[Tech Talk] Revolutionizing AI with Human-like Procedural Learning and Adaptability

[00:00:00] Speaker A: Welcome to the deep dive. Your shortcut to understanding the innovations shaping our future. Today, we're diving headfirst into something pretty fascinating in AI. Now, if you follow AI, you know, large language models, LLMs are, well, incredibly powerful. They write code, compose music, churn out articles. Amazing stuff. But for all that brilliance, they often hit a wall with one key. Adaptability. [00:00:25] Speaker B: That's absolutely right. It's a major bottleneck. [00:00:27] Speaker A: Yeah. Think about it this way. Current LLMs get trained on these huge data sets for very specific jobs, but ask them to do something slightly different, even a tiny variation. [00:00:37] Speaker B: It's like they forget everything. They often have to relearn it all from scratch every single time. [00:00:42] Speaker A: It just sounds so inefficient, doesn't it? [00:00:43] Speaker B: It's really inefficient. And it leads to these astronomical training costs. Eats up massive amounts of time, and the result is models that just lack that general flexibility we really need for truly versatile AI. [00:00:54] Speaker A: Okay, so that brings us to the big question for today's deep dive. [00:00:57] Speaker B: Yeah. [00:00:57] Speaker A: What if AI could learn and remember procedures, you know, like we do? This is where procedural memory comes in. [00:01:04] Speaker B: Yeah. [00:01:05] Speaker A: You probably know declarative memory. That's knowing that, like, you know that Paris is the capital of France. Simple fact. [00:01:10] Speaker B: Right? Factual recall. [00:01:11] Speaker A: But procedural memory is different. It's knowing how is how you ride a bike or tie your shoes or cook your favorite meal without thinking through every tiny step. [00:01:21] Speaker B: Exactly. Ingrained skills, procedures. [00:01:24] Speaker A: And that's the idea behind memp. It's this groundbreaking concept, trying to give AI that kind of human, like, knowing. [00:01:30] Speaker B: How precisely mimicking our brain's ability to learn, store, and importantly, quickly retrieve procedural. [00:01:37] Speaker A: Knowledge so the AI can learn and adapt to new procedures really fast. [00:01:40] Speaker B: That's the goal. Remarkable speed, ideally. [00:01:43] Speaker A: Okay, so in this deep dive, we're going to unpack how MEMP tackles those AI limitations we mentioned. We'll look at the practical advantages, the real world implications, and, yeah, the challenges it still faces. Our mission really is to give you a clear shortcut to understanding this potentially pivotal AI development. So let's pick up on that limitation. Again, the LLM struggle with adaptability. Can you walk us through the real world impact of that inflexibility? What happens when a system needs to change even slightly? [00:02:12] Speaker B: Yeah, that's really the core of the problem. And it's a huge bottleneck in practice. What we see is for even minor changes or slightly new tasks, you often have to basically restart the entire AI development cycle. [00:02:24] Speaker A: Start over. [00:02:25] Speaker B: Yeah, really often. Yes. Or perform very costly fine tuning. We End up building these highly specialized, task specific models, each for just one purpose. And that's why the training costs, both in computing resources and just sheer time, can be astronomical. So you get this collection of powerful but ultimately rigid tools. They just lack the generalizability needed for AI that can truly respond and adapt. [00:02:47] Speaker A: Like constantly reinventing the wheel. [00:02:49] Speaker B: Exactly. We're constantly reinventing the wheel. And that inefficiency is precisely what MEMP is designed to overcome. [00:02:55] Speaker A: It really does sound like a fundamental rethink of how AI learns. So if LLMs struggle with the how, this idea of AI remembering how to do things, it's fascinating. What's the fundamental shift MEMP brings? [00:03:09] Speaker B: It really is a fundamental shift. MEMP's core idea isn't just about processing information like LLMs do. It's about giving AI the structure to learn, store and retrieve procedural skills and an organized, accessible way. [00:03:24] Speaker A: So not just pattern matching? [00:03:26] Speaker B: No, it's different. MEMP is designed to understand sequences of actions, decision points. More like building muscle memory, you could. [00:03:33] Speaker A: Say a how to guide for the AI itself. [00:03:35] Speaker B: That's a good way to put it. Information comes in, it gets processed, tagged as a procedure, stored logically, and then it can be easily recalled and applied when that situation comes up again. [00:03:44] Speaker A: And this isn't just theory. [00:03:46] Speaker B: No, not just theory. It's already showing significant promise in practical tests, demonstrating a much more, well, human like way of acquiring skills. [00:03:55] Speaker A: That difference learning sequences of actions, not just correlations. That feels like a real game changer. What are the immediate sort of tangible benefits we're seeing from this approach? [00:04:04] Speaker B: Oh, the benefits are pretty substantial. And they really hit on some of the biggest pain points in AI development right now. First off, cost reduction, it's dramatic. MEMP significantly lowers the cost of both training and deploying AI agents. We could be talking potential cost cuts in the tens, maybe even hundreds of percent for some applications. [00:04:27] Speaker A: Wow, hundreds. [00:04:28] Speaker B: Potentially, yes. Think about a self driving car. Imagine the savings if it could learn and adapt to new road conditions, or say local traffic rules on the fly. [00:04:37] Speaker A: Instead of needing massive retraining for every single regional difference. [00:04:41] Speaker B: Exactly. Rather than ground up retraining every time. That's a huge economic shift right there. [00:04:45] Speaker A: Okay, cost is one thing, what else? [00:04:47] Speaker B: Well, it also massively simplifies things for developers. For the engineers building these systems, instead of painstakingly creating and training separate models for every single task. [00:04:58] Speaker A: Sounds exhausting. [00:04:59] Speaker B: It is. They can focus instead on building a more general purpose agent. This agent using MEMP then learns new procedures efficiently. It lets developers build more versatile Systems with less upfront slog. [00:05:12] Speaker A: Got it. Any proof points? [00:05:14] Speaker B: Yeah, we've seen really compelling evidence. For instance, in a simulated logistics scenario, think delivery routes. MEMP powered agents optimize these routes much faster and more effectively than traditional LLM systems. They just learned the how to of optimizing routes much quicker. [00:05:30] Speaker A: Okay, faster learning makes sense. [00:05:32] Speaker B: But maybe the most exciting bit is the enhanced adaptability and generalization. What we often call transfer learning. [00:05:39] Speaker A: Applying knowledge from one thing to another. [00:05:41] Speaker B: Precisely. MEMPH lets AI agents adapt to new tasks and environments way faster. The AI can apply what it learned doing one procedure to a similar but not identical task. [00:05:51] Speaker A: Okay, like what? Give me an example. [00:05:53] Speaker B: Sure. So imagine you train a robot to assemble one specific type of electronic gadget. With MeMP, it could potentially adapt quickly to assembling a slightly different model. Maybe the components are moved, or there's a new step without needing extensive retraining. [00:06:10] Speaker A: Like, if you know how to chop carrots, you can probably figure out how to chop a cucumber pretty easily. [00:06:14] Speaker B: Exactly like that. Minimal extra effort. And we saw this in studies too. In one comparison, MEMP agents Significantly outperform traditional LLMs when dealing with unexpected obstacles in a simulated warehouse. [00:06:27] Speaker A: Oh, interesting. So they could react better. [00:06:29] Speaker B: Yeah, that ability to react to the unforeseen, that's critical for the real world. [00:06:34] Speaker A: So if AI can learn to handle unexpected things, adapt to new assembly lines, what does this actually mean for us for real world applications? Where do you see MEMP powered AI really making a difference first? [00:06:46] Speaker B: Well, the potential is genuinely transformative across a huge range of fields. Really. Take robotics for example. MEMP agents could learn new assembly procedures right there on the factory floor. [00:06:56] Speaker A: On the fly. [00:06:56] Speaker B: On the fly. Leading to faster production, much greater efficiency. Imagine a robot learning to add a new part to a product in minutes, not days or weeks of reprogramming. [00:07:08] Speaker A: Okay, manufacturing makes sense. What about other areas? [00:07:10] Speaker B: Healthcare is a another big one. AI assistants could adapt to individual patient needs or changing medical situations, offering more personalized care and support based on the specific context of that patient's case, not just a general protocol. [00:07:25] Speaker A: Personalized AI care and customer service. Chatbots. [00:07:29] Speaker B: Yeah, absolutely. Chatbots could handle a much wider range of questions, more complex problems, without needing constant manual updates from developers every time something new comes up. It just provides a smoother, more effective experience for everyone. [00:07:41] Speaker A: It definitely feels like a shift from AI, AI as a set of brittle. [00:07:45] Speaker B: Single purpose tools to a truly adaptable assistant. That's the promise. [00:07:49] Speaker A: It sounds incredibly powerful, almost like, well, magic. But let's Pull back the curtain a bit. How does MEMP actually do this, this procedural learning? What's going on under the hood that gives it this muscle memory for AI, Right, yeah. [00:08:02] Speaker B: It's not magic, but it is some clever engineering at its core. MEMP uses this sophisticated system of interconnected modules. They all work together to learn, store, and retrieve that procedural knowledge. [00:08:15] Speaker A: Interconnected modules? Like different parts of a brain working together? [00:08:19] Speaker B: Sort of, yeah. You could maybe picture it as a kind of dynamic mental filing cabinet inside the AI, but it's not static. It's constantly being updated and reorganized based on new experiences and feedback. [00:08:31] Speaker A: Okay, so how does it learn a new procedure? What's the first step? [00:08:34] Speaker B: So when the AI encounters something new, it needs to learn how to do. MEMP doesn't just try to memorize a simple sequence of steps. It's A, then B, then C. Instead, it intelligently breaks the procedure down into smaller, more manageable pieces. [00:08:47] Speaker A: Components. [00:08:47] Speaker B: Exactly, components. It identifies the key actions, the important decision points, any relevant cues from the environment. Think about learning to drive. You don't just memorize one specific route. [00:08:59] Speaker A: No, you learn steering, braking, accelerating, checking mirrors, reading signs. Individual skills. [00:09:04] Speaker B: Precisely. MEMP recognizes those individual skills or components. And as nodes within a complex graph. Think of it like a network map. [00:09:14] Speaker A: Okay. [00:09:15] Speaker B: Nodes in a graph, and the connections between these nodes represent the relationships, how things depend on each other. For example, the action doing the steering wheel might be connected to. Observe, traffic and check the mirrors. Showing these things are linked often happen together or in sequence. [00:09:30] Speaker A: So it builds a map of how things are done, the relationships between the steps. [00:09:34] Speaker B: That's it. A map of how. [00:09:36] Speaker A: So it's not just memorizing the steps, but understanding how the skills connect. Okay, but how does it refine that map? Do those connections just stay fixed once learned? [00:09:45] Speaker B: No, absolutely not. They evolve. And that's actually a critical part of MEMP's power. It has a sophisticated feedback mechanism built in. [00:09:53] Speaker A: Feedback. How does that work? [00:09:54] Speaker B: Well, as the AI performs the procedure, it's constantly monitoring its own performance. And it gets feedback, maybe from sensors, maybe from results, maybe even from a human supervisor. This feedback is then used to refine the connections within that graph structure we talked about. It strengthens the pathways that lead to successful outcomes and weakens or even gets rid of ones that don't work well. [00:10:16] Speaker A: Ah, like trial and error for humans. [00:10:18] Speaker B: Very much like how we learn through trial and error. You try a new recipe, maybe it doesn't quite work. [00:10:22] Speaker A: You test it. Next time, add less salt, Cook it longer. [00:10:25] Speaker B: Exactly. MEMP does the Same thing. But computationally, it iteratively improves its procedural know how based on real world results. [00:10:34] Speaker A: And a constant refinement means it can adapt on the fly. [00:10:37] Speaker B: Yes, dynamically updating its procedural knowledge. So unlike traditional LLMs that might need massive retraining for small changes, MEMT can handle unexpected situations or variations in tasks without needing a complete overhaul. [00:10:50] Speaker A: So the example you gave earlier. The robot. [00:10:52] Speaker B: Right. A MEMP powered robot assembling electronics could maybe figure out how to adjust if a component is slightly misplaced. Whereas a traditional robot or one run by a more rigid system would likely just struggle or stop completely. [00:11:05] Speaker A: That real time learning and adapting is impressive. But how does this scale. Doesn't it just create a massive tangled mess of graphs for every single procedure? [00:11:15] Speaker B: That's a really good question. And it's addressed through a pretty clever hierarchical structure. [00:11:20] Speaker A: Hierarchical, like an org chart? [00:11:22] Speaker B: Kind of. Instead of storing every procedure as its own separate isolated graph, MEMP organizes them. IT groups related procedures together under higher level categories. [00:11:33] Speaker A: Okay, give me an example. [00:11:34] Speaker B: Sure. Like all the different procedures for assembling, things might be grouped under a general manufacturing category, or different ways of navigating could fall under a mobility category. [00:11:44] Speaker A: I see. So related skills are clustered together. [00:11:46] Speaker B: Exactly. And this hierarchical structure lets the AI leverage knowledge learned in one procedure to help learn similar ones much faster. [00:11:55] Speaker A: Ah, so it accelerates learning for related tasks significantly. [00:11:59] Speaker B: You could almost think of it as a form of transfer learning on steroids because it's so systematically organized. [00:12:04] Speaker A: Transfer learning on steroids. I like that. [00:12:06] Speaker B: And it also massively helps manage the computational side. It makes the whole system more efficient, like a highly organized library, where finding the right book, the right procedure is quick. [00:12:18] Speaker A: This idea of a scalable, adaptable architecture, the implications seem profound. It goes way beyond just single AI agents. Right. It suggests a future where AI isn't just smart individually, but maybe collectively smart. [00:12:33] Speaker B: Precisely. That's one of the most exciting possibilities. Imagine a whole fleet of autonomous vehicles. Each one is learning and adapting to its own unique environment. Maybe new roadworks, unexpected detours, even subtle local driving habits. [00:12:45] Speaker A: Okay. [00:12:46] Speaker B: With memp, these vehicles could potentially share their procedural knowledge seamlessly. What one car learns about NASA, navigating a tricky intersection could benefit the whole fleet. [00:12:55] Speaker A: Wow. So they learn from each other's experiences. [00:12:57] Speaker B: That's the vision. Creating a kind of collective intelligence that constantly improves navigation and decision making across all the vehicles. This shared learning could rapidly accelerate the overall learning curve, leading to much safer, more efficient transport systems for everyone. [00:13:11] Speaker A: That's a powerful image. What about other collaborations? [00:13:14] Speaker B: Or think About a network of robots working together on a complex assembly line. MEMP could allow these robots to learn from each other, adapt to changes in the production process. Maybe a new component is introduced, or a step changes, all in real time. [00:13:29] Speaker A: So they become more flexible as a team. [00:13:30] Speaker B: Right. Maximizing efficiency, minimizing downtime. They're not just dumbly executing pre programmed steps, they're dynamically learning and improving together as a system. [00:13:42] Speaker A: That vision of collective adaptable AI is certainly compelling, very powerful, but. Okay, let's not get ahead of ourselves just yet. MEM sounds amazing, but like any new technology, especially one this complex, it must face some challenges. Right? What are the main hurdles on the road ahead for this kind of procedural AI? [00:13:59] Speaker B: You're absolutely right to ask. It's not all smooth sailing. And there are significant hurdles. One major challenge is the need for incredibly high quality feedback. [00:14:08] Speaker A: High quality feedback? Why is that so crucial? [00:14:11] Speaker B: Well, the accuracy and the effectiveness of MEMP's learning process depend heavily on the quality and reliability of the feedback it gets. If the feedback is inaccurate or incomplete or noisy, the AI can quickly learn the wrong thing. It can ingrain incorrect or inefficient procedures. [00:14:28] Speaker A: Garbage in, garbage out, essentially. [00:14:30] Speaker B: Exactly. So a lot of current research is focused on developing more robust, more reliable feedback mechanisms. Maybe incorporating advanced techniques from reinforcement learning or sophisticated ways of having humans in the loop to guide the learning and ensure it's correct. [00:14:45] Speaker A: Okay, reliable feedback is key. What else? [00:14:48] Speaker B: Another area that needs careful work is addressing the potential for what's sometimes called catastrophic forgetting. [00:14:54] Speaker A: Catastrophic forgetting? Sounds dramatic. [00:14:56] Speaker B: It can be. While MEP's hierarchical structure helps mitigate this somewhat, there's always a risk. A risk that as the system learns lots of new procedures, it might inadvertently overwrite or simply forget procedures it learned previously. [00:15:09] Speaker A: Like a human trying to learn too many things at once and getting confused. [00:15:12] Speaker B: A bit like that, yeah. Or like learning a new language and starting to forget your native one in an extreme case. So researchers are actively exploring techniques like regularization and different kinds of memory consolidation strategies to make sure the system holds on to its existing knowledge while still being able to efficiently integrate new skills. [00:15:33] Speaker A: Protecting old memories while making new ones makes sense. [00:15:36] Speaker B: And then of course, with any powerful and adaptable AI system like this, we absolutely have to consider the responsible development side. The ethics crucial point. As MEMP gets more sophisticated, more capable, it's vital that robust safeguards are built in right from the start. [00:15:53] Speaker A: What kind of safeguards? [00:15:54] Speaker B: Well, things like carefully evaluating and actively addressing potential biases in the training data. It learns from ensuring there's transparency in how it makes decisions. Why did it choose this procedure over that one? And establishing clear mechanisms for human oversight. So we're always in control. [00:16:09] Speaker A: So fairness, transparency, accountability. [00:16:12] Speaker B: Exactly. Designing these systems with those principles. Fairness, accountability, transparency as core components, not just as an afterthought slapped on later. It has to be part of the engineering itself. [00:16:23] Speaker A: Absolutely. That seems non negotiable. So as we wrap this up then, MEMP really does sound like a significant step forward in AI. It seems to offer a clear path towards AI agents that are more adaptable, more efficient and more cost effective. [00:16:37] Speaker B: That's the potential. [00:16:38] Speaker A: It's unique approach, focusing on that human like procedural memory, that knowing how it genuinely opens up a whole world of possibilities across so many different sectors. And while there are definitely challenges still to overcome, as you've outlined, the potential benefits seem undeniable. It leaves you thinking, doesn't it? If AI can truly learn how to like we do, freeing it from that endless cycle of retraining, what really complex multi step problems could it start to solve that are just beyond our reach right now? And maybe even what new procedures might we find it can teach us about the nature of learning itself. Something to ponder. We encourage you to keep exploring these ideas and we'll certainly be back to guide you through the next big developments.