https://www.medbridge.com/rehab-and-performance-labIf you'd like to subscribe to Medbridge, visit https://www.medbridge.com/pricing/

Are there sacred objects? Do you have a mechanistic faith? Do you treat God like a vending machine? In today's episode, Jensen shares how 1 Samuel 4:1-11 encourages us to fear God and enjoy his presence. If you're listening on Spotify, comment below one takeaway from today's episode! Read the Bible with us in 2025! This year, we're exploring the Historical Books—Joshua, Judges, 1 & 2 Samuel, and 1 & 2 Kings. Download your reading plan now. Your support makes TMBT possible. Ten Minute Bible Talks is a crowd-funded project. Join the TMBTeam to reach more people with the Bible. Give now. Like this content? Make sure to leave us a rating and share it so that others can find it, too. Use #asktmbt to connect with us, ask questions, and suggest topics. We'd love to hear from you! To learn more, visit our website and follow us on Instagram, Facebook, and Twitter @TenMinuteBibleTalks. Don't forget to subscribe to the TMBT Newsletter here. Passages: 1 Samuel 4:1-11

In this episode, Martin and Nimrod dive into the cultural undercurrents shaping the future of health—and what chiropractors need to get before it's too late.Wearables are rising.AI is thinking faster than you can blink.Stress is peaking across the Western world.Amidst it all, chiropractic stands at a crossroads.⚡ Go left → Mechanistic, transactional, data-driven “healthcare.”⚡ Go right → Vitalistic, human-centered, performance-driven care.What side are you on?And what happens if you don't choose?They cover:The coming stratification of societyAI, nanotech, and the illusion of quick fixesWhy human touch still matters (and always will)The "ontological shock" shaking people's sense of meaningHow chiropractors can stay relevant without selling outThis is part philosophy, part strategy—and all signal, no noise.If you're a chiropractor wondering what the future holds, press play. It's not just about where healthcare is going. It's about where you are headed.Learn more about Daily Visit Communication 2.0https://insideoutpractices.thinkific.com/courses/daily-visitCheck out the Retention Recipe https://insideoutpractices.thinkific.com/courses/retention-recipe-2-0Check out Certainty 2.0 https://insideoutpractices.thinkific.com/courses/certainty-2-0Email me - martin@insideoutpractices.com

Connect with Francis: Research Gate: Francis Del Duchetto Review Paper: Can Foot Orthoses Beneft Symptomatic Runners? Mechanistic and Clinical Insights Through a Scoping Review Summary In this episode of the Adaptive Zone podcast, Matthew Boyd interviews Francis Del Duchetto, a podiatrist, about the role of foot orthoses in treating running injuries. They discuss the different types of orthoses, the importance of research in understanding their effects, and how they can help redistribute loads in injured runners. The episode also covers the clinical applications of orthoses, their preventative use, and the differences between custom and prefabricated options. Francis shares insights on the long-term use of orthoses and the importance of a comprehensive treatment approach for runners. Takeaways Foot orthoses are medical devices used to treat lower limb injuries. There are different types of foot orthoses: simple, prefabricated, and custom-made. Research on foot orthoses for injured runners is limited but growing. Foot orthoses can help redistribute loads from injured to non-injured structures. They are most effective when used as part of a multimodal treatment approach. Custom orthoses may not always be necessary; prefabricated options can be effective. Preventative use of orthoses is debated; other factors may be more important. Long-term use of orthoses should be monitored and adjusted as needed. The choice of running shoes can affect the effectiveness of orthoses. Research and clinical practice should continue to evolve in this area. Chapters 00:00 Introduction to Foot Orthoses 02:45 Understanding Foot Orthoses and Their Types 05:45 The Importance of Research on Foot Orthoses 09:12 Mechanisms of Action: How Orthoses Help Runners 11:48 Clinical Applications: Treating Common Running Injuries 15:00 Preventative Use of Orthoses: A Discussion 18:11 Long-term Use and Adaptation to Orthoses 21:11 Custom vs. Prefabricated Orthoses 24:00 Future Directions in Research and Practice Connect with Us: If you're an injured runner we can help you get back to running pain-free. Book a free call with us: https://matthewboydphysio.com/booking/ Running Fundamentals Course: https://matthewboydphysio.com/running-fundamentals-course/ Instagram: https://www.instagram.com/matthewboydphysio/

Duchetto (2024) Can Foot Orthoses Benefit Symptomatic Runners? Mechanistic and Clinical Insights Through a Scoping Review If you're an injured runner we can help you get back to running pain-free. Click the link to book a free call with us https://matthewboydphysio.com/booking/ Running Fundamentals Course https://matthewboydphysio.com/running-fundamentals-course/ Instagram https://www.instagram.com/matthewboydphysio/ Summary This episode explores the role of insoles and orthotics in running, discussing their purpose, historical context, and current research findings. Matthew Boyd delves into how orthotics are used to improve alignment and prevent injuries, the shift in professional attitudes towards their use, and the latest evidence regarding their effectiveness for various running-related injuries. He emphasizes that while orthotics can be beneficial for specific conditions, their prophylactic use is not supported by current research. Takeaways Insoles and orthotics are used interchangeably in running. Historically, orthotics were prescribed prophylactically for flat feet. Current research questions the effectiveness of prophylactic orthotic use. Orthotics can help reduce knee and shin pain in runners. Custom orthotics may not be significantly more effective than off-the-shelf options. Orthotics should be part of a comprehensive rehabilitation strategy. Runners can wean off orthotics if they no longer need them. The effectiveness of orthotics varies by individual and condition. Orthotics are not a silver bullet for injury prevention. Health professionals' attitudes towards orthotics have evolved over time.

Neel Nanda, a senior research scientist at Google DeepMind, leads their mechanistic interpretability team. In this extensive interview, he discusses his work trying to understand how neural networks function internally. At just 25 years old, Nanda has quickly become a prominent voice in AI research after completing his pure mathematics degree at Cambridge in 2020. Nanda reckons that machine learning is unique because we create neural networks that can perform impressive tasks (like complex reasoning and software engineering) without understanding how they work internally. He compares this to having computer programs that can do things no human programmer knows how to write. His work focuses on "mechanistic interpretability" - attempting to uncover and understand the internal structures and algorithms that emerge within these networks. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on ARC and AGI, they just acquired MindsAI - the current winners of the ARC challenge. Are you interested in working on ARC, or getting involved in their events? Goto https://tufalabs.ai/ *** SHOWNOTES, TRANSCRIPT, ALL REFERENCES (DONT MISS!): https://www.dropbox.com/scl/fi/36dvtfl3v3p56hbi30im7/NeelShow.pdf?rlkey=pq8t7lyv2z60knlifyy17jdtx&st=kiutudhc&dl=0 We riff on: * How neural networks develop meaningful internal representations beyond simple pattern matching * The effectiveness of chain-of-thought prompting and why it improves model performance * The importance of hands-on coding over extensive paper reading for new researchers * His journey from Cambridge to working with Chris Olah at Anthropic and eventually Google DeepMind * The role of mechanistic interpretability in AI safety NEEL NANDA: https://www.neelnanda.io/ https://scholar.google.com/citations?user=GLnX3MkAAAAJ&hl=en https://x.com/NeelNanda5 Interviewer - Tim Scarfe TOC: 1. Part 1: Introduction [00:00:00] 1.1 Introduction and Core Concepts Overview 2. Part 2: Outside Interview [00:06:45] 2.1 Mechanistic Interpretability Foundations 3. Part 3: Main Interview [00:32:52] 3.1 Mechanistic Interpretability 4. Neural Architecture and Circuits [01:00:31] 4.1 Biological Evolution Parallels [01:04:03] 4.2 Universal Circuit Patterns and Induction Heads [01:11:07] 4.3 Entity Detection and Knowledge Boundaries [01:14:26] 4.4 Mechanistic Interpretability and Activation Patching 5. Model Behavior Analysis [01:30:00] 5.1 Golden Gate Claude Experiment and Feature Amplification [01:33:27] 5.2 Model Personas and RLHF Behavior Modification [01:36:28] 5.3 Steering Vectors and Linear Representations [01:40:00] 5.4 Hallucinations and Model Uncertainty 6. Sparse Autoencoder Architecture [01:44:54] 6.1 Architecture and Mathematical Foundations [02:22:03] 6.2 Core Challenges and Solutions [02:32:04] 6.3 Advanced Activation Functions and Top-k Implementations [02:34:41] 6.4 Research Applications in Transformer Circuit Analysis 7. Feature Learning and Scaling [02:48:02] 7.1 Autoencoder Feature Learning and Width Parameters [03:02:46] 7.2 Scaling Laws and Training Stability [03:11:00] 7.3 Feature Identification and Bias Correction [03:19:52] 7.4 Training Dynamics Analysis Methods 8. Engineering Implementation [03:23:48] 8.1 Scale and Infrastructure Requirements [03:25:20] 8.2 Computational Requirements and Storage [03:35:22] 8.3 Chain-of-Thought Reasoning Implementation [03:37:15] 8.4 Latent Structure Inference in Language Models

The menstrual cycle is known to affect things like mood and changes in pain. But there can also be symptoms that have a serious impact on a person's function, ability to work, ability to maintain friendships and romantic relationships. This is a rare condition known as premenstrual dysphoric disorder. But it's not the only psychiatric condition that can worsen with changes in the menstrual cycle. For instance, nearly 60 percent of menstruating patients with depression can experience cyclical worsening similar to PMDD. Conditions such as these are generally referred to as menstrual cycle affective change. Menstrual cycle affective change is more common in those with chronic psychiatric disorders. The authors are interested in reframing the conversation around menstrual cycle affective change to be something that is a more fundamental process that we can study across disorders, across categories, and identify biomarkers that might help us predict who's going to have those symptoms in more complex ways than we might be able to do with categories. This paper represents how can we take this dimensional way of thinking about menstrual cycle affective change and talk about the specific ways that we can be precise in looking at the time the time characteristics of that, the specific mechanisms, et cetera. Tory Eisenlohr-Moul is an associate professor of psychiatry at the University of Illinois at Chicago in the department of psychiatry, and she's one of the authors. Jordan Barone is an MD/PhD candidate at the University of Illinois at Chicago, and she's another author. Hosted on Acast. See acast.com/privacy for more information.

In this episode, Dr. Valentin Fuster discusses groundbreaking research on SGLT2 inhibitors and their impact on heart failure, highlighting the validation of mechanistic proteomic signatures from a major clinical trial. The study reveals how empagliflozin influences over 2,000 proteins, promoting autophagy, enhancing mitochondrial health, and normalizing kidney function, offering new insights into therapeutic strategies for heart failure management.

In our latest episode, Dr. Jeff Saucerman (University of Virginia) interviews authors Dr. Naomi Chesler (University of California, Irvine) and Dr. Mitchel Colebank (University of South Carolina) about their new Guidelines in Cardiovascular Research article on incorporating mechanistic modeling into the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. The authors' goal is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. Colebank et al. outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data. Would you like to understand how to apply a cone of uncertainty to your experimental data? Listen now to find out more.   Mitchel J. Colebank, Pim A. Oomen, Colleen M. Witzenburg, Anna Grosberg, Daniel A. Beard, Dirk Husmeier, Mette S. Olufsen, and Naomi C. Chesler Guidelines for mechanistic modeling and analysis in cardiovascular research Am J Physiol Heart Circ Physiol, published August 6, 2024. DOI: 10.1152/ajpheart.00253.2024

Stan Deetz - Transforming Organizational Culture: Insights and Strategies for Modern Success In this comprehensive episode, we explore pivotal topics in organizational culture and change management with experts like Stanley Deetz. From understanding the role of communication in periods of transition and mergers to building resilience and effective leadership, our discussions cover a wide range of issues critical to the modern workplace. We delve into the historical shifts in corporate culture, the influence of Japanese practices on American companies, and the evolving mindsets driven by generational changes and Artificial Intelligence. Learn about the power of systems thinking and organic metaphors in fostering innovation and teamwork. Discover essential strategies for managing change, overcoming fear, and leveraging diversity for organizational success. Join us to gain profound insights and practical tools for navigating and transforming organizational culture. 00:00 Introduction to Organizational Culture and Change 01:07 Origins and Development of the Book 02:24 Understanding Organizational Culture 02:50 Seton Hall and Online Education 04:59 Navigating Organizational Change 05:48 Managing Hearts, Minds, and Souls 10:47 The Role of Conflict in Innovation 18:10 Historical Shifts in Corporate Culture 26:15 Internal Models vs. External Realities 26:51 Generational Shifts in Organizational Metaphors 29:06 Cultural Fragmentation and Countercultures 31:00 Mechanistic vs. Organic Metaphors 32:33 Psychologizing Organizational Change 39:38 Systemic Thinking in Organizations 44:05 Challenges in Team Dynamics 46:43 Understanding Assumptions in Change Management 51:21 Conclusion and Contact Information Find the episode we mentioned with George Lakoffat 32.25 with here: Stan Deetz, Stanley Deetz, Organizational culture, communication, Aidan McCullen, cultural change, leadership, organizational transitions, mergers, technological innovations, globalization, Seton Hall University, ethical issues, member involvement, executive master's program, organizational development,  change processes, corporate culture, workplace dynamics

Episode 140I spoke with Professor Jacob Andreas about:* Language and the world* World models* How he's developed as a scientistEnjoy!Jacob is an associate professor at MIT in the Department of Electrical Engineering and Computer Science as well as the Computer Science and Artificial Intelligence Laboratory. His research aims to understand the computational foundations of language learning, and to build intelligent systems that can learn from human guidance. Jacob earned his Ph.D. from UC Berkeley, his M.Phil. from Cambridge (where he studied as a Churchill scholar) and his B.S. from Columbia. He has received a Sloan fellowship, an NSF CAREER award, MIT's Junior Bose and Kolokotrones teaching awards, and paper awards at ACL, ICML and NAACL.Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (00:40) Jacob's relationship with grounding fundamentalism* (05:21) Jacob's reaction to LLMs* (11:24) Grounding language — is there a philosophical problem?* (15:54) Grounding and language modeling* (24:00) Analogies between humans and LMs* (30:46) Grounding language with points and paths in continuous spaces* (32:00) Neo-Davidsonian formal semantics* (36:27) Evolving assumptions about structure prediction* (40:14) Segmentation and event structure* (42:33) How much do word embeddings encode about syntax?* (43:10) Jacob's process for studying scientific questions* (45:38) Experiments and hypotheses* (53:01) Calibrating assumptions as a researcher* (54:08) Flexibility in research* (56:09) Measuring Compositionality in Representation Learning* (56:50) Developing an independent research agenda and developing a lab culture* (1:03:25) Language Models as Agent Models* (1:04:30) Background* (1:08:33) Toy experiments and interpretability research* (1:13:30) Developing effective toy experiments* (1:15:25) Language Models, World Models, and Human Model-Building* (1:15:56) OthelloGPT's bag of heuristics and multiple “world models”* (1:21:32) What is a world model?* (1:23:45) The Big Question — from meaning to world models* (1:28:21) From “meaning” to precise questions about LMs* (1:32:01) Mechanistic interpretability and reading tea leaves* (1:35:38) Language and the world* (1:38:07) Towards better language models* (1:43:45) Model editing* (1:45:50) On academia's role in NLP research* (1:49:13) On good science* (1:52:36) OutroLinks:* Jacob's homepage and Twitter* Language Models, World Models, and Human Model-Building* Papers* Semantic Parsing as Machine Translation (2013)* Grounding language with points and paths in continuous spaces (2014)* How much do word embeddings encode about syntax? (2014)* Translating neuralese (2017)* Analogs of linguistic structure in deep representations (2017)* Learning with latent language (2018)* Learning from Language (2018)* Measuring Compositionality in Representation Learning (2019)* Experience grounds language (2020)* Language Models as Agent Models (2022) Get full access to The Gradient at thegradientpub.substack.com/subscribe

Dr. Ruth Kastner is a historian of Physics and philosopher of Science who is preoccupied with rational interpretations of quantum mechanics. She serves as the third pole of the transactional quantum mechanics big tent where she, alongside John Cramer and Carver Mead, argue that the apparent mysteries of quantum mechanics can be rationalized by modeling everything from light to gravity as an exchange between atoms. Our conversation explores how she became enamored with this alternative approach to physics, the question of how accurate our models really are when it comes to the subatomic world, why the word "electron" is hopelessly confused, and why moving backwards in space and time aren't as different as they might seem at first glance. Sign up for our Patreon and get episodes early + join our weekly Patron Chat https://bit.ly/3lcAasB AND rock some Demystify Gear to spread the word: https://demystifysci.myspreadshop.com/ OR do your Amazon shopping through this link: https://amzn.to/4g2cPVV (00:00:00) Go! (00:05:21) Evolution of Transactional Interpretations in Quantum Mechanics (00:07:12) Philosophical Motivations Behind Transactional Interpretations (00:23:26) Mechanistic vs. holistic views of nature (00:27:09) Quantum mechanics and mechanistic explanations (00:32:24) Wave functions and physical reality (00:46:14) Quantum Measurement and Macroscopic Objects (00:49:00) Electron Energy Eigenstates and Stability (00:55:44) Wave-Particle Duality and Transactional Interpretation (00:59:51) Quantum Measurement Problem and Direct Action Theory (01:00:46) Quantum Field Dynamics and Mutual Communication (01:08:30) Future Influence on Present Actions (01:11:03) Actualized vs. Possible Futures (01:14:19) Quantum Indeterminacy and Hidden Variables (01:21:56) Free Will and Determinism (01:31:15) Life as entropy and disorder (01:32:24) Consciousness and non-human life forms (01:39:21) Complexity in living systems (01:45:20) Challenges in quantum theory interpretations (01:53:30) Ontology and Space-Time Concept (01:55:07) Relativistic Quantum Direct Action Theory (02:00:47) Charge Interpretation and Field Source Dynamics #sciencepodcast, #longformpodcast, #QuantumMechanics #TransactionalInterpretation #PhilosophyOfScience #MechanisticVsHolistic #WaveFunctionReality #QuantumMeasurement #MacroscopicObjects #ElectronEnergy #WaveParticleDuality #DirectActionTheory #QuantumFieldDynamics #FutureInfluence #PossibleFutures #QuantumIndeterminacy #HiddenVariables #FreeWillVsDeterminism #LifeAndEntropy #ConsciousnessStudies #NonHumanLife #ComplexSystems #QuantumTheoryChallenges #Ontology #SpaceTime #RelativisticQuantum #ChargeDynamics #FieldSourceTheory #QuantumPhysics #QuantumPhilosophy #Determinism #QuantumFieldTheory #InterpretationOfQuantumTheory #WaveFunction Check our short-films channel, @DemystifySci: https://www.youtube.com/c/DemystifyingScience AND our material science investigations of atomics, @MaterialAtomics https://www.youtube.com/@MaterialAtomics Join our mailing list https://bit.ly/3v3kz2S PODCAST INFO: Anastasia completed her PhD studying bioelectricity at Columbia University. When not talking to brilliant people or making movies, she spends her time painting, reading, and guiding backcountry excursions. Shilo also did his PhD at Columbia studying the elastic properties of molecular water. When he's not in the film studio, he's exploring sound in music. They are both freelance professors at various universities. - Blog: http://DemystifySci.com/blog - RSS: https://anchor.fm/s/2be66934/podcast/rss - Donate: https://bit.ly/3wkPqaD - Swag: https://bit.ly/2PXdC2y SOCIAL: - Discord: https://discord.gg/MJzKT8CQub - Facebook: https://www.facebook.com/groups/DemystifySci - Instagram: https://www.instagram.com/DemystifySci/ - Twitter: https://twitter.com/DemystifySci MUSIC: -Shilo Delay: https://g.co/kgs/oty671

This is the fifth episode of our new "Program on JAK Inhibitors for Atopic Dermatitis" series, hosted by expert dermatologist Dr. Christopher Bunick, MD, PhD. This episode, "Choosing the Right Path: JAK Inhibitors or Biologics for Atopic Dermatitis?", featuring expert dermatologist Dr. Naiem Issa, MD, PhD, discusses the following topics: Mechanistic differences between JAKi and biologics for treatment of AD Clinical data supporting the safety of JAKi in head-to-head trials compared to biologics Pearls for prescribing JAKi to AD patients Strategies for choosing JAKi vs biologics based on safety and patient factors

Support the show to get full episodes and join the Discord community. Àlex Gómez-Marín heads The Behavior of Organisms Laboratory at the Institute of Neuroscience in Alicante, Spain. He's one of those theoretical physicist turned neuroscientist, and he has studied a wide range of topics over his career. Most recently, he has become interested in what he calls the "edges of consciousness", which encompasses the many trying to explain what may be happening when we have experiences outside our normal everyday experiences. For example, when we are under the influence of hallucinogens, when have near-death experiences (as Alex has), paranormal experiences, and so on. So we discuss what led up to his interests in these edges of consciousness, how he now thinks about consciousness and doing science in general, how important it is to make room for all possible explanations of phenomena, and to leave our metaphysics open all the while. Alex's website: The Behavior of Organisms Laboratory. Twitter: @behaviOrganisms. Previous episodes: BI 168 Frauke Sandig and Eric Black w Alex Gomez-Marin: AWARE: Glimpses of Consciousness. BI 136 Michel Bitbol and Alex Gomez-Marin: Phenomenology. Related: The Consciousness of Neuroscience. Seeing the consciousness forest for the trees. The stairway to transhumanist heaven. 0:00 - Intro 4:13 - Evolving viewpoints 10:05 - Near-death experience 18:30 - Mechanistic neuroscience vs. the rest 22:46 - Are you doing science? 33:46 - Where is my. mind? 44:55 - Productive vs. permissive brain 59:30 - Panpsychism 1:07:58 - Materialism 1:10:38 - How to choose what to do 1:16:54 - Fruit flies 1:19:52 - AI and the Singularity

Nathan explores the cutting-edge field of mechanistic interpretability with Dan Balsam and Tom McGrath, co-founders of Goodfire. In this episode of The Cognitive Revolution, we delve into the science of understanding AI models' inner workings, recent breakthroughs, and the potential impact on AI safety and control. Join us for an insightful discussion on sparse autoencoders, polysemanticity, and the future of interpretable AI. Papers Very accessible article on types of representations: Local vs Distributed Coding Theoretical understanding of how models might pack concepts into their representations: Toy Models of Superposition How structure in the world gives rise to structure in the latent space: The Geometry of Categorical and Hierarchical Concepts in Large Language Models Using sparse autoencoders to pull apart language model representations: Sparse Autoencoders / Towards Monosemanticity / Scaling Monosemanticity Finding & teaching concepts in superhuman systems: Acquisition of Chess Knowledge in AlphaZero / Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero Connecting microscopic learning to macroscopic phenomena: The Quantization Model of Neural Scaling Understanding at scale: Language models can explain neurons in language models Apply to join over 400 founders and execs in the Turpentine Network: https://hmplogxqz0y.typeform.com/to/JCkphVqj SPONSORS: Oracle Cloud Infrastructure (OCI) is a single platform for your infrastructure, database, application development, and AI needs. OCI has four to eight times the bandwidth of other clouds; offers one consistent price, and nobody does data better than Oracle. If you want to do more and spend less, take a free test drive of OCI at https://oracle.com/cognitive The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://bit.ly/BraveTCR Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off https://www.omneky.com/ Head to Squad to access global engineering without the headache and at a fraction of the cost: head to https://choosesquad.com/ and mention “Turpentine” to skip the waitlist. CHAPTERS: (00:00:00) About the Show (00:00:22) About the Episode (00:03:52) Introduction and Background (00:08:43) State of Interpretability Research (00:12:06) Key Insights in Interpretability (00:16:53) Polysemanticity and Model Compression (Part 1) (00:17:00) Sponsors: Oracle | Brave (00:19:04) Polysemanticity and Model Compression (Part 2) (00:22:50) Sparse Autoencoders Explained (00:27:19) Challenges in Interpretability Research (Part 1) (00:30:54) Sponsors: Omneky | Squad (00:32:41) Challenges in Interpretability Research (Part 2) (00:33:51) Goodfire's Vision and Mission (00:37:08) Interpretability and Scientific Models (00:43:48) Architecture and Interpretability Techniques (00:50:08) Quantization and Model Representation (00:54:07) Future of Interpretability Research (01:01:38) Skepticism and Challenges in Interpretability (01:07:51) Alternative Architectures and Universality (01:13:39) Goodfire's Business Model and Funding (01:18:47) Building the Team and Future Plans (01:31:03) Hiring and Getting Involved in Interpretability (01:51:28) Closing Remarks (01:51:38) Outro

Coach Ted talks about the artistry in mechanistic methods.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Efficient Dictionary Learning with Switch Sparse Autoencoders, published by Anish Mudide on July 22, 2024 on LessWrong. Produced as part of the ML Alignment & Theory Scholars Program - Summer 2024 Cohort 0. Summary To recover all the relevant features from a superintelligent language model, we will likely need to scale sparse autoencoders (SAEs) to billions of features. Using current architectures, training extremely wide SAEs across multiple layers and sublayers at various sparsity levels is computationally intractable. Conditional computation has been used to scale transformers (Fedus et al.) to trillions of parameters while retaining computational efficiency. We introduce the Switch SAE, a novel architecture that leverages conditional computation to efficiently scale SAEs to many more features. 1. Introduction The internal computations of large language models are inscrutable to humans. We can observe the inputs and the outputs, as well as every intermediate step in between, and yet, we have little to no sense of what the model is actually doing. For example, is the model inserting security vulnerabilities or backdoors into the code that it writes? Is the model lying, deceiving or seeking power? Deploying a superintelligent model into the real world without being aware of when these dangerous capabilities may arise leaves humanity vulnerable. Mechanistic interpretability (Olah et al.) aims to open the black-box of neural networks and rigorously explain the underlying computations. Early attempts to identify the behavior of individual neurons were thwarted by polysemanticity, the phenomenon in which a single neuron is activated by several unrelated features (Olah et al.). Language models must pack an extremely vast amount of information (e.g., the entire internet) within a limited capacity, encouraging the model to rely on superposition to represent many more features than there are dimensions in the model state (Elhage et al.). Sharkey et al. and Cunningham et al. propose to disentangle superimposed model representations into monosemantic, cleanly interpretable features by training unsupervised sparse autoencoders (SAEs) on intermediate language model activations. Recent work (Templeton et al., Gao et al.) has focused on scaling sparse autoencoders to frontier language models such as Claude 3 Sonnet and GPT-4. Despite scaling SAEs to 34 million features, Templeton et al. estimate that they are likely orders of magnitude short of capturing all features. Furthermore, Gao et al. train SAEs on a series of language models and find that larger models require more features to achieve the same reconstruction error. Thus, to capture all relevant features of future large, superintelligent models, we will likely need to scale SAEs to several billions of features. With current methodologies, training SAEs with billions of features at various layers, sublayers and sparsity levels is computationally infeasible. Training a sparse autoencoder generally consists of six major computations: the encoder forward pass, the encoder gradient, the decoder forward pass, the decoder gradient, the latent gradient and the pre-bias gradient. Gao et al. introduce kernels and tricks that leverage the sparsity of the TopK activation function to dramatically optimize all computations excluding the encoder forward pass, which is not (yet) sparse. After implementing these optimizations, Gao et al. attribute the majority of the compute to the dense encoder forward pass and the majority of the memory to the latent pre-activations. No work has attempted to accelerate or improve the memory efficiency of the encoder forward pass, which remains the sole dense matrix multiplication. In a standard deep learning model, every parameter is used for every input. An alternative approach is conditional computatio...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: An Extremely Opinionated Annotated List of My Favourite Mechanistic Interpretability Papers v2, published by Neel Nanda on July 7, 2024 on The AI Alignment Forum. This post represents my personal hot takes, not the opinions of my team or employer. This is a massively updated version of a similar list I made two years ago There's a lot of mechanistic interpretability papers, and more come out all the time. This can be pretty intimidating if you're new to the field! To try helping out, here's a reading list of my favourite mech interp papers: papers which I think are important to be aware of, often worth skimming, and something worth reading deeply (time permitting). I've annotated these with my key takeaways, what I like about each paper, which bits to deeply engage with vs skim, etc. I wrote a similar post 2 years ago, but a lot has changed since then, thus v2! Note that this is not trying to be a comprehensive literature review - this is my answer to "if you have limited time and want to get up to speed on the field as fast as you can, what should you do". I'm deliberately not following academic norms like necessarily citing the first paper introducing something, or all papers doing some work, and am massively biased towards recent work that is more relevant to the cutting edge. I also shamelessly recommend a bunch of my own work here, sorry! How to read this post: I've bolded the most important papers to read, which I recommend prioritising. All of the papers are annotated with my interpretation and key takeaways, and tbh I think reading that may be comparable good to skimming the paper. And there's far too many papers to read all of them deeply unless you want to make that a significant priority. I recommend reading all my summaries, noting the papers and areas that excite you, and then trying to dive deeply into those. Foundational Work A Mathematical Framework for Transformer Circuits (Nelson Elhage et al, Anthropic) - absolute classic, foundational ideas for how to think about transformers (see my blog post for what to skip). See my youtube tutorial (I hear this is best watched after reading the paper, and adds additional clarity) Deeply engage with: All the ideas in the overview section, especially: Understanding the residual stream and why it's fundamental. The notion of interpreting paths between interpretable bits (eg input tokens and output logits) where the path is a composition of matrices and how this is different from interpreting every intermediate activations And understanding attention heads: what a QK and OV matrix is, how attention heads are independent and additive and how attention and OV are semi-independent. Skip Trigrams & Skip Trigram bugs, esp understanding why these are a really easy thing to do with attention, and how the bugs are inherent to attention heads separating where to attend to (QK) and what to do once you attend somewhere (OV) Induction heads, esp why this is K-Composition (and how that's different from Q & V composition), how the circuit works mechanistically, and why this is too hard to do in a 1L model Skim or skip: Eigenvalues or tensor products. They have the worst effort per unit insight of the paper and aren't very important. Superposition Superposition is a core principle/problem in model internals. For any given activation (eg the output of MLP13), we believe that there's a massive dictionary of concepts/features the model knows of. Each feature has a corresponding vector, and model activations are a sparse linear combination of these meaningful feature vectors. Further, there are more features in the dictionary than activation dimensions, and they are thus compressed in and interfere with each other, essentially causing cascading errors. This phenomena of compression is called superposition. Toy models of superpositio...

In the world of chiropractic, the philosophy is our compass—it's the 'why' that shapes the 'what' and 'how' of our practice. But what happens when your 'why' evolves? How do you transform your approach to stay true to your new understanding? Join us in this episode as Martin sits down with Glenn Duffy, a chiropractor who has navigated this very journey. Glenn transitioned from running a successful mechanistic practice in the UK to embracing a vitalistic approach in Spain. Along the way, he's authored books that illuminate the true value of chiropractic and holistic health. Tune in to explore Glenn's inspiring shift and discover insights that could revolutionize your own practice. Check out Glenn's book https://www.amazon.com.au/Befriend-Gravity-Commonsense-Approach-Rebuild-ebook/dp/B0BLHYVHQL/ref=tmm_kin_swatch_0?_encoding=UTF8&dib_tag=se&dib=eyJ2IjoiMSJ9.SsegccMj6tr2fz90QioSZ8pY2uCPlfpodwMwzbhI0Vim569As_aIfPnUv82z3w9yC2kqE9MvjXpxu9riYy5MFAa7c5J2N17X1nrCKzWWakIAVnFqbCDwNM3Gm-2VXGP4MHvYn75Us2NZ9zFcHyTLJw.PTfZ6_6PH59ApdHWEFMo60NXapi-rqEpmLe8pPpfXm8&qid=1718696930&sr=8-1 To learn more and register for the ACA Conference 27-28 July https://www.chiropractors.org.au/event/aca-conference-2024-cairns To learn more about the Retention Recipe 2.0 https://insideoutpractices.thinkific.com/courses/retention-recipe-2-0 Check out Certainty 2.0 https://insideoutpractices.thinkific.com/courses/certainty-2-0 To listen to my other podcast "Cut The Crap" on Spotify https://open.spotify.com/show/6CNsDhnm93RXHNCIudicoc?si=da7bda2c44794d36 To listen on Apple Podcasts: https://podcasts.apple.com/au/podcast/cut-the-crap-with-craig-and-martin/id1744483428 Email me - martin@insideoutpractices.com --- Send in a voice message: https://podcasters.spotify.com/pod/show/martin-harvey/message

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Compact Proofs of Model Performance via Mechanistic Interpretability, published by Lawrence Chan on June 24, 2024 on The AI Alignment Forum. We recently released a paper on using mechanistic interpretability to generate compact formal guarantees on model performance. In this companion blog post to our paper, we'll summarize the paper and flesh out some of the motivation and inspiration behind our work. Paper abstract In this work, we propose using mechanistic interpretability - techniques for reverse engineering model weights into human-interpretable algorithms - to derive and compactly prove formal guarantees on model performance. We prototype this approach by formally proving lower bounds on the accuracy of 151 small transformers trained on a Max-of-K task. We create 102 different computer-assisted proof strategies and assess their length and tightness of bound on each of our models. Using quantitative metrics, we find that shorter proofs seem to require and provide more mechanistic understanding. Moreover, we find that more faithful mechanistic understanding leads to tighter performance bounds. We confirm these connections by qualitatively examining a subset of our proofs. Finally, we identify compounding structureless noise as a key challenge for using mechanistic interpretability to generate compact proofs on model performance. Introduction One hope for interpretability is that as we get AGI, we'll be able to use increasingly capable automation to accelerate the pace at which we can interpret ever more powerful models. These automatically generated interpretations need to satisfy two criteria: 1. Compression: Explanations compress the particular behavior of interest. Not just so that it fits in our heads, but also so that it generalizes well and is feasible to find and check. 2. Correspondence (or faithfulness): Explanations must accurately reflect the actual model mechanisms we aim to explain, allowing us to confidently constrain our models for guarantees or other practical applications. Progress happens best when there are clear and unambiguous targets and quantitative metrics. For correspondence, the field has developed increasingly targeted metrics for measuring performance: ablations, patching, and causal scrubbing. In our paper, we use mathematical proof to ensure correspondence, and present proof length as the first quantitative measure of explanation compression that is theoretically grounded, less subject to human judgement, and avoids trivial Goodharting. We see our core contributions in the paper as: 1. We push informal mechanistic interpretability arguments all the way to proofs of generalization bounds on toy transformers trained on the Max-of-$K$ task. This is a first step in getting formal guarantees about global properties of specific models, which is the approach of post-hoc mechanistic interpretability. 2. We introduce compactness of proof as a metric on explanation compression. We find that compactifying proofs requires deeper understanding of model behavior, and more compact proofs of the same bound necessarily encode more understanding of the model. 3. It is a common intuition that "proofs are hard for neural networks", and we flesh this intuition out as the problem of efficiently reasoning about structureless noise, which is an artifact of explanations being lossy approximations of the model's learned weights. While we believe that the proofs themselves (and in particular our proof which achieves a length that is linear in the number of model parameters for the parts of the model we understand adequately) may be of particular interest to those interested in guarantees, we believe that the insights about explanation compression from this methodology and our results are applicable more broadly to the field of mechanistic interpretability. Cor...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Rational Animations' intro to mechanistic interpretability, published by Writer on June 15, 2024 on LessWrong. In our new video, we talk about research on interpreting InceptionV1, a convolutional neural network. Researchers have been able to understand the function of neurons and channels inside the network and uncover visual processing algorithms by looking at the weights. The work on InceptionV1 is early but landmark mechanistic interpretability research, and it functions well as an introduction to the field. We also go into the rationale and goals of the field and mention some more recent research near the end. Our main source material is the circuits thread in the Distill journal and this article on feature visualization. The author of the script is Arthur Frost. I have included the script below, although I recommend watching the video since the script has been written with accompanying moving visuals in mind. Intro In 2018, researchers trained an AI to find out if people were at risk of heart conditions based on pictures of their eyes, and somehow the AI also learned to tell people's biological sex with incredibly high accuracy. How? We're not entirely sure. The crazy thing about Deep Learning is that you can give an AI a set of inputs and outputs, and it will slowly work out for itself what the relationship between them is. We didn't teach AIs how to play chess, go, and atari games by showing them human experts - we taught them how to work it out for themselves. And the issue is, now they have worked it out for themselves, and we don't know what it is they worked out. Current state-of-the-art AIs are huge. Meta's largest LLaMA2 model uses 70 billion parameters spread across 80 layers, all doing different things. It's deep learning models like these which are being used for everything from hiring decisions to healthcare and criminal justice to what youtube videos get recommended. Many experts believe that these models might even one day pose existential risks. So as these automated processes become more widespread and significant, it will really matter that we understand how these models make choices. The good news is, we've got a bit of experience uncovering the mysteries of the universe. We know that humans are made up of trillions of cells, and by investigating those individual cells we've made huge advances in medicine and genetics. And learning the properties of the atoms which make up objects has allowed us to develop modern material science and high-precision technology like computers. If you want to understand a complex system with billions of moving parts, sometimes you have to zoom in. That's exactly what Chris Olah and his team did starting in 2015. They focused on small groups of neurons inside image models, and they were able to find distinct parts responsible for detecting everything from curves and circles to dog heads and cars. In this video we'll Briefly explain how (convolutional) neural networks work Visualise what individual neurons are doing Look at how neurons - the most basic building blocks of the neural network - combine into 'circuits' to perform tasks Explore why interpreting networks is so hard There will also be lots of pictures of dogs, like this one. Let's get going. We'll start with a brief explanation of how convolutional neural networks are built. Here's a network that's trained to label images. An input image comes in on the left, and it flows along through the layers until we get an output on the right - the model's attempt to classify the image into one of the categories. This particular model is called InceptionV1, and the images it's learned to classify are from a massive collection called ImageNet. ImageNet has 1000 different categories of image, like "sandal" and "saxophone" and "sarong" (which, if you don't know, is a k...

Dr. David Liew reports on abstracts OP0007 and OP0069 at Eular 2024 in Vienna, Austria.

This week, I am excited to have Layne Norton with me on the podcast. Layne has been a huge inspiration and a source of knowledge since my health and fitness journey began. After years of study and research, he obtained a PhD in nutritional sciences, and his extensive knowledge is evident in our conversation. We'll be delving into topics ranging from the science and psychology of pain to navigating misinformation to the science behind our eating habits. You're bound to come away from this episode smarter than before. Save 10% at BPN Supps: https://bit.ly/nickbare10audio Follow for more:  IG: https://www.instagram.com/nickbarefitness/ YT: https://www.youtube.com/@nickbarefitness Keep up with Layne:IG: https://www.instagram.com/biolayne/ Topics: 0:00 Intro 0:47 Welcome 5:13 The science of pain 19:21 Injuries during training tapers 25:13 Consistency is an equalizer 36:34 Mechanistic studies 46:51 Do the research 54:51 Identifying who an expert is 59:06 Why we're addicted to negativity 1:04:41 Managing the misinformation 1:15:47 Types of testing and research 1:25:10 Intermittent fasting 1:39:37 Eating habits 1:32:21 Cell autophagy and fasting 1:36:40 Blood sugar levels 1:45:04 Eating frequency 1:48:48 Tracking serving sizes 1:54:34 Stepping over rocks to pick up pebbles 2:10:00 Deadlifting

Dive into an in-depth conversation with Nathan and Robert Wright as they discuss AI's transformative potential, mechanistic interpretability, and the sobering realities of AI alignment research. Learn about the defensive strategies and safety measures necessary for managing advanced AI risks in an open source world. Don't miss the insights on AI-powered VR, and be sure to check out part one on the non-zero feed. Checkout the Part 1 of the conversation here : https://www.youtube.com/watch?v=s8bgB8TCdBs SPONSORS: Oracle Cloud Infrastructure (OCI) is a single platform for your infrastructure, database, application development, and AI needs. OCI has four to eight times the bandwidth of other clouds; offers one consistent price, and nobody does data better than Oracle. If you want to do more and spend less, take a free test drive of OCI at https://oracle.com/cognitive The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://bit.ly/BraveTCR Head to Squad to access global engineering without the headache and at a fraction of the cost: head to https://choosesquad.com/ and mention "Turpentine" to skip the waitlist. Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off https://www.omneky.com/ CHAPTERS: (00:00:00) Introduction (00:07:13) AI in Governance (00:11:08) Sci-fi doomer (00:13:58) Sponsors: Oracle | Brave (00:16:05) The frontier models (00:20:22) Emergent behavior (00:23:48) Theory of mind (00:28:09) Mechanistic interpretability (00:34:12) Sponsors: Squad | Omneky (00:38:12) AI Alignment Techniques (00:42:38) The Sweet Spot of AI