Full Transcript

Dean W. Ball: (0:00) Computers already are neural interfaces. You don't control them directly with your brain, but they obviously interface with your brain, and you are using your brain via touch control to manipulate them. Throughout the history of technology, 1 of the things we see is that information technologies don't just change the way information propagates. They change the way we think. The impulse that people have a lot of the time is to think, oh, no. If there's an incentive to use this thing, then everybody will have to use it. What about the people that don't wanna use it? I I go there too. I have sympathy for those people. I think about the Amish. And I do think that there's probably going to be forms of digital Amish in the future that we need to be thinking about. At the same time, the people who want to enhance their cognition also should have the liberty to do that. And we should want there to be more cognition in the world, especially human cognition.

Nathan Labenz: (0:58) Hello, and welcome to the Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week, we'll explore their revolutionary ideas, and together, we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathanlabenz, joined by my cohost, Eric Thornburg. Hello, and welcome back to the Cognitive Revolution. Today, I'm very excited to share my conversation with Dean w Ball, research fellow at the Mercatus Center and author of the Hyperdimensional Substack. Dean is a kindred spirit who shares my obsession with frontier technologies and their near term possibilities, and he's done a super deep dive into the current state of brain computer interfaces. In this wide ranging discussion, Dean explains the major approaches being pursued today, from the noninvasive techniques like EEG and ultrasound stimulation to the invasive implants like those being developed by Neuralink. He gives us a sense for the current state of the art across important technical dimensions, such as the number of electrodes used in a given device, the spatial and temporal resolution that's achievable with different technologies, and the major technical challenges that still need to be overcome. Along the way, we touch on big picture questions about the dimensionality of thought, the challenges of interpreting signal that's transmitted through the human skull, and the ethical and societal implications of being able to increasingly read from and even write to the human brain. Could we 1 day induce complex thoughts or download knowledge and skills matrix style? Will brain monitoring become a routine part of legal proceedings or job interviews? How might the widespread adoption of cognitive enhancement change human culture and even our biological evolution? While he maintains an appropriate degree of epistemic humility throughout, Dean's well informed intuitions paint a picture of consumer devices that will soon provide powerful brain reading and crude but potentially useful brain writing capabilities. More advanced applications, like high bandwidth 2 way brain computer communication, will likely require invasive implants. But the pieces there too are coming together faster than many realize. Whether you're an AI expert looking to understand a related technology trend, a policymaker grappling with the implications, or just a curious observer of the human journey, this episode will give you a solid grounding in the emerging neurotech landscape. Special thanks to Dean for doing all the work to make this episode possible, and definitely check out his newsletter at hyperdimensional.substack.com. As always, we welcome your comments, questions, and suggestions for future episodes. You can contact us via our website, cognitiverevolution.ai. And if you do, you might also enjoy the chance to chat with my new AI clone, which has been trained on the full history of the cognitive revolution and is powered by Delphi AI. For now, I hope you enjoy my conversation about brain computer interfaces and the potential for a man machine merge with Dean w Ball. Dean w Ball, research fellow at the Mercatus Center and author of the Hyperdimensional Substack. Welcome to the cognitive revolution.

Dean W. Ball: (4:08) Thank you, Nathan. Great to be here.

Nathan Labenz: (4:10) I'm really excited for this conversation. You and I are kindred spirits in that we both have had our day jobs, and then we've had our obsessions with different areas of technology. We connected online after a couple pieces of writing that you put out that were excellent. And as I learned more about you, I was fascinated to see just how much time and energy you have put into learning all about the brain computer interface space. And so today, we are gonna do a survey on all things neurotech. And I'm really excited to learn from you in this domain and to share that with our audience.

Dean W. Ball: (4:45) Awesome. Yeah. Me too.

Nathan Labenz: (4:47) So you wanna start by just maybe introducing yourself? You've got a varied set of experiences and and background interests, and then we can dive into your kinda high level thesis and and dig into the technologies that will take us there 1 x 1.

Dean W. Ball: (5:00) Yeah. As the title of my Substack suggests, I have varied background with a lot of different brands going into it. I spent most of my career in the think tank world. So about a a decade working in state and local economic policy for the most part, housing, infrastructure, things like that, but have always maintained a very strong interest in technology. I started open source forum software. If there's anyone who remembers PHP BB from way back in the day, I was doing commits to that and writing technical documentation for that at a young age, and the coding bug hit me at that point. And I never went into it professionally, but it's always stuck with me. And just a general interest in technology, and that morphed as the deep learning revolution unfolded in the last decade into following the ML literature pretty closely. And once ChatGPT came on the scene, I realized that this was a perfect moment for me because I have a background in policy, and there's a lot of interest in the societal and policy implications of this technology. And at the same time, I've got a a pretty good ground truth technical sense of what's actually going on, which is often lacking in the policy space. So I try to bring that to everything that I do.

Nathan Labenz: (6:17) I love that. I often say that we really need to figure out what is before what ought to be done about it, And there's all too much jumping to what ought to be done about it that is not based in a good ground truth understanding of what technology actually exists today, what can be done with it, where that's heading in the near term, and and so on. So I appreciate that, perspective, and I'm excited to get into the details of what you think is gonna happen in the big picture of the the possibility of a merge between man and machine.

Dean W. Ball: (6:50) Yeah. So I'll explain a little more about how I came to this particular issue. I am a longtime fan of of J. C. R. Licklider, the, DARPA senior researcher who wrote a an article back in 1960 called man machine symbiosis. It's not quite the physical merge that we're gonna be talking. It anticipates the development of something like AGI as a lot of people did in the sixties being, like, way easier. The thesis of that paper is AGI is gonna be here in the next 5 to 10 years, and who knows what happens after that? Probably, it'll become super intelligent and replace us. But in the meantime, we'll have a lot of fun. That's basically the thesis of this senior government official at that time. It shows you how much things really changed in terms of

Nathan Labenz: (7:35) Although I feel like I've heard similar quotes from some notable technology leaders more recently From as

Dean W. Ball: (7:42) the technologists, for sure. But coming directly from the DOD is interesting, and it shows you how how greenfield everything felt with computing at that time. And what's exciting about right now is I I do feel like we're at a similar moment where a lot of things are possible, but I've always thought about this issue of of symbiosis and thought about it in a fairly broad way. But in the last few years is the the concept of artificial superintelligence has become maybe a little closer than any of us thought, possibly imminent if you believe some people on Twitter. The concept of the merge just keeps coming up. Prominent technologist, as you say, Sam Altman, has a famous blog post called The Merge where he essentially describes it as an inevitability. Elon Musk is not just called it an inevitability, but is in fact working on technical solutions. So he is probably taking this more seriously than anyone else. And just in general, in my conversations with people building in the AI space at advanced levels, there is a very common sense that this is something that will happen. And if artificial superintelligence is even remotely eminent, then the merge must be not too far off either if that's going to happen. And so my fundamental question is, what does the technology really look like? And that's what I sought to answer in some articles that I've written. And do that I I first looked at the actual scientific tech technologies that exist right now and and it's like, where is the literature? But also thought about it in a broader sense because in a lot of ways, computers already are neural interfaces. You don't control them directly with your brain, but they obviously interface with your brain, and you are using your brain via touch control to manipulate them. And throughout the history of technology, 1 of the things we see is that information technologies don't just change the way information propagates. They change the way we think. So that was my starting point of almost seeing the merge as something that started a long time ago, a path that we are still on and that will get more vivid and more visceral over time, but not a fundamentally

Nathan Labenz: (10:01) new Yeah. That's interesting. Certainly, feel I like I've been through a couple waves already of technology changing how I think. It feels so quaint now. But there was the idea of we don't have to remember anything anymore because we can just go search for it on the Internet. And certainly, that is true. Or even GPS. Right? Just we don't have to make spatial maps of our general environments because so many of us just default to using the GPS. For me right now, I wonder if you have experienced something similar with the current wave of technology. I do find myself getting into some pretty deep habits, at least, with how I relate to language models. Coding tasks are a pretty good example of this when I reflect on how I used to code versus how I approach coding tasks now. Probably making a mistake in the past because I've never been an outstanding coder, but I feel like I often used to go into the editor first and maybe start writing some comments and trying to break things down or starting to scaffold out the the classes. But very often, I was jumping too quickly into code. And 1 of the things I've learned from using the language models is I'm really rewarded for taking the time first to think, what do I actually want here? Can I define that in conceptual terms first before I start to diffuse as it were into the finer grained structure of the code? And in general, I think that's starting to happen to me. We recently did an episode with Shortwave, the email client as well, that has a really good email AI assistant. I see an evolution there too, where I used to remember keywords to get to navigate my vastly overflowing and unmaintained inbox. And now I don't have to be so keyword oriented anymore. I can ask, like, questions, and that's definitely very helpful for me. But also, I'm starting to feel already a little bit like my keyword search ability is beginning to atrophy. So certainly, I I recognize and experientially feel the idea that the ways of technology have changed how I thought. I don't know if you have any similar stories, and this probably pales compared to what might be ahead of us still.

Dean W. Ball: (12:08) Yeah. No. Absolutely. And it is important to think about that kind of thing. For me, in in a similar vein, LLMs just fundamentally changed the way I ask questions and made me much better at asking questions. Oftentimes, I I remember it's about a year ago since my first experiences with g p t 4, and 3.5 was not quite as vivid in this way. But when g p t 4 came out, I would ask it a question and I would find, this isn't quite what I wanted. This isn't quite what I was looking for, but I know this model must be capable of giving me what I look for, so the problem must be with me. And so I kind of refining my question and getting down to what is it that you're really asking? And so often in fields of intellectual inquiry, that is actually the more important thing. But finding the right question is 90% of the intellectual journey. And so something that accelerates that, that makes us better at that, accelerates all kind of intellectual endeavor. And I am excited to see things like Claude 3 opus, which it seems that the qualitative difference in that model versus g p t 4 is really at the frontier of all kinds of different fields, that that people that are doing middle of the bell curve activities and not even coding necessarily, but just, like, various kinds of questions, science, history, whatever it may be, that those questions, the middle of the bell curve stuff, the difference between g p t 4 and plot 3 up is not necessarily all that much. But that when you're asking things that are more at the frontier, FLAWD 3 really shines vis a vis GPT 4. I can definitely tell you, having used LLMs a lot to help me accelerate this process of of, you know, researching these articles on neuroscience, that's definitely been the case with Claude. It's just so much better at answering those questions than GPT 4, where GPT 4 would get answers that I had enough context to know, like, this is kind of BS here. This is just filling in the blank. Whereas, Claw 3 is more capable. I think as the LLMs get more capable, that will continue to be very relevant. And then on another level, obviously, anyone paying attention to this space is asking themselves philosophical questions about what is the nature of cognition? What is the nature of intelligence? What distinguishes my intelligence from the kind of intelligence that I'm seeing here exhibited on this screen? And it's actually a journey that we should all go through anyway regardless of whether LLMs exist. Because asking what special value do you bring to the table and what unique perspective do you have is a question that everybody should be seriously asking themselves all the time and not enough people do. And I think LLMs are a kind of forcing function for a lot of people for labor market concerns just as much as anything else to just really probe at that. So in a way, for me, my experience is as I look back on the last year using advanced LLMs, it's made me more reflective and and just more inquisitive. And that is Excel. That has all kinds of compounding benefits. And, obviously, the other thing is that once so much about the history of technology is not how it impacts 1 person or 1 building or 1 place. It is instead about the nonlinear compounding effects when everybody has access to to that same capability. And I think we're in the very early days of that, and that's part of why I find this to be such an enormously exciting time to be alive.

Nathan Labenz: (15:53) Hey. We'll continue our interview in a moment after a word from our sponsors. Yeah. It's crazy. You hit on something there that I often think about as well, which is just that the future dynamics of the world at large as AI starts to infuse into everything are dramatically under theorized. When I go around talking to everybody in AI today, largely, their implicit world model seems to be the world is the world. I'm applying AI here. Therefore, we're gonna have the world plus my AI intervention, and that'll be as sweet because we'll get this, you know, productivity gain or whatever. And that is not wrong, but it sort of misses the fact that everybody else is doing this at the same time. And therefore, there's gonna be just a lot of, in my view, unpredictability to how all these new arrangements interact with each other at at a higher level.

Dean W. Ball: (16:45) I think about that all the time, and I think that we're in for just a order of magnitude complexification of the world beyond what we've already seen. If you think about the world has just become denser in the last 30 years. You think about what was New York City like 400 years ago? The Island Of Manhattan versus what it is today, it's the same geographic land mass today, landfill parts of it, but basically the same thing. And the difference is that it's just dramatically denser and more complex. And I think that we are in for perhaps not geographical densification, but another substantial round of conceptual and cognitive densification. And 1 of the things I worry about and another thing that attracted me to this issue area is the question of how we're all gonna keep up with that. I worry now that people like you and me are already in a bubble. We're we're extrapolating multiple points out on this LLM on on the dynamics of AI agents being all over the world. Other people haven't even contemplated the idea of AI agents and have probably asked chat GPT 3.51 basic question 18 months ago and haven't thought about it really that much since. And so I worry about people being shocked and left behind. And I think that maybe neurotechnology is our way, at least I hope, for us all to just keep better pace with the complexity that I suspect is coming.

Nathan Labenz: (18:14) Yeah. There was a recent study just this week that came out that said that 30 percent of young people are using CHET GPT for work or whatever, And it it is a good reminder that that's a lot because it's only been a year and change since the first version and a year and just a few days since g p t 4. But it's also not that many yet, and the tools are still certainly relatively basic compared to where it seems quite clear that we're headed. In terms of going along for the ride, which is an Elon Musk framing on this, this may be a little shocking for, people that aren't aware of how much progress has already been made. Because 1 way I think about it is, like, the way we use computers has first of all, it's a bidirectional relationship. Right? We put information into them. They give us information back. The way in which we have entered information into the computers historically has been this sort of finite action space of, like, you can type on the keyboard, and you can click the mouse, and you can do a lot with that. But at any given time, you have a finite and and often quite prescribed set of actions that you can take, and outside of that, just nothing happens. And that's also interesting. That's 1 of the reasons that they cite for why the web agents historically didn't take off. They tried to do reinforcement learning on the web, and the reward signal was just too sparse. Agents couldn't get anywhere, and they they couldn't chain enough success together to get any reward. And so the whole project went nowhere for that reason. Of course, the signals that we get back have been getting richer and richer over time, like higher resolution, more realistic graphics, etcetera, etcetera. Now with this language moment, we have the ability to speak a natural language to the computers, and this really opens up a sort of richness of communication for what they can understand in terms of our mental states and desires. Ilya from OpenAI once and possibly still from OpenAI put that when he said, look, the thing that's most incredible about this is I speak to the computer, and I feel I am understood. And that's a really good baseline reality check of what makes this different than before. But now recalling your sub stack title hyperdimensional, we're moving we're on the verge of potentially moving from this language mode of communication, which has just opened up for us to talk to computers in our own natural language. That is obviously a richer space, much more wide open space than you can click on these particular buttons within an interface. But it is still, of course, the compressed form token by token that we're reducing our internal thoughts to this output stream that we can encode in language. And 1 big way to think about the set of technologies that we're gonna talk about today is that it goes up yet another level in terms of the richness of the dimensionality of the space in which we can communicate with computers.

Dean W. Ball: (21:09) Yeah. Absolutely. So let's get into it. I think at a fundamental level, all of this, the neural technologies, it's really a story of very advanced sensor fusion and signal processing, which is the story of so much technological innovation over the last hundred years. We don't think about it in that way. But from from Bell Labs on, it's been a story of signal processing. In this case, are the biomarkers of thought. Thought is fundamentally mediated by electricity and magnetism, and so you can measure electrical and magnetic fields generated by brain activity. That's the the primary way that this is done, or at least it's the most straightforward way. Let's put it that way. There there are other interesting ways that we'll get into. The most common that you see for neuromonitoring, monitoring brain state, not so much modifying them, but understanding what a person is thinking, is called EEG, electroencephalography, and it has a closely related cousin called MEG, magnetoencephalography, which is measuring the magnetic fields. So kinda same idea. The difference is that EEG can be done with tiny electrodes placed on the head. Magnetoencephalography requires pretty substantial equipment that I don't think is gonna make it out of the lab anytime soon. The thing that a lot of people don't know even in the the tech space is that it's not just Neuralink in this field shipping products. For more than a decade, there have been neural technologies using EEG especially because it's relatively cheap to do all kinds of practical things, and it's obviously also been used in a lot of lab settings. The difference between really a lab EEG and a consumer EEG is just the number of channels that you get. So consumer EEG might be stereo, just 2 electrodes up to yeah, maybe at the maximum 32. That would be a lot. 32 would be, like, a whole helmet that you're wearing. Something like 8 16 is more common in the consumer setting. Lab could go all the way up to 256. And this is just taking an enormous amount of data. That's something that is also not appreciated. It's just that the brain generates a torrent of data. A lot of EEG you can change the sampling rate, but the sample rate is usually around 1000 hertz, so 1000 samples per second. An 8 channel EEG in a consumer headset is gonna be generating 8,000 data points from your brain per second. So you quickly get into millions if you're using it for for any substantial period of time. Fundamentally, the problem with all of these technologies really is the human skull. The skull was designed to protect your brain, and it does a good job of that. And it is not particularly conductive, So it tends to attenuate signal coming out of the brain and going in. There's other things too. There's cerebrospinal fluid. There's other mediating layers between the actual surface of the cortex and the brain and the scalp, but the skull is the most important 1. And so modeling that modeling the skull, which also varies between people. In thickness, density is very difficult. It also varies not just between people, but across your skull. So your skull is not homogenous in terms of its density or thickness. And so it has to be modeled in real time for an ideal reading. And that is where I think we're at the frontier of these technologies, but I'll talk a little about what has been done so far, particularly with with EEG. EEG devices can do things like screen for cognitive for signs of cognitive decline. They can read, for example, Parkinson's. There are biomarkers of mental illness that can be picked up in EEG devices. Generally, you don't see that on the consumer side of things. And the reason for that is that it relates to FDA regulation. We can get into that later. But for consumer devices, you'll often see devices to help you meditate better. The brain waves, they fall into a few broad families, and it is actually kind of intuitive. Your brain, the electrical fields that your brain is generating oscillate at higher frequency when you are thinking more intensely. That is an intuitive relationship. So when you're in deep sleep, the frequency is very low. And when you are focusing or in a panic, the frequency is very high. And so those kind of crude measures of, is this person sleeping? Is this person in a deep meditative state? Is this person attentive and focused, or are they scared? These are the kinds of things that can be read right now in consumer devices, still fairly crudely, but it can be done.

Nathan Labenz: (25:56) So a couple just very practical rookie level questions about exactly what we're measuring and exactly what we're doing with it here. The electrodes that you put on your head to measure these electrical signals, You said the the frequency with which they can take a measurement is about a thousandth of a second. 1000 hertz is 1000 cycles a second, so it's a 1 1 thousandth of a second measurement. The rate at which a neuron can fire is also roughly 1000 times a second. Is that right?

Dean W. Ball: (26:28) It can be quicker than that. Neural activations can be quicker. So I I should actually just step back. First of all, we're talking here about noninvasive technology. There's a whole school of invasive stuff, which we can get into later. But on the noninvasive side, yeah, that's right. An EEG is also useful because of that temporal resolution, as it's called in the literature, is excellent for EEG. As a comparison, the kinda gold standard for spatially imaging the brain is fMRI, and that, at its best, can take 1 image every 2 seconds. So in the space that fMRI if you're using fMRI, you're in a lab because fMRI requires superconducting materials.

Nathan Labenz: (27:11) You're

Dean W. Ball: (27:12) not wearing that on your head anytime soon. If you're wearing, for ex let's just say a 64 channel EEG, then in the space that an MRI can take 1 image of your brain, the EEG has generated more than a 100 samples from the brain. So the temporal resolution is great. Not quite at the neural activation level from a temporal perspective and far from it from a spatial perspective. So an electrode at its most precise can see millions of neurons still, down to a spatial resolution of centimeters or or millimeters in some cases.

Nathan Labenz: (27:47) I wanna understand a little bit better what the electrodes are actually measuring, like what their output is, and then how that gets translated into this sort of spatial model of what's going on in the brain. Is it accurate to say that an electrode creates 1 number every thousandth of a second that would represent sort of the strength of the electrical field?

Dean W. Ball: (28:09) A point in time.

Nathan Labenz: (28:10) Yeah. Exactly. And it seems like there's Fourier transform kind of math that must be happening here where this is similar to, like, how cell phone towers work from what, like, limited understanding I have of this branch of physics. But, basically, we have all of these neurons firing off with these sort of spikes. Right? And there there's a very short duration hot relatively high voltage signal that is getting sent very locally. Stop me if I'm wrong about this at any point because I'm really not very expert in this at all. Because that's now happening in all these billions of locations concurrently throughout this whole region of the brain, that then sends out this massive signal to the outside world, much like a a cell phone tower is sending out a massive signal that is communicating with all of the phones in the area at once. And then in a similar way, all these electrodes are receiving this sort of messy signal where it's like, okay. I'm here, and this is what I feel right now. And that is the aggregate of all of the the signals that have been created from these individual neuron firings. And then there's, like, a real computational challenge after that to say, okay. We've got 64 different signals because we got 64 electrodes on the head. What does that translate into? So can you Yeah. Tell us a little bit more about how that is happening? Like, how how does that sort of 64 numbers get translated into a spatial understanding of of what's going on inside?

Dean W. Ball: (29:39) Yeah. That's a great question. First of all, yeah, your intuition here and what you sketched out is is basically correct. And, yeah, a lot of the basic analysis that's happening here is your standard Fourier transform.

Nathan Labenz: (29:50) To define that, that basically takes a composite signal and breaks it down into the intensity of the signal at different frequencies over time. So you have some wobbly signal that you're measuring, and this says, okay. How could you rerepresent that signal as the sum of intensities at certain frequencies throughout the frequency spectrum?

Dean W. Ball: (30:16) Yeah. The way I think about it is almost like if if your brain is playing a chord, the Fourier transform separates it into individual notes. And so you can see it that way. And that's essentially what's going on with EEG signal processing. I would just add that there's competing electrical signals. Right? Your eyes, when you blink, are generating electrical fields. All of the muscles in your face are doing the same thing. The device itself that you're wearing is generating electrical fields. So there's all of this competing electrical activity around the head, which is being sampled. The electrodes are gonna sample some of that. That's gotta be processed out. And then there is what I was alluding to earlier, that the electrical field itself from the brain is is going to be attenuated in different ways dynamically by the skull and other sort of cranial media. Difficulty of processing that, that's where there have been a lot of traditional techniques, traditional statistical analysis techniques that have been used and older MEL techniques, convolutional neural nets, recurrent neural nets, all that kind of stuff that have shown some promise. Basically, everything that I'll talk about actually is really using those kind of older hat forms of statistical analysis to break this apart and figure out what's relevant and figure out what it means. There hasn't been a lot of transformer based work and and other types of things, and that's where I think there's just a lot of low hanging fruit. And it's actually still an open question to me. The transformer has been around for a while now. There are some papers that show EEG transformers and other types of neural signal processing with transformers, but why aren't there much more? Is it the conservatism of academia? My first intuition was maybe it's a data problem, but these things generate a lot of data. So I really don't know. I'd love to know. If if any of your listeners have insights, I'd I'd love to hear.

Nathan Labenz: (32:14) Yeah. I have a couple intuitions, which may or may not be right, and people can correct me on this as well. But we just did an episode on the first 90 days of mamba literature. And 1 of the things that is really interesting about this new mechanism, the selective state space mechanism, is that it does have different strengths and weaknesses compared to the attention mechanism, both in terms of how much memory it consumes, where attention mechanism is quadratic in the length of the input. And that might be, by the way, 1 of the reasons, like, just as you talk about, like, a torrent of data and 1000 samples per second, if that were to be naively translated to 1000 tokens per second, then very quickly, you're getting to a level of tokens that we have only very recently reached with frontier grade transformers. It was only with GPT 4 a year ago that the public first got to see a quality 8,000 token transformer. And before that, it was like, it's just a couple months where we had just seen the 4,000. And before that, as of, like, 18 months ago, 2,000 tokens was what you could really get from, like, the OpenAI API. So just the sheer volume of data may not lend itself super well to the transformer. But also another interesting thing is that when they break down these micro tasks and look at what the transformer can do and and can't do, 1 of the things it really struggles on is the hyper noisy environment. There there was a re interesting result in this 1 mamba versus transformer comparison paper. It's more about the selective state space mechanism and the attention mechanism. Those are really the the 2 things that are more dueling it out than the higher level architectures. And they're not even dueling it out because they actually work best together. Spoiler. But in the super noisy environment where what actually matters is quite rare in what you're signaling, then the transformer sometimes has a hard time converging. And the intuition I've developed for that is because it's changing all the weights at the same time across, like, the entire range of the input, it may be that the gradient is often dominated by noise and has a hard time converging on the signal. Whereas when I don't wanna make everything about the selective states based model, though I do have an obsession about this as folks know, it is updating per token. And so it seems like it has a more natural mechanism when the actual signal hits to say, oh, and I'm this is where I start to violate my no anthropomorphizing policy, but it it has an ability to recognize when the signal hits and update in a more focused way on that 1 thing that really was supposed to matter, whereas the transformer is updating everything all across. It's considering everything at once. And so the it seems like the signal can get lost in all that noise. The the recurrent nature of the selective state space mechanism allows you to kinda 0 in and and do the gradient on the signal when you have the signal. And then, of course, there's still a lot of noise, but that maybe can get separated from the signal because of this bit by bit level processing and updating. I'm not a 100% confident in that theory, but it is consistent with all the evidence that I know of so far. So we'll see how that evolves through time.

Dean W. Ball: (35:36) Then we might be in for an exciting couple of years, uh-huh, if that's true. Yeah. That's a great point. Very well.

Nathan Labenz: (35:42) Hey. We'll continue our interview in a moment after a word from our sponsors.

Dean W. Ball: (35:46) Maybe that. I've talked a lot about the downsides is what's hard. Once you get into this literature, what ends up happening is you just admire the very, very challenging problems that there are and just how complicated the brain is. But there is a lot that you can do. And what's shocking about EEG is that it works as well as it does noninvasively, especially when you consider that the only electrical signals really that can reach the brain or that can reach the electrodes on the scalp from the brain are very much on the surface, very much cortex, maybe 1 to 3 mm below at the most. Everything else just gets totally attenuated. So any deep brain structure, there's just nothing that EEG can really read. So everything we're talking about is stuff that's coming from just the surface of the brain, and that can do things like predict seizures with nearly 100% accuracy up to 1 hour before. That was demonstrated, in fact, about 8 years ago in a lab setting, and there are devices that that do this for people with epilepsy. So the the telltale signs during which the patient feels absolutely nothing. There's no external sign for the patient that a seizure is coming, but there are brain patterns that can be interpreted by consumer EEG hardware that can predict with almost a 100% accuracy whether a seizure is going to happen. Same thing with Parkinson's. Early onset Parkinson's has a pretty distinct neuro signature that can be read. That's something like 90 plus percent accuracy. So already, the potential of a medical device I mean, I'm wearing AirPods right now. In fact, Apple has a patent that they've explored of of putting electrodes on AirPods to do this exact thing. Just simply something, a pair of AirPods that can monitor for important medical conditions like that, and maybe help me relax. That's already something that could be quite useful and seems like it will happen within the next few years. Maybe not exactly in the AirPods form factor. And when I said, you know, make you relaxed, that refers to something called neurofeedback. So rather than a direct modulation by hardware of your neural state, Neurofeedback is basically giving you the data and allowing you to change your patterns of thought in response to the data. So there are neurofeedback devices that have EEG sensors on a headband that you might wear, and it pairs with a phone app. And the phone app will show you your neural activity in real time and maybe give you a game or some other kind of cognitive stimulator that you play with, and it will tell you how focused you are. And you can get focused more. The idea is that you can actually train circuits in the brain to become more focused. There are devices that do this for sleep without you having to do anything. A lot of devices, for example, there's 1 called the BIA, I believe, that resembles like a sleep mask that you wear around your head. And I've never tried this, by the way. I don't think the BIA is actually shipping yet, and I'm certainly not endorsing it, just to be clear. But you can wear it like a sleep mask, and it will play music. And that music is dynamically tuned in response to EEG signal and meant to bring you into a more relaxed state. And, ultimately, I don't think there's a lot of scientific literature backing this up, but there is general scientific literature to support the idea that neurofeedback is a thing. Your brain will develop circuits to get into a more relaxed state more easily on its own with enough practice of that. So that's the kind of thing that we're talking about in consumer devices. There's also motor control, which is a whole different interesting field that may maybe you wanna go into. But first, if you have any questions on the first set of things.

Nathan Labenz: (39:39) Yeah. This sort of same paradigm seems to happen all over the place. There's, like, echoes of this going with the increasingly rich signal, then we also have these sort of increasingly meaningful states that we're able to identify. Right? Within the transformer, this is pretty well studied now where in these sort of late middle layers, the inputs have been worked up to these rich concepts, and you can even identify the direction in activation space that corresponds to justice or fairness or love or these kind of things you're like, wow. How did this AI learn to represent that when all it's doing is next token prediction? And here, there's, like, a somewhat analogous thing where as the ability to read this signal gets better and better, we're able to see, like, okay. It takes 2 electrodes on the brain to detect if you're sleeping or awake. With 4, you can get to, are you stressed or are you relaxed? With 8, you can get to general emotional state, like fear, happiness, disgust, anger. And then with 16 and 32, more advanced things are starting to happen. So I am actually really interested in the motor thing because that seems like an interesting lens into just, like, how much resolution do we already have in the ability to decode this stuff? And then I also wonder if you have thoughts about the limits of that with this technology. Of course, we're gonna discuss some other technologies too.

Dean W. Ball: (41:04) Yeah. So motor control, it really does turn out that when you think about moving something, it is tense enough to sending the electrical impulses to actually move that thing. And that can be translated for people that are unable to make that motion themselves. So there's demonstrations, maybe it's just my algorithm, but I see this on x all the time, of a person wearing a helmet controlling a robotic arm, for example. And oftentimes, the motions are jerky or slow, or you'll see sped up footage. And those are the signal processing issues that we have now. If if you see someone wearing a whole helmet, that's gonna be a lot of of EEG signal that that they're reading. But you can interpret some basic motor control from the cortex, from cortical activity. Now a lot of motor control is coming from deeper brain structures. The limits on that, at least to me as someone who's not a neuroscientist, unclear exactly. But I actually think it it it's important to say at this point that some of the highest quality datasets for this kind of application are datasets that combine EEG readings with fMRI or PET scans. Now, you know, PET scans and fMRI, again, not the kind of thing that are going to to make it into consumer use cases. Maybe ever, certainly not anytime soon. In lab settings, you can record that, then you have an interesting relationship because the fMRI in particular is a 3 d voxel, as it's called in the literature, representation of brain activity. FMRI uses blood flow to infer a blood oxygenation, to be precise, to infer brain activity. And then you can connect that with EEG signal. So all of a sudden, that's an interesting AI application because you've got a much richer but more sparse signal coming from the fMRI and then a super noisy signal coming out of EEG. And there's an interesting connection. What are subtle differences that we might not notice, subtle patterns that we might not notice between different states of mind that we can easily see on the fMRI that we can't see on EEG, but perhaps they can be pulled out of the data. I think that's a very rich area of research, and we'll talk later about some companies that are using models exactly of that kind to do interesting things, but that could really advance, especially on the motor control. But noninvasive EEG based brain computer interfaces already exist, and that's to understand. Are they super useful? No. Not really. They're for enthusiasts of this kind of thing, and they're not cheap. But you can, today, buy the 32 jam channel EEG headset and pair it with software that you can use to manipulate objects on the screen of your brain. It requires some calibration. Can't just do that out of the box, but it can do it. It's like they have a block that you can move left and right and things like that. Nothing like the cursor control that Neuralink has displayed has shown, but that is possible in digital environments. And then certainly also it's possible, to move to move robotic devices, and there are prosthetics that work in this way already.

Nathan Labenz: (44:21) 1 question I've been pondering here is, what is the kind of breakdown? We talked a little bit about traditional signal processing and Fourier transforms and whatever. And then I'm also reminded of Elon Musk saying on the Neuralink show until day a year and change ago now that it turns out the best thing to decode a neural net is another neural net. And so I'm wondering what parts of the interpretation process you've got, like, 1000 numbers coming off each electrode per second, and then ultimately that needs to be translated into something. Right? A classification of your state or, like, a direction in motion space that you're gonna try to move your robot arm or whatever you're trying to do. And between there, there's you could imagine, like, decoding with all traditional methods. You could imagine an entirely neural net thing that just, like, takes in these raw numbers and translates that to an output with basically no principled approach other than just throw a model and a bunch of data at it and let it figure it out. Do you have a sense for what the right balance is there and whether today's balance is likely to hold, or are we headed for another bitter lesson where just all of this gets thrown into neural nets ultimately?

Dean W. Ball: (45:36) It does feel that way. We see problem after problem falling to the unprincipled application of neural nets. Obviously, it's more complicated than just throwing data into an architecture, but, basically, we've seen that over and over again. And every time it happened, the experts and the scientists who were specific to that field told us that will never work. It's impossible, and then it works. So I've learned to be humble in this regard, and I wouldn't wanna hazard too much of a guess. My intuition right now is that the field, frankly, is probably relying on a little bit more primitive methods of doing the signal processing than it could, and that sort of moving in the direction of a fully neural net is at least worth experimenting with. And I think part of the reason that the scientific community doesn't do that is because you want comparability with studies that have come before. There is this, like, innate conservatism in this field of science that a fresh crop of startups focusing on this will be entirely uninterested in. And I think we're at the stage where we need to get this research out of scientific labs and more into at least corporate r and d labs. And, obviously, something that's important to understand here is that every 1 of the big tech companies employs neuroscientists who work on things that are at least adjacent to this. Apple's got a lot of patents in the field. Apple's exploratory design group is probably looking at this. That's back to their kinda skunkworks operation. Meta's been more public about it. Meta's public re and in fact, Mark Zuckerberg, it's worth noting in his review of the Apple Vision Pro, which we should talk about, by the way, because that's, like, an interesting kind of half step towards all this. But Mark Zuckerberg, in his review of the Apple Vision Pro, said basically, something along the lines of the eye tracking interface they've built is okay until we get the neural interface hooked up. Basically, viewing it as an inevitability. There are probably right now inside of both startups and large corporate r and d labs fresher approaches to this being tried than at least what I have seen in the academic literature. Though, fairness, academic literature doesn't always go into a ton of detail about exactly how the signal processing is done at that statistical analysis level. But, yeah, that that's my general sense.

Nathan Labenz: (48:08) Availability of data also may be a real issue for some of these things. It seems like the way that this has progressed in neuroscience has backfilling a narrative here, but I feel like it is the the brain is the ultimate black box. Right? Even more messy and and black boxy and just hard to get into, obviously, for all sorts of medical and ethical reasons as compared to a digital neural network, which you can be into with clarity and chop parts off and see how it works with different permutations. So there's been just a huge initial challenge of figuring out, in rough terms, what is going on and how does it work. And so people are doing these very small sample sizes of looking at people in FMRIs and trying to deduce what brain region does what and, like, what frequency of waves seem to correspond to what. And this super low data regime figuring out, in general terms, what's going on then seems to naturally lend itself to, okay. Now let's try to apply principled approaches to identify when that's happening versus I could see this shifting very quickly, and probably would happen in private companies first, just because if nothing else, they're gonna see the opportunity and really invest in scaling up the data. Just having the data to say, what were you trying to do? When we recorded that brain state, what were you trying to do with your robot armor, with the cursor on the screen? Like, that data just has never existed. So you can't really take the better lesson approach until you have the scale of recorded data to make it go. And it seems like we've only recently realized this is gonna work for everything, and we need that scale of data, and nobody has really collected it globally. We just did this episode with Paul Scottie, who is the author of the mind eye 2 paper. They are beginning to work on a multimodal brain state interpretation model, which would take in different kinds of signals and try to output different kinds of predictions. And it sounds like that's really only getting underway. Like, you know, data is super fragmented. It's all kinds of different places and forms. And so that that also seems like a big part of why this hasn't happened yet. Would you agree?

Dean W. Ball: (50:24) Yeah. I do. And I think we're at a very primitive state when it comes to the data. I don't think we have a good sort of population level modeling of the variation in neurosignatures, even things like skull thickness. Right now, it's like the literature is, well, everyone's skull is different. That's probably not true. It's probably not literally true that it's like a snowflake. Right? Like, my skull is it might be unique at a very granular level of detail, but you can probably model population dynamics for this sort of thing, and that would make your imaging challenges substantially easier. Same thing goes for the neural activity. It's definitely quite likely that high dimensional thought in particular is probably pretty unique signature. My associations that I have with the concept of flawed 3 opus or Mozart or something are very different from yours, and it's not entirely clear that we're gonna be able to get to the point of, like, oh, Dean and Nathan are both thinking about Mozart. That seems hard to be able to reach, but there's a lot lower level thought interesting, that it seems like you could model, that it's also more that's higher level somewhere in between Mozart and, you know, I'm scared of this tiger. I'm trying to run away from it. We can read that pretty easily. We know what that looks like. That has a pretty common signature that's easy to pick up on.

Nathan Labenz: (51:52) The MyGuide tube paper actually may shed a little light on this because, first of all, the dataset is only from 8 people that they work on. So people it should be right before this on the feed. Basically, what they're doing is looking at your brain state as measured in that case by fMRI, and reconstructing what you were looking at. They had an earlier version of this where they created a custom model for each of the 8 people that are represented in this 1 open source dataset. Each person had to spend, like, 30 to 40 hours in an MRI machine over presumably a bunch of different sessions, and they're being shown an image every few seconds. And then they just had to sit there and click a button if they recognize that they had seen that image before just to kind of make sure that people are, you know, engaged in the task on an ongoing basis. So the jump between mind I 1 and mind I 2 was that instead of creating a single model trained on all of the 30 to 40 hours of data per individual, they were able to train a single model based on 7 of the 8 individuals' data. By the way, each of the brains is quite different in shape. They report the number of voxels per individual, Voxel being, roughly speaking, a centimeter or a 2 mm cube. The lowest number of voxels from 1 person is, like, 12. How they choose the number of voxels is by, like, the anatomy of the brain. So they're looking at basically the visual cortex, segmenting that off, and then just splitting it into voxels. And the the resolution of the voxel is constant. So how many voxels you get depends on the total space of the portion of the brain that they identify as your the relevant visual cortex for this purpose. So 12,000 and some is the low end, and the high end is over 17,000. So you have a 12,000, a couple in the 13,000 range, a couple in the 14,000 range, a couple in the 15,000 range, and then 1 over 17 close to 18,000 is the highest. So you see, like, a roughly 40% difference from the lowest number of voxels to the highest. And that create requires then a little bit of a bespoke adapter portion of the model for each person because they just have literally different numbers of input number. The vector that is measured is a different length for each of these individuals. So the adapter to then get to the shared latent space has to be a bit different for each individual. But once they create that shared latent space, then they are able to do an additional person with just 1 hour of data. So the key finding there is that they go from a a re like a previous technology, same dataset, interestingly, same raw information. These are, like, the same sessions that were recorded, but they're able to go from a version where it works at 30 to 40 hours, which is obviously prohibitive for most usage, down to now they can get it to work with 1 hour of data because they're tapping into this, like, shared latent space that they've created from other individuals. So I think that is pretty interesting. I asked him a question about, can we say anything about how similarly people perceive the same thing? And he said, unfortunately, in the dataset that they were working with, there's very little overlap between the different images that people saw. So on the 1 hand, you would say, like, in in some sense, that kind of maybe suggests that there is, like, a high level of generality because they're able to get this shared latent space to work even when people mostly didn't see the same images. On the other hand, it kind of creates a limitation in terms of, can we say, well, the inner product of my Claude 3, conception and your Claude 3 conception is, like, unfortunately, in this study, people just didn't see the same thing enough for them to be able to do that sort of analysis. But I did find it quite interesting, truly profound. Quite interesting is an understatement. It's a profound observation that you can get with only 7 individuals whose brains vary in size by up to 40% to a shared latent space that is general enough that you can just come plug another individual onto it with basically 1 hour's worth of calibration data from an fMRI.

Dean W. Ball: (56:15) Yeah. No. That's that's wild. It really is. And I think we're just scratching the surface there. Like I said, I I don't know that we're gonna get to my Mozart and your Mozart or whatever, but I also think that there's a really wide space. There's a lot of surface area that you can reach. So yeah. And anything that makes it easier to collect data is particularly appealing because having to have people go through these things for 30, 40 hours is an unreasonable collection. So I suspect that we will see an acceleration of all this pretty soon. It's going to happen pretty soon, and I don't know that EEG will be the technology specifically. There's 1 other approach called FMR's functional near infrared spectroscopy. If you almost think of it as, like, fMRI, but that you can wear around in a consumer device, It's not gonna give you the 3 d depth that fMRI is, but it's doing the same thing where it's blood oxygenation to measure brain activity. It is actually pretty simple. It it's pretty comparable to EEG in terms of what it can do and in terms of its cost and and things like that. So it could be that, but I think that there are devices that I can imagine existing that are not the full brain computer interface that we're all dreaming of or, like, what Neuralink is doing, but it get you to some pretty interesting directions. And by the way, just as a side note, what Neuralink is doing is really kind of just an invasive version of everything we're talking about here. So instead of putting the EEGs or the electrodes on your scalp, they're putting a lot of them, very high density, implanted in the brain. With some silicon on board, they can do some of this ML sensor processing right there for latency purposes. So what you saw with the guy playing chess on a computer with an implanted device is very impressive, and it's amazing to see some awareness of these issues coming up. Also, shocking.

Nathan Labenz: (58:14) You wanna describe that just in case people haven't actually seen it?

Dean W. Ball: (58:17) Yeah. There there I go thinking that everyone's as as engaged in this as I am. So, yeah, this is the first patient of Neuralink. He's a young quadriplegic. Very sad case. He had a diving accident or something like that. Doesn't have any use of his arms. And he had the Neuralink implanted and was able to manipulate standard Windows computer. If you've ever used Windows, this is exactly the same in your vain. It's not some special software. He's just using Windows to move the cursor around to play chess. The first night that he got all this hooked up, he stayed up all night playing civilization 6, which I can relate to. And yeah. So just this amazing ability to more or less fully use a computer. And with devices like this, implanted typing has been demonstrated, not in a way that a consumer would ever wanna use, but typing has been demonstrated with noninvasive technology. You can do that, with noninvasive EEG. But being able to just fully manipulate a computer and live at least a a digital life surely with the brain was what he was able to do. And, again, amazing, but not something that would come as a galloping shock to anyone who's been paying attention to this. We've seen stuff like that before. And a lot of what Neuralink actually has done in kind of a classic Elon Musk fashion, 1 of the most important innovations that they have done is the automated surgery device, the robotic surgeon that can do this more or less without human intervention. There's Elon Musk thinking about not just how do I bring innovative technology, but how do I change the cost structure? He's thinking along those lines. So he's obviously thinking about wide scale deployment or something like this.

Nathan Labenz: (1:00:00) Okay. So can we summarize, or maybe there's a couple other high level data points that could bring all this into focus to zoom out and give this the sort of survey view of where we are in the taxonomy. So it's 2 directions in which information can flow. We have focused on reading of the brain states, not yet the ability to change the brain states, except in as much as there is sort of a feedback thing of, like, you measure and then you show that to the user, then they can get into a certain, you know, rhythm and and react to the measurement that they're seeing. But that neurofeedback is not direct manipulation of the brain state. And then within this reading half of the equation, there's a lot of different technologies. We've got stuff that's outside the skull is subject to just really noisy signal, a lot of challenges with that. But with the EEG, you do get high frequency of signal. Then you've got the FMRI, which is 1000000 dollar machine or whatever, and certainly not something you can wear around, gives you a much better spatial resolution. You can really see what's going on at a at a finer grain level, but less fast. There's some idea that these 2 modalities might be merged, and there could be some really interesting generalization based on that. You can maybe impact that a little bit more. And then in terms of what we can do with it, it's like with just a couple electrodes on the head, you can do basic stuff, Like, are you asleep or are you awake? With a lot of electrodes on the head, you can do reasonably advanced stuff, although it's still kind of clumsy and slow, like, you can type with your brain. Neuralink then is going inside the skull, and and that gives them a much cleaner signal and gives them the ability to have higher bandwidth. That's kind of the the whole value prop that Anuska's talked about over time. Are there other memorable kind of striking demos or products that you think people should understand that are coming out of all this work that they could go check out or watch a little demo videos of?

Dean W. Ball: (1:01:57) Yeah. I would say robotic control is definitely always worth looking at, and and you can find if you just Google EEG robotic control, there aren't really devices. There are not, like, marketed devices to do this, but you can find a lot of laboratory settings where it's been done. Beyond that, there are all kinds of interesting headbands, things like that that you can wear sometimes, like headphones too. Over ear headphones is very common with a band over them, and they integrate the EEG into the band that goes over your head. There's a lot of products like that out there, and they're not fantastically expensive. They're not super cheap. Usually, somewhere between 500 and $1,000 would be roughly my estimate. In terms of the brain computer interface, though, the company that I know of that is furthest along in this regard is a company called Emotive, e m o t I v, and they are the ones that I was referencing earlier. It's a little DIY that you can buy them and use them at home and pair it with software that allows you to do some very low level manipulation of objects. You mentioned though, like, what might be the next sort of level of generalization that we reach as we get better data and we apply better model architectures? Obviously, I don't know. There's been some interesting work on this in the lab is better decoding of language. So the best way to put it is mind reading. So we've seen that with image. There have been a lot of MindEye is an example. Meta has had some research in this regard. There's a lot of people that are basically decoding images that you're imagining in in your mind's eye into using AI into a predictive digital image. Basically, doing the same thing with text word by word at first, eventually getting higher bandwidth and higher latency. That seems achievable. And that seems very interesting, particularly when you think about it in the context of communicating with an LLM. Prompting an LLM with thoughts. As we were saying at the beginning of this conversation, the challenge, but also sort of opportunity with a frontier LLM is how good is your question? And the better your question, the more precise and tailored your question, the better an answer you will get out of these systems. So some people just don't have the communication capability to ask exactly the question that they want to, but they can surely think of it. So can that start to be translated into questions that that could prompt an an LLM? I don't know. Maybe. Certainly, it seems to me that basic motives could be. So I think about something like this this device that came out at CES this year, the rabbit r 1, which uses that thing that they call a large action model, an LLM that translates what you want into intents, basic intents. I also think about something like the iPhone and Apple devices in general have these automation layer called shortcuts where you can make little modular pieces of software to do a simple thing. Could we start to translate thought into basic actions like that? Maybe not my thoughts on a painting by Picasso, very complex set of thoughts, but maybe I wanna see what my schedule is for today. I wanna see what the weather is for today. I wanna turn the bedroom lights, things like that. Can we translate that and then use existing AI infrastructure, sort of automation infrastructure currently being built to actually just perform that? And, essentially, that might feel shockingly like telepathy. If you can think, I wanna turn the lights downstairs off, and they will turn off. That might feel shockingly like telepathy. So it in a certain sense, the really stone cold reality of where we are, but everything feels like it's converging to this point where a real qualitative leap is possible. At least it seems to me like it could be in the relatively near future. But to your point, to get to the the brain computer interface that we all really want, you need write access, not just read access.

Nathan Labenz: (1:06:04) Yeah. Let's sketch out a little bit more the how does this tip over the next couple of years? It seems like the model is basically, you know, this is a bit hackneyed now in the AI space to put everything in terms of what GPT level are we at. But it seems like we're at GPT 1 on this, maybe between 1 and 2, where it's like, g p t 2 was just barely starting to be useful. When fine tuned, you could do some interesting things with it, but you couldn't do much with it. Right? It certainly wasn't doing much in the way of reasoning. It didn't have have the few shot learning ability that emerged with g p t 3. And so you could do classification type tasks or something like sentence completion perhaps, but very limited application. And it seems like we've sort of managed through mostly basic science to get to the point where we've got a similar level of capability. And you've got links here in the show notes that we can include so people can go check out in visual form, like, what these devices look like. But there's 8 or so different consumer devices that have seen enough there where they're like, somebody's gonna wanna buy this. Let's get started building a company on it. And where I see this really changing is akin to robotics too. The amount of data that is going to be generated as the products hit the wild, even with the early adopter set, seems like it just goes vertical compared to what has existed before. Keeping in mind that mind eye 2 is 8 patients, a total of 200 and some hours in an MRI across 8 individuals. Now you've got things that are going consumer. So you've got orders of magnitude more data flowing in, and you have people that are actually attempting to use them. And so you're gonna start to get feedback on what is working, what is not working. And so the regime the data regime is just totally changing. So I think there's ultimately a very simple story. It's like, we've gotten through enough basic science to get to the point where there's just this kernel of utility, which is going to tip us into a a much faster bootstrapping dynamic where we're gonna soar through orders of magnitude of data available. That, of course, we've already got architectures that can probably decode that signal if there is enough data to learn from. The same machine learning techniques are working everywhere. So unless there's some very odd reason why they won't work here, the data is about to come online, and that's gonna be the big unlock that's going to allow for just tremendously more utility. And we might hit some limits around just how good of a signal can we ultimately get, But it seems like at a minimum, we are headed for this sort of stored procedure triggering that you mentioned for image decoding is obviously what you're seeing is already quite decodable. What you're thinking about in turning that into verbal form seems like very achievable, and then even control seems like it likely gets refined to the point where it's practically useful, if not, like, super smooth, just based on collecting a ton of data and applying known machine learning techniques to those signals. Is that basically your world model, or what how would you refine what I just said?

Dean W. Ball: (1:09:25) I think that's, you're pretty much spot on. The GPT 2 comparison is an interesting 1. I recall back in my housing policy days, kind of YMV adjacent. Build more housing is what YMV means. Build more housing to make it cheaper, To put it simply, I tried to use GPT 2 to analyze municipal zoning codes, segments of municipal zoning codes to give me just a thumbs up or down on, you know, how restrictive was it, how not restrictive was it in an automated way. And the answers that you could get out of it compared to now are just so simian. It was like, good, bad, kind of thing. And you look at where we are now. The difference, of course, is that language is a kind of ground truth that you can refine your understanding of over time. And the question really is how consistent is the language of thought across people? I think that's the fundamental barrier potential barrier is how calibrated does this have to be. And, ultimately, if I have to go get an fMRI scan to make any of this useful to me, then that kinda really changes it a lot. Maybe I'll do that. They're like the craziest thing in the world, actually. How long does it take to go buy an Apple Vision Pro? About half an hour.

Nathan Labenz: (1:10:41) Demo is 27 minutes. Yeah.

Dean W. Ball: (1:10:43) Yes. How how hard would it be to put an fMRI in the back of an Apple store or something? I don't know. But still, it changes it for sure. It the level of calibration to me is really the big open question here, both on the interpretation side and the imaging of the skull side. But I have to think that substantial progress can be made and that it is possible to at least translate impulses, desires, basic desires, and that that can be fed into various kinds of AI architectures that can take actions on your behalf. That just seems super possible in the near term. I would be shocked if that didn't happen in the next 5 to 10 years, Maybe less. Maybe a lot less. That that that basically is not an all.

Nathan Labenz: (1:11:30) Yeah. I see a parallel also between the fact that all these devices exist and the current state of AI agents broadly, where it's like everybody sees where the technology is going. In the case of the agents, it's like, yeah, g b d 4 wasn't quite trained till there's this weird juxtaposition where it's like closing in on expert level on things like medical diagnosis, but then it sometimes can't click the right button on a very simple user interface, or it gets stuck. It gets into, like, loops that it sometimes can't break out of. Like, why is that disconnect there? Presumably, it's because there wasn't really the sort of task completion mid length episode data available to train the first version of g v d 4 on. I strongly believe now that that big tech leaders are investing heavily in creating that sort of mid length episode data. And that in all likelihood, I would bet reasonably confidently that the next big shift is going to be that those sorts of things are gonna start to work even if, like, the MMLU score doesn't go up that much in the immediate term. And sort of close that thread. When that happens, then all these agent products start to work dramatically better all at once. And that gets us back to that dynamics question we talked about earlier. On the brain computer interface side, it it does seem similar where people are developing all these different form factors. You've got glasses. You've got helmets. You've got headbands. And they all kind of don't work that well because there wasn't enough of the right kind of data to train them on, but you also had to get these things out there to get that data. I don't know that OpenAI necessarily had to have all these agent products created. I think they probably could have created their own data. But in this field, you actually need readings off of a lot of brains. And so it seems like we're in this kind of similar space where the hardware is starting to ship. It doesn't quite work. But again, it's gonna collect a lot of data, and then you can imagine a lot of these things turning on relatively quickly and and being just a lot more advanced, perhaps without even necessarily needing major upgrades to the hardware.

Dean W. Ball: (1:13:32) If you

Nathan Labenz: (1:13:33) already have a 32 channel thing, that may well be enough for a lot of use cases if you can decode the data effectively. So maybe it'll take more than 32, but it it's do you know what what is the number of electrodes that the guy from, the NERLYNX patient had?

Dean W. Ball: (1:13:47) That's a great question. I don't know exactly, but it's a lot. They're very dense. It might be, like, 1000 plus. I'm not actually a 100% sure of that, but it's quite a bit because of the the sort of fibrous way that they're doing the implant. It is, like, very dense and fine.

Nathan Labenz: (1:14:03) Google both traditional search result and generative search result, comes back with a 24 electrodes.

Dean W. Ball: (1:14:11) Yeah. So that's a lot that they're doing. Yeah. The problem though with that, obviously, is that it's a lot in a very specific place, and there are long term issues with that. Invasive is very promising in terms of capabilities, but you gotta have them all over the place. The the EEGs, they do have the same problem that the noninvasive ones have when they're excited in the skull. They can't read everything in the brain. They can read in very local areas. They can read down to the neuron level invasively, but they can only read locally. So same problem. And, also, obviously, like, the idea of connecting an EEG in my brain to the Internet is absolutely terrifying, right, just from a cybersecurity perspective. So there's obstacles there too. Yeah. But 1 other thing that I I do think about though is in as much as these various platforms have the problem with foundation model agents is that their test environment is the real world. The actual full they don't have a baby version of the Internet or computing environment that they can interact in. They have to use our computing environment, which is weird and has all kinds of history to it and affordances that are for us that they don't necessarily need, that probably ultimately serve to confuse them and complexify the environment for them. It is why I I mentioned the shortcuts idea from Apple's platforms, because what they have kind of done is modularized the functionality of not just their whole operating system, but developers can plug into this too. Third party apps are modularized too. And Android, I believe, has something similar. It's not just Apple. That's the kind of thing that, again, it could, like, take off pretty quickly, and it could actually be the local. So it could be fast. You could do this inference locally in theory if you had a sufficiently powerful compute onboard, but it wouldn't be like, you don't need, like, an h 100 to do this kind of thing. Yeah. And that and then the other shocking thing is just how little compute has been applied to this problem. Yeah. There's low hanging fruit. I have no doubt about it. So, yeah, that takes us to the right section of this, which from a technological perspective is actually pretty simple for me to explain in a narrative way because it's the same basic principle. We wanna manipulate electrical and magnetic fields. The problem is that everything I describe in terms of getting signal out of the brain in a clean way, those problems are magnified substantially for getting signal into the brain because that's really what your skull is intended to do. It's not so much to keep things in. It's to keep things out. So there's direct current and alternating current stimulation transcranially. This is noninvasive deck. Transcranial magnetic stimulation would be the magnetic field equivalent of that. The problem is that there's been some promise. There's been some stuff that's been shown in the lab, But it's either very expensive hardware. It's not writing that good hardware curve like the electrodes are, by the way, which are getting denser, cheaper all the time. And the signal just diffuses. So it's just erratic. It's impossible to focus, and it's very varied between people. So I don't really see the magnetic or electric field manipulation as being all that promising in the long term for for neuromodulation. Some people disagree with what's called transcranial magnetic stimulation. Some people disagree and think that is gonna be the path if we can just get the cost down. But right now, the cost is very, very, very high. So it's just not what I'm focusing on because I'm interested in things that seem like they could happen in the relatively near future. There is a technology, though. This really was my exclusive focus when it comes to neuro noninvasive neuromodulation. Is called transcranial focused ultrasound. It's a very old technology. It goes back a 100, but, really, its rebirth is in the past 10 or 20 years. What this is firing very high frequency sound waves at the brain far outside the range of human hearing, far outside the range of the hearing of of dogs and cats, though there are animals that can hear it. Rats, bats, I think, can hear it too. But that you can fire at a sufficient frequency and amplitude and pulse to target pretty precisely into the brain and deeper than anything else can. You can't get to deep brain regions. You can't write to the hippocampus, which is memory from with this, but you can go a few millimeters, as much as a centimeter, into the brain, and you can target it very precisely. So it's a few millimeters to a centimeter of depth and then millimeter level resolution spatially. So it's a very promising technology. Also, it's cheap. And, also, much like the electrodes, ultrasonic transducers are on a good hardware curve. They are getting cheaper and denser year by year. The weird thing about TFUS, as it's called, it's not very well understood how it works. Yeah. And it's just weird in principle that firing high frequency sound waves into the brain makes you that changes your thought in any way. That's just weird, but it does. There's some who theorize that it changes the electrical, receptivity of brain tissue in certain ways that just make electrical signal flow more readily. There's a theory that it just stimulates neurotransmitter release in the targeted regions of the brain, but no one's really found any safety problems with it at reasonable dosages. At high dosages, it has a thermal effect as we see with other ultrasound. At a very high dosage, it can cause brain tissue to heat in a way that you probably don't want. And the other thing from a safety perspective, of course, is that all of these things are done 1 time in a lab. Nobody has modeled what the long term effects are of using this, for example, every day. But it's very promising. The FDA says it's safe below a certain level, and it's been shown to do some shocking things in my mind. So we've talked a little bit about improving bandwidth. Maybe the single most interesting finding to me of this whole chain of research that I've done is this finding, that with TFUS at the sense at the somatosensory cortex fired at the at specific regions of basically your tactile sensation, the part of your brain that measures tactile sensation. You can perform tactile discrimination tests. So, basically, you cover the user's eyes and rub a pin on their hand and then rub 2 pins really close together and if they can distinguish between the 1 pin and the 2 pins. And TFUS was shown to improve that when it was being fired at the brain and also had some offline effect too, and offline and versus an online effect. An online effect is do we observe an improvement in the desired cognitive activity when the treatment is actually being applied? Offline is do we observe it afterwards? And 40 minutes afterwards, the discrimination had attenuated a little bit, but was still there. So it had a long term effect on what it would seem to me is the brain's channel bandwidth to the hand. So that is very interesting. There's a lot of other things that it's done too. Mood, focus, visual acuity, and visual discrimination tasks in the same way that I described with the tactile discrimination. You can actually induce complete changes to the visual field or or at least noticeable changes to the visual field by inducing these what's called phosphines. If you imagine when you shut your eyes and you lightly press with your fingers on your eyelids, those kind of blobby shapes you see, those are phosphines. You can induce that in a person's visual field using TFUS. Auditory discrimination also goes up. Another finding that interests me too is the people can discriminate between vibrations at different frequencies that are very close together. They can do that discrimination not substantially, but marginally better with TFUS. And all of these studies, for the most part, are TFUS applied at fairly low dosage levels, well below where that thermal effect is seen that I mentioned. So there might be an interesting range of capabilities that you could obtain if you were to go beyond that dosage level. And I've talked to both of the leading scientists in the world who have had the top TFUS papers in the last 10 years, and they've both said, we are very confident that you can increase dose safely at this point. That's TFUS. And can it write thoughts to the brain? Like, absolutely not. And I don't even wanna suggest that I think that's, like, remotely close. I think that's really hard. That's my intuition. But this idea of being able to improve not just mood, but things like channel bandwidth in the senses. That's really interesting to me. So, yeah, any questions?

Nathan Labenz: (1:23:32) So you had said that the skull is a huge barrier in the most obvious way to getting these signals into the brain. It seems like that's also a problem on the way out, but we sort of compensate for it by just, like, having a bunch of electrodes all around and collecting the mess and then trying to clean up the mess. But on the way in, where the precision really matters, that becomes a a big barrier. Is there an evolutionary story for why the brain would be good at this, or do you think this is just, like, an accident of biological history that the brain blocks these electrical signals?

Dean W. Ball: (1:24:07) I I think it probably has to do with the fact that it's probably useful for all kinds of other reasons for bone, just in general, to be low conductivity, and the skull is made of bone, and you just benefited there. I have no idea, though. That's an interesting question. I'd love to ask an evolutionary biologist that question. I will say, though, with TFUS, the exact same imaging problems are present. Weirdly enough, in certain cases, the skull can actually have a beneficial effect on getting ultrasound signal into the brain. If the kind of signal you're trying to create and the specific region of the skull is just right, it can actually have a lensing effect. But in general, you still have to deal with signal processing and sort of attenuate modifying your signal based on a dynamic model of the skull. You still have to do that.

Nathan Labenz: (1:24:56) It is crazy that this ultrasound is basically just vibration. Right? Sound is vibration. This is just high frequency vibration. And I don't know if you know the frequency off top of your head that this operates at. Like, middle a or middle c, the tuning note in the orchestra is, like, 4 40. Right? So this would be, like, you go up several octaves, we can definitely still hear up into the range of a few thousand hertz, it seems. And then I guess this would be, like, north of 10,000 maybe?

Dean W. Ball: (1:25:27) I think it's 5 to 10, usually, in that range. 5 to 5 to 10,000 hertz.

Nathan Labenz: (1:25:32) Yeah. It's weird. Right? At a neuron level, this basically amounts to shaking the neuron. Right? It's like this thing can sort of fire off its electrical signal a 1000 ish times a second or sometimes even faster as you said earlier. Now we bring in a literal physical shaking that comes in at an even higher frequency than that. And that seems to kind of wobble things loose and just this is stimulus. Right? Is there a suppressive effect that can be achieved this way, or is it is this purely a stimulus technology?

Dean W. Ball: (1:26:07) Most of the literature I've seen is for excitatory signals, but, no, you can do inhibitory effects as well. So, yeah, it it you can block certain things too.

Nathan Labenz: (1:26:16) And, Dure, do you have a sense of what is the difference? If I'm imagining shrinking myself down to neuron scale and sitting in this region of the brain, it seems that the vibrations coming through, like, how would I know whether they're supposed to make me do stuff or not do stuff in my particular local area?

Dean W. Ball: (1:26:35) Yeah. That's a very good question. I'd love to ask 1 of the scientists who helped me with this research. I'd love to ask them this question. That's a great practical question for them. My general sense is that most of the inhibitory stuff is not that much inhibitory stuff that actually gets done. Let me actually quickly pull up my notes. I'll get you an answer to that question. On what specifically has been inhibited at least, because that might answer the question. Yeah. So the inhibitory TFUS, most of it has been used for pain attenuation. So it's looking at the parts of the brain that are sensing pain and are targeting that. And that seems to work for whatever reason. I don't think that is well understood at all. I don't know for sure. But that also has the side effect of reducing motor time, reaction time for various kinds of motor tasks, can reduce some of

Nathan Labenz: (1:27:31) the things we were talking about. Yeah. It seems like there's also even if it was purely stimulating activity in a particular region, it's an important fact to keep in mind that the brain is self regulating in all sorts of ways. Right? So we see this at the cellular level too. There is a a gene that gets expressed to suppress the expression of another gene, and in at the brain level, there are regions that activate to suppress other regions. And so you can imagine, even if you could only turn things on in many ways, that might allow you to turn things off if you can figure out the indirect pathway to get there. I don't doesn't sound like that's what's going on here, but conception

Dean W. Ball: (1:28:09) It it might well be what they're doing. That that that could very well be the case. It is definitely inhibitory in terms of activity, though. And I don't have a good model for why that works. The excitatory is at least intuitive. If anything, it's a little like, well, if you shake the TV set, it generally fixes the signal. There's a primitive part of us that can understand how that works. But, yeah, the inhibitory is is a bit more of a puzzle to me as to why that works.

Nathan Labenz: (1:28:36) So this question of writing thoughts, I'm not sure we ever would want this exactly anyway. But putting that aside, I can start to imagine how you might close the loop here. Maybe there are some barriers that I'm not immediately seeing. I'm sure there are plenty of challenges that would have to be overcome, and I'm not seeing any fundamental barriers. The resolution on this technology was down to a couple millimeters as well. Right?

Dean W. Ball: (1:29:03) That's right. Yeah. And so that's notably,

Nathan Labenz: (1:29:05) like, the same scale as the voxels that come out of the fMRI. And so if I connect this to the mind eye paper again, and I'm like, okay. There are 12 to 17,000 voxels per person back in the visual cortex. And I got a mini lesson on this in that episode around how much semantic information is encoded in the visual cortex. Is that just like raw sensory workup until kind of lines and edges, and then the front of the brain does the sort of that's a tiger type stuff? And Paul, the author of this paper, he said, no. Still in the back of the brain, as part of the visual cortex, there is a lot of semantic information Yeah. That understands,

Dean W. Ball: (1:29:45) like,

Nathan Labenz: (1:29:46) what it is that, you know, and actually recognizes conceptually what it is you are looking at. So from these 12 to 17,000 voxels, 2 ish millimeters cubed, they're able to extract both a general sense of the image that you're looking at, like what are the colors and what are the regions of the image, which part is dark, which part is light, whatever. But then also straight from that reading, they can predict a caption for the image from the brain state. And that's because there is enough semantic information there that not only do you have this kind of blurry, purely visual information, but you also have this conceptual information. So, anyway, point there is that you could read that at a 2 mm cube level, and now you're saying that you can also focus this signal down to that same kind of scale. It seems like the possibility, at least conceptually, exists to create a feedback loop where you might say, okay. I want to send some signal. How do I know that I sent that signal? I also need to then read the signal back. So you can focus the signal down to a couple millimeters region, but maybe you can't do that 10,000 wide. Maybe you would have to do that 10,000 voxels at a time to induce meaningful higher order concepts in the brain. But it does seem like you at least now start to have some ability to make a perturbation and then also read it on the other end. And if you have a sense for what it is you're targeting, I want you to be thinking about 3. There seems to be at least some ability to start to be like, okay. I'm gonna send a signal and then decode what states arise from that. And was the person thinking about Cloud 3? Yes or no. Then I update my network that is deciding what signal to send in based on the signal that is later read out. And this seems like a leap, but given what we have seen work, it doesn't seem too crazy to think that you could start to close this loop with, was I able to induce what I was trying to induce? Maybe the reward is too sparse. Maybe you need to have, like, broader regions subject to receiving a signal at the same time to really get anywhere. If you if you can only target 1 2 mm cube, maybe that just gets lost in the overall broader state of the brain. But I'm starting to at least imagine how you can close a loop and begin to reliably induce certain things because you can read those things, and that gives you some ability to correct or to gradually learn how to do the inducement in the first place. I'm not sure what other big barriers there would be to doing something like this. But it it also seems like there's plenty of possibility that things could just be quite odd and that maybe you don't actually need that. If you really could figure out how the brain works, like, these sort of thoughts arise somewhere, and they presumably arise locally first. There's some sort of trigger that ultimately propagates through broader regions of the brain, but starts with some sort of input or some sort of signal that, you know, becomes dominant in the moment and and ultimately rises to the level of consciousness. It It seems like it's not crazy to think that maybe you could find the key levers through this process of attempting to now would we do this on humans? Maybe not, but you could definitely do it on monkeys. And I bet we're similar enough to monkeys. We could even create, like, a shared latent space with some monkeys, at some point to get to the point where you could say, yeah. I'm just gonna run this loop a ton. I'm gonna try to induce these things. I'll measure how close did I come. I'll update on that. And even if I don't have the ability to stimulate that much of the brain at a single time, potentially, I can find with a key that unlocks the lock to induce the the states that I want to induce. Possibly crazy, but what, if anything, would say that can't happen?

Dean W. Ball: (1:33:37) The main thing is that we don't really understand at all. As far as I know, we don't have a good understanding of how information is encoded in the brain, and it can be read back when you're doing things like the mind eye, the fMRI, and especially when you're pairing it. Like, when you're looking at the visual cortex, you can kind of, like, extract out of it. But actually, the right process, like, how do you do that? Where does the right happen? Probably more than 1 place. Right? Like, it the thing that distinguishes brains from even the most sophisticated neural networks is just how much crosstalk there is and how much weird resonance and just all sorts of feedback that's constantly being conveyed. And I don't think we have a good understanding of that at all. Would I be shocked if you were able to induce a simple thought in someone's mind by firing ultrasound at v 2, the visual cortex? Well, no. I would be pretty surprised if that worked. It it wouldn't be a galloping shock to me because of the informational aspect of that. I'm not sure that high dimensional information can be, first of all, encoded in instructions to ultrasound to ultrasonic transducers and then successfully transmitted into the parts of the brain, probably several, where it would need to go in order to actually write information, particularly of any I'm I'm thinking of the moment in the matrix where Trinity downloads the instructions for how to operate a helicopter. Thinking of that as being your your North Star. That seems hard to encode in all the various ways they would need to be encoded, but I wouldn't rule it out. At the very least, though, like, brain activity is possible to stimulate, and it is possible to change the mood of the wearer, the level of focus that they have, things of that nature. All possible. We don't know how much and how high dimensional that can get. Obviously, like, a good mood is a pretty general concept that could be refined quite a bit. There's a few TFUS devices that are on the market. There's a company called Prophetic AI that is based in New York. They've had a lot of buzz on x and other social media with some of their various announcements. And there's a couple things that are really interesting about what they're doing because it is actually kind of the convergence of a few of the things that we've talked about in this discussion. First of all, they have developed what they call an ultrasonic transformer, which is a transformer based system trained on EEG and fMRI data, which takes as its input EEG readings from the headband that they're going to sell and outputs instructions to the ultrasonic transducers also on that same headband. So it's inferring your brain state, and then it's turning that into instructions for neuromodulation. And their ambition is to do this with lucid dreaming. It's actually a pretty easy signature. From an EEG perspective, this is not hard. Like, you can absolutely recognize that somebody is lucid dreaming while they're sleeping because the the neurocognitive of sleep is pretty consistent, pretty easy to recognize even for low channel EEG. And then there's a spike of gamma waves in the prefrontal cortex. It recognizes that spike, and then it directs the transducers to just keep firing to maintain that brain state and keep you in that lucid state. They might have slight corrections to that description, but the basic idea is that it can keep you inside of a lucid dreamy state. They also have ambition to to do quite a lot of other stuff, conscious experiences of of all kinds, much kind of higher dimensional conscious experiences. They wanna start with focus and and a positive mood, but they have ambition to go beyond that. A lot of other people that are operating in this space have ambition to go beyond that. And, again, you're writing a positive hard record. So just like you are with E.

Nathan Labenz: (1:37:51) Yeah. The how high dimensional is thought is it's really I'm thinking about the representation engineering work that recently came out of Case and other there's, you know, a bunch of authors on that paper. But they identify these high order concepts through clever, but honestly, it's not even that crazy of a technique where they create a bunch of different contrasting pairs that show, like, the presence and absence or the positive and the negative of a a concept of interest and then create enough pairs of those and then run them all through and then look at these intermediate states and then take the averages and are able to basically say, okay. This appears to be the general direction trying to kind of let the noise cancel out that represents the direction from unfairness to fairness or the direction from sadness to happiness. And then they can start to use that vector direction to detect those states in further downstream inferences, blind. Right? Just looking at the activations and saying, is this a happy state, or is it is it an unhappy state? And then they can also start to layer those in and shape behavior based on just adding it to the whatever the the thing is doing at a given time. If you just add on the happiness direction, then you can see that you actually steer the the downstream model behavior in a reasonably intuitive direction where it seems like, okay. Now it is actually generating happier outputs. So that is way, way easier to do in a neural network than it is to do in the brain, and the dimensionality of that is pretty high. So it might just be an engineering challenge that is, like, at least in a noninvasive way, just too crazy to get to. But it does seem like we have something pretty similar going on. And you're citing all these examples where it's like, you're doing that at basically a low dimensional, very crude sort of way. And as we get into these higher and higher level concepts, I I also think about the anthropic sparse autoencoders with this where they show that, basically, from these high dimensional states, you wanna identify these human intuitive concepts. And the more space you give the sparse autoencoder, the more concepts it will find. They call this feature splitting. So if you don't give it that many if you give it, like, a limited number of concepts that it can differentiate its activation space into, then you'll get high leveled kind of more general concepts out. And if you give it a lot more space, then the features will split, and you'll start to see these very granular level representations. And it seems like we're just operating right now at a very, very general level. We have not yet got to the level of resolution where the features can split. And so we're making these very basic modifications to mood, but it seems like it's really a question of resolution more than anything else over time.

Dean W. Ball: (1:40:48) Yeah. Resolution and and it might also just be data. The the same data problems that we talked about earlier, and then obviously with TFUS, in particular, that's even more limited of a dataset. It might be some combination of all those things. And yeah. But I I don't know how far it can go. In principle, any conscious state is inducible, but how far can that go with noninvasive? My guess would be somewhere between exactly what I'm experiencing right now at the high end and being happy at the low end. I would suspect you can do more than that, more than being happy, less than what I'm experiencing right now. But where exactly? I think we just have to find out. It is, though, possible. It takes some time to to even get your head around what that would be like. The closest thing is probably drug use. That's why I I really don't wanna say that this is, like, using a drug because I think that this might be much more than that. And I don't even necessarily mean illicit drug use. I just mean something that's meant to enhance your mood. But even then, my guess would be that because of the sort of slower mechanism of action that most pharmaceutical mood drugs have, it's gonna feel like it's coming from inside you more. Whereas something like TFUS, you will feel good or focused suddenly. I don't know exactly how long the feelings persist. I would be shocked if it were, like, a steep drop off, like, a completely, like, cliff, but it is also probably fairly quick that it drops off. And, certainly, I I would guess that the experience of using something like the prophetic headband to lucid dream, if you use that every day, it would be like taking LSD every single day, and you would eventually go into a Sid Barrett doom loop doom loop would be my guess. But, yeah, how far can you go? I I have no idea. It's it's very early days. This is very much at the frontier. At the same time, eminently degradable into consumer hardware, not expensive, and FDA approved below a certain limit. I'm happy to explore this more. That might be a good segue into some of the policy. I am a policy person.

Nathan Labenz: (1:43:00) Before we go into policy, let me just ask 1 more kind of technical intuition question. If I had to make a high level guess on everything we've talked about, it feels like I would put my money on. We're going to have pretty good brain rating in consumer devices over the next few years. Because the hardware is already there, and the cost curve is good, and the data is about to explode, and the best thing to interpret 1 neural net is another neural net. On the other end of the right or the modification to brain state side, it seems like resolution and just the overall ability to accurately send the signal that you wanna send is limited and seems like it's probably gonna continue to be limited relative to the bandwidth that you would actually need to create really rich input signal to the brain from outside the skull anyway. And so we're probably headed for something that is, like, fairly crude and sort of valence level, but we're not gonna be invoking or inducing thoughts of Claude 3 opus with precision with those sort of noninvasive techniques. And so if those things are to be done, it seems like they would require invasive techniques. And at that level, then you can actually send the signal where you want at the obvious cost of having to implant thousands or potentially many thousands of electrodes into the brain. But what would the path be if there is to be a path to something like the matrix moment where you could randomly download skill sets or knowledge bases or whatever or even just create the sort of mind control to get people to act in whatever way you want them to act. With the current technology landscape, that seems only plausibly feasible through invasive methods.

Dean W. Ball: (1:44:55) Yeah. I totally agree. The only thing I'll say just as a caveat is that the spatial resolution of TFUS and the spatial resolution of EEG are basically the same. There might be practical differences in spatial resolution that I'm not thinking of. The obvious 1 being that with with EEG, you can't differentiate between all the neural signals between individual neurons, but you are picking up the collective the aggregate result of their electrical activity. Whereas with TFUS, maybe it is the case that you need neuron level or close to neuron level targeting to create really rich experiences. But I don't know. At the same time, neural networks tend to have, like, a fair amount of redundancy. Would you say that's, like, a a fair observation that there's possible that you don't need to get down to the level of the individual neuron to do quite a bit? So Yeah. That but in general, I agree with your intuition. That's about where I am too. Cool.

Nathan Labenz: (1:45:55) Then let's get to policy. And also, I I wonder if you have any intuition for sort of as this technology maybe follows its natural course, how does life start to change? Have you started to game out dynamics at all and policy will be a part of shaping those dynamics. I I used to ask people all the time, if the Neuralink had reached 1000000 patients and was generally considered to be safe, would you get 1? And interesting for people that are, like, very much on the frontier of AI, I get very different answers there. But some of the interesting answers that I got were rooted in the idea that I'd have to. How else would I keep up? So I feel like there's some game theoretic aspect to this, and policy can shape the sort of game theoretic environment perhaps, and maybe can do other things too, you know, including encouraging or discouraging the development of different kinds of technologies. But I'm interested in both kinda how you think this in the absence of governmental intervention, how it evolves, and then what questions government can and and should be asking, and what maybe the most likely things are to happen in that respect.

Dean W. Ball: (1:47:01) Yeah. 1 of my favorite anecdotes I'll I'll go back to the 19 tens for 1 second. The interior design of the average American's house before and you can just imagine, like, an old house. Right? Victorian house, whatever. Beautiful houses in Detroit. Dark red, dark green walls you often think about, those kinda, like, burgundy. Why? Why were all the walls dark back then? That seems weird. The reason is that interior illumination was provided by kerosene lamps, which stained walls. So you had dark colored walls to hide the stain. White walls are a luxury enabled by electricity. My point only is that it is very hard to predict the outcomes of what happens when these things are at scale, obviously. Like, the things I can predict are gonna pale in comparison to what will actually happen. That being said, tough things do seem apparent to me. I always try to think about the legal system first, and this is an area that interacts with the legal system, I think, in really interesting ways. The concept of being under oath changes if we want it to, And that's the question. And a interesting way of thinking about the next 20 years of technology in general is will we want to impose artificial constraints on various aspects of social life and technological development. And I'm not saying that we should. I don't have a strong model of that yet, but it does occur to me that if we're headed towards the Nicholas Bostrom solved world, which I don't think we are, by the way. But if he's right, then 1 of the things you will need is artificial constraints. Because my strong intuition is that a solved world quickly devolved into hell. I really do believe that's

Nathan Labenz: (1:48:54) an issue. Impact the what what exactly you mean by solved world? And then you're probably going there anyway, but more kind of granular specific questions. I take it that you're asking you can imagine a lot of different regimes, but would we require you to wear 1 of these headsets to testify so we can also look at your internal brain states in addition to your, like, spoken testimony? That is that's the sort of thing that you are getting at. Right?

Dean W. Ball: (1:49:18) Yeah. Exactly. The solved world is a concept from Nick Bostrom, essentially post singularity tech techno utopia style thinking, where every conceivable good is available in in enormous abundance and humans have godlike powers to assemble atoms in whatever way that they find desirable or something does. Some form of god just like that. Not us, but I'm not a big subscriber to any of that sort of techno utopian thinking. But, yeah, ultimately, it's a very different kind of society if we can actually know at a biological level whether or not you are lying, for example. Right? That's just a profoundly different society. Do we want that? Is a society with no deception actually a desirable thing? Is an AI model with no deception actually a desirable thing? I'm not confident the answer to either of those questions is yes. In fact, I'm, like, pretty confident that the opposite is the case. I'm pretty confident that some degree of deception is an important part of life. I think some degree of deception is probably an important part of judicial life. In court, we assume that there will be some degree of bending of the truth. And I I think that thinking about court is a fascinating way to think about how society will digest this. It's not the only way, but I just think it's a very concrete way to think about it. What are the limits on something like a warrant or a subpoena in a world where varying degrees of dimensionality into human thought is recorded and, in theory, something that you can be examined for a court case. My instincts on that kind of a question is that it's probably beneficial to a certain point, but there is an extreme where if everyone really has Neuralink and really, truly every thought you have can be recorded in a high resolution compression, you you probably don't want that to be available to certainly not for advertising purposes. That's another thing I think about. Within limits, it seems fine. At the extreme, it seems dangerous to have to figure that 1 out. And my general sense is that from a policy perspective, sure. I mean, if this exists, the Europeans will regulate it. Right? And know that. And half of America at least will want to regulate it. Whether or not they'll be able to is a separate question. Actually, it's probably worth talking about just for a moment what the current regulatory state of all this stuff is. So these are not considered to be high risk medical devices by the FDA by and large. They fall into this category 2, this mid level of risk, but there's an exception to that, which allows you to evade most of the FDA's regulatory processes if you are marketing a general wellness device. So I remember when the Apple Watch came out with its blood oxygen sensor a few years ago. It was during COVID. It was, like, in 2020 that watch came out. Apple was extremely careful, and blood oxygen is an important measure for COVID. Apple was very careful to say, this has nothing to do with measuring for COVID. This is purely telling you your blood oxygen level. Because if you connect the device to diagnosing or treating any specific medical condition, then all of a sudden, you're in a whole different world from a regulatory perspective. Neural technologies have generally gone for this general wellness exemption from the FDA. The FDA has not actually been clear that that exemption applies to them. They've been asked to make that clear, and they have refused to do so, which is a common thing that the FDA and other aspects of American bureaucracy tend to do. Anyone who knows cryptocurrency will also be familiar with this that, like, a regulatory tactic is actually uncertainty. Tactic. That's a form of regulation that regulators use is creating uncertainty and creating gray areas. So that's where most of these neurotechnologies exist right now is sitting in a gray area. They're being marketed. And the reason that the FDA does that, I suspect, is maybe they don't know. Maybe they don't know how they feel about it. That could be true. But, also, for sure, they probably wanna preserve the optionality to cut back on this stuff if they want to and to remove all the general wellness devices from the market in a heartbeat. But at the moment, if you could make a noninvasive brain computer interface that can induce all kinds of conscious experiences, and as long as you're not trying to treat or diagnose a specific medical condition, and as long as you're staying below certain thresholds of DFUS and other things, like, there's certain safety guidelines. But as long as you're mechanically underneath those things, then you can just sell this on the general That's the way that's that's how it currently applies. The final part of your question was about long term dynamics of adopt And, yeah, I think if if these things are cognitive enhanced devices, then there will be evolutionary incentives. There are evolutionary incentives to do all sorts of things right now that most people do not do. Right? Like, we have enough data to know that I have an evolutionary incentive to go on a jog after this podcast. Will I do that? Probably not. That's the threat. Or that I should only whatever Andrew Huberman recommends. Like, all that stuff is true, and yet we don't do it. Right? We don't do it all the time. We shouldn't be drinking alcohol, but a lot of people drink alcohol. So I don't know. And I see this in the AI safety community in general. A lot of people think that legible Darwinian evolutionary impulses are going to drive technology adoption in society, and I think it's more complicated than that. I don't think it is a straight Darwinian algorithm evolutionary algorithm that's being applied here. So that's my read on that. And I also think that there's a flip side. I think the impulse that people have a lot of the time is to think, oh, no. If there's an incentive to use this thing, then everybody will have to use it. And what about the people that don't wanna use it? I I go there too. Right? I have sympathy for those people. I think about the Amish. And I do think that there's probably going to be forms of digital Amish in the future that we need to be thinking about. At the same time, the people who want to enhance their cognition also should have the liberty to do that. And we should want there to be more cognition in the world, especially human cognition. There's a part of me that says, my god. Like, the idea that we would want there to be less human cognition in a world where GPT 5 is right around the corner. I am not sure from what world model that impulse derives, but it is a world model that I could poke holes at. Not that it's wrong, but I could certainly poke holes at it. So that's like my mind is not at all made up on any of this, but that is how I model this

Nathan Labenz: (1:56:16) right now. I know you're potentially just a couple doors down the hall from Robin Hanson, who's also a recent guest on the podcast. And he makes a extremely compelling case that we are in a, what he calls, strange dream time between what are almost sure to be much longer eras both before and after us in which evolutionary dynamics and just the practical constraints of, like, available resources are in fact the dominant drivers of how things unfold. And we're in this weird moment now where we've created way more capital per capita, and birth rates are down, and it seems like the sort of strangeness probably can't last in the course of evolutionary time scales. Then he also it's funny you mentioned the Amish too because he also has a part of his near term world model, which I think is less compelling personally, but you can debate that with him over lunch perhaps where he thinks because of the lower birth rates in general, but higher among the Amish, we may be headed for a period of technology stagnation and Amish domination. So that gets a little fine grained in terms of, like, how, precise the crystal ball has to be to advance a theory like that for me. But there's definitely a couple of interesting lunch debate topics for you now at the Mercado Center with him.

Dean W. Ball: (1:57:30) Well, for sure. And and I think part of his point is, like, not to put words in his mouth at all, but we have a lot of artificial hyperparameters on the way society works, and those things are called laws and policies. And that's what I study for a living. And, yeah, they've created all kinds of unintended weird effects, and that's not to say they're bad or good or really anything, just to say that they exist, and to say that things don't proceed according to mathematical models of the way that history or nature has unfolded in the past for those reasons. Because they didn't have occupational licensing 1000000 years ago, and that does change the way that evolution works at a certain level, at least the evolution of society. And yeah. Like, just 1 other point about the kind of digital Amish thing and and just in general, my read on there's some polling that gets put out about AI and things like that. I don't put a lot of stock in issue polling personally, and I don't think they're coming from organizations that are motivated to find the truth. I will just put it that way about what Americans think. I think it the questions are along the lines of someone's gonna make Hal 9,000. How do you feel about that? It's coming to kill you. It's gonna be here next year. What do you think? 98% of Americans are opposed to like, okay. That's not that useful of a question. But issue polling is also not that useful of a field unless you do it really carefully. But I do think that that these things, adoption of technology is going to start to have a political valence to it. And whether that is coded as AI or whether that ends up getting coded in terms of these neural technologies or the AppleVision pro. Is the AppleVision pro a political statement of a sort? Yeah. I see it as 1 already. I think a lot more people will in the future. So I I don't know exactly how to model that, but I do think that that we should expect this all to become more political, not just about who gets to control the existing platforms, but about sort of whether you are interested in these technologies at all.

Nathan Labenz: (1:59:29) Yeah. Unfortunately, I would love to see the discourse around AI and technologies like this remain separated from day to day political discourse as long as possible just because it seems like everything gets worse once it gets cast through the lens of certainly partisan politics. I'm not sure I agree with your view on polling in as much as I would say, like, my read of a lot of those answers is that I certainly would agree that they're subject to framing effects majorly. So definitely buy that argument. My sense of just the people that I talk to in life and they're outside of the bubble when I get outside of the bubble, their kind of gut reactions to things is that it is negative by default. Yeah. And arguably, that has been shaped by culture and fiction and the terminator and whatever. But I do think there is something quite real there that those answers are getting at. Though, I also do think they're coming largely from a position of ignorance, certainly when you're just doing general public opinion polling. So I always advise people. I don't think we don't have too many chat GPT novices listening to this show. But, again, when I get outside the bubble and present to, like, business leaders or whatever, I'm always just like, okay. First thing you gotta do, spend some actual time with the technology. Like, you need to develop your own experiential understanding of what this stuff is. You can't just have it all be filtered through the media for you because the surface area is just so vast and the the range of different use cases, and it's just so big compared to what you can get in an article or whatever. So you really do need to get hands on with it, feel it, mess around with it, give it your data, see how it it reacts to stuff that is really personal to you, and if that's useful to you. And that also gives you a great sense of its strengths and weaknesses in doing that. Maybe in closing, I wonder if you could recommend some things that people might do to start to get themselves acclimated to this merge technology tree. Again, a lot of people that follow this show will be very familiar with the language models. They'll have a sense for where AI is and some sense of where it's headed. I would guess that most people have never worn any of these devices. Even starting with an Apple Vision Pro, I would still guess that sub 5% of the audience has even done the demo at the Apple Store at this point. So what would you, you know, suggest? Is it, like, going and watching the demo videos on the company websites that we can, you know, put links to these companies in the show notes? Is it going and trying an Apple Vision Pro? Is it what what do people do to start to orient themselves to this and and start to develop their own, not just, like, through the media, not just by listening to you and me, but how do they get their own sense for how they should start to feel about this technology?

Dean W. Ball: (2:02:11) Yeah. First of all, I would just say in response to the polling thing, I I totally agree with you. People are passing their stake about this. That is a fact. I think that the techno optimists like myself, we have an uphill battle to fight. So I don't wanna suggest that the polling is, like, manipulating the reality. I think it's exaggerating a tendency that already exists. That's how I would put it. Anyway, it's a very good question. I've never thought exactly about what other people should do. I guess my somewhat myopic reaction is, like, the path I took was useful enough for me. There's a good book about all this that came out recently called the battle for your brain by Nita Farahany. She's done some great reporting almost about this kind of stuff, and I would recommend reading that. I actually would recommend trying on an Apple Vision Pro. And the reason for that is that you would be shocked how close your gaze is to a kind of neural interface. And it is something we didn't talk about at all, but it is probably the case that the ideal form factor for any of this kind of neural technology we're talking about is probably something that wraps around the head much as the Meta Quest and the Vision Pro do. And so when you start to combine the idea of, well, we've already got eye tracking and we've got hand tracking, and now we're adding in the neural technology, maybe the neural signal doesn't need to be that good to do something really interesting. Right? That's a whole different angle to approach this is a sensor fusion. So that's just 1 note. But, yeah, I would try a VisionPro. I would do that. And I would

Nathan Labenz: (2:03:52) a breathtaking experience. I was really floored by just the demo. And I haven't bought 1 yet only because I'm not sure how much content there is and how much time I would really spend in it, but it was definitely an eye opener for me in the sense that it was, like, akin to a a GPT 4 type experience where I have the Oculus 2. And when I first got the GPT 4 access, I had been using a lot of GPT 3. But the step up in terms of the quality and the, like, oh my god. Like, this is just an arrestingly different experience. It is a similar like, you you have to experience it to feel the difference. I could tell you how much better g p d 4 is versus g p d 3, but the best way is to get your hands on it. Would say the same thing. Even if you you have done, like, relatively recent VR but not done the Apple Vision Pro yet, I would say you do kinda owe it to your own worldview, if not necessarily to buy it, but at least to get that sense of what this thing can do, how high resolution it can be, how immersive it is, just how compelling the overall sensory experience of it is. Because it is genuinely next level, and it it absolutely feels to me like a part of the future. We haven't quite figured out how to use it yet. The experience of it is, yeah, this is definitely gonna be a thing.

Dean W. Ball: (2:05:12) Yeah. No. I I totally agree. And I think that it's not entirely a neural interface, but it's also not not a neural interface. So I would say, like, reading that book, trying an Apple Vision Pro. And beyond that, unfortunately, my answer is boring, which is always try to proceed from ground truth of actual, empirically demonstrated knowledge and not narrative. Because the narrative surrounding this technology in particular is likely to be quite toxic and quite misleading in a variety of different ways. So model it for yourself. That is my best advice. Don't rely on someone else's model, including mine.

Nathan Labenz: (2:05:51) In the future technology scouting business, it is important to, maintain always a high degree of epistemic humility, and I think that's a a great note potentially to close on. D w Ball, research fellow at the Mercatus Center and author of the hyperdimensional substack, thank you for being part of the cognitive revolution.

Dean W. Ball: (2:06:11) Thank you, Nathan.

Nathan Labenz: (2:06:12) It is both energizing and enlightening to hear why people listen and learn what they value about the show. So please don't hesitate to reach out via email at tcr@turpentine.co, or you can DM me on the social media platform of your choice.