\u003C/p>\u003Cp>How AI can level the playing field between top performers and less experienced staff\u003C/p>\u003Cp>The potential for massive cost savings and efficiency gains across various industries\u003C/p>\u003Cp>The ethical implications of AI in the workplace - threat or opportunity?\u003C/p>\u003Cp>Real-world implementation strategies and challenges\u003C/p>\u003Cp>\u003Cbr />\u003C/p>\u003Cp>Whether you're a CEO looking to gain a competitive edge, an HR director aiming to optimize your workforce, or simply curious about the future of work, this episode is a must-listen. We'll separate hype from reality and give you actionable insights on how AI might transform your professional life.\u003C/p>\u003Cp>Tune in for a fascinating glimpse into a future where humans and AI work side by side. \u003C/p>\u003Cp>The workplace revolution is here - are you ready?\u003C/p>","episodic","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/images/bb0d16b6-e14e-4b9f-8a31-8f81469302e9.jpg",{"image_80":13,"image_180":14,"image_240":15,"image_600":16,"image_1280":17},"storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/images/bb0d16b6-e14e-4b9f-8a31-8f81469302e9_80.jpg","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/images/bb0d16b6-e14e-4b9f-8a31-8f81469302e9_180.jpg","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/images/bb0d16b6-e14e-4b9f-8a31-8f81469302e9_240.jpg","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/images/bb0d16b6-e14e-4b9f-8a31-8f81469302e9_600.jpg","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/images/bb0d16b6-e14e-4b9f-8a31-8f81469302e9_1280.jpg","https://cloud.mave.digital/58641","Sergio Voropaev",false,32,2,{"rate":24,"count":22},5,[26,29,32],{"name":27,"subcategory":28,"is_main":20},"Образование","Самосовершенствование",{"name":30,"subcategory":31,"is_main":20},"Бизнес","Управление",{"name":33,"is_main":34},"Технологии",true,[36],1,"Lets connect","ceo@greatleveler.com",{"facebook":40,"twitter":41,"instagram":40,"telegram":42,"vk":40,"patreon":40,"boosty":40},null,"https://x.com/greatlevelercom","https://t.me/greatlevelercom",{"apple_id":44,"apple":45,"google":40,"spotify":46,"yandex":47,"vk":40,"castbox":48,"soundstream":40,"deezer":49,"overcast":50,"podcastAddict":50,"pocketCasts":50,"youtube":51,"soundcloud":40,"zvuk":50,"youtubeMusic":52,"myBook":40,"litres":53},1774183463,"https://podcasts.apple.com/ru/podcast/ai-synergy/id1774183463","https://open.spotify.com/show/2799vuVV6ZM7ipuxqHsEmM?si=LFkhdF-2QqWpMAE5xAC0FQ&nd=1&dlsi=0518d31c491e497b","https://music.yandex.ru/album/33938902","https://castbox.fm/channel/id6318548?country=ru","https://deezer.com/show/1001326571","","https://www.youtube.com/playlist?list=PLinPRXtk3-haYmjeEt_urdTKOji-r07l5","https://music.youtube.com/playlist?list=PLinPRXtk3-haYmjeEt_urdTKOji-r07l5","https://www.litres.ru/podcast/sergio-voropaev/ai-synergy-71218483/",[55],{"id":56,"podcast_id":7,"name":19,"info":57,"image":58,"createdAt":59,"updatedAt":60,"contact_id":40},"dba1999e-f8b8-4181-9f09-f7bd44a86280","Founder of Great Leveler AI - a platform helping tech leaders boost productivity by 43% through AI implementation. Former Swiss VC mentor, successful founder of multiple tech startups, and expert in AI business integration and scaling.","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/contacts/0361e8d5-08a5-4aab-b563-7c950643919e.jpeg","2024-11-14T10:46:22.583Z","2024-11-14T10:46:22.727Z",{"id":62,"number":63,"season":36,"title":64,"description":65,"type":66,"image":11,"audio":67,"duration":68,"is_explicit":20,"code":63,"publish_date":69,"listenings":70,"is_transcription_hidden":20,"text":71,"is_private":20,"plans":40,"video":40,"images":72,"reactions":73,"chapters":79,"relevantEpisodes":80},"d7427338-815f-469d-b655-9cf0acf5747f",7,"The Centaur Conundrum. Cognitive AI model.","Dive into the fascinating world of \u003Cb>\u003Ca href=\"https://huggingface.co/marcelbinz/Llama-3.1-Centaur-70B\">Centaur\u003C/a>\u003C/b>, an ambitious AI model. \u003Cp>Explore how this groundbreaking technology is blurring the lines between artificial intelligence and cognitive science, and uncover the incredible potential it holds for unlocking the secrets of human behavior and cognition.\u003C/p>","full","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/episodes/d7427338-815f-469d-b655-9cf0acf5747f.mp3",1598,"2024-10-29T14:14:44.823Z",15,"Speaker 01 00:00:00\n\nOK, so get this. Imagine a model like an actual model that could predict how people behave, not just in like some niche area, but across a ton of tasks and situations. It'd be like like having a crystal ball, but for human behavior. Yeah. Well, researchers are trying to do that with a new computational model called Centaur. And you guys have sent us a bunch of research on it. And it's really interesting stuff. So we're going to do a deep dive into Centaur. And...\n\nSpeaker 00 00:00:38\n\nYou're right to compare it to a crystal ball because one of the biggest challenges in AI has always been capturing that incredible versatility of human thinking.\n\nSpeaker 01 00:00:45\n\nRight, because it's like current AI models are amazing, but they're very specialized, right? They can do these incredible things, but only within their, like, narrow lane. So, like, AlphaGo, amazing. It can be, you know, the world champion at Go, but you ask it to write a poem or plan a vacation, forget about it. So how is Centaur different?\n\nSpeaker 00 00:01:07\n\nWell, Centaur is aiming to be more of a generalist. So instead of mastering one specific task, It's designed to reflect the broad spectrum of human cognition. And the way they did this is really fascinating. They trained a powerful language model on a massive data set of human behavior called Psych 101.\n\nSpeaker 01 00:01:28\n\nPsych 101. That sounds familiar. Yeah. Like a college course.\n\nSpeaker 00 00:01:31\n\nThat's a fitting name. because it's essentially what it is, like a crash course in human psychology. Right. We're talking data from 160 different psychology experiments covering everything from decision making to memory to learning.\n\nSpeaker 01 00:01:48\n\n160 experiments, that's a crazy amount of data.\n\nSpeaker 00 00:01:52\n\nIt is. It is. We're talking trial by trial data from over 60,000 participants. Wow. Which translates to over 10 million choices. It's like having this giant database of how humans make decisions in all sorts of contexts.\n\nSpeaker 01 00:02:05\n\nOK, so we've got this AI model. It's basically been schooled in human behavior. What did the researchers actually do with it? What kind of tests did they run?\n\nSpeaker 00 00:02:16\n\nSo they put it through its paces for sure. They tested it against existing cognitive models. you know, the kind designed for very specific tasks. And also against like a baseline language model, which is like a control group. And what's really remarkable is that Centaur consistently predicted how participants would behave better than those specialized cognitive models. It was better in almost every single experiment.\n\nSpeaker 01 00:02:42\n\nHold on, so it outperformed the models that were specifically designed for those tasks?\n\nSpeaker 00 00:02:46\n\nYeah. That's wild. It's pretty remarkable.\n\nSpeaker 01 00:02:49\n\nHow is that even possible?\n\nSpeaker 00 00:02:50\n\nWell, to measure performance, they use this thing called pseudo R2. It's basically a way to gauge how well the model's predictions line up with real human choices. So like a pseudo R2 of zero means it's just guessing randomly. And a score of one means it's predicting perfectly.\n\nSpeaker 01 00:03:09\n\nGotcha. So like where on that pseudo R2 scale was Centaur scoring?\n\nSpeaker 00 00:03:15\n\nYeah. So it wasn't just a little bit better. It was significantly more accurate. Across all the experiments, it averaged a pseudo-R2 of 0.50, which is a huge leap from the base language model, which scored 0.36. And get this, it even outperformed all those specialized cognitive models by an average of 0.18.\n\nSpeaker 01 00:03:37\n\nSo it wasn't just like slightly better, it was making predictions that are significantly closer to how real people behave. Yeah. That's amazing. It is. Did they notice anything about the experiments themselves that influenced how well Centaur performed? Did it do better on certain types of tasks or was it pretty consistent?\n\nSpeaker 00 00:03:57\n\none really interesting finding. was that Centaur's performance actually got better. as the number of participants in the dataset increased. So that kind of suggests that the model thrives on the diversity of human behavior. The more data it has to learn from, the better it captures all the nuances of how people think.\n\nSpeaker 01 00:04:17\n\nThat makes sense. The more varied the training data. The better the model can be at understanding how different humans approach a problem. Right. But could centaur do more than just predict like single choices? Right. Could it like simulate a whole sequence of decisions as if it were a person interacting with the world?\n\nSpeaker 00 00:04:37\n\nExactly. They wanted to see if Centaur could go beyond making one-off predictions and actually simulate human-like behavior over a series of choices. This is what they call open-loop simulation.\n\nSpeaker 01 00:04:50\n\nOpen loop simulation. Okay, break that down for me.\n\nSpeaker 00 00:04:52\n\nOkay, so imagine you're playing a game. In a normal test, the model might say, okay, given this situation, a person would probably choose option A. But in open loop simulation, it's more like Centaur is actually playing the game. So it makes a choice. Sees the outcome of that choice, then makes another choice based on that new situation, so on and so forth. So it's like it's actually interacting with the environment.\n\nSpeaker 01 00:05:16\n\nSo it's not just predicting what someone would do. It's like making decisions and learning from the consequences. Like we do in real life.\n\nSpeaker 00 00:05:24\n\nExactly. And what's amazing is that Centaur was successful at this. Wow. It didn't just imitate some average behavior. It actually captured the variability that you see in a whole population of people.\n\nSpeaker 01 00:05:38\n\nThis is where things get even more mind-blowing. Remember how Centaur was trained purely on behavior? Well, the researchers discovered something crazy about its inner workings. It turns out that by training on behavior, they accidentally made the model's internal representations more human-like as well.\n\nSpeaker 00 00:05:56\n\nYou're jumping ahead a bit. We need to talk about how they tested its ability to generalize first.\n\nSpeaker 01 00:06:01\n\nyou're right. You're right.\n\nSpeaker 00 00:06:03\n\nThat's a crucial part of a unified theory of cognition.\n\nSpeaker 01 00:06:06\n\nYou're totally right, I got ahead of myself. So they wanted to see if Centaur could handle brand new situations, not just the ones that it was trained on. How'd they go about testing that?\n\nSpeaker 00 00:06:16\n\nThey came up with some really clever experiments. One of them involved what they call cover story robustness. Remember that open loop simulation with the two-step task we were talking about?\n\nSpeaker 01 00:06:28\n\nYeah, where Centaur was basically playing that game, making decisions based on the outcome of previous choices.\n\nSpeaker 00 00:06:33\n\nRight. Well, they use two different versions of that task. One used a classic spaceship theme. So you're imagining you're piloting a spaceship. trying to find treasure on different planets. used a magic carpet theme. So instead of spaceships and planets, You're on a magical carpet searching for gems.\n\nSpeaker 01 00:06:56\n\nSo same basic task structure.\n\nSpeaker 00 00:07:00\n\nExactly. And Centaur was only trained on one of those. Centaur only saw the spaceship version of the task during training. But when they tested it on the Magic Carpet version, it still accurately predicted how people would behave.\n\nSpeaker 01 00:07:15\n\nWait, so it was able to figure out the underlying logic of the task? Yeah. even with this total different story. That's a pretty good sign that it's not just memorizing patterns. It's like actually understanding something deeper.\n\nSpeaker 00 00:07:30\n\nYeah it really is.\n\nSpeaker 01 00:07:31\n\nabout how decisions are made.\n\nSpeaker 00 00:07:32\n\nYeah. And it gets even more impressive. They also wanted to test Centaur's robustness to actual changes in the structure of the task itself.\n\nSpeaker 01 00:07:41\n\nOh, okay. So they were changing the rules of the game this time. Right. Not just the theme.\n\nSpeaker 00 00:07:46\n\nExactly. So what do they do?\n\nSpeaker 01 00:07:48\n\nThey used a task called Maggie's Farm. It's kind of like a classic two armed bandit task. like the slot machines where you have to choose between two levers.\n\nSpeaker 00 00:07:57\n\nYeah, yeah, I get it. Well, in Maggie's farm, they added a third option. Oh, okay. So instead of just two choices, there are three. which makes the task more complex.\n\nSpeaker 01 00:08:09\n\nSo they threw a curve ball at Centaur. Yeah. Something it had explicitly not seen before. How'd it handle that?\n\nSpeaker 00 00:08:16\n\nremarkably well. even though it had never encountered like a three armed bandit task before. it still performed well on Maggie's farm. This suggests that Centaur has this surprising level of adaptability when it comes to changes in the task structure itself.\n\nSpeaker 01 00:08:34\n\nBut did they ever throw out like a truly wild card?\n\nSpeaker 00 00:08:38\n\nThey tested Centaur on a set of logical reasoning tasks. and these were completely different from the decision-making tasks it had been trained on.\n\nSpeaker 01 00:08:47\n\nSo completely different type of thinking.\n\nSpeaker 00 00:08:49\n\nYes, totally different.\n\nSpeaker 01 00:08:50\n\nWow, that's a huge challenge. So how to do was it able to hold its own?\n\nSpeaker 00 00:08:56\n\nWell, its accuracy wasn't quite as high as in those other experiments, which isn't surprising. given that it was venturing into brand new territory. Totally. But here's the kicker. It still performed better than the base language model.\n\nSpeaker 01 00:09:10\n\nSo even though it was way outside its comfort zone, it still showed some ability to generalize, to learn.\n\nSpeaker 00 00:09:17\n\nIt did, which is promising. Yeah, it suggests that Centaur has the potential to tackle new domains. with further training Yeah, that's a really encouraging sign.\n\nSpeaker 01 00:09:29\n\nThis ability to generalize beyond its training data is one of the most interesting things about Centaur. It hints at the possibility of like a real breakthrough. Yeah. In AI. Totally. One that could fundamentally change how we understand the human mind. Yeah. Okay. All right. So now we're getting to the part that I was really excited about. Okay. We've talked about how well Centaur can predict behavior, even in new situations. Right. But remember, it was only trained on how people act. Not necessarily on how their brains work. So did the researchers find any connection between centaurs internal workings and what's going on in a real human brain?\n\nSpeaker 00 00:10:10\n\nThat is the million dollar question. And that's what they wanted to know. and see if they could use Centaur's internal representations. Think of it like the model's thought process. to predict actual human brain activity.\n\nSpeaker 01 00:10:26\n\nWait, so they were able to connect what's happening inside the AI to like brain scans? How is that even possible?\n\nSpeaker 00 00:10:33\n\nThey used a technique called fMRI, which measures brain activity by detecting changes in blood flow. They ran two separate experiments to see if Centaur's internal workings, you know, signed up with real human brain data.\n\nSpeaker 01 00:10:49\n\nOkay, what were the experiments?\n\nSpeaker 00 00:10:51\n\nSo in the first experiment, they focused on that two-step task we keep talking about. Right. And they wanted to see if they could use Centaur's internal representations to predict the brain activity of people, you know. while they were performing that task. Well, the results were pretty amazing. Centaur's predictions of human brain activity were significantly better. than the predictions for the base language model.\n\nSpeaker 01 00:11:15\n\nHold on. So just by training it on human behavior, they kind of accidentally made it step process more aligned with the human brain.\n\nSpeaker 00 00:11:23\n\nThat's what it suggests, yeah. And they saw a similar pattern in their second experiment which was even more intriguing.\n\nSpeaker 01 00:11:30\n\nOkay, I'm on the edge of my seat. Tell me about this second experiment.\n\nSpeaker 00 00:11:34\n\nOkay, so for this one, they used fMRI data. from people reading simple sentences.\n\nSpeaker 01 00:11:41\n\nThat's a far cry from the decision-making tasks was trained on.\n\nSpeaker 00 00:11:46\n\nI know that's what makes it interesting. They were just simple six-word tenses.\n\nSpeaker 01 00:11:52\n\nsentences, that's a big jump. So what did they find?\n\nSpeaker 00 00:11:55\n\nThey found that Centaur's internal representations, especially those from the middle layers of the model, We're much better at predicting brain activity in the language areas of the brain than the base language model.\n\nSpeaker 01 00:12:08\n\nWait, so Centaur was never specifically trained on brain data?\n\nSpeaker 00 00:12:12\n\nnever. It was purely based on behavior.\n\nSpeaker 01 00:12:14\n\nThat's insane. It's like the model developed a rudimentary understanding of language. just by learning to predict how humans behave. In a range of tasks.\n\nSpeaker 00 00:12:27\n\nThis unexpected alignment. between Centaur's internal workings and human neural activity opens up a whole new world of questions and possibilities. It suggests this deep connection between behavior and brain function that we're only just beginning to understand.\n\nSpeaker 01 00:12:45\n\nOkay, so we've got this model that's amazingly good at predicting human behavior. even generalizing to new situations. And now we're seeing that its inner workings are starting to resemble This is huge. Where does this lead us? What's the big picture here?\n\nSpeaker 00 00:13:04\n\nWell, it brings us a step closer to answering of the fundamental questions in cognitive science. Is it even possible to build a model that truly reflects the incredible versatility of the human mind.\n\nSpeaker 01 00:13:18\n\nYeah, and is Centaur the answer?\n\nSpeaker 00 00:13:20\n\nI mean, it's a giant leap in the right direction. You know, do you remember back in the 1990s when Alan Newell This prominent computer scientist proposed this set of criteria for what a unified theory of cognition should look like. It was like a checklist for any model. that aims to explain how the human mind works Like a blueprint for building a mine. Exactly. And what's exciting is that Centaur is actually ticking off many of the boxes. It operates in real time, meaning it processes information and makes decisions as quickly as humans can. it responds dynamically to changes in the environment. adapting to new information, just like we do. And it has access to vast knowledge thanks to that massive language model.\n\nSpeaker 01 00:14:12\n\nIt sounds like Centaur is fulfilling that vision in a way that no other model has before. But this is all still pretty theoretical, right? Right. What are the real world... implications of Centaur's success.\n\nSpeaker 00 00:14:25\n\nYeah, so think about it this way. If you have a model that can reliably predict how people will behave, you can use it to design and test all sorts of things without involving real people.\n\nSpeaker 01 00:14:39\n\nOh, so like a virtual lab. for psychology experiments.\n\nSpeaker 00 00:14:42\n\nExactly. You could run simulations and get a sense of how people might react to certain situations or stimuli.\n\nSpeaker 01 00:14:48\n\nThat'd be really valuable for researchers.\n\nSpeaker 00 00:14:50\n\nincredibly it would save so much time effort and resources I mean, think about it.\n\nSpeaker 01 00:14:55\n\nYou could use it to figure out which experimental designs would be most effective, how to reduce the number of participants needed for a study, maybe even estimate the likely outcome of an experiment before you even run it. That would be incredibly valuable.\n\nSpeaker 00 00:15:11\n\nAbsolutely. But it goes beyond just research. Okay. Imagine the potential in fields like education, healthcare, even marketing.\n\nSpeaker 01 00:15:20\n\nOK, so Centaur could help us design better teaching methods. more targeted treatments. even like more persuasive advertising campaigns.\n\nSpeaker 00 00:15:29\n\nExactly. By understanding how people are likely to behave in different situations. Right. we can create systems and interventions that are tailored to their specific needs and preferences this is incredible it's pretty amazing It sounds like Centaur could revolutionize the way we approach research and problem solving in a ton of different fields. It is. Yeah, it really does open up a world of possibilities. It does. But it's important to remember. that Centaur was trained to mimic human behavior. Not necessarily to think exactly like a human. So the researchers wanted to know, did this trading on behavior also makes centaurs inner workings more human like.\n\nSpeaker 01 00:16:11\n\nThat's a great question. It's like if it walks like a duck and quacks like a duck, Does it also have the brain of a duck? Did they actually figure out a way to peek inside Centaur's mind?\n\nSpeaker 00 00:16:23\n\nThey did. They used Centaur's internal representations. Think of it as the model's thought process and compared them to actual human brain activity measured using fMRI.\n\nSpeaker 01 00:16:33\n\nWait, so they were able to connect what's going on inside this AI to actual like brain scans? That's that's crazy.\n\nSpeaker 00 00:16:43\n\nIt is pretty groundbreaking stuff. They did two separate experiments to test this. The first one used the two-step task we've been talking about. They wanted to see if Centaur's internal representation, so the way it processes information, up with the brain activity of people while they were doing the task.\n\nSpeaker 01 00:17:00\n\nOkay, so they were basically looking for similarities in the patterns of activity. Was Centaur thinking like a human?\n\nSpeaker 00 00:17:07\n\nThe results were really compelling. Centaur's predictions of human brain activity were significantly more accurate than the predictions made by the base language model.\n\nSpeaker 01 00:17:19\n\nSo just by training it on human behavior, They sort of accidentally made its thought process. like mirror the human brain.\n\nSpeaker 00 00:17:28\n\nTo some degree, yes.\n\nSpeaker 01 00:17:29\n\nTo some degree, that's amazing. What about the second experiment?\n\nSpeaker 00 00:17:32\n\nThe second experiment was even more fascinating because they took it in a completely different direction. This time they used fMRI data collected from people reading simple sentences. Just six words long.\n\nSpeaker 01 00:17:45\n\nIt seems like such a jump from the decision-making tasks was trained on.\n\nSpeaker 00 00:17:50\n\nI know that's what makes the results so fascinating.\n\nSpeaker 01 00:17:53\n\nYeah. How could it possibly predict brain activity related to language?\n\nSpeaker 00 00:17:58\n\nWell, they discovered that Centaur's internal representations, particularly those from the middle layers of the model... We're better at predicting brain activity in the language processing areas of the brain. compared to the base language model.\n\nSpeaker 01 00:18:13\n\nHold on, I need to make sure I'm understanding this correctly. Centaur was never specifically fed any information about language or how the brain processes it.\n\nSpeaker 00 00:18:23\n\nThat's right. It was purely trained on human behavior.\n\nSpeaker 01 00:18:26\n\nThat is absolutely mind blowing. It's as if simply by learning to predict human behavior across a diverse range of tasks. Right. Centaur developed this fundamental understanding of language how the brain handles Precisely.\n\nSpeaker 00 00:18:41\n\nYeah. And this unexpected alignment. between centaurs, internal workings and human neural activity. raises so many fascinating questions. opens up all these new avenues for research. Totally. It suggests this deep connection between behavior and brain function. Like I said, we're only just beginning to understand.\n\nSpeaker 01 00:19:01\n\nOkay, so big question. We've got this model that can accurately predict human behavior, even in these unfamiliar situations. And now we're seeing that its internal workings actually resemble the human brain in some ways. Does this mean we finally cracked the code? That's the question. Of human cognition. Is Centaur the key to unlocking all the mysteries of the mind?\n\nSpeaker 00 00:19:26\n\nIt's certainly a massive step towards that ultimate goal. Remember when we talked about Alan Newell's criteria for unified theory of cognition?\n\nSpeaker 01 00:19:34\n\nRight. Like he laid out a roadmap for what he did. Such a model should be capable of. Exactly. Checklist. for building a model that truly represents the human Exactly.\n\nSpeaker 00 00:19:45\n\nAnd what's so exciting is that Centaur is fulfilling many of those criteria. It operates in real time, just like the human brain. It processes information and makes decisions.\n\nSpeaker 01 00:19:58\n\nIt's also incredibly adaptable, able to respond to changes in the environment, learn from new information. And thanks to that massive language model at its core, it has access to this vast storehouse of knowledge.\n\nSpeaker 00 00:20:17\n\nIt really sounds like Centaur is bringing that vision to life in a way that we haven't seen before. It is. But how does all of this translate to the real world? What are the practical applications of Centaur's capabilities? How can it benefit us beyond these theoretical achievements?\n\nSpeaker 01 00:20:33\n\nYeah, the possibilities are vast. And we're only just scratching the surface. Just imagine if you have a model that can reliably predict human behavior. You could use it to design and test virtually anything. without involving real people.\n\nSpeaker 00 00:20:47\n\nThat's an interesting concept. What kind of applications are we talking about?\n\nSpeaker 01 00:20:51\n\nWell, think about fields like psychology, education, healthcare, even marketing. Centaur could revolutionize research by allowing scientists to run virtual experiments and test hypotheses in simulated environments.\n\nSpeaker 00 00:21:05\n\nThis would be incredibly valuable for understanding complex human behaviors and developing more effective interventions.\n\nSpeaker 01 00:21:12\n\nSo you're saying we could like test new therapies or teaching methods without having to like conduct large scale trials with human participants.\n\nSpeaker 00 00:21:22\n\nExactly. It would accelerate the pace of research. allow us to explore a wider range of possibilities. without the ethical concerns and logistical challenges of traditional human studies.\n\nSpeaker 01 00:21:35\n\nWhat about other fields? How could Centaur be applied in like health care or marketing?\n\nSpeaker 00 00:21:42\n\nWell, in healthcare, Centaur could be used to develop personalized treatment plans. tailored to individual patients. By analyzing a patient's medical history, lifestyle, behavioral patterns, Centaur could predict how they might respond to different treatments and recommend the most effective course of action.\n\nSpeaker 01 00:22:02\n\nRight. So it would lead to like more targeted and hopefully successful interventions.\n\nSpeaker 00 00:22:07\n\nExactly. Improving patient outcomes. potentially saving lives.\n\nSpeaker 01 00:22:12\n\nAnd what about marketing? How could like an AI that understands human behavior be used in that field?\n\nSpeaker 00 00:22:18\n\nMarketing is all about understanding consumer behavior. So with Centaur, marketers could gain unprecedented insights into how people make purchasing decisions, what motivates them. What influences their preferences? It could help them craft more effective advertising campaigns. Yeah. Tailor their messaging to specific target audiences. Yeah. Ultimately create more engaging and successful marketing strategies. Right.\n\nSpeaker 01 00:22:43\n\nThis all sounds incredibly promising, But I can't help but wonder about the potential downsides. If we're talking about an AI that can predict human behavior, with this level of accuracy. Are there any ethical concerns?\n\nSpeaker 00 00:23:00\n\nOf course, that's a crucial question. And it's one that researchers are actively grappling with as these technologies advance. It's important to remember that Centaur, like any AI, And like any tool, it can be used for good or for ill.\n\nSpeaker 01 00:23:16\n\nSo the responsibility lies with us, the humans. Who develop and deploy these technologies to ensure that they're used ethically and for the benefit of humanity. It sounds like we're entering uncharted territory here, and there are a lot of challenges ahead. but the potential rewards are immense. Imagine a world where we can leverage the power of AI to like understand ourselves better, create more effective solutions to these complex problems. and ultimately build a brighter future.\n\nSpeaker 00 00:23:51\n\nIt's a future worth striving for. It is. And Centaur is showing us that it might be closer than we think.\n\nSpeaker 01 00:23:56\n\nWell, this has been an absolute mind-blowing deep dive. into the world of AI and its potential to unlock the secrets of the human mind. It seems like Centaur is just the beginning of a very exciting journey And I, for one, can't wait to see what the future holds.\n\nSpeaker 00 00:24:15\n\nYeah. It really is incredible to think about, you know, we might be on the verge of like a whole new era. understanding the And it's all thanks to this fascinating intersection of AI and cognitive science.\n\nSpeaker 01 00:24:30\n\nYeah, absolutely.\n\nSpeaker 00 00:24:32\n\nCentaur is clearly a remarkable achievement. Where do you think the long-term implications of this could lead?\n\nSpeaker 01 00:24:41\n\nWell, I think what's really fascinating is that Centaur success could like pave the way for training even more sophisticated cognitive models. Models that are like directly informed by brain data and theories from neuroscience.\n\nSpeaker 00 00:24:54\n\nSo instead of just learning from behavior, we could build models that actually incorporate what we know about the physical structure and function of the brain.\n\nSpeaker 01 00:25:02\n\nLike, we could combine the power of AI with the incredible complexity of the human brain.\n\nSpeaker 00 00:25:09\n\nExactly. Imagine the insights we might gain. about the architecture of the human mind. If we could bring those two worlds together, could unravel mysteries that have puzzled scientists for centuries. we might finally gain a deeper understanding of consciousness. Creativity. All those things that make us uniquely human.\n\nSpeaker 01 00:25:31\n\nIt's almost too much to comprehend, you know? It is. We're talking about potentially unlocking the deepest secrets of the human mind. But Centaur's just the beginning, right?\n\nSpeaker 00 00:25:44\n\nIt is, think of it as a crucial stepping stone. It's shown us that this ambitious goal, this dream of a unified theory of cognition might actually be within reach. And it's opened up this whole new set of possibilities for how we'd approach the study of the mind.\n\nSpeaker 01 00:26:02\n\nWell, this has been an absolutely mind blowing deep dive into the world of AI. Thanks to Centaur. it feels like we're on the cusp of a new era of discovery. An era where those boundaries between AI and cognitive science continue to blur. Who knows what amazing breakthroughs await us on this incredible journey.\n\nSpeaker 00 00:26:29\n\nIt's a journey full of potential. I'm excited to see where it leads.\n\nSpeaker 01 00:26:32\n\nYeah, me too. Thanks for joining us on this exploration of the frontiers of human understanding.\n\nSpeaker 00 00:26:37\n\nAbsolutely. My pleasure.",{"image_80":13,"image_180":14,"image_240":15,"image_600":16,"image_1280":17},[74,77],{"type":75,"count":76},"like","0",{"type":78,"count":76},"dislike",[],[81,91,100,109,119,128],{"id":82,"number":83,"season":36,"title":84,"description":85,"type":66,"image":11,"audio":86,"duration":87,"is_explicit":20,"code":83,"publish_date":88,"listenings":89,"is_private":20,"plans":40,"video":40,"images":90},"54fcc72a-a8dc-468b-aeee-88e169dae27c",6,"Digital Minds at Work: The Revolution of Large Action Models","In this episode, we dive deep into the groundbreaking world of Large Action Models (LAMs), with a special focus on Anthropic's Claude 3.5 Haiku. We'll explore how this lightning-fast AI isn't just chatting anymore – it's actively using computers like a human would, opening files, navigating websites, and handling complex digital tasks through innovative pixel-based interaction. \u003Cp>\u003Cbr />\u003C/p>\u003Cp>Keywords: AI technology, Large Action Models, Anthropic, Claude 3.5 Haiku, computer automation, future of work, AI innovation\u003C/p>","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/episodes/54fcc72a-a8dc-468b-aeee-88e169dae27c.mp3",182,"2024-10-22T16:28:01.262Z",21,{"image_80":13,"image_180":14,"image_240":15,"image_600":16,"image_1280":17},{"id":92,"number":24,"season":36,"title":93,"description":94,"type":66,"image":11,"audio":95,"duration":96,"is_explicit":20,"code":24,"publish_date":97,"listenings":98,"is_private":20,"plans":40,"video":40,"images":99},"a265ee3a-0825-4a6c-a7a0-89fba9eb267f","AI Dream Teams: How Multi-Agent Platforms Are Revolutionizing Business","Dive into the world of \u003Cb>Asilisc Scope\u003C/b> and multi-agent \u003Cb>AI platforms\u003C/b> that are transforming how businesses operate. Discover how interconnected\u003Cb> AI specialists\u003C/b> can streamline your company's workflow - from accounting to customer service and beyond. Learn how these \u003Cb>AI teams\u003C/b> collaborate under human supervision to tackle complex problems, potentially boosting efficiency and innovation across your entire organization. Join us as we explore the future of AI in business, where your next star employee might just be a team of artificial intelligences.","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/episodes/a265ee3a-0825-4a6c-a7a0-89fba9eb267f.mp3",494,"2024-10-20T12:58:43.907Z",22,{"image_80":13,"image_180":14,"image_240":15,"image_600":16,"image_1280":17},{"id":101,"number":102,"season":36,"title":103,"description":104,"type":66,"image":11,"audio":105,"duration":106,"is_explicit":20,"code":102,"publish_date":107,"listenings":89,"is_private":20,"plans":40,"video":40,"images":108},"05137fef-b1e0-4222-9de1-844a074a8e08",4,"The Action AI Revolution: How Large Action Models Are","Explore the game-changing world of Large Action Models - AI that doesn't just advise, but acts. Learn how this cutting-edge technology is dramatically accelerating productivity by automating tasks across various business software platforms. We'll dive into the potential benefits, challenges, and ethical considerations of AI that works alongside humans, potentially reshaping the future of work as we know it.","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/episodes/05137fef-b1e0-4222-9de1-844a074a8e08.mp3",225,"2024-10-20T12:47:01.424Z",{"image_80":13,"image_180":14,"image_240":15,"image_600":16,"image_1280":17},{"id":110,"number":111,"season":36,"title":112,"description":113,"type":66,"image":11,"audio":114,"duration":115,"is_explicit":20,"code":111,"publish_date":116,"listenings":117,"is_private":20,"plans":40,"video":40,"images":118},"aac64a7b-1308-4487-b065-4e2b19b41f00",3,"Colossus Chronicles: Musk, Grok-3, and the Future of AI","This episode explores the creation of \u003Cb>Colossus\u003C/b>, a supercomputer of unprecedented power, built to train the next-generation AI model, \u003Cb>Grok 3.\u003C/b> We'll delve into the astonishing specs of this machine. \u003Cp>\u003Cbr />\u003C/p>\u003Cp>Buckle up for a journey into the cutting edge of AI, where the future is being written at warp speed in a repurposed factory in \u003Cb>Memphis, Tennessee.\u003C/b>\u003C/p>","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/episodes/aac64a7b-1308-4487-b065-4e2b19b41f00.mp3",297,"2024-10-17T05:57:37.589Z",37,{"image_80":13,"image_180":14,"image_240":15,"image_600":16,"image_1280":17},{"id":120,"number":22,"season":36,"title":121,"description":122,"type":66,"image":11,"audio":123,"duration":124,"is_explicit":20,"code":22,"publish_date":125,"listenings":126,"is_private":20,"plans":40,"video":40,"images":127},"ca3dcdba-512d-46db-a902-4fa1d5a54664","Watts Up With AI: Powering the Digital Brain","\u003Cb>\"Watts Up With AI\"\u003C/b> dives deep into the rarely discussed but critically important topic of AI's massive energy consumption. As we marvel at AI's capabilities in generating images and engaging in conversations, this podcast uncovers the hidden giant powering it all: the enormous energy appetite of AI systems.\u003Cp>\u003Cbr />\u003C/p>\u003Cp>The podcast delves into the challenges of sustainably powering the AI revolution, discussing innovative solutions like \u003Cb>Google\u003C/b>'s exploration of small \u003Cb>nuclear reactors\u003C/b> for data centers.\u003C/p>","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/episodes/ca3dcdba-512d-46db-a902-4fa1d5a54664.mp3",210,"2024-10-16T13:51:51.794Z",20,{"image_80":13,"image_180":14,"image_240":15,"image_600":16,"image_1280":17},{"id":129,"number":36,"season":36,"title":130,"description":131,"type":66,"image":11,"audio":132,"duration":133,"is_explicit":20,"code":36,"publish_date":134,"listenings":135,"is_private":20,"plans":40,"video":40,"images":136},"6c0bdf6b-3686-4620-97db-a02b58bf4766","Exploring the Collaborative Potential of AI in Enhancing Employee Productivity and Work Quality","The content explores the transformative potential of artificial intelligence (AI) in enhancing productivity through collaboration with human workers rather than serving as a replacement. A study titled \"Symbiotic Enhancement of Employee Productivity Using Generative AI\" involved over a thousand participants from various sectors, including finance, construction, and retail, demonstrating the broad applicability of AI in diverse work environments.\u003Cp>\u003Cbr />\u003C/p>\u003Cp>Two primary AI methodologies were evaluated: the Collaborative Cognitive Agent (CCA) and the Continuous Cognitive Agent (Cochia). The CCA functions akin to a skilled intern, managing tasks such as scheduling, thereby enabling employees to concentrate on more significant responsibilities. In contrast, Cochia serves as a mentor, offering real-time feedback and suggestions during tasks. Both approaches resulted in participants completing an average of 12.2% more tasks and achieving approximately 25% faster completion rates, showcasing a marked improvement in efficiency.\u003C/p>\u003Cp>\u003Cbr />\u003C/p>\u003Cp>Importantly, the study found that less productive individuals experienced the most significant gains, with a 43% increase in productivity, while high performers saw a 17% boost. This indicates that AI can provide crucial support for those struggling with organization. Furthermore, work completed with AI assistance was rated 40% higher in quality by independent experts, challenging the notion that speed compromises quality. The key takeaway emphasizes the importance of maintaining control over one’s time and focusing on meaningful tasks, with AI playing a role in enhancing decision-making and error correction.\u003C/p>\u003Cp>\u003Cbr />\u003C/p>\u003Cp>The discussion also highlights the necessity for a thoughtful integration of AI technologies, advocating for an understanding of their capabilities to foster productivity within teams. Participants envision a future where AI promotes collaboration, creativity, and improved work dynamics, positioning technology as a partner rather than a threat. This partnership requires a mutual commitment to openness and strategic integration of AI with existing skills.\u003C/p>\u003Cp>\u003Cbr />\u003C/p>\u003Cp>Ultimately, the objective extends beyond merely increasing output; it aims to enhance the quality of work and enrich the human experience. Listeners are encouraged to reflect on applying these insights in their work environments and to engage in the ongoing dialogue regarding AI's evolving role in the workplace, underscoring the importance of individual contributions to this conversation. The segment concludes with a call for continued exploration into AI’s implications, emphasizing its potential to redefine work, learning, and success.\u003C/p>","storage/podcasts/a916dc01-1db2-4f42-aaf0-e30bf94c491d/episodes/6c0bdf6b-3686-4620-97db-a02b58bf4766.mp3",458,"2024-10-16T07:20:38.355Z",18,{"image_80":13,"image_180":14,"image_240":15,"image_600":16,"image_1280":17},["Reactive",138],{"$ssite-config":139},{"_priority":140,"env":144,"name":145,"url":146},{"name":141,"env":142,"url":143},-10,-15,-4,"production","podcast-website","https://greatleveler.mave.digital/",["Set"],["ShallowReactive",149],{"$63LOZx6kQb":-1},"/ep-7",{"common":152},{"activeTab":153,"isShareActive":20,"episodes":154,"contentPosition":20,"podcast":5,"podcastSlug":155,"showPlayer":20,"activeTrack":40,"pauseTrack":20,"activeEpisode":61,"titleHeight":156,"website":157,"listenUrl":40,"isMobileShareActive":20,"isDataLoaded":34,"favicon":50,"customDomain":40,"episodesCount":156},"listen",[],"greatleveler",0,{"button_text":37,"button_link":38,"is_indexing":34,"ym_id":-1,"gtm_id":-1}]