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: I spent twenty minutes yesterday, literally twenty minutes, trying to
Speaker 2: get a supposedly state of the art AI to figure
Speaker 2: out this completely absurd riddle.
Speaker 3: Oh a riddle. Let me guess. It didn't go exactly
Speaker 3: as planned.
Speaker 2: No, it was a disaster. My seven year old told
Speaker 2: it to me. It was something about a penguin, a
Speaker 2: flashlight and a jar of peanut butter.
Speaker 3: Right, not exactly standard training data.
Speaker 2: Exactly, And the AI confidently generated this five paragraph, highly articulate,
Speaker 2: just beautifully written essay that completely missed the punchline. I
Speaker 2: mean it fundamentally violated basic physic confidently incorrect.
Speaker 3: That is the hallmark of the current architecture.
Speaker 2: Yes, and yet I open up my feed right after that,
Speaker 2: and the headlines are absolutely screaming. They're saying, this exact
Speaker 2: same architecture is about to autonomously replace human doctors and
Speaker 2: lawyers and engineers. There is this massive dizzying disconnect happening
Speaker 2: right now for you listening, You are constantly surrounded by
Speaker 2: these claims that artificial intelligence is reaching human levels of comprehension.
Speaker 3: You hear about them acing the bar, exam.
Speaker 2: Breezing through advanced medical licensing.
Speaker 3: Tests, mastering the exact standardized testing frameworks we've used for generations.
Speaker 2: Right it paints this incredibly vivid picture for all of us,
Speaker 2: a picture of algorithms that are practically breathing, thinking, and
Speaker 2: understanding the world exactly the way you and I do.
Speaker 2: But and this is the big question for today, what
Speaker 2: if the very yardsticks we have been using to measure
Speaker 2: artificial intelligence are just fundamentally broken.
Speaker 3: They are obsolete, completely broken.
Speaker 2: Today we are exploring a massive paradigm shift. We are
Speaker 2: looking at the core reality that those traditional academic benchmarks
Speaker 2: have completely and utterly lost their diagnostic utility.
Speaker 3: That is precisely our mission today. We have to completely
Speaker 3: deconstruct how we evaluate the artificial mind. We are no
Speaker 3: longer just talking about machines getting smarter in some vague sense.
Speaker 2: We're stepping into a high stakes intellectual mystery.
Speaker 3: We are because we are examining a profound shift in
Speaker 3: how we test computational intelligence, we're transitioning entirely away from
Speaker 3: testing generalized models on standard educational curricula. Instead, we're looking
Speaker 3: at how we evaluate them against the absolute limits of
Speaker 3: highly specialized human expertise, at.
Speaker 2: The very frontier of scientific and historical discovery.
Speaker 3: Exactly. The central theme here is understanding the stark delineation
Speaker 3: between the statistical probability operations of a machine, the pattern matching, right,
Speaker 3: the pattern matching, and the actualized, deep causal reasoning of
Speaker 3: a human mind. It is about separating the illusion of
Speaker 3: comprehension from genuine contextual synthesis.
Speaker 2: Okay, let's unpack this collapse of the old standard, because
Speaker 2: I think we all know how these models work at
Speaker 2: a baseline, right, They are incredibly sophisticated.
Speaker 3: Autocorrects navigating vector spaces to predict the next hope exactly.
Speaker 2: But for a long time, the gold standard, the ultimate
Speaker 2: proving ground for these architectures was something called the MMLU,
Speaker 2: the Massive Multitask Language Understanding Exam.
Speaker 3: If you're building a multi billion dollar machine learning model,
Speaker 3: this was your benchmark.
Speaker 2: It covered this incredibly broad generalized knowledge base, everything from
Speaker 2: basic high school European history to complex professional level medical
Speaker 2: diagnostic microeconomics tort law.
Speaker 1: Right.
Speaker 2: It was supposed to be the ultimate test of an
Speaker 2: AI's generalized knowledge.
Speaker 3: And when the MMLU was initially introduced, it did provide
Speaker 3: a highly effective metric, a percentage increase in accuracy on
Speaker 3: that exam directly correlated with handible architectural improvements in the
Speaker 3: neural networks.
Speaker 2: It gave the developer, for is, a clear roadmap, a
Speaker 2: clear empirical trajectory.
Speaker 3: Yes, But then we witnessed a phenomenon that completely destabilized
Speaker 3: this metric, the exponential scaling of neural networks.
Speaker 2: The tech giants just started throwing hardware at it.
Speaker 3: Massive hardware developers began massively increasing both the parameter counts
Speaker 3: of these models and the sheer volume of their training
Speaker 3: data sets. They were essentially scraping the entire indexed Internet,
Speaker 3: and as a.
Speaker 2: Direct result of that scaling, the systems began achieving near
Speaker 2: perfect scores on the MMLU.
Speaker 3: They effectively maxed out the test.
Speaker 2: Which creates a massive structural flaw. If you have a
Speaker 2: diagnostic tool, any kind of test, and it routinely starts
Speaker 2: returning the maximum possible values across all these diverse subjects.
Speaker 2: It stops giving you any meaningful variants.
Speaker 3: It goes blind. It is a phenomenon known as saturation.
Speaker 2: Saturation. To put this in perspective for you listening, imagine
Speaker 2: you are a sports scientist. You're trying to test the
Speaker 2: absolute physical limits of an elite Olympic decathlete, a gold
Speaker 2: me right, But the only diagnostic tool you have in
Speaker 2: your lab is the standard middle school presidential fitness test, the.
Speaker 3: One we all took in seventh grade.
Speaker 2: Exactly sure, the olympian is going to get a perfect score.
Speaker 2: They're going to do all the pull ups, run the
Speaker 2: shuttle sprint, stretch past their toes without breaking a sweat.
Speaker 2: But that perfect score tells you absolutely nothing about their
Speaker 2: actual absolute physical limits.
Speaker 3: It doesn't tell you how their cardiovascular system handles the
Speaker 3: complex stress of it to caflon, or.
Speaker 2: How they adapt to unpredictable physical challenges. It just tells
Speaker 2: you that they are stronger than a twelve year old.
Speaker 2: The test is saturated, it ceases to provide any insight
Speaker 2: into the underlying capabilities, or more importantly, the limitations of
Speaker 2: the system being tested.
Speaker 3: What's fascinating here is how the saturation exposes a deep
Speaker 3: fundamental difference between high performance on tasks designed by humans
Speaker 3: and actual generalizable intelligence.
Speaker 2: Because getting an A on a human test doesn't mean
Speaker 2: you think like a human exactly.
Speaker 3: When these models achieve those near perfect scores on the MMLU,
Speaker 3: those strong empirical results are frequently just manifestations of highly
Speaker 3: sophisticated pattern matching. They are processing an unimaginably vast amount
Speaker 3: of ubiquitous online data and finding the correlations.
Speaker 2: They've read every prep book ever public millions of them.
Speaker 2: But and this is the crucial distinction, that pattern matching
Speaker 2: does not represent deep synthesized understanding. The saturation of the
Speaker 2: MMLU prove that our old diagnostic tools were fundamentally incapable
Speaker 2: of mapping the computational differences between a machine executing a
Speaker 2: statistical operation and a human engaging in true causal comprehension.
Speaker 3: Right, which brings us to the mechanics of that illusion,
Speaker 3: Because what this exposes is just how completely that specific
Speaker 3: architecture shatters when you take off the training wheels of
Speaker 3: the Internet's data.
Speaker 2: It breaks down fundamentally. So let's get into the technicals
Speaker 2: of vector embeddings, and let's go beyond the basic IT
Speaker 2: maps coordinates explanation that we always hear what is actually
Speaker 2: happening in side that high dimensional space when a model
Speaker 2: occurs to be thinking.
Speaker 3: To understand the illusion, we have to look at the
Speaker 3: intersection of vector embeddings, attention mechanisms, and co sign similarity.
Speaker 2: Okay, lay it out for us.
Speaker 3: When an artificial intelligence processes text, it mathematically maps words
Speaker 3: and concepts into a space that can have tens of
Speaker 3: thousands of dimensions. Concepts that frequently appear together in the
Speaker 3: training data form dense.
Speaker 2: Clusters, so they live in the same mathematical neighborhood.
Speaker 3: Yes, the model uses attention heads to weigh the importance
Speaker 3: of different words in your prompt and then uses a
Speaker 3: mathematical function, often co sign similarity, to find the closest,
Speaker 3: most statistically relevant cluster of vectors to generate its response.
Speaker 2: So, if I ask it about a widely documented historical
Speaker 2: event like the moon landing, it's operating in a highly
Speaker 2: dense cluster. There are millions of articles, transcripts, and books
Speaker 2: in its training data, linking Apollo eleven, Armstrong Moon and
Speaker 2: nineteen sixty nine. The cosigine similarity points it directly to
Speaker 2: the center of a very tight well defined mathematical neighborhood.
Speaker 3: It's essentially impossible for it to miss. The density of
Speaker 3: the data cluster allows for highly accurate statistical retrieval. It
Speaker 3: looks like mastery.
Speaker 2: It looks like it knows what the moon is, but
Speaker 2: it doesn't.
Speaker 3: And that is the problem. What happens when you introduce
Speaker 3: sparse data? What happens when you ask it to synthesize
Speaker 3: concepts that do not reside in a dense mathematical neighborhood like.
Speaker 2: My seven year old's penguin riddle?
Speaker 3: Precisely, the cluster density is too low for reliable statistical retrieval.
Speaker 3: The attention mechanisms attempt to draw connections between vectors that
Speaker 3: are mathematically distant, leading to what we call hallucinations.
Speaker 2: Because it's forced to answer.
Speaker 3: The system is mathematically forced to predict the next token,
Speaker 3: so it wanders into a low density neighborhood and simply
Speaker 3: starts generating plausible sounding nonsense based on superficial syntactical patterns.
Speaker 2: Because it doesn't actually possess an internal model of reality.
Speaker 2: It's just doing high dimensional.
Speaker 3: Geometry geometry disguised as language.
Speaker 2: And this brings us to doctor Tong New New's analytical
Speaker 2: warning about the anthropomorphic fallacy. We are so incredibly wired
Speaker 2: evolutionarily to assume that if something can speak to us,
Speaker 2: if it uses syntax and grammar, it must think like us.
Speaker 3: Doctor Nunu identifies this pervasive cognitive bias perfectly. Because these
Speaker 3: models are successfully completing tasks that were historically designed to
Speaker 3: require human cognition, like passing a medical board exam, observers
Speaker 3: incorrectly deduce that the machine must possess an equivalent cognitive framework.
Speaker 2: We project human thought onto a statistical calculator.
Speaker 3: Yes, a machine can predict the next token perfectly in
Speaker 3: a highly structured, well documented academic test solely because that
Speaker 3: data exists in abundance within its training corpuses.
Speaker 2: That's all just correlations.
Speaker 3: But when confronted with a novel situation that requires actual
Speaker 3: contextual synthesis, a scenario it hasn't mapped the mathematical coordinates
Speaker 3: for the statistical probability, mapping completely breaks down.
Speaker 2: It's the ultimate trick of the light, and it's exactly
Speaker 2: bactly what catalyzed this massive global shift in how we
Speaker 2: evaluate intelligence. The structural gaps have become so profound that
Speaker 2: they could no longer be mapped by isolated teams of
Speaker 2: computer scientists just working in their Silicon Valley silos.
Speaker 3: They needed a much broader perspective.
Speaker 2: It required a massive interdisciplinary intervention. We were talking about
Speaker 2: the engineering of the ultimate metric, something known as Humanity's
Speaker 2: Last Exam or the HLE. And let's clarify that name
Speaker 2: right now, because Humanity's Last Exam sounds incredibly melodramatic.
Speaker 3: It does sound like a cinematic apocalypse.
Speaker 2: It sounds like the title of a dystopian sci fi
Speaker 2: novel where we are all plugging into the matrix for
Speaker 2: the final time.
Speaker 3: It is a provocative title, certainly, but the nomenclature is
Speaker 3: purely a clinical rhetorical framing device. It is not an
Speaker 3: expression of apocalyptic dread regarding human relevance.
Speaker 2: We're not throwing in the towel, not at all.
Speaker 3: Rather, it is a highly specialized initiative designed to systematically
Speaker 3: delineate the boundary between algorithmic operations and genuine human reasoning.
Speaker 3: The objective is to identify operational strengths and computational vulnerabilities
Speaker 3: so that we can engineer safer, more reliable technologies.
Speaker 2: It is about understanding exactly where the machines fail to
Speaker 2: synthesize reality exactly.
Speaker 3: It's about precision, and the.
Speaker 2: Scale of the consortium that built this test is just staggering.
Speaker 2: We are looking at nearly one thousand researchers globally, and crucially,
Speaker 2: they weren't just computer engineers. They realized that generalized domains
Speaker 2: were totally insufficient to test for true understanding.
Speaker 3: To break a statistical machine, you have to force a
Speaker 3: fusion of disparate knowledge bases.
Speaker 2: So they integrated historians, physicists, linguists, and medical researchers right
Speaker 2: alongside the computer scientists.
Speaker 3: That interdisciplinary composition is critical because conceptual integration is exactly
Speaker 3: where the statistical probability mapping of current architectures falters advance.
Speaker 3: Human expertise is uniquely characterized by the ability to fuse disparate,
Speaker 3: seemingly unrelated domains of knowledge, drawing connections across disciplines. Yes.
Speaker 3: To test for this, the consortium published a highly rigorous
Speaker 3: assessment in the journal Nature, specifically under the doi ten
Speaker 3: point one zero three eight four one five eight six
Speaker 3: zero two five zero nine nine six two four. This
Speaker 3: examination consists of exactly two thousand, five hundred questions, and
Speaker 3: it is bound by incredibly strict, unforgiving methodological constraints.
Speaker 2: Let's look at those constraints, because they are brilliantly designed
Speaker 2: to trap an AI. The first constraint is binary greeting.
Speaker 2: Every single query among those twenty five hundred questions must
Speaker 2: possess exactly one clear, verifiable answer.
Speaker 3: There is no partial credit none.
Speaker 2: There is no room for a beautifully written, eloquent essay
Speaker 2: that dances around the topic and sounds smart but says
Speaker 2: absolutely nothing.
Speaker 3: This binary constraint is absolutely essential for empirical validity. One
Speaker 3: of the greatest challenges in evaluating open ended algorithmic generation
Speaker 3: is subjective human interpretation.
Speaker 2: We get tripped by good grammar who do If.
Speaker 3: A model generates a highly articulate response, human evaluators could
Speaker 3: be easily deceived, even if the output is factually hallucinatory.
Speaker 3: The model syntactical fluency masks its lack of actual comprehension.
Speaker 2: It speaks with so much confidence.
Speaker 3: But by enforcing strict binary grading, the test entirely eliminates
Speaker 3: that subjective vulnerability. The machine either successfully executed the complex
Speaker 3: logical deduction to arrive at the single verifiable truth, or
Speaker 3: it failed entirely.
Speaker 2: It strips away the AI's ability to smooth talk its
Speaker 2: way out of a corner. But the second constraint is
Speaker 2: the real killer, absolute immunity to rapid online search queries.
Speaker 3: This is where the paradigm shifts entirely.
Speaker 2: By engineering the test to be immune to basic search
Speaker 2: engine retrieval, the consortium forces the system entirely away from
Speaker 2: its primary operational strength. If an answer can be located
Speaker 2: as a contiguous factual string within an index database anywhere
Speaker 2: on the Internet, it completely fails to test structural comprehension.
Speaker 3: It just proves the machine can look things up incredibly fast.
Speaker 2: Right if I can google the exact phrase, it's not
Speaker 2: a good test of intelligence exactly.
Speaker 3: If the answer exists in a unified format within the
Speaker 3: training data, the model can simply rely on that high
Speaker 3: density vector cluster we discussed earlier. Therefore, the questions designed
Speaker 3: for the HLE demand multi step logical deduction, intricate spatial reasoning,
Speaker 3: or the synthesis of deeply obscured information that does not
Speaker 3: exist in a single location anywhere.
Speaker 2: It forces them to build something new.
Speaker 3: The system must piece together fragments of knowledge to derive
Speaker 3: an answer that hasn't been explicitly written.
Speaker 2: Down before and to guarantee that these constraints were actually met,
Speaker 2: the consortium implemented an adversarial pre testing phase that I
Speaker 2: just find brilliant. They built a filtration protocol. Imagine a
Speaker 2: massive room of these thousand researchers, and every single proposed
Speaker 2: question was systematically administered to the leading state of the
Speaker 2: art artificial intelligence systems available at the.
Speaker 3: Time, all the top tier models.
Speaker 2: If any of those models managed to produce the correct answer,
Speaker 2: that specific question was instantly destroyed, ripped up, and thrown out.
Speaker 3: This pre testing methodology is what ensures the exam remains
Speaker 3: perpetually stationed just beyond the frontier of current computational performance.
Speaker 3: It does not measure what the models can already do.
Speaker 3: It maps the exact perimeter of algorithmic ignorance.
Speaker 2: The perimeter of ignorance. I love that phrasing.
Speaker 3: It defines the precise boundary where statistical probability fails and
Speaker 3: causal deduction is required.
Speaker 2: This brings us to a specific area where this boundary
Speaker 2: mapping is most devastating the deterministic vulnerability of these models.
Speaker 2: Let's look at the objective contributions of doctor Tungwan from
Speaker 2: Texas A and M University's Department of Computer Science and Engineering.
Speaker 3: He was a major player in this consortium.
Speaker 2: He authored seventy three questions for the assessment, which was
Speaker 2: the second highest individual contribution globally, and his queries were
Speaker 2: highly concentrated within the domains of rigorous mathematics and computer science.
Speaker 3: Doctor Juan's contrabutions are vital because they isolate a critical
Speaker 3: vulnerability inherent in all probabilistic models, the fundamental conflict between
Speaker 3: stochastic prediction and deterministic execution.
Speaker 2: Okay, let's break that down for the listener.
Speaker 3: Mathematical and computational logic requires step by step rigid determinism.
Speaker 3: A sarcastic prediction model cannot navigate a rigorous mathematical proof.
Speaker 2: So say you give the AI a highly complex, fifty
Speaker 2: step mathematical proof that has never been solved in this
Speaker 2: specific way before. If you are a machine learning model
Speaker 2: relying on probabilistic guessing, just predicting the most likely next
Speaker 2: mathematical operation based on BASS training data, you might get
Speaker 2: step one right with ninety nine point nine percent certainty.
Speaker 3: You might even get step two right.
Speaker 2: But eventually you are going to make a tiny minor
Speaker 2: variable error because you are guessing, you're not deducing precisely.
Speaker 3: And in a rigorous mathematical proof, what happens when you
Speaker 3: introduce a single minor variable error at step fourteen.
Speaker 2: The entire logical structure collapses.
Speaker 3: The error compounds exponentially. A stochastic model might get the
Speaker 3: first steps right because those operational sequences are common in
Speaker 3: its training data, but the moment has to logically deduce
Speaker 3: a novel sequence. Its probabilistic nature forces a guess. The
Speaker 3: guess introduces an error, and the final answer is completely wrong.
Speaker 2: You're building a fifty story house of cards in a windstorm.
Speaker 2: It just takes one microscopic miscalculation at the base and
Speaker 2: the whole thing comes down. We often think of computers
Speaker 2: as being inherently perfect at math, like a giant calculator, But.
Speaker 3: These large language models are not calculators.
Speaker 2: There are language prediction engines trying to speak math.
Speaker 3: That is exactly what they are doing, and that is
Speaker 3: why they stumble when forced out of language and into pure,
Speaker 3: unforgiving deterministic logic.
Speaker 2: Now, to truly comprehend the massive cognitive divide that this
Speaker 2: exam is measuring, we need to spend some serious time
Speaker 2: analyzing the typeology of the expert level assessment domains. This
Speaker 2: is where it gets incredibly fascinating.
Speaker 3: The domains themselves are extraordinary.
Speaker 2: Look three specific examples of the types of questions that
Speaker 2: survived that brutal filtration process, and these are completely wild.
Speaker 2: Let's start with domain one linguistic synthesis, specifically the translation
Speaker 2: of ancient Palmerine inscriptions.
Speaker 3: Agent Palmerine represents a dialect that severely disrupts standard computational processing.
Speaker 3: It is an extinct language from the ancient city of Palmyra,
Speaker 3: located in present day Syria.
Speaker 2: A vital oasis hub on the Silk Road.
Speaker 3: Crucially, its linguistic record possesses highly limited fragmentary representation. Because
Speaker 3: it is so obscure, it completely lacks the massive digital
Speaker 3: corpus required to train statistical engines effectively.
Speaker 2: Right, there just aren't millions of pages of ancient Palmerines
Speaker 2: sitting on Wikipedia for the AI to ingest and map
Speaker 2: into its multidimensional vector space. The cluster density is practically zero.
Speaker 3: There is no broad pattern to recall.
Speaker 2: So when the AI encounters this dialect, its cosine similarity
Speaker 2: functions just hit a brick. Wall. But how does a
Speaker 2: human expert handle this? Because a human epigrapher doesn't just
Speaker 2: throw up their hands and give up when they don't
Speaker 2: have enough data points.
Speaker 3: No, they engage in something called epigraphic deduction.
Speaker 2: Let's walk through exactly what that looks like.
Speaker 3: Epigraphic deduction is a masterful example of multimodal contextual reasoning.
Speaker 3: A human epigrapher cross references disparate fields of knowledge that
Speaker 3: on the surface have nothing to do with linguistics. Let's
Speaker 3: say they are looking at a partially destroyed stone tablet
Speaker 3: containing a tax record from the year two fifty AD.
Speaker 2: Okay, setting the same.
Speaker 3: The word indicating this specific tax commodity is chipped away.
Speaker 3: An AI cannot statistically predict the missing word because the
Speaker 3: linguistic data is too sparse.
Speaker 2: But the human epigrapher steps back. They look at the
Speaker 2: chisel marks on the stone and realize it matches the
Speaker 2: craftsmanship of a specific merchant class exactly.
Speaker 3: They expand the context window to reality itself.
Speaker 2: They analyze the regional historical context. They know that around
Speaker 2: two hundred and fifty eight there was a massive drought
Speaker 2: in the region that decimated local agriculture, which meant trade
Speaker 2: routes had to shift significantly to import grain from Egypt.
Speaker 3: They know about the political shifts, perhaps a specific marriage
Speaker 3: between a Palmerine noble and a Roman patrician that altered
Speaker 3: tariff laws for that exact decade.
Speaker 2: So the human expert understands the human context in which
Speaker 2: the inscription was created.
Speaker 3: They use their causal understanding of history, geology, economics, and
Speaker 3: politics to infer the missing linguistic data. They deduce that
Speaker 3: the missing word must be the specific term for Egyptian grain.
Speaker 3: Based on the convergence of all these non linguistic variables.
Speaker 2: They solve the puzzle where half the pieces are missing
Speaker 2: by understanding the history of the factory that made the puzzle.
Speaker 3: That is a brilliant way to phrase it.
Speaker 2: The AI architecture completely lacks this multimodal contextual reasoning. Its
Speaker 2: standard statistical models fundamentally failed to synthesize the ancient texts
Speaker 2: because the variables involved in ancient political shifts and ecological
Speaker 2: disasters entirely evade their mathematical parameterization.
Speaker 3: They cannot compute the causal link between a drought and
Speaker 3: a missing chisel mark because those concepts don't live in
Speaker 3: the same mathematical neighborhood in their training data.
Speaker 2: This fundamental integration deficit leads us perfectly to the second domain,
Speaker 2: which forces a completely different kind of synthesis spatial and
Speaker 2: biological reasoning. The designated task in this domain involves the
Speaker 2: identification of microscopic anatomical structures within avian biology.
Speaker 3: Specifically the complex physiological taxonomy of birds.
Speaker 2: Okay, so we are shifting from dead languages on the
Speaker 2: silk road to microscopic bird anatomy. Talk about interdisciplinary So
Speaker 2: why does bird anatomy break a multi billion dollar AI.
Speaker 3: It comes down to the operational difficulty of dealing with
Speaker 3: messi real world data. The nature paper task requires deriving
Speaker 3: three dimensional spatial relationships purely from chaotic two dimensional microscopic imaging.
Speaker 2: Okay, elaborate on that operational difficulty.
Speaker 3: The core computecational challenge is that the system must map abstract,
Speaker 3: obscure taxonomic classifications onto highly variable, often visually unclear, microscopic data.
Speaker 3: When a human biological researcher looks at a slide of
Speaker 3: avian tissue under a microscope, they are not looking at
Speaker 3: a perfectly formatted, color coded textbook diagram.
Speaker 2: No, I've seen these slides, they look like Jackson Pollock
Speaker 2: paintings made of pink and purple blobs. There are no
Speaker 2: clean lines.
Speaker 3: Precisely, they're looking at a chaotic field of overlapping cells,
Speaker 3: artifacts from the staining process, and structural anomalies. The human
Speaker 3: researcher has to mentally execute a three dimensional spatial rotation
Speaker 3: of those two D anomalies in.
Speaker 2: Their mind, while simultaneously applying highly specialized obscure biological rules
Speaker 2: regarding Avian taxonomy to figure out what specific celluar structure
Speaker 2: they are observing, and.
Speaker 3: The machine struggles profoundly to synthesize that specialized visual geometry
Speaker 3: with the necessary biological context.
Speaker 2: It exposes how coudled these models are by their training data.
Speaker 2: These current architectures are so use to being spoon fed pristine,
Speaker 2: unified textbook inputs, but the real world of scientific discovery
Speaker 2: is incredibly noisy.
Speaker 3: It is entirely unstructured.
Speaker 2: You can't just read a million Wikipedia articles about bird
Speaker 2: anatomy and suddenly understand a chaotic microscopic slide. You have
Speaker 2: to mentally build a three D model of that tissue
Speaker 2: in your head, apply of obscure physiological rules to it.
Speaker 2: And filter out the visual noise.
Speaker 3: The AI just gets entirely lost in that noise because
Speaker 3: it lacks a causal understanding of biology and spatial physics.
Speaker 2: And this leads us to our third example, which incorporates
Speaker 2: rigorous phonological and theological analysis. The designated task requires the
Speaker 2: examination of detailed sound patterns within Biblical Hebrew.
Speaker 3: This domain isolates the intersection of phonetic, historical linguistics, and
Speaker 3: complex textual analysis.
Speaker 2: This one is fascinating because we aren't just talking about
Speaker 2: translating a sentence. We are talking about analyzing the historical
Speaker 2: evolution of specific sound structures within a highly dense, centuries
Speaker 2: old theological context.
Speaker 3: The system is to map orthographic symbols the written characters
Speaker 3: on the page to their historical phonetic realities. Consider the
Speaker 3: Masoretic text and the Tiberian vocalization tradition.
Speaker 2: Which is incredibly layered.
Speaker 3: Yes, the system must understand how morphological rules, vowel points,
Speaker 3: and consonant pronunciations were altered through centuries of human transmission,
Speaker 3: oral tradition, and theological preservation.
Speaker 2: It's requiring the system to hold an internal temporal model
Speaker 2: of linguistic evolution. Let's say a scribe in the ninth
Speaker 2: century made a slight, localized adjustment to a vowel pointing
Speaker 2: based on a highly specific theological debate happening in their
Speaker 2: specific community at that time.
Speaker 3: A shift motivated by human belief, not mathematical probability.
Speaker 2: Exactly, that tiny adjustment changes the phonetics of the word,
Speaker 2: and AI can't just look at that Hebrew word and
Speaker 2: spit out the English equivalent based on a lookup table.
Speaker 2: It has to understand the why and the how that
Speaker 2: word sounded a specific way a thousand years ago, based
Speaker 2: on the theological rules of the time, and how those
Speaker 2: rules shifted across generations.
Speaker 3: This completely neutralizes the superficial semantic processing of large language models.
Speaker 3: It forces them into a depth of historical phonetics and
Speaker 3: theological reasoning that extends far beyond the parameterization of current
Speaker 3: statistical text generators. They cannot infer the phonetic shift without
Speaker 3: an internal temporal model of the culture that produced it.
Speaker 2: Synthesizing these three examples, the contextual epigraphic deduction of ancient Palmyrene,
Speaker 2: the chaotic spatial reasoning required for Avian microscopic anatomy, and
Speaker 2: the phonetic evolution of Biblical Hebrew. It demonstrates the profound
Speaker 2: depth and specialized expertise that constitutes true intelligence.
Speaker 3: These are the things that require a mind, not just
Speaker 3: a map, and.
Speaker 2: They contrast so sharply with the remarkably shallow knowledge base
Speaker 2: of standard language models, and the data backing this up
Speaker 2: is staggering. Let's look at the empirical trajectory and the
Speaker 2: performance metrics, because this is where the theoretical hits the concrete.
Speaker 3: The baseline measurements were.
Speaker 2: Defa when they established those baseline measurements for the early models.
Speaker 2: Taking this exam it revealed an initial diagnostic floor that
Speaker 2: was frankly shocking to the industry.
Speaker 3: The severe underperformance of these highly regarded state of the
Speaker 3: art models is statistically significant. When evaluated against the hl
Speaker 3: GPT four achieved an accuracy rate of just two point
Speaker 3: seven percent.
Speaker 2: Two point seven percent.
Speaker 3: CLAUDE three point five, sonnet achieved four point one percent.
Speaker 3: Open AI Specialized Reasoning Model A one reached a threshold
Speaker 3: of only eight percent.
Speaker 2: I want you listening to really let those numbers sink
Speaker 2: in two point seven percent, four point one percent, eight percent.
Speaker 2: These single digit percentiles indicate an absolute baseline collapse of
Speaker 2: reasoning capabilities.
Speaker 3: A total collapse.
Speaker 2: When these massive architectures are stripped of easily searchable data
Speaker 2: and forced to synthesize novel information across disciplines, they fall
Speaker 2: apart entirely. In fact, on a binary or multiple choice format,
Speaker 2: these scores are statistically worse than random guessing.
Speaker 3: Yes, statistically worse.
Speaker 2: If you just closed your eyes and flipped a coin
Speaker 2: or picked answers at random, you would mathematically score higher
Speaker 2: than these multi billion dollar supercomputers did on this exam.
Speaker 3: That is a critical observation. The reason they perform worse
Speaker 3: than random guessing is due to the mechanics of their failure.
Speaker 3: They engage in systematic hallucination because of their structural compulsions,
Speaker 3: their mathematical mandate to predict the next token. They are
Speaker 3: driven to generate a response.
Speaker 2: They can just say I don't know exactly.
Speaker 3: They lack the epistemic humility to simply state I do
Speaker 3: not have sufficient data to synthesize a conclusion. Therefore, they
Speaker 3: generate statistically plausible, beautifully articulated, but entirely logically fallacious answers.
Speaker 2: They confidently lead you off a cliff.
Speaker 3: They are confidently incorrect drawn off course by superficial patterns
Speaker 3: in the prompt that lead them away from the actual
Speaker 3: complex truth.
Speaker 2: However, we have to acknowledge the rapid iteration that followed.
Speaker 2: The tech industry does not just sit still and accept
Speaker 2: a two percent score. Subsequent models showed a steep improvement curve.
Speaker 3: The optimization was rapid.
Speaker 2: We saw Gemini three point one pro and Clawed four
Speaker 2: point six eventually elevate their accuracy rates to approximately forty
Speaker 2: to fifty percent. Now I have to challenge you here.
Speaker 2: If I'm an AI developer listening to this, I'm screaming
Speaker 2: at my dashboard right now.
Speaker 3: I'm sure they are.
Speaker 2: I'm saying, wait, hold on, we jumped from two percent
Speaker 2: to fifty percent in just a few iterations. We improve
Speaker 2: the system's performance by twenty five times. Give us another year,
Speaker 2: throw another trillion dollars of compute at it, and we'll
Speaker 2: hit one hundred percent. Why are you so certain that
Speaker 2: fifty percent is an unbreakable ceiling and not just a
Speaker 2: speed bump on the way to artificial superintelligence.
Speaker 3: It is a valid counter argument, absolutely, but rigorous architectural
Speaker 3: analysis must be applied to the persistence of the remaining
Speaker 3: competency gap. The models hit a wall at that fifty
Speaker 3: percent threshold. The advancement from the single digits to fifty
Speaker 3: percent was achieved largely by optimizing logical routing protocols and
Speaker 3: expanding what we call content xtual processing.
Speaker 2: Windows, basically making their short term memory bigger.
Speaker 3: Essentially, yes, the developers gave the AI a massively larger
Speaker 3: short term memory to hold more variables in its active context.
Speaker 2: Simultaneously, so they built a bigger desk for it to
Speaker 2: spread all its papers out.
Speaker 3: On exactly but bridging the final gap, causing that fifty
Speaker 3: percent chasm. To reach true one hundred percent expert level
Speaker 3: mastery across all domains requires fundamentally different cognitive architecture. It
Speaker 3: requires true causal.
Speaker 2: Reasoning, which they don't have it don't.
Speaker 3: It requires the internal representation of reality that humans have,
Speaker 3: which these statistical architectures inherently lack. You cannot simply add
Speaker 3: more memory or a bigger desk to a statistical engine
Speaker 3: and magically spark causal understanding.
Speaker 2: The difference between retrieving a complex correlated path and constructing
Speaker 2: a novel causal graph is non trivial.
Speaker 3: It is the defining limitation that is why this fifty
Speaker 3: percent deficit likely represents a structural symptope.
Speaker 2: A structural essmp top, meaning a mathematical limit that a
Speaker 2: curve approaches but can never quite reach, no matter how
Speaker 2: far it extends or how much money you pour into
Speaker 2: the server farms.
Speaker 3: Precisely and to ensure that this a symptote remains a valid,
Speaker 3: uncorrupted measurement of the cognitive divide, the consortium had to
Speaker 3: implement critical future proofing mechanisms for the benchmark itself. The
Speaker 3: most vital of these is maintaining the strict opacity of
Speaker 3: the exam. The vast majority of those twenty five hundred
Speaker 3: questions are securely hidden from the public domain.
Speaker 2: They have to keep it locked in a vault because
Speaker 2: if they published the full data set of questions and
Speaker 2: verified answers, the artificial intelligence models, which are constantly scraping
Speaker 2: the Internet for their continuous training pipelines, would instantly ingest
Speaker 2: the exam.
Speaker 3: If they would memorize the exact sequence of tokens.
Speaker 2: The next time they took the test, they would achieve
Speaker 2: perfect scores through the statistical weightings of memorized data, instantly
Speaker 2: invalidating the entire diagnostic benchmark. The test would saturate again
Speaker 2: just like the MMLU, and we would be back to
Speaker 2: square one.
Speaker 3: This brings us to a crucial phase of our analysis,
Speaker 3: the strategic implications, the inherent risks, and the theoretical projections
Speaker 3: surrounding these developments. The risk of misinterpretation regarding AI capabilities
Speaker 3: is incredibly severe. We must issue a stark warning concerning
Speaker 3: the danger of legacy testing.
Speaker 2: This is where the rubber meets the road and impacts
Speaker 2: the real world for you and me. If policymakers, hospital administrators,
Speaker 2: software developers, and end users deploy these systems under the
Speaker 2: false assumption that they possess human level competence, an assumption
Speaker 2: based entirely on those saturated obsolete MMLU scores, the consequences
Speaker 2: could be disastrous.
Speaker 3: The systemic vulnerabilities are terrifying.
Speaker 2: Imagine integrating a machine learning model into critical medical diagnostic infrastructure,
Speaker 2: or utilizing it for complex regulatory compliance, or even embedding
Speaker 2: it within judicial sentencing algorithms.
Speaker 3: Doing so grants operational autonomy to mathematical models and high
Speaker 3: stakes environments that far exceed their actual cognitive capacities. You
Speaker 3: are trusting a system to execute complex CAUs fuzzle reasoning
Speaker 3: in a life or death medical scenario when its underlying
Speaker 3: architecture is only capable of probabilistic token generation.
Speaker 2: It's like asking a really good autocorrect to perform surgery.
Speaker 3: The diagnostic reality provided by the HIL demands a total
Speaker 3: recalibration of how and where we deploy these systems safely.
Speaker 2: It forces us to ask, if standard academic tests, even
Speaker 2: incredible hard ones, are constantly at risk of being memorized
Speaker 2: and gamed by these models, what is the right way
Speaker 2: to measure functional intelligence? This leads us to some fascinating
Speaker 2: mind bending theoretical frameworks emerging in public discourse. The first
Speaker 2: one I want to introduce is the financial Turing test.
Speaker 3: The underlying argument of the financial Turing test is that
Speaker 3: static academic testing, no matter how rigorous or heavily obfuscated,
Speaker 3: will always retain an element of artificiality. Instead, proponents posit
Speaker 3: that financial accumulation, specifically operating autonomously within dynamic global financial markets,
Speaker 3: serves as a much more pragmatic, ungamable mayer of functional
Speaker 3: and intelligence.
Speaker 2: Let's walk through a scenario to really illustrate why this
Speaker 2: is such a compelling idea. Imagine there is a sudden,
Speaker 2: unexpected military coup in a minor lithium producing country in
Speaker 2: South America. The AI needs to instantly recognize this news, but.
Speaker 3: It's not enough to just summarize the news article exactly.
Speaker 2: To extract maximal capital, it has to realize that the
Speaker 2: new dictator's brother happens to own a controlling stake in
Speaker 2: a very specific, mid sized shipping company that operates out
Speaker 2: of a neighboring port. It has to deduce that this
Speaker 2: shipping company is about to get an exclusive monopoly on
Speaker 2: lithium exports, and it has to aggressively buy stock in
Speaker 2: that shipping company before the rest of the world. Human
Speaker 2: analysts make the same connection.
Speaker 3: The financial markets represent a hyperdynamic, fiercely adversarial environment. To
Speaker 3: succeed in your scenario requires the real time synthesis of
Speaker 3: highly obscure geopolitical shifts, economic indicators, and unpredictable human behavior patterns.
Speaker 2: A current AI fails in this scenario because the causal
Speaker 2: link between the coup, the brother, and the shipping company
Speaker 2: hasn't been written down in a thousand news articles yet
Speaker 2: the clustered density doesn't exist for it to retrieve the correlation.
Speaker 3: A human hedge fund manager succeeds through rapid novel causal inference.
Speaker 2: The proposition is that the autonomous navigation of such chaotic
Speaker 2: real world systems demonstrates a far more generalizable, robust intelligence
Speaker 2: than deep, isolated academic synthesis ever could. It is the
Speaker 2: ultimate test of adapting to unstructured reality. You can't memorize
Speaker 2: the stock market.
Speaker 3: A second theoretical framework we must examine involves the application
Speaker 3: of Goodheart's law and its connection to the IQ test paradox.
Speaker 3: Goodheart's law is a well established principle in economics and
Speaker 3: measurement theory, which dictates that when a measure becomes a target,
Speaker 3: it ceases to be a reliable measure.
Speaker 2: When a measure becomes a target, it ceases to be
Speaker 2: a reliable measure. We have seen this historically with human
Speaker 2: IQ tests. Originally, they were designed to measure underlying generalized
Speaker 2: cognitive comprehension, but as society t he placed more and
Speaker 2: more emphasis on the scores, using them for school admissions
Speaker 2: and job placements. People started exposing themselves to the structural
Speaker 2: formats of the tests.
Speaker 3: They bought prep books.
Speaker 2: They learned how to take the test. This format optimization
Speaker 2: artificially inflated their goals, but it generated absolutely no corresponding
Speaker 2: increase in their actual underlying cognitive comprehension. They just got
Speaker 2: better at the game of the test.
Speaker 3: We are observing this exact phenomenon currently with AI benchmarking.
Speaker 3: It is commonly referred to in educational theory as teaching
Speaker 3: to the test. Developers are continuously optimizing their algorithmic architectures,
Speaker 3: specifically to maximize performance on localized metrics and standardized data sets.
Speaker 2: They are engineering the models to beat the test rather
Speaker 2: than fundamentally improving generalized causal reasoning.
Speaker 3: This is precisely why the HL must remain strictly obfuscated
Speaker 3: to prevent the measure from becoming a mere target for
Speaker 3: algorithmic optimization.
Speaker 2: And if we extrapolate this arms race out to its
Speaker 2: logical conclusion, we arrive at a third theoretical projection that
Speaker 2: is truly a paradigm shifter algorithmic exam generation. Think about
Speaker 2: this logical progression. What happens after the theoretical mastery of
Speaker 2: the HL E. Let's say, decades from now, an entirely
Speaker 2: new architecture is invented that finally crosses that structural asymptote
Speaker 2: and genuinely masters these two thy five hundred expert level
Speaker 2: questions using true causal reasoning. What is the next step?
Speaker 3: The next methodological inversion is to task those advanced AI
Speaker 3: systems with designing subsequent iterations of testing themselves.
Speaker 2: That's a wild thought.
Speaker 3: It posits a future where the computational system itself generates
Speaker 3: multidisciplinary diagnostic queries designed to map the conceptual limits of
Speaker 3: human cognition or to benchmark next generation computational architectures. The
Speaker 3: AI would generate queries that require a level of conceptual
Speaker 3: integration that far exceeds the limits of current human experts.
Speaker 2: We would essentially rely on the machine to define the
Speaker 2: new boundaries of intelligence, creating tests that we ourselves could
Speaker 2: not pass. The student becomes the master, designing the test
Speaker 2: for the next generation of minds.
Speaker 3: It's an incredible structural shift.
Speaker 2: But shifting the tone slightly, we also have to confront
Speaker 2: the impending technological collision that makes all of this urgency,
Speaker 2: all of this precise boundary mapping so incredibly palpable. I
Speaker 2: am talking about the quantum threat horizon. We have been
Speaker 2: discussing the limits of current AI running on classical silicon
Speaker 2: computer chips. But what happens when advanced machine learning merges
Speaker 2: with theoretical quantum hardware developments.
Speaker 3: The intersection of speed and synthesis in a quantum augmented
Speaker 3: AI model forces us to analyze projected security vulnerabilities on
Speaker 3: an entirely different scale. This structural projection involves theoretical quantum
Speaker 3: chips executing operations exponentially faster than any classical benchmark.
Speaker 2: If machine learning capabilities scale alongside this quantum processing power,
Speaker 2: the threat matrix expands exponentially. Let's get specific about that
Speaker 2: fret matrix because it involves something called Shores algorithm.
Speaker 3: Yes, Shores algorithm is the critical vulnerability.
Speaker 2: And to understand why this is so terrifying, you have
Speaker 2: to understand how classical encryption works. Things like RSA encryption,
Speaker 2: which essentially secures the entire modern digital world, from your
Speaker 2: online banking to classified government communications to the power grid,
Speaker 2: rely on the fact that classical computers are really bad
Speaker 2: at factoring massive prime numbers.
Speaker 3: They are computationally inefficient at it.
Speaker 2: Let's use an analogy. Imagine classical encryption is a colossal,
Speaker 2: incredibly complex maze. A classical computer trying to break that
Speaker 2: encryption has to run down every single path one by one,
Speaker 2: it hits a dead end, turns around, and tries the
Speaker 2: next path. It would take a classical supercomputer millions of
Speaker 2: years to check every path in the RSA maze, But a.
Speaker 3: Quantum computer using Shores algorithm doesn't run down the paths
Speaker 3: one by one.
Speaker 2: No, it essentially floods the entire maze with water, simultaneously
Speaker 2: finding the exit instantly.
Speaker 3: That is a highly effective analogy. Shor's algorithm utilizes quantum
Speaker 3: super position to shatter classical encryption by factoring those massive
Speaker 3: prime numbers at speeds impossible for classical architectures. Classical encryption
Speaker 3: protocols face immediate critical failure in this scenario.
Speaker 2: Now imagine integrating that raw maze flooding computational speed with
Speaker 2: an AI model that has actually achieved expert level contextual synthesis.
Speaker 2: You no longer just have a really fast calculator breaking codes.
Speaker 2: You have an autonomous system capable of creatively deducing the
Speaker 2: contextual architecture of a target system.
Speaker 3: That is the systemic dismantling scenario. We were talking about
Speaker 3: an AI capable of bypassing encryption understanding the contextual logic
Speaker 3: of a foundational financial infrastructure and systematically dismantling it.
Speaker 2: Or infiltrating and encrypting governmental firewalls, entirely locking us out
Speaker 2: of our own defensive systems.
Speaker 3: And it would execute this systemic dismantling at a pace
Speaker 3: that far exceeds any human defensive response protocol. By the
Speaker 3: time human analysts realized the breach has occurred, the AI
Speaker 3: has already re written the architecture of the system. The
Speaker 3: intersection of quantum speed and advanced contextual reasoning is a
Speaker 3: horizon we must prepare for with extreme precision.
Speaker 2: So what does this all mean in final synthesis? This
Speaker 2: is why the current diagnostic efforts this massive global consortium
Speaker 2: are so crucial. The assessment serves as the most precise
Speaker 2: current instrument for quantifying the persistent cognitive divide.
Speaker 3: It systematically delineates the profound separation between human specialized expertise,
Speaker 3: our capacity for multimodal contextual causal reasoning, and machine learnings
Speaker 3: reliance on statistical pattern recognition.
Speaker 2: It leaves the entire scientific community to confront a deeply
Speaker 2: rigorous theoretical question regarding the epistemology of artificial intelligence.
Speaker 3: How must we define intelligence when the very instruments utilized
Speaker 3: to measure it must be continuously elevated in complexity. We
Speaker 3: have to strictly obfuscate the data, dynamically redesign the questions,
Speaker 3: and constantly build higher walls solely to evade the road
Speaker 3: statistical memorization capabilities of the subjects being tested.
Speaker 2: Are we measuring true comprehension or are we simply engaged
Speaker 2: in an escalating, multi billion dollar arms race against an
Speaker 2: increasingly sophisticated statistical parrot. The nature of intelligence itself becomes
Speaker 2: a moving part it, and that leads me to a
Speaker 2: final lingering thought for you to mull over as we
Speaker 2: wrap up this exploration. The core philosophical dilimma, Actually, if
Speaker 2: human intelligence is increasingly being defined purely in the negative,
Speaker 2: defined simply as the things a statistical machine cannot yet do,
Speaker 2: what happens to our fundamental understanding of human identity and
Speaker 2: human exceptionalism on the day a machine finally does cross
Speaker 2: that structural asymp tode. Are we just going to endlessly
Speaker 2: move the goalposts of our own uniqueness forever, creating harder
Speaker 2: and harder tests to prove we are still special?
Speaker 3: Or is there an unquantifiable, deeply intrinsic essence to human
Speaker 3: causal reasoning, a spark of true comprehension that no mathematical algorithm,
Speaker 3: no matter how complex or how quantum, will ever be
Speaker 3: able to map.
Speaker 2: It's a question that the challenge is the very core
Speaker 2: of who we are in the algorithmic age.
Speaker 3: It is a profound inquiry, one that demands continuous critical thinking,
Speaker 3: questioning of our baseline assumptions, and an appreciation for the vast,
Speaker 3: uncharted complexities of both the human and the artificial mind.
Speaker 2: It truly is Keep questioning those headlines, keep looking beyond
Speaker 2: the near perfect test scores, and never stop exploring the
Speaker 2: incredible boundary between math and mind. Keep your curiosity sharp.