Speaker 1: Welcome to the Sentient Code, where intelligence is engineered, autonomy
Speaker 1: is emerging, and a line between human and machine grows thinner.
Speaker 1: Each episode, we decode the algorithms, explore the robotics, and
Speaker 1: examine the ideas shaping the future of artificial minds.
Speaker 2: You know that voice in your head, the one you're
Speaker 2: using right now to process what I'm saying. Maybe it
Speaker 2: is saying, Okay, where is he going with this? Or
Speaker 2: maybe it's just reminding you that you forgot to switch
Speaker 2: the laundry right.
Speaker 3: The internal monologue, the narrator of the documentary that is
Speaker 3: your life exactly.
Speaker 2: We have always thought of that inner voice as just
Speaker 2: a human quirk, maybe even a byproduct of consciousness, something
Speaker 2: that just happens because we have language. But what if
Speaker 2: it is not just noise? What if that little voice
Speaker 2: is actually the engine of intelligence?
Speaker 3: That is the billion dollar question. And if you ask
Speaker 3: the researchers at the Okinawa Institute of Science and Technology
Speaker 3: or OIST, they will tell you that the reason AI
Speaker 3: has been hitting a wall lately is precisely because it
Speaker 3: doesn't have that voice. It doesn't mumble to itself.
Speaker 2: Mumbling that was the technical term they use.
Speaker 3: Well, they call it self directed internal speech. But yeah,
Speaker 3: expectively it is mumbling, and we are looking at a
Speaker 3: really fascinating study today. This was published just this January
Speaker 3: twenty eighth, twenty twenty six, in the journal Neural Computation.
Speaker 3: It is led by first author doctor Jeffrey Kaiser of
Speaker 3: the Cognitive Neurorobotics Research Unit, and it proposes something that
Speaker 3: frankly sounds a little sci fi.
Speaker 2: Yeah. I read through the material for this steep dive
Speaker 2: and my immediate thought was, great, now the robots are
Speaker 2: going to be talking to themselves on.
Speaker 3: The bus exactly, just muttering in the corner.
Speaker 2: But the implications here are massive, right. We aren't just
Speaker 2: talking about a chatbot that is a little bit wittier
Speaker 2: or more conversational, No, not at all.
Speaker 3: We are talking about a fundamental restructuring of how machines learn.
Speaker 3: We are moving away from the whole big data approach.
Speaker 2: Where you just feed a computer the entire Internet.
Speaker 3: Right, just scraping everything. We are moving away from that
Speaker 3: and towards something much more biological, something that learns a
Speaker 3: lot more like a human child does.
Speaker 2: So the mission for our deep dive today is to
Speaker 2: really figure out why giving an artificial intelligence, A mumble
Speaker 2: and a scratch pad might actually be the key to
Speaker 2: the next generation of robotics. Because usually when we hear
Speaker 2: about AI upgrades, it's always, oh, we need more chips,
Speaker 2: or we need massive new data centers.
Speaker 3: More compute, more power, alway.
Speaker 2: Right, But this is different. This is about the architecture itself.
Speaker 3: It is about architecture, but honestly, it's also about psychology
Speaker 3: because to understand this machine architecture, we actually have to
Speaker 3: start with a human brain. We have to ask why
Speaker 3: do you talk to yourself?
Speaker 1: Usually to keep.
Speaker 2: From panicking, to be honest, or if I'm cooking, if
Speaker 2: I am making a really complex recipe, I am definitely muttering. Okay,
Speaker 2: onions are done. Now I need the garlic. Where to
Speaker 2: put the garlic?
Speaker 3: Exactly, you are using self talk as an executive function.
Speaker 3: You aren't just making noise into the void. You are
Speaker 3: actively organizing disparate ideas. You are weighing conflicting choices. You
Speaker 3: are processing sensory data in real time. So it have
Speaker 3: a purpose, a very specific purpose. In psychology. We call
Speaker 3: this metacognition.
Speaker 2: Thinking about thinking, right.
Speaker 3: It allows you to objectify your own thought process. It
Speaker 3: creates a feedback loop where the output of one thought,
Speaker 3: like the onions are done, becomes the direct input for
Speaker 3: the next thought, which is get the garlic.
Speaker 2: So it's essentially a chain of logic.
Speaker 3: It is a chain. And doctor Kwiser's team is saying, look,
Speaker 3: this biological habit isn't a glitch in the human system.
Speaker 3: It is a highly functional mechanism. It is literally how
Speaker 3: we organize our minds. And if we want AI to
Speaker 3: navigate ambiguity the way humans do, we need to import
Speaker 3: this biology directly into the code.
Speaker 2: But it is not just the voice, right. There is
Speaker 2: this other piece of the puzzle that the paper emphasizes
Speaker 2: called working memory. And I really want to pause on
Speaker 2: this because in the study they talk a lot about slots, Yes, slots, slots, right,
Speaker 2: and they make a really big deal about how this
Speaker 2: is entirely different from how a normal neural network remembers things.
Speaker 2: So help me out here, because I think a lot
Speaker 2: of people would assume doesn't the standard chatbot already have
Speaker 2: a memory. It remembers what I typed three prompts ago.
Speaker 3: It does, but it is a completely different kind of memory.
Speaker 3: Think of a standard neural network like the ones running
Speaker 3: most current large language models as a giant piece of
Speaker 3: tied fabric.
Speaker 2: Hi die Okay, I am picturing a vintage T shirt
Speaker 2: from the sixties.
Speaker 3: Perfect. When a standard neural network learns something new, the
Speaker 3: die spreads out everywhere. The information is distributed across all
Speaker 3: the connections, all the mathematical weights simultaneously. It is a
Speaker 3: holographic kind of storage.
Speaker 2: I see.
Speaker 3: So if you want to change one specific fact, or
Speaker 3: if you just need to hold one specific number in
Speaker 3: your head for a second, it is really hard to
Speaker 3: do that without messing up the pattern of the whole.
Speaker 2: Shirt because it's messy. You can't just one specific thread
Speaker 2: out without unraveling the entire image or changing the surrounding
Speaker 2: colors exactly.
Speaker 3: That architecture is fantastic for recognizing broad patterns, but it
Speaker 3: is actually really bad for holding specifics. Now, what doctor
Speaker 3: Kwaiser and his team did was introduce these explicit slots.
Speaker 3: Imagine that on top of that TIDI shirt, you sew
Speaker 3: on a few clear plastic pockets.
Speaker 2: Okay, like a plastic badge holder or a bocket protector.
Speaker 3: Right, These are distinct protected containers. You can write a
Speaker 3: number on a piece of paper, put it in slot,
Speaker 3: A and it stays perfectly safe. It doesn't bleed into
Speaker 3: the TIDI fabric at all. It functions as a true
Speaker 3: variable I see.
Speaker 2: So the AI can say, okay, I am currently holding
Speaker 2: the number seven in my left hand, and it completely
Speaker 2: doesn't matter what the rest of the network is doing.
Speaker 2: That seven is safe and isolated precisely.
Speaker 3: And this is absolutely crucial for formal logic. If I
Speaker 3: tell you to reverse the sequence seven, two, nine, you
Speaker 3: need to hold those three numbers in your head, in
Speaker 3: your slots and shuffle them around a standard AI struggles
Speaker 3: with this because it tries to memorize the concept of seven, two,
Speaker 3: nine based on how often it has seen those specific
Speaker 3: numbers grouped together in the past.
Speaker 2: So it's essentially trying to vibe its way vibe.
Speaker 3: Its way through a math problem. Yes, that is hilarious,
Speaker 3: but it's true. It is vibing based purely on statistics.
Speaker 3: It looks at the data and says, well, usually seven
Speaker 3: is followed by eight, but here it's two, and it
Speaker 3: just gets confused. But the OIC model is different. It
Speaker 3: puts seven and slot one two and slot two and
Speaker 3: nine and slot three, and then that is when the
Speaker 3: inner voice, the mumbling kicks in, and.
Speaker 2: The mumble says, swap slot one and slot three.
Speaker 3: Bingo. It generates a symbolic command directed at its own
Speaker 3: memory system, swap one in three. It absolutely does not
Speaker 3: care that the numbers are seven and nine. They could
Speaker 3: be an apple and an orange. They could be completely
Speaker 3: made up words. The logic holds perfectly because the slots
Speaker 3: are entirely separate from the content inside them.
Speaker 2: And this sounds exactly like what computer scientists called generalization.
Speaker 3: That is the magic word here, generalization.
Speaker 2: Because in the paper they use this incredibly dense phrase
Speaker 2: they call it content agnostic information processing. It is a mouthful,
Speaker 2: it really is, but it seems to be the core
Speaker 2: of why this works.
Speaker 3: It is a mouthful, but it is the holy grail
Speaker 3: of artificial intelligence research. Content agnostic means the AI understands
Speaker 3: the underlying rule, regardless of the specific data it is
Speaker 3: looking at. Think about basic algebra. If you know the
Speaker 3: A plus B equal C, you can solve that equation
Speaker 3: whether A is five or a is five million, or
Speaker 3: a is a banana.
Speaker 2: Right because I know the relationship between the parts, not
Speaker 2: just the parts themselves exactly.
Speaker 3: Traditional AI is often just memorizing millions examples, if it
Speaker 3: has seen the sequence one two three reverses three to
Speaker 3: one a million times in its training data, it can
Speaker 3: do it easily. But if you give it xyz, it
Speaker 3: might fail simply because it hasn't seen those specific letters
Speaker 3: in that specific.
Speaker 2: Order before, which seems so brittle.
Speaker 3: It is extremely britle. But the OIST researchers found that
Speaker 3: their model, the one equipped with the memory slaw and
Speaker 3: the internal mumbling, could look at a sequence it had
Speaker 3: literally never seen before in its life and apply the
Speaker 3: reverse rule perfectly on the first try.
Speaker 2: Because it wasn't looking at the letters themselves, it was
Speaker 2: looking at the containers. Take what is in slot one
Speaker 2: and move it to slot three exactly.
Speaker 3: It completely separates the algorithm from the data, and that
Speaker 3: is something humans do naturally all day long, but neural
Speaker 3: networks have historically been terrible at it.
Speaker 2: Okay, so let's dig into the actual mumbling mechanism itself,
Speaker 2: because I am trying to visualize this. Yeah, how does
Speaker 2: a computer actually mumble? I mean, is it generating a
Speaker 2: tiny sound file? Is there microphone involved?
Speaker 3: No, no audio is being generated. It is generating tokens.
Speaker 3: In AI terminology, a token is just a fundamental unit
Speaker 3: of information, like a word or a piece of a word.
Speaker 3: In a normal chatbot that you might use online, the
Speaker 3: tokens it generates come out immediately as text on your screen.
Speaker 3: But in this OHES system, the researchers created a recurrent loop.
Speaker 2: A loop, so it feeds back on itself.
Speaker 3: Right, the system generates a token. Let's say it generates
Speaker 3: the token for the word swat, but instead of showing
Speaker 3: that word to the user, it feeds that token directly
Speaker 3: back into its own input layer for the very next
Speaker 3: millisecond of processing.
Speaker 2: So it is whispering back into its own ear.
Speaker 3: It is a quiet mumble. The paper describes it as
Speaker 3: the low level generation of tokens. It acts as an
Speaker 3: intermediate computational step. And the researchers did something very specific
Speaker 3: here to make this happen. They actively encouraged the system
Speaker 3: to do this during training.
Speaker 2: Encouraged like they gave it a digital cookie.
Speaker 3: Sort of yeah. In machine learning we use things called
Speaker 3: loss functions and targets to guide behavior. They essentially set
Speaker 3: a strict target where the system was required to produce
Speaker 3: a certain amount of internal speech while it was attempting
Speaker 3: to solve the problem. They basically said to the AI,
Speaker 3: you cannot just guess the final answer. You have to
Speaker 3: show your work. You have to talk it through step
Speaker 3: by step.
Speaker 2: Man That instantly reminds me of my high school algebra teacher.
Speaker 2: I don't care if you got the right answer, show
Speaker 2: me the steps, and.
Speaker 3: Your teacher was exactly right, because if you show the steps,
Speaker 3: you actually prove that you understand the logic behind the solution.
Speaker 3: If you just write down the final number, you might
Speaker 3: have just memorized it from the textbook or taken a
Speaker 3: lucky guess. By forcing the AI to mumble the intermediate steps,
Speaker 3: they forced it to break the complex problem down into
Speaker 3: manageable logical.
Speaker 2: Chunks, which brings us directly to the specific tasks they
Speaker 2: use to test this theory. We mentioned reversing sequences earlier.
Speaker 2: The paper also talks about pattern creation. But why are
Speaker 2: these specific tasks so important for the researchers to use.
Speaker 2: They seem, I don't know, almost too simple, like reversing
Speaker 2: a list of items. A cheap pocket calculator can do that.
Speaker 3: A calculator can do that because a calculator is hard
Speaker 3: coded by a human software engineer to do exactly that.
Speaker 3: A neural network, on the other hand, has to learn
Speaker 3: how to do it completely from scratch just by looking
Speaker 3: at examples. And for a neural network, these types of
Speaker 3: tasks are actually brutal. They are highly computationally because they
Speaker 3: require what we call sequential processing.
Speaker 2: Sequential meaning involves time.
Speaker 3: Yes, time and order. You have to remember the beginning
Speaker 3: of the sentence while you are simultaneously reading the end
Speaker 3: of the sentence, and then you have to purposefully manipulate
Speaker 3: the order of those elements. This requires holding multiple distinct
Speaker 3: data points in your head simultaneously without them overwriting each other.
Speaker 2: Ah, we are back to the tid eye problem.
Speaker 3: Exactly, if I add blue dye for the end of
Speaker 3: the sentence, it might bleed over and turn the red
Speaker 3: dye at the beginning of the sentence into purple.
Speaker 2: So the information corrupts itself just by existing in the
Speaker 2: same space.
Speaker 3: Right, And the study showed that the models equipped with
Speaker 3: the explicit slots and the internal mumble just blue the
Speaker 3: standard models out of the water. They could handle significantly
Speaker 3: longer sequences, much more complex patterns. And here's the really
Speaker 3: mind blowing part. They could switch between different tasks without crashing.
Speaker 2: Multitasking. Multitasking Now, humans are pretty famous for thinking we
Speaker 2: are amazing at multitask while actually being terrible at it.
Speaker 2: But for AI, it's usually a complete disaster, isn't it.
Speaker 3: It is usually catastrophic. In fact, there is an official
Speaker 3: term for it in the field. It's called catastrophic forgetting.
Speaker 2: That sounds incredibly dramatic, it really is.
Speaker 3: If you train a standard artificial intelligence to play chess
Speaker 3: and it gets really good, and then you try to
Speaker 3: teach that exact same model to play checkers, it will
Speaker 3: almost always completely forget how to play chess. That's totally why,
Speaker 3: completely overwritten, because it overwrites the mathematical weights to learn
Speaker 3: the new game. The tiedeie pattern essentially gets entirely redied
Speaker 3: with new colors.
Speaker 2: But the OST model didn't do that.
Speaker 3: It remember both it did, and doctor Kwoiser observed that
Speaker 3: the mumbling was the absolute key to this capability. The
Speaker 3: internal speech acted as a dynamic context manager.
Speaker 2: Break that down for me. A context manager.
Speaker 3: Thing of a professional chef working in a really busy kitchen.
Speaker 3: They are chopping onions on the cutting board, but they
Speaker 3: also have a delicate sauce simmering over on the stove.
Speaker 3: They chop chop, chop, and they fit physically stop and
Speaker 3: say out loud to themselves, Okay, check the sauce. They
Speaker 3: walk over, they stir the sauce. Then they say, sauce
Speaker 3: is good. Back to onions.
Speaker 2: That little phrase back to onions. It resets their mental state, It.
Speaker 3: Resets the context. The OST system uses its mumble to
Speaker 3: explicitly label which task it is currently performing. It says internally,
Speaker 3: I am now doing task A, and it uses the
Speaker 3: memory slots specifically assigned for task A. Then it mumbles
Speaker 3: switching to task B, and that command clears the slots
Speaker 3: or moves its attention to new slots. It actively prevents
Speaker 3: the parameters of the tasks from bleeding into each other.
Speaker 2: That is wild. That essentially bridges the huge gap between
Speaker 2: the rigid, single task focus of traditional AI, where you
Speaker 2: have one specific bot for playing chess and a totally
Speaker 2: different bot for chatting, and the flexible, fluid adaptability that
Speaker 2: human beings have.
Speaker 3: It is a massive step towards general purpose intelligence, and
Speaker 3: it leads us directly to another incredible benefit outline in
Speaker 3: the research, which is data efficiency.
Speaker 2: Yes, this is a huge topic right now in the
Speaker 2: tech world. I like constantly keep reading articles saying that
Speaker 2: we are basically running out of Internet, that the big
Speaker 2: tech companies have scraped every single book, every news article,
Speaker 2: every Reddit post, and there is literally nothing left of
Speaker 2: high quality to train the next generation of models on.
Speaker 3: That is a very real, very pressing problem. The current
Speaker 3: dominant paradigm in AI development is essentially scale is all
Speaker 3: you need. Just make the model bigger, throw more processing
Speaker 3: power at it, and give it more data. But we
Speaker 3: are rapidly hitting the hard seiling of what is actually
Speaker 3: available out there. The OST research suggests a completely viable way.
Speaker 2: Out of that trap, sparse data utilization.
Speaker 3: Right. Because the OST system is learning how to think,
Speaker 3: meaning the general underlying rules, rather than just what to think,
Speaker 3: which is just memorizing specific answers, it needs significantly less
Speaker 3: data to achieve the same or better performance.
Speaker 2: Going back to your algebra analogy earlier, if I teach
Speaker 2: a student the actual rules of algebra, I really only
Speaker 2: need to show them maybe ten practice problems, and they
Speaker 2: get the concept they can apply it anywhere. But if
Speaker 2: I try to teach them malogy but purely by showing
Speaker 2: them every single possible math problem in existence so they
Speaker 2: can memorize the answers. I would literally need infinite data.
Speaker 3: That is a perfect analogy. The OIS model is learning
Speaker 3: the rules of the game. Doctor Kawiser explicitly calls it
Speaker 3: a complementary, lightweight alternative to these massive, heavy data models.
Speaker 3: And you have to imagine what that means for the
Speaker 3: real world. Think about the environment, think about the energy costs.
Speaker 3: We wouldn't need to build these massive city size data
Speaker 3: centers that consume as much electricity as a small country
Speaker 3: just to train a smart AI, and we.
Speaker 2: Would need a supercomputer to actually run the AI once
Speaker 2: it's trained. Yeah, which brings us to the part of
Speaker 2: the paper that got me really truly excited, and that
Speaker 2: is robotics.
Speaker 3: Yes, the real world application of all this theory.
Speaker 2: Because right now, let's be honest, robots are kind of dumb.
Speaker 2: They work perfectly in a car factory where everything is
Speaker 2: literally bolted down to the floor, the lighting never changes
Speaker 2: and the exact same part comes down the assembly line
Speaker 2: every three seconds. But you put a state of the
Speaker 2: art robot in my messy living it is total chaos.
Speaker 2: It gets stuck on a rug exactly.
Speaker 3: The paper explicitly talks about the challenge of transitioning AI
Speaker 3: from controlled environments to dynamic environments.
Speaker 2: Let's really take this out of the laboratory, because the
Speaker 2: paper specifically mentions agricultural robots as a use case. Let's
Speaker 2: visualize that you have got a robotic tractor, or let's
Speaker 2: go at a weed bot out in a massive cornfield.
Speaker 3: Okay, so you have this robot. Its sole job is
Speaker 3: to drive down the row, visually identify a weed and
Speaker 3: pull it out, but obviously leave the valuable corn alone. Now,
Speaker 3: in a sterile lab setting, that is incredibly easy. The
Speaker 3: lighting is perfectly calibrated, the corn is bright green, the
Speaker 3: weed has a distinct leaf shape. The cameras process it instantly.
Speaker 2: But out in the actual real war, in the.
Speaker 3: Real world, a dark cloud passes over the sun. The
Speaker 3: ambient light drops by fifty percent in two seconds. A
Speaker 3: gust of wind blows the corn stock, so it is
Speaker 3: suddenly leaning over at a forty five degree angle. Maybe
Speaker 3: there's a splash of mud that gets splattered right on
Speaker 3: the robot's camera lens.
Speaker 2: To a standard vision based AI, that visual input just
Speaker 2: changed completely. It completely freaks out. It thinks the leaning
Speaker 2: corns an entirely new, unrecognized object. It thinks the shadow
Speaker 2: from the cloud is a deep hole in the.
Speaker 3: Ground, exactly, it throws an error and crashes, or worse,
Speaker 3: it just happily pulls up all the expensive corn But
Speaker 3: a robot equipped with this inner voice architecture and working
Speaker 3: memory can actually self correct in real time. It can
Speaker 3: literally talk itself through the sensory confusion, so it.
Speaker 2: Is internally mumbling, Okay, the light just got a lot darker,
Speaker 2: but my sensors say I didn't actually move forward, so
Speaker 2: the object directly in front of me is highly likely
Speaker 2: to still be the cornstock I was just looking at.
Speaker 3: Yes, exactly. It maintains a continuous state. It explicitly says
Speaker 3: to itself. Current state is weeding row four. Event is
Speaker 3: sudden light reduction. Action is continue current task. It bridges
Speaker 3: the sudden gap in its sensory data by relying on
Speaker 3: a logical internal narrative. It creates a cognitive buffer against
Speaker 3: the unpredictability and chaos of the physical world.
Speaker 2: That is just remarkably human. I mean, that is exactly
Speaker 2: what I do. When I am driving on the highway
Speaker 2: in a sudden rainstorm. I am talking to myself saying, okay,
Speaker 2: I can't see much, just slow down, keep the wheel straight,
Speaker 2: look for the tail lights ahead. I am not completely
Speaker 2: relearning how to drive a car every single second. I
Speaker 2: am actively talking myself through the noise and the fear.
Speaker 3: And that is exactly why this research is so huge
Speaker 3: for the field of robotics. You simply cannot upload the
Speaker 3: entire Internet into the memory banks of a farm tractor.
Speaker 3: It is impossible. You need a centralized brain that is small,
Speaker 3: highly efficient, and capable of actively reasoning its way out
Speaker 3: of a novel problem, rather than just cross referencing a
Speaker 3: massive database to remember a pre programmed solution. This is
Speaker 3: the fundamental difference between simple automation and true autonomy.
Speaker 2: Break that distinction down from me a bit more automation
Speaker 2: versus autonomy. People use those words interchangeably a lot.
Speaker 3: They do, but they are very different. Automation is like
Speaker 3: a train on a track. It is incredibly powerful, it
Speaker 3: is fast, it is efficient, but if a cow suddenly
Speaker 3: wanders on to the track, the train doesn't know how
Speaker 3: to evaluate the situation. It just hits the brakes and stops,
Speaker 3: or it crashes. Current robots are still largely just automated.
Speaker 3: They rigidly follow a script. Go forward ten feet, turn left,
Speaker 3: ninety degrees stop, but.
Speaker 2: The real world does not have tracks exactly.
Speaker 3: Autonomy, on the other hand, is like driving a car.
Speaker 3: You can see the cow, evaluate the shoulder of the road,
Speaker 3: and steer around it. You can decide to go off
Speaker 3: road if you have to. Autonomy means you are writing
Speaker 3: the behavioral script in real time as the situation unfolds.
Speaker 3: The mumbling we are talking about is effectively that real
Speaker 3: time script writing process.
Speaker 2: And because this whole architecture is so lightweight, as doctor
Speaker 2: Quaser puts it, you can actually put this brain physically
Speaker 2: inside the robot itself.
Speaker 3: Right, it becomes an embedded system. The robot doesn't need
Speaker 3: to constantly talk to a massive cloud server for every
Speaker 3: single micro decision, and that drastically reduces latency. If that
Speaker 3: automated tractor sees a itch suddenly appear, it needs to
Speaker 3: stop right now, not in the two seconds it takes
Speaker 3: to send a video frame to a server farm in
Speaker 3: Virginia and wait to get a stop command back.
Speaker 2: Two seconds is an eternity when you are driving a
Speaker 2: tractor into.
Speaker 3: A ditch exactly.
Speaker 2: So, if I'm tracking this right, we have a proposed
Speaker 2: system that learns significantly faster uses a fraction of the
Speaker 2: data can multitask without forgetting its primary directive and functions
Speaker 2: exponentially better in the messy, unstructured real world, and honestly
Speaker 2: sounds almost too good to be true. There has to
Speaker 2: be a catch, or rather, what does this actually mean
Speaker 2: for the future of how we as humans are going
Speaker 2: to interact with these things?
Speaker 3: Well, there is actually one more really interesting side effect
Speaker 3: of this mumbling architecture that we haven't touched on yet,
Speaker 3: and it solves a major headache in the field interpretability.
Speaker 2: Interpretability like being able to translate.
Speaker 3: It more like being able to understand its motives. One
Speaker 3: of the absolute biggest fears about modern AI, especially the
Speaker 3: massive deep learning models, is that they are essentially a
Speaker 3: black box. We feed data in, we get an answer out,
Speaker 3: but we really don't know why made that specific decision.
Speaker 3: It just spits out the final output based on billions
Speaker 3: obscure mathematical weights. But with this ost system, the actual
Speaker 3: thought process is explicit and trackable.
Speaker 2: Because it is actively generating and mumbling those tokens.
Speaker 3: Yes, we can theoretically open up the system and read
Speaker 3: the literal transcript of its internal deliberation. So if that
Speaker 3: agricultural robot does something crazy and drives straight through a
Speaker 3: wooden fence. The engineers don't have to just throw their
Speaker 3: hands up and guess why the weights failed. They can
Speaker 3: look at the internal log and literally read the mumble.
Speaker 3: They can see it said identified wooden fence as tall
Speaker 3: dry grass proceeding forward.
Speaker 2: Oh wow, So it gives us an actual readable audit
Speaker 2: trail of its thought process.
Speaker 3: Exactly. It makes safety engineering and debugging so much easier
Speaker 3: and more transparent. We can debug the actual logic of
Speaker 3: the machine, not just try to tweak the underlying math
Speaker 3: and hope for the best. We can see exactly where
Speaker 3: the reason is went off the rails.
Speaker 2: That is fascinating. It's exactly like being able to read
Speaker 2: a student's rough draft of an essay to see exactly
Speaker 2: where they misunderstood the core assignment, rather than just giving
Speaker 2: the final paper an f and moving on.
Speaker 3: It really is and all of this leads us to
Speaker 3: a final sort of philosophical point that the paper touches on.
Speaker 3: Doctor Kwiser and his team make a point to mention
Speaker 3: that this research isn't just about building better, more efficient
Speaker 3: robots for industry. It is fundamentally about understanding ourselves.
Speaker 2: Getting back to the biological blueprint we talked about the
Speaker 2: start right.
Speaker 3: By successfully modeling inner speech and working memory as a
Speaker 3: distinct computational advantage in a machine, the study strongly validates
Speaker 3: the hypothesis that our own internal monologue is a critical
Speaker 3: functional component of human intelligence. It's not just some weird
Speaker 3: evolutionary quirk or a side effect of learning to speak
Speaker 3: out loud. We evolve the ability to talk to ourselves
Speaker 3: because it is quite literally the most efficient way to
Speaker 3: run our own biological software.
Speaker 2: So we are basically biological machines running a continuous mumble
Speaker 2: algorithm on a wetwear neural network.
Speaker 3: In a very real way. Yes, it suggests that the
Speaker 3: act of thinking is really just internal self.
Speaker 2: Communication, and that is that is heavy.
Speaker 3: It is heavy. It really changes how we look at
Speaker 3: artificial intelligence going forward. Instead of seeing AI as this alien,
Speaker 3: hyperfast super calculator that just knows things instantly, we might
Speaker 3: start to see it as something that occasionally needs to
Speaker 3: just take a minute pause and think things through before
Speaker 3: it acts.
Speaker 2: We are moving towards systems that do not just blindly
Speaker 2: process incoming data, but actively interact with their own internal
Speaker 2: states to figure out the world.
Speaker 3: Correct the future of robust, reliable artificial intelligence lies in
Speaker 3: machines that quite literally talk to themselves.
Speaker 2: I really love that perspective. It makes the AI feel
Speaker 2: a lot less like a terrifying magic box and a
Speaker 2: lot more like a well like a thinker, a.
Speaker 3: Thinker with a very organized scratch pad.
Speaker 2: A thinker with a scratch pad and a habit of
Speaker 2: uttering under its breath.
Speaker 3: Exactly.
Speaker 2: So let's recap the really big takeaways here for everyone.
Speaker 2: We started with the observation that traditional AI has been
Speaker 2: somewhat stuck in this big data trap, right.
Speaker 3: Relying purely on massive, unsustainable data inputs to essentially memorize
Speaker 3: the entire world.
Speaker 2: And then this team, it always he comes along and says, no,
Speaker 2: stop building bigger data centers. Look at the architecture of
Speaker 2: the human brain. Instead, we have working memory, which gives
Speaker 2: us those isolated slots, and we have internal speech, which
Speaker 2: gives us the mumble.
Speaker 3: And when you successfully combine those two biological concepts and code,
Speaker 3: you achieve content agnostic information processing. You get a system
Speaker 3: with the ability to learn the underlying rules of a problem,
Speaker 3: not just memorize the facts of the training.
Speaker 2: Data, which directly leads to much better generalization, the ability
Speaker 2: to deal with the alien alphabet or the completely novel
Speaker 2: sequence without crashing.
Speaker 3: And significantly better multitasking, the chef managing the kitchen without
Speaker 3: catastrophic forgetting.
Speaker 2: And finally, it unlocks the potential for true real world robotics.
Speaker 2: The autonomous tractor navigating a sudden storm without needing to
Speaker 2: ask a cloud server what a shadow.
Speaker 3: Is, achieving true autonomy through internal self regulation.
Speaker 2: It really seems like we are witnessing a true maturing
Speaker 2: of the entire field of AI, moving away from just
Speaker 2: brute force computing and moving toward truly elegant, biologically inspired design.
Speaker 3: I would go so far as to call it a
Speaker 3: fundamental shift from artificial intelligence to artificial cognition. We aren't
Speaker 3: just trying to simulate the final results of human thinking anymore.
Speaker 3: We are actually simulating the step by step process of thinking.
Speaker 2: That is a very crucial distinction to make.
Speaker 3: It is, and doctor Kaiser's work really challenges all of
Speaker 3: us to reconsider our basic definition of what learning actually is.
Speaker 3: Learning is not just the massive accumulation of disjointed facts.
Speaker 3: It is the act of development of the internal logical
Speaker 3: processes required to manipulate and understand those facts.
Speaker 2: And the memory slots provide the stable canvas upon which
Speaker 2: that complex process is drawn Together.
Speaker 3: They enabled the machine to finally step out of the rigid,
Speaker 3: fragile constraints of its historical training data and dynamically engage
Speaker 3: with a sheer, novelty and chaos of the real world.
Speaker 2: As we wrap up this deep dive, I want to
Speaker 2: leave you the listener, with a final thought to mole
Speaker 2: over the next time you catch yourself talking to yourself,
Speaker 2: maybe you are rehearsing an argument in the shower, or
Speaker 2: muttering under your breath while you tear apart the house
Speaker 2: looking for your car keys. Do not feel crazy, definitely not.
Speaker 3: You are highly functional.
Speaker 2: You're just actively optimizing your working memory. You are running
Speaker 2: some very high level executive code, and pretty soon your
Speaker 2: smart toaster might be doing the exact same thing while
Speaker 2: it figures out how to perfectly brown your bagel.
Speaker 3: Let's just hope the toaster doesn't start arguing back about
Speaker 3: what settings you chose.
Speaker 2: That is definitely a problem for another deep dive. This
Speaker 2: has been a truly fascinating look into the study. AI
Speaker 2: that talks to itself learns faster and smarter coming out
Speaker 2: of the Okanau Institute of Science and Technology.
Speaker 3: A very signific can step forward in the field of
Speaker 3: cognitive neurorobotics.
Speaker 2: Thank you so much for taking the time to break
Speaker 2: all of this complex architecture down with us today. It
Speaker 2: was my absolute pleasure, and thank you for listening. Keep
Speaker 2: talking to yourselves everyone, It is genuinely good for your brain.