Speaker 1: You know, if you look at a microprocessor under an
Speaker 1: electron microscope, there's this this expectation of just absolute crystalline perfection.
Speaker 2: Oh for sure. I mean it's engineering in its purest,
Speaker 2: most rigid form, right.
Speaker 1: You see these immaculate grids of silicon, and you just
Speaker 1: know exactly what the hardware is doing, like millions of
Speaker 1: transistors are flipping between one and zero true or false,
Speaker 1: on or off.
Speaker 2: Yeah, it's an architecture that's fundamentally built on absolute certainty exactly.
Speaker 1: But the paradox we're unpacking in today's deep dive is
Speaker 1: that this rigid certainty, it actually creates a structural.
Speaker 2: Trap, right, because we construct these flawlessly deterministic environments for
Speaker 2: our machines, and then.
Speaker 1: We ask them to step outside of.
Speaker 2: That environment exactly. We ask them to process the real
Speaker 2: world like we expect them to understand a casual conversation
Speaker 2: or a Parsi poem, or you know, navigate the nuances
Speaker 2: of a simple joke, and the moment.
Speaker 1: That microscopic perfection collides with the chaotic, messy reality of
Speaker 1: human coog ignition, the system just breaks down. Because human
Speaker 1: intelligence isn't a spreadsheet, it's murky it's totally subjective.
Speaker 2: It really is, and the gap between the flawless binary
Speaker 2: machine and the wonderfully messy human mind is basically the
Speaker 2: central hurdle of modern computing.
Speaker 1: Which brings us to the stack of notes and excerpts
Speaker 1: we have for today. We're looking at a deeply technical,
Speaker 1: really paradigm shifting academic book called Artificial Intelligence with Uncertainty,
Speaker 1: second edition.
Speaker 2: Yes, by Researchers Daily, and you do. It's a dense read,
Speaker 2: but fascinating.
Speaker 1: So dense, But our mission for you today is to
Speaker 1: explore why, after literally decades of trying to make computers
Speaker 1: perfectly precise, scientists are now intentionally teaching AI how to
Speaker 1: be uncertain.
Speaker 2: Yeah. They're using this revolutionary framework called the cloud model
Speaker 2: to force precise machines to think in the beautiful, uncertain
Speaker 2: gray areas of human language.
Speaker 1: Okay, let's unpack this because to really glasp why injecting
Speaker 1: uncertainty into AI is a necessity and it's not like
Speaker 1: a flaw. We have to start with the universe itself right.
Speaker 2: Before we even touch algorithms. We have to recognize that
Speaker 2: reality refuses to be put into a neat, predictable.
Speaker 1: Box, which wasn't always the scientific consensus, right, I mean,
Speaker 1: for a long time, the dominant paradigm was totally deterministic. Oh.
Speaker 2: Absolutely. Think back to the nineteenth century. You had Newton
Speaker 2: in la Place and this classical view of the universe
Speaker 2: as this giant, perfect clockwork mechanism, a clockwork universe exactly.
Speaker 2: The assumption was that every particle moves according to fixed
Speaker 2: early rules, and the uncertainty we experience was just well
Speaker 2: a byproduct of our own ignorance, Like if we just
Speaker 2: built better measuring tools, the uncertainty would completely vanish, right,
Speaker 2: But modern physics dismantled that pretty quickly.
Speaker 1: Yeah. The source text pulls this amazing quote from the
Speaker 1: philosopher Carl Popper that serves as the thesis for this
Speaker 1: whole problem. He said, all clocks are clouds.
Speaker 2: I love that quote. It perfectly captures the illusion of determinism,
Speaker 2: because even the most incredibly precise clocks we can engineer,
Speaker 2: like atomic clocks measuring the decay of isotopes.
Speaker 1: They're actually just clouds of probability at their core.
Speaker 2: Exactly at the quantum level, it's all clouds. Werner Heisenberg's
Speaker 2: uncertainty principle prove that you literally cannot know both the
Speaker 2: exact position and momentum of particles simultaneously.
Speaker 1: So uncertainty isn't just a lack of knowledge, No, it's
Speaker 1: an objective, mathematical attribute of nature itself. And the book
Speaker 1: gives this phenomenal example of how this uncertainty scales, which
Speaker 1: I think is so cool. Like it points out that
Speaker 1: the dimension of a system is entirely relative to your
Speaker 1: vantage point of the Earth.
Speaker 2: Example.
Speaker 1: Yeah, if you look at the Earth from outside the
Speaker 1: Milky Way, it's a single certain point, a zero dimensional dot.
Speaker 1: But zoom into the Solar system and it becomes a
Speaker 1: one dimensional elliptical orbit.
Speaker 2: Right. If you're standing on the surface, it operates as
Speaker 2: a flat two dimensional plane exactly.
Speaker 1: Or look at the coastline paradox, The length of a
Speaker 1: coastline actually changes depending on the size of the ruler
Speaker 1: use to measure it. The closer you look, the more
Speaker 1: chaotic the boundary becomes.
Speaker 2: Which is exactly why the scientific community developed entropy is a.
Speaker 1: Metric to mathematically quantify that disorder.
Speaker 2: Right, yeah, I mean it started in thermodynamics back in
Speaker 2: the eighteen fifties with Rudolph Clausis tracking how heat dissipates,
Speaker 2: and then o Lidvig Boltzmann mapped it to the random
Speaker 2: movement of molecules.
Speaker 1: But the big leap for AI happened later.
Speaker 2: Right right. In nineteen forty eight, Claude Shannon introduced information entropy.
Speaker 2: He proved that you could mathematically measure the uncertainty of
Speaker 2: information as it travels through a communication channel.
Speaker 1: Okay, but hang on, let me push back on this
Speaker 1: premise for a second.
Speaker 2: Sure, good, I.
Speaker 1: Understand that the physical universe is a cloud of chaos,
Speaker 1: but computers are literally artificial environments. They are the clocks
Speaker 1: we built to escape the clouds.
Speaker 2: I see where you're going.
Speaker 1: Right, Like, if our hardware is fundamentally binary, isn't injecting
Speaker 1: chaos into a computational system counterproductive? I mean, if I'm
Speaker 1: building an AI for medical diagnostics or autonomous driving, forcing
Speaker 1: a perfect machine to act imperfectly sounds like a recipe
Speaker 1: for catastrophic failure.
Speaker 2: It seems counterintuitive. I'll give you that. But what's fascinating
Speaker 2: here is that rigid systems are actually more brittle in
Speaker 2: the real world. Brittle if you force a machine to
Speaker 2: only view the world through strict, deterministic boundaries, it will
Speaker 2: fail the second it encounters an edge case that doesn't
Speaker 2: fit its exact parameters.
Speaker 1: Oh, because the real world isn't binary exactly.
Speaker 2: Think about a self driving car. It doesn't encounter a
Speaker 2: neat pedestrian or no pedestrian scenario. It encounters like a
Speaker 2: plastic bag blowing across the street. That might be a
Speaker 2: small animal under lighting conditions, that might be a shadow.
Speaker 1: Right right, It's a gray area.
Speaker 2: And for an AI to safely navigate that reality, it
Speaker 2: can't just throw an error code when things get fuzzy.
Speaker 2: It must be mathematically equipped to process and compute that entropy.
Speaker 1: That makes a lot of sense. And I mean since
Speaker 1: the physical world is uncertain, the way human beings p
Speaker 1: and describe it is also totally uncertain, which leads us
Speaker 1: directly to the ultimate carrier of human.
Speaker 2: Intelligence, natural language.
Speaker 1: Exactly natural language. If we want to simulate human thought,
Speaker 1: we have to decode our language, and human language is
Speaker 1: just full of randomness and fuzziness.
Speaker 2: It really is the ultimate expression of cognitive fuzziness, and
Speaker 2: it's the essential distinction between a machine processing data and
Speaker 2: an entity demonstrating actual common sense.
Speaker 1: The text highlights this beautifully by looking at how language
Speaker 1: completely resists numerical substitution. There are these uh poetic examples
Speaker 1: they use, oh.
Speaker 2: With Tang dynasty poems.
Speaker 1: Yeah, like Wang Bo writing about a proud eagle flying
Speaker 1: with rosy clouds, or Cowzyugen's description of Lindaiu's eyebrows which
Speaker 1: seem to knit and yet not to knit.
Speaker 2: I mean, you cannot map an RGBX code to rosy.
Speaker 1: No, you really can't, and you certainly can't assign a
Speaker 1: boolean value to eyebrows that seem to knit and yet
Speaker 1: not to knit. Like the concept exists entirely in a
Speaker 1: state of conradiction.
Speaker 2: But any human reading that sentence understands the exact emotional
Speaker 2: weight being conveyed exactly.
Speaker 1: And we don't even need to look at poetry to
Speaker 1: see this. We see it in the most basic functional
Speaker 1: definitions of common sense. Like the text breaks down how
Speaker 1: we distinguish between a cup, a plate, and a bowl.
Speaker 2: Which is fascinating because there's absolutely no strict mathematical ratio
Speaker 2: of width to height that defines a bowl.
Speaker 1: Right, A cup has a handle, a plate is flat
Speaker 1: and holds rice. A bowl has an edge and holds soup.
Speaker 2: But if I hand you an object that is like
Speaker 2: slightly too wide for a standard cup, but a little
Speaker 2: too deep for a standard plate. Your brain doesn't short circuit.
Speaker 1: No, you don't need a ruler to categorize it. Your
Speaker 1: common sense just steps in and flexes the definition based
Speaker 1: on context.
Speaker 2: And in AI. This human common sense is highly relative.
Speaker 2: It changes by time, place, and people. But that flexibility
Speaker 2: is what allows it to survive.
Speaker 1: I want you, the listener, to think about this practically
Speaker 1: for a second. Think about how often you lie on
Speaker 1: this fuzziness just to get through a work day.
Speaker 2: Oh, all the time.
Speaker 1: Right. You'll sit in a meeting and say the project
Speaker 1: is probably going to take about three weeks.
Speaker 2: Usually that sentence is a mathematical nightmare.
Speaker 1: It really is probably about and usually are massive variables.
Speaker 1: But your colleagues understand the exact level of confidence you
Speaker 1: are projecting. You don't get confused by the uncertainty. It
Speaker 1: actually gives your brain the flexibility to understand the context.
Speaker 2: If you instead said the project will conclude in exactly
Speaker 2: five hundred and four hours, twelve minutes and six seconds,
Speaker 2: they wouldn't think you were smart.
Speaker 1: No, they'd think you fundamentally misunderstood the chaotic nature of
Speaker 1: project management. The uncertainty in your language doesn't impede understanding,
Speaker 1: it actively enables it.
Speaker 2: But the problem is the dominant schools of AI development
Speaker 2: have basically spent the last sixty years trying to strip
Speaker 2: that vital fuzziness out of the system.
Speaker 1: Okay, so let's contextualize that. Because if human thought is
Speaker 1: fluid and language is fuzzy, how have computer scientists spent
Speaker 1: the last six decades trying to shove this messy reality
Speaker 1: into rigid code.
Speaker 2: What's been a journey?
Speaker 1: Yeah, let's do a rapid fire history of AI. Here.
Speaker 1: It basically starts at the nineteen fifty six Dartmouth symposium,
Speaker 1: right with guys like McCarthy, Minski, and Shannon. That's where
Speaker 1: the term artificial intelligence was born exactly.
Speaker 2: And over the years we've seen huge milestones the logic,
Speaker 2: theorist proving math theorems, gameplaying AI evolving from Samuel's Checkers
Speaker 2: to Deep Blue beating Gary kasprov at chess, and eventually
Speaker 2: Google's Alpha Go defeating Fanwei.
Speaker 1: But academically, the text breaks down three major schools of
Speaker 1: thought that emerged from all this.
Speaker 2: Right, First, you have symbolism. This is the idea that
Speaker 2: thinking is just computation. It's rule based logic.
Speaker 1: If A then B, So like giving the AI a
Speaker 1: strict recipe book.
Speaker 2: Exactly. This gave us early expert systems like mycion for
Speaker 2: medical diagnoses, but they were incredibly brittle. Then you have connectionism.
Speaker 1: This is simulating the brain structure right, artificial neural networks
Speaker 1: and backpropagation like modern deep learning.
Speaker 2: Yes, instead of hard coding rules, you let the system
Speaker 2: adjust the weights of its own connections across massive data sets.
Speaker 1: So connectionism is like trying to build the AI's muscle
Speaker 1: memory by mimicking the brain's wiring.
Speaker 2: That's a great way to put it. And the third
Speaker 2: school is behaviorism. This focus is on the perception behavior model,
Speaker 2: intelligent control, and adaptation.
Speaker 1: Like giving the AI reflexes to react to its environment
Speaker 1: like that inverted pendulum robot mentioned in the text.
Speaker 2: Exactly reflexes, muscle memory, and recipe books. But while these
Speaker 2: methods conquered chess and go, they still struggle with the fluid,
Speaker 2: non mathematical nature of human language in consciousness.
Speaker 1: So to bridge this gap between precise math and fuzzy
Speaker 1: human language, the authors introduce this groundbreaking framework, the cloud model.
Speaker 2: And this is where things get really paradigm shifting.
Speaker 1: To explain it, we have to talk about this classic
Speaker 1: experiment from the text by the Chinese scholar Nan Lunzang.
Speaker 1: He wanted to look at fuzziness versus randomness, so he
Speaker 1: asked one hundred and twenty nine people to define the
Speaker 1: age range for the concept of youth.
Speaker 2: Such a seemingly simple concept, but the data is wild.
Speaker 1: It really is. So Zeg measured the membership degree, like,
Speaker 1: what percentage of people agree this age is youth? At
Speaker 1: age twenty to twenty four, the membership degree is one
Speaker 1: one hundred percent agreement.
Speaker 2: Everyone agrees a twenty two year old is a youth, right.
Speaker 1: But at age thirty it drops two point five nine
Speaker 1: sixty nine, and by age thirty six it's just point
Speaker 1: zero zero zero seven eight.
Speaker 2: What this points out is that the concept of youth
Speaker 2: isn't just fuzzy, it's genuinely random.
Speaker 1: Wait random? How so?
Speaker 2: Well, if you ask the exact same person on a
Speaker 2: Tuesday if a thirty year old is a youth, they
Speaker 2: might say yes. But if you ask them on a Friday,
Speaker 2: depending on their mood or context, their answer might change
Speaker 2: oh wow.
Speaker 1: So it completely violates traditional probabilities law of excluded middle.
Speaker 2: Exactly, a person can't be simultaneously a youth and not
Speaker 2: a youth in classical logic, but in human cognition they
Speaker 2: absolutely can be.
Speaker 1: You're sure? It gets really interesting. The cloud model takes
Speaker 1: this qualitative concept like youth, and translates it into a
Speaker 1: quantitative distribution of cloud drops.
Speaker 2: Right. It doesn't treat a word as a single data point.
Speaker 2: It treats it like a literal cloud made of thousands
Speaker 2: of randomized drops of data.
Speaker 1: But if a qualitative word is a cloud made of
Speaker 1: data drops, how do we mathematically describe the shape of
Speaker 1: that cloud to a computer? Like? What's the digital DNA
Speaker 1: of a concept?
Speaker 2: The researchers broke it down into three digital characteristics, expectation, entropy,
Speaker 2: and hyper entropy X and he yes. The first one,
Speaker 2: expectation or X, is the most representative point the classic sample,
Speaker 2: the dead center of the target.
Speaker 1: Okay, let me test this. If the concept is a
Speaker 1: normal commute to work. The expectation the X is twenty
Speaker 1: five minutes, that's the center point perfect.
Speaker 2: The second is entropy or INQ. This is the uncertainty measurement.
Speaker 2: It dictates how far the drops disperse.
Speaker 1: So the entropy is the standard traffic variance, say between
Speaker 1: twenty and thirty minutes.
Speaker 2: Exactly, it's the width of the cloud. But the third
Speaker 2: characteristic is the trickiest hyper entropy or he.
Speaker 1: Yeah, the uncertainty of the entropy itself. What is that?
Speaker 1: Is that like a sutty snowstorm messing up the commute?
Speaker 2: Not quite?
Speaker 1: Yeah.
Speaker 2: If we connect this to the bigger picture, hyper entropy
Speaker 2: is essentially the measure of consensus. Consensus, Yeah, it measures
Speaker 2: the thickness of the cloud's fuzziness. If a concept has
Speaker 2: hyper entropy, it means it's a concept people fundamentally disagree
Speaker 2: on it.
Speaker 1: Oh I see, so a normal commute has low hyper
Speaker 1: entropy because we mostly agree on what that means.
Speaker 2: The line is thin, right, But think about a concept
Speaker 2: like a fair salary.
Speaker 1: Oh man, the hyper entropy on that would be massive.
Speaker 1: My definition of a fair salary variant is going to
Speaker 1: be wildly different from a CEO's definition.
Speaker 2: Exactly. The conceptual boundary is incredibly thick and heavily debated.
Speaker 2: Low hyper entropy means it's universally accepted common sense.
Speaker 1: So once we have this DNA, the expectation entropy and
Speaker 1: hyper entropy. We can oftually build algorithms that allow computers
Speaker 1: to generate these concepts.
Speaker 2: Yes, the researchers call them forward cloud generators, and it
Speaker 2: turns out these concepts take on very specific visual shapes.
Speaker 1: The text describes this forward Gaussian cloud algorithm, and it's
Speaker 1: fundamentally different from a standard Gaussian distribution right because it
Speaker 1: uses iterative randomness.
Speaker 2: Right, randomness built on top of randomness. It takes the hyperanthropy,
Speaker 2: uses it to generate a random variance and uses that
Speaker 2: variance to plot a single cloud drop.
Speaker 1: That is wild and depending on the inputs, it constructs
Speaker 1: different shapes of clouds to model real world concepts, like
Speaker 1: the symmetric cloud.
Speaker 2: Yes, that's for a concept like medium height. It tapers
Speaker 2: off evenly in both directions. You can be too tall
Speaker 2: to be medium or too short.
Speaker 1: But then there's the half cloud.
Speaker 2: Right for asymmetrical concepts like having a fever.
Speaker 1: Because normal body temp is thirty seven degrees celsius and
Speaker 1: a fever only goes up. You can't have a fever
Speaker 1: of thirty five degrees. So the cloud is literally sliced
Speaker 1: in half exactly.
Speaker 2: And for really complex realities you use a combined cloud
Speaker 2: like the concept of a white collar salary.
Speaker 1: Right because salaries clumped tightly at the entry level plateau
Speaker 1: in middle management and then have this long chaotic tail
Speaker 1: for executives. A simple bell curve can't model that, but
Speaker 1: a combined cloud maps it perfectly.
Speaker 2: And these multi dimensional models are what allow AI to
Speaker 2: perform soft reasoning.
Speaker 1: Thinking with words instead of just numbers.
Speaker 2: Exactly. It opens the door for massive leaps in data mining,
Speaker 2: intelligent control, and network science.
Speaker 1: Like an autonomous drone trying to land in a storm,
Speaker 1: a rigid AI would overcorrect it every wind, gust and crash,
Speaker 1: but a cloud model controller understands the fuzziness of the environment.
Speaker 1: It blends the overlapping data clouds to execute a smooth landing.
Speaker 2: It makes decisions based on the shape of the uncertainty.
Speaker 1: That is just mind blowing. So what does this all
Speaker 1: mean for you, the listener? Let's recap this journey. We
Speaker 1: started by seeing that the universe is a cloud, not
Speaker 1: a perfect.
Speaker 2: Clock, and our natural language reflects that beautiful chaos exactly.
Speaker 1: But for sixty years we tried to shove that fluid
Speaker 1: language into rigid binary systems. Now by using the cloud
Speaker 1: model mapping, the expectation, the entropy and the hyper entropy
Speaker 1: of our concepts. We are finally teaching AI that it's
Speaker 1: okay not to be one hundred percent.
Speaker 2: Certain, because uncertainty is the very foundation of intelligence. And
Speaker 2: I think it leads us with a final, really provocative
Speaker 2: thought to mull over.
Speaker 1: Oh, what's that.
Speaker 2: We've spent decades trying to eliminate uncertainty in machines, to
Speaker 2: make them perfect, flawless tools. But if true intelligence requires
Speaker 2: the fuzziness of clouds, the subjectivity of hyper entropy, and
Speaker 2: the flexibility of common sense, Yeah, at what point does
Speaker 2: programming a machine to be uncertain make it less like
Speaker 2: a tool and more like us?
Speaker 1: Wow? That really is the question, isn't it. If we
Speaker 1: teach them how to be unsure, we might just be
Speaker 1: teaching them how to be human. Thank you so much
Speaker 1: for joining us on this deep dive. I hope the
Speaker 1: next time you use words like about or probably you
Speaker 1: appreciate the hyperintropic chaos in your own mind. Embrace the
Speaker 1: gray areas. Everyone. We'll catch you next time.