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: Welcome back. It is Wednesday, February eighteenth, twenty twenty six.
Speaker 2: And if you've been looking at the tech headlines this morning,
Speaker 2: or really any morning for the past month, you're seeing
Speaker 2: one phrase just repeated over and over.
Speaker 3: You really are. It's everywhere.
Speaker 2: The industry is trying very, very hard to make the
Speaker 2: AI coscientist the defining concept of the year.
Speaker 3: It's absolutely the buzzword of the quarter. You can't open
Speaker 3: a journal or a tech blog without seeing it.
Speaker 2: But I want to push back on this a little.
Speaker 2: Right from the start, we've been calling AI an assistant
Speaker 2: or a copilot since what twenty twenty three at least. Yeah,
Speaker 2: we've had tools that can summarize PDFs for us and
Speaker 2: write email a drafts for years now. So my question
Speaker 2: is is co scientist just a branding pivot? Is this
Speaker 2: just marketing trying to sell us the same old llms
Speaker 2: with a new code of paint or is there an
Speaker 2: actual fundamental architectural difference in the stack that we're seeing today.
Speaker 3: That's actually, that is exactly the right question to be asking.
Speaker 3: And it's fair to be skeptical because the hype cycle
Speaker 3: is very real. It's always real, always, But if you
Speaker 3: actually look at the engineering, the shift that we're seeing
Speaker 3: in early twenty twenty six is not just marketing. It
Speaker 3: really isn't. If you look back at that, say, twenty
Speaker 3: twenty three, twenty twenty four era, the architecture was fundamentally
Speaker 3: something called retrieval augmented generation RIGG.
Speaker 2: We've talked about that before.
Speaker 3: Exactly ria you'd ask a question, the model would go
Speaker 3: look up a document, summarize it, and then give you
Speaker 3: an answer based on what it found. It was a librarian,
Speaker 3: a very very fast library, very fast, incredibly well read librarian. Sure,
Speaker 3: but it was still just retrieving existing information, right.
Speaker 2: It's fetching, it's not it's not thinking, it's not creating.
Speaker 3: Precisely, the shift to coscientists here in twenty twenty six
Speaker 3: is really about two core things, agency and closed loop systems.
Speaker 3: We are not just talking about a chatbot that answers
Speaker 3: a prompt anymore.
Speaker 2: Okay, so that sounds like engineering jargon. Let's break that
Speaker 2: down close. Loop, what does it actually look like in
Speaker 2: a real lab setting?
Speaker 3: So it means the system is self correcting. It observes data,
Speaker 3: it generates a hypothesis. It then writes the code to
Speaker 3: test that hypothesis. It executes a simulation or even a
Speaker 3: physical experiment. Okay, it reads the error logs or the results.
Speaker 3: And then, and this is the absolute key part, it
Speaker 3: refines its own hypothesis based on whether it's succeeded or failed.
Speaker 3: It doesn't stop and ask the human what should I
Speaker 3: do next?
Speaker 2: Ah?
Speaker 3: It iterates that self correction loop is what earns it
Speaker 3: the title of scientist rather than just search engine.
Speaker 2: So it's the difference between asking a sus chef to
Speaker 2: chop an onion for you and asking a chef to
Speaker 2: invent a completely new soup.
Speaker 3: That's a great analogy.
Speaker 2: The chef tastes, it realizes that it needs more salt,
Speaker 2: adds the salt tasted. Again, that loop is internal. It
Speaker 2: doesn't need external direction for every.
Speaker 3: Step precisely, and the capabilities that we're seeing now things
Speaker 3: like designing experiments from the ground up, simulating molecular interactions
Speaker 3: at a dynamic level, not static, and even controlling physical
Speaker 3: robots in a lab. These are things the copilots of
Speaker 3: twenty twenty four simply could not do.
Speaker 2: There was this quote from the research steck we looked
Speaker 2: at that really stuck with me. AI has moved from
Speaker 2: summarizing papers to making discoveries.
Speaker 3: That's the headline.
Speaker 2: It's a bold claim, and today we're going to test
Speaker 2: that claim. We're going to look at the evolution of
Speaker 2: this technology, specifically, how we got from you know, the
Speaker 2: static images of alpha fold to the dynamic simulation engines
Speaker 2: we have now, and we have to open.
Speaker 3: Up the hood and look at the brain of these systems,
Speaker 3: the whole egenic workflow, this test time compute that everyone's
Speaker 3: talking about.
Speaker 2: Absolutely, and then we have to talk about the physical layer,
Speaker 2: the robotics, because that's where the digital world actually touches
Speaker 2: the real one.
Speaker 3: That's where code moves.
Speaker 2: Yeah, and we'll get into the medical break twos that
Speaker 2: are happening right now, real world stuff in cancer, in Alzheimer's,
Speaker 2: and we'll wrap up with how you our listeners can
Speaker 2: actually get your hands on these tools, because apparently you
Speaker 2: don't need a PhD and a million dollar grant to
Speaker 2: play in the sandbox anymore.
Speaker 3: No, not at all. The barrier to entry has completely collapsed.
Speaker 3: It is a very very different world than it was
Speaker 3: even eighteen months ago.
Speaker 2: Okay, so let's rewind a little bit to set the
Speaker 2: foundation here. To really understand twenty twenty six, we have
Speaker 2: to understand where we came from. And you can't talk
Speaker 2: about AI and science without talking about alpha fold.
Speaker 3: Right. Alpha fold is really patient zero for this entire revolution.
Speaker 3: If we go back to that twenty twenty one to
Speaker 3: twenty twenty four window, Google deep Mind effectively solved the
Speaker 3: protein folding.
Speaker 2: Problem, which was a fifty year old grand challenge in biology.
Speaker 2: This wasn't a minor problem, oh no.
Speaker 3: It was the grand challenge for half a century. We
Speaker 3: knew the chemical formula of a protein, you know the
Speaker 3: list of ingreen and seemino ass, but we had no
Speaker 3: reliable way of knowing how it folded up into a
Speaker 3: complex three D shape.
Speaker 2: And in biology, shape is everything everything.
Speaker 3: Shape determines function. If you don't know the shape, you
Speaker 3: don't know if that protein is going to build a
Speaker 3: muscle or digest your food, or you know, causet disease.
Speaker 3: It's the whole ballgame.
Speaker 2: I remember the analogy everyone was using back then. It
Speaker 2: was like Google Maps for the molecular world.
Speaker 3: It's a pretty decent analogy. Actually, before alpha fold, we
Speaker 3: were navigating the cellular world by stars and intuition, doing
Speaker 3: painstaking X ray crystallography.
Speaker 2: Which could take months or years for a single protein exactly.
Speaker 3: Suddenly, with alpha fold we had a street view for
Speaker 3: nearly every protein known to science. Then alpha fold three
Speaker 3: came along and predicted the structures of not just proteins,
Speaker 3: but their interactions with DNA, RNA other molecules.
Speaker 2: It was a massive leap, and the creators won the
Speaker 2: Nobel Prize in Chemistry for it in twenty twenty four.
Speaker 3: Rightfully so it was the moment of validation. It was
Speaker 3: the proof that AI had truly arrived in serious hard science.
Speaker 3: It wasn't just for you know, identifying cats and pictures anymore.
Speaker 2: But here we are in twenty twenty six and looking
Speaker 2: at the research we have in front of us for today,
Speaker 2: alpha fold almost looks I don't want to say basic
Speaker 2: because that feels disrespectful. No I know em but it
Speaker 2: looks like just the starting line.
Speaker 3: It was the foundation. Absolutely, the key limitation of alpha fold,
Speaker 3: and this is so important to understand the current moment,
Speaker 3: is that it gave you a static structure. It gave
Speaker 3: you a snapshot, a beautiful, high resolution statue of a protein.
Speaker 2: But biology is in the statue, not at all.
Speaker 3: Biology is wet, it's warm, it's noisy. Proteins don't just
Speaker 3: sit still. They vibrate, they twist, they dance, They adopt
Speaker 3: all these different conformations based on what they're touching, or
Speaker 3: the temperature of the cell or the pH level.
Speaker 2: And computing that dance is exponentially harder than just computing
Speaker 2: the one static shape, right yeah, because you aren't just
Speaker 2: solving for X, you're solving for eds over time in
Speaker 2: a changing environment exactly.
Speaker 3: You're dealing with what's called an energy landscape. Alpha fold
Speaker 3: was incredible at finding the lowest energy state, the most
Speaker 3: stable resting pose.
Speaker 2: Of the protein, the bottom of the valley, the.
Speaker 3: Very bottom of valley. But the twenty twenty six systems,
Speaker 3: these successors, like alpha proteo and a SOD are probabilistic.
Speaker 3: They don't just give you the bottom of the valley.
Speaker 3: They map out the entire landscape. They tell you the
Speaker 3: probability of a protein shifting into a different shape that can, say, suddenly,
Speaker 3: lock onto a drug molecule.
Speaker 2: So it's a difference between a photograph and a movie.
Speaker 3: It's a movie where you can see all the possible
Speaker 3: alternate endings, and even better, a movie where you can
Speaker 3: influence which ending happens. This gets us to the concept
Speaker 3: of inverse folding.
Speaker 2: Okay, inverse folding, I saw that in the isomorphic labs papers.
Speaker 2: Break that down for us.
Speaker 3: Okay, So traditional protein folding is here is a sequence
Speaker 3: of amino acids. What three D shape does it make?
Speaker 2: Input is the sequence? Output is the shape?
Speaker 3: Right, inverse folding flips that entire problem on its head.
Speaker 3: It says, here is the shape I need. For example,
Speaker 3: I need a shape that perfectly blocks this specific virus receptor.
Speaker 3: Now you tell me this sequence of amino acids that
Speaker 3: will reliably build that exact shape.
Speaker 2: So it's generative. It's like using an image generator like Dally,
Speaker 2: but instead of the prompt being an astronaut riding a
Speaker 2: horse on the moon. Okay, the prompt is a protein
Speaker 2: that binds to and neutralizes the COVID twenty six spike.
Speaker 3: Precisely. It is generative biological engineering, and this one capability
Speaker 3: to design interactions, not just predict them, has completely utterly
Speaker 3: collapsed the timeline for drug discovery.
Speaker 2: Okay, let's talk about those timelines, because traditionally, discovering a
Speaker 2: new drug is a marathon. We're talking ten to fifteen
Speaker 2: years from initial idea to pharmacy shelf.
Speaker 3: And billions of dollars, and you have to remember most
Speaker 3: of that time is just failure. It's high throughput screening
Speaker 3: where you're just trying a key in a lock and
Speaker 3: it doesn't fit, so you file it down a little,
Speaker 3: You try again, it still doesn't fit. You do that
Speaker 3: ten thousand, one hundred thousand times. Route force pure route force. Now,
Speaker 3: because we can simulate the wiggling and jiggling of both
Speaker 3: the lock and the key in the computer, we can
Speaker 3: eliminate ninety nine percent of the failures before we ever
Speaker 3: mix a single chemical in a day tube.
Speaker 2: It's a huge shift in efficiency. The sources mentioned that
Speaker 2: AI designed molecules. Molecules that were invented in silico entered
Speaker 2: phase three clinical trials back in twenty twenty five.
Speaker 3: That is the holy grail. Phase three is the final
Speaker 3: most expensive step right before FDA approval. Getting a drug
Speaker 3: to phase three and twenty twenty five means the foundational
Speaker 3: discovery work probably started in what twenty twenty two or
Speaker 3: twenty twenty three.
Speaker 2: That's a compression of the timeline that is almost hard
Speaker 2: to believe.
Speaker 3: It's why we're seeing these market forecasts for AI driven
Speaker 3: drug discovery just exploding. They're hitting eight to ten billion
Speaker 3: dollars in twenty twenty six alone. This isn't a future trend,
Speaker 3: it's the economic reality of the industry right now.
Speaker 2: So it's speed, yes, but it's also precision. We aren't
Speaker 2: just throwing spaghetti at the wall faster. We're guiding each
Speaker 2: trand of spaghetti to the wall with I don't know,
Speaker 2: laser beams.
Speaker 3: That's a colorful image, but yes, that's it. And that
Speaker 3: precision comes from the fact that we aren't just using
Speaker 3: a single monolithic algorithm anymore. We've moved into this era
Speaker 3: of agentic workflows, and this is where we really need
Speaker 3: to get into the inside the machine part of our
Speaker 3: discussion today.
Speaker 2: Yes, let's unpack this because when I hear AI, my
Speaker 2: brain still defaults to a text box where a type
Speaker 2: of question and he gives me an answer a chatbot.
Speaker 2: But the co scientists system that Google deep Mind has built,
Speaker 2: which is running on their new Gemini two point zero model,
Speaker 2: isn't just one thing. It's a team. The outline describes
Speaker 2: a cast of agents, right.
Speaker 3: And you should think of it almost like a corporate
Speaker 3: structure or a research lab team. In any functional lab,
Speaker 3: you have different roles, right. You have the creative postdoc
Speaker 3: who has one hundred wild ideas a day brainstormer exactly.
Speaker 3: Then you have the critical senior scientist, the principal investigator,
Speaker 3: who pokes holes in ninety nine of those ideas and
Speaker 3: finds the one that might actually work. You have the
Speaker 3: lab tech who actually runs the experiments, and you have
Speaker 3: the lab manager making sure it all runs smoothly.
Speaker 2: And the aicoscientist replicates that entire structure using specialized, fine
Speaker 2: tuned software agents. It does, But hang on a second,
Speaker 2: why is that necessary? Isn't that just the model talking
Speaker 2: to itself. Why do we need distinct agents? Why can't
Speaker 2: I just put it all into one big prompt and say, hey, Gemini,
Speaker 2: be creative, but also be critical and also manage the project.
Speaker 3: You can try that, people have, but you run into
Speaker 3: a problem that engineers call context contamination, or having conflicting
Speaker 3: objective functions. Okay, if you ask one single model to
Speaker 3: be both creative and critical at the very same time,
Speaker 3: the two objectives bleed into each other. You get mediocre
Speaker 3: creativity because the model is presensoring its own wild ideas.
Speaker 2: It's pulling its punches exactly.
Speaker 3: And you get soft, ineffective criticism because it subconsciously wants
Speaker 3: to support the ideas it just generated. It's not a
Speaker 3: true adversarial process.
Speaker 2: So by splitting them up into separate agents, you create
Speaker 2: a genuine adversarial dynamic. You let them argue, you let
Speaker 2: them argue, you optimize them against each other. So you
Speaker 2: have the generation agent. Its only job is quantity and variety.
Speaker 2: It looks at all the available literature, all the data,
Speaker 2: and it generates hundreds of potential hypotheses. It's rewarded purely
Speaker 2: for novelty. It doesn't care if an idea is perfect
Speaker 2: or even feasible. It just wants to explore the entire
Speaker 2: solution space.
Speaker 3: Okay, so that's the wild idea engine. Then the supervisor
Speaker 3: passes those ideas to the next stage, the review and
Speaker 3: ranking agents.
Speaker 2: The critics. Yeah, the peer review panel. These agents take
Speaker 2: those hundreds of hypotheses and they filter them through a
Speaker 2: brutal gauntlet. They have three main jobs. First, check for novelty.
Speaker 2: Has this been published before? Is this just rediscovering something
Speaker 2: from a nineteen eighty two paper, which is a huge
Speaker 2: problem in science, a huge problem. Second, check for feasibility.
Speaker 2: Does this violate the known laws of physics? Is this
Speaker 2: actually chemically possible? And Third, and this is maybe the
Speaker 2: most important, they check for testability. Is there an experiment
Speaker 2: we can actually run to prove or disprove this hypothesis.
Speaker 2: So an idea could be brilliant and true, but if
Speaker 2: it's untestable, it's not science.
Speaker 3: It's philosophy. The ranking agents discard it. So out of
Speaker 3: one hundred ideas, maybe only five or six make it
Speaker 3: through this filter.
Speaker 2: And then those survivors go to the evolution agent.
Speaker 3: The improver, the polisher. It takes the best ideas that
Speaker 3: survive the critics, and it iterates on them. It refines
Speaker 3: the hypothesis, It tightens the parameters, It suggests modifications. It
Speaker 3: turns a rough diamond into a cut gem.
Speaker 2: And the conductor of this whole orchestra is the supervisor agent.
Speaker 3: The manager, the supervisor is the traffic cop. It decides
Speaker 3: when to send an idea back to the generation agent
Speaker 3: for more brainstorming, when to push it forward to the
Speaker 3: evolution agent, and crucially, it manages the compute budget. It
Speaker 3: manages this thing called test time compute right.
Speaker 2: I saw that term in the papers, test time compute scaling.
Speaker 2: It sounds incredibly technical. But in plain English, does that
Speaker 2: just mean thinking longer?
Speaker 3: That's a perfect way to put it. Yes, But it's
Speaker 3: how it thinks longer that's revolutionary. Instead of just giving
Speaker 3: you the first, most probable answer, it allows the model
Speaker 3: to generate thousands of internal thoughts or reasoning.
Speaker 2: Steps, a chain of thought, a whole.
Speaker 3: Branching tree of thoughts, and it explores all the different paths,
Speaker 3: and most of them it discards as dead ends before
Speaker 3: it ever gives you the final, polished answer. This is
Speaker 3: a system too thinking that the psychologist Daniel Khannaman talks about.
Speaker 3: It's slow, it's deliberate, it's logical.
Speaker 2: So it's not just retrieving an answer from its training data.
Speaker 2: It's actively deliberating. It's reasoning through the problem.
Speaker 3: It's simulating multiple paths of reasoning. It's fact checking itself
Speaker 3: against its own internal knowledge and external tools. It's the
Speaker 3: difference between a gut reflex and a deep considered contemplation,
Speaker 3: and the expert analysis is unambiguous. The more time you
Speaker 3: give the model to think at test time, the better
Speaker 3: and more robust the reasoning becomes.
Speaker 2: We have a perfect real world example of this in
Speaker 2: the sources the bacterial mystery, and I want to go
Speaker 2: deep on this one because the timeline is just wild.
Speaker 2: This is the story about cfpicis.
Speaker 3: Right, capsid forming phage, inducible chromosomal.
Speaker 2: Islands, the zood height.
Speaker 3: Thank you. So these are basically little mobile genetic elements
Speaker 3: in bacteria. The mystery for science was how do they
Speaker 3: spread between different bacterial species. They're not viruses, but they
Speaker 3: act a bit like them.
Speaker 2: And humans had been working on this problem for how long, over.
Speaker 3: A decade, really smart people publishing papers, making incremental progress,
Speaker 3: but nobody could figure out the exact mechanism for how
Speaker 3: they moved around.
Speaker 2: So enter the aiicoscientist walk us through exactly what happened here.
Speaker 2: It didn't just guess the answer.
Speaker 3: Right, no, not at all. It followed the exact agentic
Speaker 3: workflow we just described. The system started by ingesting a
Speaker 3: massive data set of bacterial genomes and all the existing
Speaker 3: scientific literature on the topic.
Speaker 2: Okay, so read everything.
Speaker 3: It read everything. Then the generation agent went to work.
Speaker 3: It proposed a bunch of hypotheses. One of the most
Speaker 3: promising was this, maybe the cfpicis steal proteins from actual
Speaker 3: viruses that happened to be in the same cell. They
Speaker 3: hijack the viral proteins to build their own transport capsules.
Speaker 2: Okay, that's a clever, plausible theory, like a car thief
Speaker 2: stealing an engine to put in their own chassis.
Speaker 3: It's a great analogy. But a theory isn't enough. The
Speaker 3: review agent immediately kicked in. It said, Okay, if that's true,
Speaker 3: then we should be able to see a specific cluster
Speaker 3: of genes activating at a specific time when a bacterium
Speaker 3: is co infected. It made a testable prediction.
Speaker 2: It defined the evidence it needed to define exactly.
Speaker 3: So the supervisor agent then directed the system to run
Speaker 3: a massive search across forty thousand bacterial genomes to look
Speaker 3: for that specific genetic signature, that coactivation pattern, and it
Speaker 3: found it. It found a candidate, strong one. But it
Speaker 3: didn't stop there. The evolution agent took over and refined
Speaker 3: the hypothesis. It predicted exactly which proteins from the virus
Speaker 3: were being stolen and how they were being incorporated. And then,
Speaker 3: and this is the kicker, it designed the follow up
Speaker 3: analysis and recapitulated the necessary findings to prove it.
Speaker 2: And the total time for this whole process.
Speaker 3: The system went from here is a decade old mystery
Speaker 3: to here is a highly probable data supported mechanism in
Speaker 3: two days.
Speaker 2: Two days versus ten years of human strung that is
Speaker 2: just staggering.
Speaker 3: And it was validated. The human scientist took the AI's output,
Speaker 3: ran the physical lab experiments and confirmed it. The paper
Speaker 3: was published in the journal Cell in September of twenty
Speaker 3: twenty five, and importantly, it wasn't a computer science paper
Speaker 3: about how cool the AI is. It was a biology
Speaker 3: paper about a fundamental discovery made by the AI.
Speaker 2: That distinction is so critical. The AI isn't the story anymore.
Speaker 2: The discovery that it enables is the story.
Speaker 3: That's when you know the technology has matured. But you
Speaker 3: know it's not just biology. This deep think capability, this
Speaker 3: ability to reason through these long, complex chains of logic,
Speaker 3: is showing up in the most abstract fields like pure
Speaker 3: mathematics and physics.
Speaker 2: Which brings us to open ais. GBT five point two.
Speaker 3: Right GPT five point two, which came out late last year.
Speaker 3: It introduced these thinking modes or deep think modes as
Speaker 3: a default feature, and the benchmark that just blew everyone
Speaker 3: away was its performance on the International Mathematics Olympiad the IMO.
Speaker 2: This is a competition for the most brilliant high school
Speaker 2: math kids on the planet. These are problems that can
Speaker 2: stump professional mathematicians with PhDs.
Speaker 3: They're designed to require not just knowledge, but true ingenuity,
Speaker 3: and GPT five point two achieved a gold metal level performance.
Speaker 2: But wait, math has always been the achilles heel of
Speaker 2: large language models, hasn't it? Because lms are probabilistic. They're
Speaker 2: just predicting the next most likely word that's right. And
Speaker 2: math is pure logic. It's precise. If you are ninety
Speaker 2: nine percent right in a mathematical proof, you are one
Speaker 2: hundred percent wrong. So how did they finally crack that problem?
Speaker 3: They cracked it by combining that test time compute we
Speaker 3: talked about with sophisticated tool use. When GPT five point
Speaker 3: two faces a hard math problem, now it doesn't just
Speaker 3: try to guess the answer in one shot. It breaks
Speaker 3: the problem down. It says, okay, first I need to
Speaker 3: solve this integral. Then it actually writes a small Python
Speaker 3: script to call a symbolic math library like simpi.
Speaker 2: Ah. So it's using the computer as a calculator the
Speaker 2: same way human mathematician would use Wolfram alpha or a
Speaker 2: physical calculator.
Speaker 3: Exactly. It runs the script, it gets a definitive grounded
Speaker 3: truth as the outpuint, and then it incorporates that result
Speaker 3: into the next step of its logical proof. It's orchestrating
Speaker 3: a whole suite of computational tools.
Speaker 2: It's not just a language model anymore. It's a reasoning
Speaker 2: engine that uses language.
Speaker 3: That's the perfect description, and this capability has allowed it
Speaker 3: and to solve multiple open problems from the famous mathematician
Speaker 3: Paul Erders.
Speaker 2: The Erdor's problems. I remember hearing about these. He was
Speaker 2: this prolific mathematician who left behind hundreds of unsolved conjectures
Speaker 2: with cash prizes.
Speaker 3: Attached, and GPT five point two managed to crack several
Speaker 3: of them. But here's the real seal of approval. The
Speaker 3: proofs that generated were validated by Terrence Taw.
Speaker 2: Wow, Terrence Taw For anyone who doesn't know, he's basically
Speaker 2: considered the Mozart of modern mathematics. It feels metalist.
Speaker 3: He's the gold standard. So having Terrence twe review and
Speaker 3: validate an AI's mathematical proof is like I don't know,
Speaker 3: having Einstein check your physics homework. It signals that the
Speaker 3: AI is engaging in genuine, novel mathematical rus not just
Speaker 3: regurgitating the things that saw in its training data.
Speaker 2: And the sources mentioned this new benjmark called frontier math.
Speaker 2: It sounds like the old tests just got too easy.
Speaker 3: They did the AIS were acing them, so they had
Speaker 3: to build a new harder test. Frontier math consists of
Speaker 3: these ultra hard problems from the frontiers of research that
Speaker 3: usually required days or weeks of work by teams of
Speaker 3: expert mathematicians. GPT five point two is scoring over forty
Speaker 3: percent on the benchmark.
Speaker 2: Forty percent might not sound that high to a layperson, right.
Speaker 3: But for this level of difficulty, it's absolutely superhuman. It
Speaker 3: means it's making significant headway on nearly half of the
Speaker 3: hardest problems humanity has to offer.
Speaker 2: Let's shift gears from the abstract world of math to physics,
Speaker 2: because we aren't just solving equations on a chalkboard anymore.
Speaker 2: We are and I can't believe I'm saying this, controlling
Speaker 2: plasma infusion reactors.
Speaker 3: Yeah, this is where it gets really tangible.
Speaker 2: The Genesis mission, which was launched by the Trump administration
Speaker 2: back in November twenty twenty five, specifically mentions a collaboration
Speaker 2: between OPENING and the Department of Energy on tokeomagmagnets.
Speaker 3: Fusion energy the dream of unlimited clean power. The core
Speaker 3: problem with fusion has always been containment. You have to
Speaker 3: contain plasma that is literally hotter than the surface of
Speaker 3: the Sun, and.
Speaker 2: You do that with incredibly powerful magnetic.
Speaker 3: Fields, right, but the plasma is violently unstable. It wants
Speaker 3: to burst out. The analogy I've heard is that it's
Speaker 3: like trying to hold a giant, angry ball of jello
Speaker 3: together using only rubber bands while the jello is also exploding,
Speaker 3: and if.
Speaker 2: You fail, even for a millisecond, the reaction just.
Speaker 3: Fizzles out or you seriously damage the multi billion dollar reactor.
Speaker 3: The new AI models, specifically the ones developed in this
Speaker 3: Open AI and DOE collaboration, are now in direct control
Speaker 3: of these magnetic coils. They can adjust the magnetic field
Speaker 3: in microseconds, reacting to the plasma's turbulence, faster than any
Speaker 3: human operator or any traditional control algorithm ever could.
Speaker 2: So the AI is essentially surfing the plasma wave in
Speaker 2: real time.
Speaker 3: It is it's predicting the install ability a fraction of
Speaker 3: a second before it happens, and then preemptively countering it.
Speaker 3: It's a perfect example of why this high speed inference
Speaker 3: reasoning is so critical in the physical world.
Speaker 2: Speaking of the physical world, this is the perfect transition.
Speaker 2: Let's move to section four, the physical loop, because this
Speaker 2: is the part that to me really feels like science
Speaker 2: fiction becoming everyday reality. We are moving from just code
Speaker 2: to actual beakers.
Speaker 3: This is the concept of closed loop autonomy. It's the
Speaker 3: final piece of the puzzle. It's the bridge between the
Speaker 3: digital world of simulation and the physical world of experimentation.
Speaker 2: So we have the cycle. The AI generates a hypothesis.
Speaker 3: It runs a simulation to see if it's plausible.
Speaker 2: And then, and this is the new part, it designs
Speaker 2: and executes a robotic experiment to test it in the
Speaker 2: real world.
Speaker 3: It analyzes the results from that physical experiment and uses
Speaker 3: that data to generate a new better hypothesis. The loop
Speaker 3: is closed.
Speaker 2: I get the chemistry side of it, but the robotics
Speaker 2: and how does a large language model, which fundamentally just
Speaker 2: deals in tokens and text know how to grip a
Speaker 2: glass test tube without crushing it. We're not just setting
Speaker 2: a spreadsheet to a machine anymore.
Speaker 3: That is the big breakthrough of what are called VLA models.
Speaker 3: That's vision language action.
Speaker 2: Okay, vision language action. How did that work in practice?
Speaker 3: So traditionally lab robots were hard coded. A programmer had
Speaker 3: to tell them specific coordinates. Move your arm on the
Speaker 3: X axis ten point five millimeters, rotate your gripper on
Speaker 3: the Z axis ninety degrees, close scripper. It was incredibly brittle.
Speaker 3: If the beaker was one inch to the left of
Speaker 3: where it was supposed to be, a robot would just
Speaker 3: grab empty air.
Speaker 2: Right. They were powerful, but they were dumb robots.
Speaker 3: Extremely dumb. The news systems like the one being used
Speaker 3: in the cost Scientist project at Carnegie Mellon use these
Speaker 3: VLA models. They essentially tokenize physical actions in the same
Speaker 3: way they tokenize words to the AI. The command pick
Speaker 3: up the vile from the rack is just a sequence
Speaker 3: of vectors, just like the sentence write a poem about
Speaker 3: a robot is a sequence of vectors.
Speaker 2: So it's predicting the x most likely physical movement in
Speaker 2: the sequence, the same way chat GPT predicts the next
Speaker 2: most likely word in a sentence.
Speaker 3: Precisely. It learns the grammar and syntax of chemistry experiments
Speaker 3: by watching videos of humans doing them. It looks at
Speaker 3: the live camera feed that's the vision part. It understands
Speaker 3: the high level instruction from the scientist that's the language part,
Speaker 3: and it outputs the precise motor controls that's the action part.
Speaker 2: That is just wild. And because it's a language model,
Speaker 2: it can read the instruction manuals for the lab equipment.
Speaker 3: Yes, it literally reads the PDF manuals for the liquid
Speaker 3: handler or the spectrometer, so it knows the API calls
Speaker 3: for the hardware. It writes the Python script to drive
Speaker 3: the equipment on the fly, tailored to the specific experiment
Speaker 3: it just designed.
Speaker 2: She could literally give it a plain English command like
Speaker 2: synthesized iboprofen, and it just figures out all the steps. Yes.
Speaker 3: In the co Scientists demo from late last year, they
Speaker 3: did exactly that it looked up the chemical reaction in
Speaker 3: the literature, It calculated the precise amounts of the reagent,
Speaker 3: It translated all of that into a sequence of robotic protocols,
Speaker 3: and it executed the synthesis from start to finish autonomously.
Speaker 2: And then there's this other project from Berkeley Lab, the
Speaker 2: DTCs the Digital Twin for Chemical Science.
Speaker 3: That one is so cool. It takes it a step further.
Speaker 3: It creates a real time, physics based digital replica of
Speaker 3: the experiment as it's running. So while the robot is
Speaker 3: mixing chemicals, you have a digital twin of that beaker
Speaker 3: on your computer screen.
Speaker 2: And you can interact with the digital twin.
Speaker 3: You can you can adjust parameters on the screen, say
Speaker 3: you turn up the virtual temperature dial, and the AI
Speaker 3: uses that to optimize the reaction in the real world
Speaker 3: in real time. It can compress months of painstaking trial
Speaker 3: and error optimization into a few minutes.
Speaker 2: We're seeing this in industry too. Google's robotic labs are
Speaker 2: using this approach for materials science.
Speaker 3: Yes, specifically for discovering new inorganic materials. In twenty twenty
Speaker 3: five alone, there are autonomous labs discovered dozens of previously
Speaker 3: un known stable inorganic compounds. These are materials that could
Speaker 3: be the basis for the next generation of battery electrolytes,
Speaker 3: or a new type of superconductor, or a more efficient
Speaker 3: catalyst for carbon capture.
Speaker 2: And they're designing them in silico in the computer and
Speaker 2: then immediately synthesizing and verifying them autonomously.
Speaker 3: The discovery pipeline is becoming fully automated.
Speaker 2: This brings us to what is for me, the most
Speaker 2: important and personal part of this whole deep dive, saving
Speaker 2: lives Section five, The medical realities in twenty twenty six.
Speaker 2: Because all this technology is fascinating, but if it doesn't
Speaker 2: actually help people, it's just a very expensive toy.
Speaker 3: And in twenty twenty six it is definitely helping people.
Speaker 3: The impact is becoming undeniable.
Speaker 2: Let's start with Alzheimer's. This is a disease that has
Speaker 2: baffled researchers for decades. We've had so many false starts,
Speaker 2: so many promising drugs that looked great in mice and
Speaker 2: then failed spectacularly in human trials we have.
Speaker 3: It's been a graveyard for pharmaceutical R and D. But
Speaker 3: the AI cooscientist approach has given us a completely new
Speaker 3: angle on the disease. There's a gene called PHGDH. For
Speaker 3: a long time, scientists thought it was just a biomarker.
Speaker 2: Meaning it was just a signpost a correlation.
Speaker 3: Exactly like smoke indicating there might be a fire nearby.
Speaker 3: It was elevated in patients with Alzheimer's, but we didn't
Speaker 3: think it was actually causing the fire, just a symptom, right,
Speaker 3: But the AI systems modeled the incredibly complex three D
Speaker 3: protein structures and the entire gene regulation network around this
Speaker 3: gene and found something completely surprising. It's not just smoke,
Speaker 3: it's part of the fire. How so, the AI discovered
Speaker 3: that the protein made by the PHGDH gene is an
Speaker 3: active disruptor. It directly interferes with the normal process of
Speaker 3: gene regulation inside brain cells, causing a cascade of downstream problems.
Speaker 2: So it's not a bystander. It's one of the villains.
Speaker 3: It is a key villain in the story. And because
Speaker 3: we now know it's a villain, and thanks to tools
Speaker 3: like alpha fold and alpha proteo, we know its exact
Speaker 3: three D shape, we can design a drug to specifically
Speaker 3: target it and inhibit its function.
Speaker 2: New therapeutic candidates have been derived directly from this AI
Speaker 2: generated insight exactly.
Speaker 3: It has opened up a whole new avenue for treatment
Speaker 3: that we were completely blind to before because the chain
Speaker 3: of interactions was simply too complex for the human mind
Speaker 3: to piece together from the data.
Speaker 2: That's incredible. And then there's cancer, specifically the field of immunotherapy.
Speaker 3: This is a major collaboration we're seeing between the pharmaceutical
Speaker 3: giant Astrosenica and the AI company Tempeses. AI immunotherapy can
Speaker 3: be miraculous when it works.
Speaker 2: It can literally cure stage four cancer.
Speaker 3: It can, but it only works for a fraction of patients.
Speaker 3: The grand challenge is knowing ahead of time which patient
Speaker 3: is going to respond. Giving a multi hundred thousand dollars
Speaker 3: drug to someone it won't help is a terrible outcome.
Speaker 2: So they use what they call a predictive biomarker modeling framework.
Speaker 2: The notes mention contrastive learning. We hear that term a
Speaker 2: lot these days. Can we demystify it? What is contrastive
Speaker 2: learning actually doing here?
Speaker 3: Sure, standard machine learning often looks for patterns in one
Speaker 3: big bucket of data. Contrastive learning is more clever, more aggressive.
Speaker 3: It takes two distinct data sets, in this case, pathology
Speaker 3: images and genetic data from patients who survived, and the
Speaker 3: same data from patients who didn't, and it forces the
Speaker 3: model to find the maximum possible difference between those two groups.
Speaker 2: So it's not just looking for a pattern, it's looking
Speaker 2: for the one thing that most clearly separates winners from losers.
Speaker 3: It's forcing the two groups as far apart as possible
Speaker 3: in the high dimensional data space to find the signal
Speaker 3: in all the noise. It's like looking for the needle
Speaker 3: in the haystack by systematically burning away all the hay
Speaker 3: and what do you find? It identified a complex multimodal signal,
Speaker 3: a subtle pattern combining features from the pathology slides with
Speaker 3: specific genetic sequences that no human pathologist would ever be
Speaker 3: able to spot. It was a unique fingerprint for patients
Speaker 3: who would respond well to the.
Speaker 2: Therapy and the resulting clinical trials.
Speaker 3: A fifteen percent survival benefit.
Speaker 2: Fifteen percent that is not a rounding error in oncology.
Speaker 2: That is a assive victory. That is thousands of lives.
Speaker 3: It is. It's the promise of precision medicine finally being delivered.
Speaker 3: It's about getting the right drug to the right person
Speaker 3: at the right time.
Speaker 2: And finally, on the medical front, antimicrobial resistance, the superbugs.
Speaker 2: This is the one that really keeps me up at night,
Speaker 2: the slow motion pandemic.
Speaker 3: It should keep everyone up at night. We are running
Speaker 3: out of effective antibiotics. Bacteria are evolving resistance faster than
Speaker 3: we can invent new drugs to fight them. But the
Speaker 3: aiicoscientist is helping us catch up.
Speaker 2: How it's helping us anticipate the enemy's moves. There was
Speaker 2: a paper where the system predicted these highly complex gene
Speaker 2: transfer mechanisms, the ways that different bacteria swap armor plating
Speaker 2: and weapons to protect themselves from our drugs, before those
Speaker 2: mechanisms were ever observed and published experimentally.
Speaker 3: It saw the future. It predicted their evolution.
Speaker 2: It did, and by understanding these defense mechanisms before they
Speaker 2: become widespread, we can start designing new antibiotics that bypass them,
Speaker 2: or even turn their own to against them. It gives
Speaker 2: us a fighting chance in a race we were starting
Speaker 2: to lose.
Speaker 3: It's just it's breathtaking stuff. But I want to pause
Speaker 3: here for a second. We've been talking for a while
Speaker 3: about the AI doing everything generating the ideas, running the robots,
Speaker 3: predicting the genes. So where do the humans fit in?
Speaker 3: Are we just becoming the janitors for the server form?
Speaker 2: That is the big anxiety, isn't it the existential question?
Speaker 2: But the expert consensus in twenty twenty six from everyone
Speaker 2: we've read is remarkably clear. Humans are not only still necessary,
Speaker 2: they are more important than ever.
Speaker 3: Why if it's so smart, why does it still need us? Well,
Speaker 3: for one, hallucinations, AI models, even the very best ones
Speaker 3: like GPT five point two, can still confidently make things up.
Speaker 3: They can sound incredibly convincing while being totally fundamentally wrong
Speaker 3: about a physical constant or a biological fact.
Speaker 2: They don't know what they don't know exactly.
Speaker 3: Novelty requires rigorous verification. You cannot just blindly trust the
Speaker 3: output of the black box, especially when human lives are
Speaker 3: on the line.
Speaker 2: So the human is the safety valve, the final fact checker.
Speaker 3: The human is the safety valve, absolutely, but they're also
Speaker 3: the conductor of the orchestra. The ideal setup we're seeing
Speaker 3: emerge is the human plus AI team. Think about that
Speaker 3: workflow we discussed earlier. The AI proposes one hundred different hypotheses.
Speaker 3: It's not the AI that decides which three of those
Speaker 3: are the most scientifically important or the most ethically sound
Speaker 3: to pursue. It's the human. The human brings high level context, intuition,
Speaker 3: and most importantly, values to the table.
Speaker 2: So we picked the destination. The AI figures out the
Speaker 2: fastest way to build a road.
Speaker 3: That's a perfect way to put it. And then you
Speaker 3: have the massive looming ethical and legal questions. For example,
Speaker 3: who owns an AI discovered drug? Oh?
Speaker 2: The ownership question this is a big one. If I,
Speaker 2: as a researcher, read a clever prompt for the AI,
Speaker 2: and the AI invents a new life saving molecule, do
Speaker 2: I own the patent? Does my university? Does Google? Who
Speaker 2: built the AI? Or does nobody?
Speaker 3: It is a massive legal and philosophical battleground right now.
Speaker 3: If an AI that was trained on decades of publicly
Speaker 3: funded research data discovers a cure for cancer, should a
Speaker 3: private pharmaceutical company be able to hold an exclusive twenty
Speaker 3: year patent on it?
Speaker 2: These are not questions that an ADI can answer for us.
Speaker 3: Oh. These are uniquely human problems that require human judgment
Speaker 3: and societal debate.
Speaker 2: So we aren't being replaced. We are being augmented.
Speaker 3: Promoted augmented is the right word. Our jobs are changing.
Speaker 3: We're moving from being the people physically pipetting liquid into
Speaker 3: tubes to being the people who are orchestrating fleets of
Speaker 3: robotic scientists to solve humanity's biggest problems.
Speaker 2: This brings us to what I think is the most
Speaker 2: exciting and empowering part for our listeners, because you might
Speaker 2: be listening to all of this and thinking, well, this
Speaker 2: is great for the scientists at Google or after Zeneca
Speaker 2: with their billion dollar budgets. But I'm just a regular
Speaker 2: person with a laptop, and that is.
Speaker 3: Where you would be completely wrong. The barrier to entry
Speaker 3: for high level scientific discovery has never ever been lower
Speaker 3: than it is today. We are seeing the exciting ride
Speaker 3: of the citizen coscientists.
Speaker 2: I love that term. So what can I do today?
Speaker 2: February eighteenth, twenty twenty six, sitting at home with just
Speaker 2: my computer.
Speaker 3: You have free access to tools that would have cost
Speaker 3: millions of dollars and required a supercomputer just a decade ago.
Speaker 3: The Alpha fold server is public. You can go right
Speaker 3: now and submit a protein sequence and get a state
Speaker 3: of the art three D structure back free for free.
Speaker 3: Google's Aicoscientist has a trusted tester weight list that many
Speaker 3: hobbyists and students are getting access to, and perhaps most importantly,
Speaker 3: hugging Face hosts hundreds of open source chemistry and biology
Speaker 3: models that you can run locally on a good gaming
Speaker 3: PC or very cheaply in the cloud.
Speaker 2: So let's give our listeners some actual homework. What are
Speaker 2: some specific actionable things they can try this week?
Speaker 3: Okay, sign it number one. Design a new molecule. You
Speaker 3: can use an open source LM like LAMA three or mistrawl.
Speaker 3: Combine with a free software library called rd kit, which
Speaker 3: is a standard toolkit for computational chemistry.
Speaker 2: And what would it prompt be?
Speaker 3: You could prompt it act as an expert medicinal chemist.
Speaker 3: My goal is to design a novel small molecule to
Speaker 3: inhibit the PHGDH protein implicated in Alzheimer's Generate five potential
Speaker 3: molecular structures. Then you can take those designs and actually
Speaker 3: simulate how well they might bind to the target protein.
Speaker 3: Using the public.
Speaker 2: Alpha fold server, you can literally do the first steps
Speaker 2: of drug discovery on your couch.
Speaker 3: You can generate the initial hypothesis. Now you can't synthesize
Speaker 3: it in your kitchen. Please please do not try that disclaimer, right,
Speaker 3: but you can do the foundational computational work that used
Speaker 3: to be the exclusive domain of major pharmaceutical companies.
Speaker 2: Okay, that's incredible.
Speaker 3: Assignment number two, use the advanced chatbots Gemini, Claude, GPT
Speaker 3: five point two, but prompt them like a professional. Don't
Speaker 3: just ask a simple question, give them a roll. Say
Speaker 3: you are my AI coscientist. My grand challenge is to
Speaker 3: find a new sustainable battery electrolyte that doesn't rely on lithium.
Speaker 3: Please generate five novel research hypotheses, rank them based on
Speaker 3: scientific feasibility and potential impact, and for the top one,
Speaker 3: suggest a concrete experimental plan to test it.
Speaker 2: So it's all about the prompt treating it like a brilliant,
Speaker 2: tireless colleague, not like a simple search engine exactly.
Speaker 3: And finally, Assignment number three, join a citizen science platform.
Speaker 3: There are platforms right now that are feeding training data
Speaker 3: into the Genesis mission models. By helping to classify images
Speaker 3: or fold proteins in a game, or even donating your
Speaker 3: idle GPU time you are actively contributing to the National
Speaker 3: Scientific effort.
Speaker 2: You can be a part of the Genesis mission. That
Speaker 2: sounds pretty epic.
Speaker 3: It is. This is a collective societal effort. Now science
Speaker 3: is being democratized.
Speaker 2: So as we wrap up this deep dive, let's just
Speaker 2: zoom out one last time. We've covered the history, the tech,
Speaker 2: the robots, the medicine, the citizen science. What does this
Speaker 2: all mean for the big picture of human progress?
Speaker 3: It means we are in the early days of a
Speaker 3: new scientific renaissance. I don't think that's an exaggeration. By
Speaker 3: the end of this year, late twenty twenty six, many
Speaker 3: experts predicting that we will see the first fully AI
Speaker 3: led discovery to publication pipeline.
Speaker 2: Meaning from start to finish.
Speaker 3: From start to finish, the AI has the initial idea,
Speaker 3: it does all the work, it writes the paper, it
Speaker 3: submits it to a journal, and it gets published, a
Speaker 3: completely autonomous discovery.
Speaker 2: And this changes the scale of the problems we can
Speaker 2: even dare to tackle completely.
Speaker 3: The grand challenge is curing cancer, solving climate change, achieving
Speaker 3: fusion energy. They have always been limited by human bandwidth.
Speaker 3: We only have so many brilliant scientists in the world,
Speaker 3: and they only have so many hours in the day.
Speaker 2: They have to sleep, but silicon doesn't sleep.
Speaker 3: Silicon doesn't sleep. The limit on the pace of discovery
Speaker 3: is no longer a collective bandwidth. It's becoming our collector imagination.
Speaker 3: It's our willingness to collaborate with these new silicon colleagues.
Speaker 2: It's a genuinely hopeful message. I think the AI cooscientist
Speaker 2: isn't just accelerating the science we already do. It seems
Speaker 2: like it's redefining what discovery even means.
Speaker 3: I think so it allows us to see patterns and
Speaker 3: ask questions that we're previously too complex for our brains
Speaker 3: to even formulate. It's a new lens for viewing reality.
Speaker 2: That is a powerful thought to leave everyone with. So
Speaker 2: here is my final question to you listening right now,
Speaker 2: what hypothesis will you explore with your aicoscientists today? You
Speaker 2: have the tools, you now have the knowledge. The rest
Speaker 2: is up to you.
Speaker 3: Go discover something new.