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 want you to do something for me right now.
Speaker 2: It's going to sound a little ridiculous, maybe a bit
Speaker 2: too simple, but just humor me for a second. Okay,
Speaker 2: if you're sitting at a desk, or maybe you're holding
Speaker 2: your phone while you're walking, I want you to reach
Speaker 2: out and touch something. Just reach your glass of water
Speaker 2: or pick up a pin. Go ahead, do it.
Speaker 3: It feels instant, doesn't it.
Speaker 2: It feels like absolutely nothing. It's seamless. I think I
Speaker 2: want that pen and poof my hand. Is there, my
Speaker 2: fingers wrap around it, I lift it. It's the most
Speaker 2: mundane thing in the world. We do it thousands of
Speaker 2: times a day without even registering it.
Speaker 3: But if we actually slow that fraction of a second down,
Speaker 3: I mean, really, if we could just freeze time right
Speaker 3: between the thought and the action, what just happened is
Speaker 3: arguably the most complex engineering feet in the known universe.
Speaker 2: Okay, hang on that's a big claim.
Speaker 3: I don't say that lightly.
Speaker 2: Let's unpack that. Yeah, because to me it just felt
Speaker 2: like magic. I didn't feel any engineering. I didn't feel
Speaker 2: any gears turning or you know, wires firing.
Speaker 3: That's because the interface is completely invisible to you. But
Speaker 3: here is the reality. Before your muscle fiber even twitched,
Speaker 3: a thought existed, an intention right get pin exactly. And
Speaker 3: that intention started as a literal storm of electrical activity
Speaker 3: in your motor cortex. It's a specific part of your brain,
Speaker 3: the scorm I like that. From there, it cascaded down
Speaker 3: through your brainstem, It rocketed down your spinal cord, branched
Speaker 3: out through all these periperal nerves in your arm, and
Speaker 3: then finally it hit the neuromuscular junction to tell your
Speaker 3: hand to close. All of that happened and in the
Speaker 3: time it took you to just.
Speaker 2: Reach It's a biological cascade.
Speaker 3: It's a massive data pipeline, and the biology is so fast,
Speaker 3: so integrated that for a healthy person, the thought and
Speaker 3: the movement feel identical.
Speaker 2: There's no lag.
Speaker 3: There is no lag, there is no user innercise. You
Speaker 3: are the machine.
Speaker 2: But and this is the heavy part, the part that
Speaker 2: really grounds why we're here today. That circuit isn't guaranteed. No,
Speaker 2: it's incredibly fragileve for millions of people. That lightning fast
Speaker 2: highway is cut. We're talking about things like als, spinal
Speaker 2: cord injuries.
Speaker 3: Strokes, praumatic brain injuries.
Speaker 2: Yep. The heartbreaking thing I read in the research for
Speaker 2: this exploration is the description of what that actually feels like.
Speaker 2: It's not that the mind stops working. It's not that
Speaker 2: the desire.
Speaker 3: Fades precisely, that is the absolute key in many of
Speaker 3: these conditions. The motor cortex is still firing perfectly. The
Speaker 3: intention to move is there, the plan is perfect, The
Speaker 3: storm is brewing.
Speaker 2: That the wire is cut. The wire is.
Speaker 3: Cut, the signal screams down on the spinal cord, and
Speaker 3: it just it hits a dead end.
Speaker 2: It's a form of imprisonment. I think that's the phrase
Speaker 2: that's stuck with me. A fully intact, active mind locked
Speaker 2: inside a body that has stopped responding. It's a terrifying thought,
Speaker 2: it is, But it sets the stage perfectly for what
Speaker 2: we are exploring today. We are looking at the technology
Speaker 2: that is trying to bridge that gap. We're talking about
Speaker 2: brain computer interfaces, or.
Speaker 3: BCIs this is the frontier. If the biological highway is broken,
Speaker 3: the question becomes can we build a digital one?
Speaker 2: So we're not trying to fix the old road, we're
Speaker 2: building a whole new interstate.
Speaker 3: Exactly can we bypass the spinal cord entirely and wire
Speaker 3: the brain directly to a machine.
Speaker 2: And we aren't just talking about moving a mouse cursor
Speaker 2: on a screen, right, I mean that's where it started.
Speaker 2: The scope now is huge.
Speaker 3: Oh, it's way beyond that. We're talking about controlling advanced
Speaker 3: robotic arms. We're talking about synthesizing speech directly from the.
Speaker 2: Brain and eventually, well, stuff that sounds like straight up
Speaker 2: sci fi human augmentation.
Speaker 3: It does sound like sci fi, and it's easy to
Speaker 3: get carried away, but I do want to be the
Speaker 3: voice of let's say, engineering caution here.
Speaker 2: Around us a little.
Speaker 3: This is happening now, but it is incredibly hard, I
Speaker 3: mean unbelievably difficult. It is a collision of neuroscience, machine learning, robotics,
Speaker 3: and crucially ethics. And while the future possibilities are wild,
Speaker 3: the current reality is actually in some ways even more
Speaker 3: fascinating because of the sheer difficulty of the problem.
Speaker 2: So our mission today is to understand how we hack
Speaker 2: the brain. How do we listen to that electrical storm
Speaker 2: and translate it into action.
Speaker 3: Let's get into it.
Speaker 2: Okay, So to understand how we plug a computer into
Speaker 2: the brain, we first have to understand how the brain talks.
Speaker 2: I think most people have this vague idea that it's electrical.
Speaker 2: But yeah, it's not like a wall socket, right, you know,
Speaker 2: just you know, find the right port and play in
Speaker 2: a USB cable.
Speaker 3: Not exactly. The brain is an electrochemical organ. It's wet,
Speaker 3: it's salty, right, not great for electronics, not at all.
Speaker 3: And you have roughly one hundred billion neurons. That number
Speaker 3: is staggering, one hundred billion. And when they communicate, they
Speaker 3: fire what's called an action potential, and all that is
Speaker 3: really is a tiny sharp pulse of voltage. It's caused
Speaker 3: by ions, things like sodium and potassium rushing in and
Speaker 3: out of a cell membrane.
Speaker 2: So it's a little chemical battery firing off a spark.
Speaker 3: That's a great way to think about it, a tiny
Speaker 3: little spark. And this isn't a sporadic thing. This is
Speaker 3: happening constantly all over your.
Speaker 2: Brain, even when you're just sitting here doing nothing constantly.
Speaker 3: It's a symphony of noise. Even when you are sleeping,
Speaker 3: it's humming with activity. But for our purposes today, for BCI,
Speaker 3: we are interested in a very specific part of the
Speaker 3: brain called the primary motor cortex.
Speaker 2: Okay, walk us through the geography. Here. Where is this
Speaker 2: command center for movement?
Speaker 3: Imagine a headband, a strip of tissue running roughly from
Speaker 3: ear to ear right across the top of your head.
Speaker 3: That is your command center for voluntary movement.
Speaker 2: And everything I consciously decide to move command starts there.
Speaker 3: Every single voluntary movement, Yes, from wiggling your to speaking
Speaker 3: a word.
Speaker 2: I've seen those diagrams of the homunculus. Yeah, it's that
Speaker 2: weird distorted little man drawn over the brain. It's a
Speaker 2: map right of the body.
Speaker 3: It is a map, a very famous one. If you
Speaker 3: were to open up the skull and.
Speaker 2: Please don't do this at humeh right, disclaimer.
Speaker 3: And you were to electrically stimulate specific spots on that strip,
Speaker 3: you would see specific body parts twitch. Stimulate. Here, the
Speaker 3: thumb moves move a centimeter over, the lip twitches.
Speaker 2: So it's one to one map it is.
Speaker 3: But here is the catch. The map isn't drawn to
Speaker 3: scale based on physical size. It's drawn to scale based
Speaker 3: on complexity of movement.
Speaker 2: Ah okay, so my back is physically huge, but on
Speaker 2: the brain map it's tiny.
Speaker 3: Your back takes up a minuscule amount of space on
Speaker 3: that map. You don't need fine motor control for your
Speaker 3: lower back.
Speaker 2: I just needed to, you know, hold me up right.
Speaker 3: But your hands, your face, your tongue, they take up
Speaker 3: massive amounts of real estate on that strip. Why the
Speaker 3: amount of neural processing power required to move your thumb
Speaker 3: and forefinger with precision to pick up a dime is well,
Speaker 3: it's astronomical compared to the processing power it takes to
Speaker 3: move your leg to walk.
Speaker 2: Because of the decks terity involved. We have incredible fine
Speaker 2: motor skills in our hands. Speech is another one, I
Speaker 2: imagine exactly.
Speaker 3: And this is where the engineering challenge really begins. When
Speaker 3: you decide to move that hand. It's not just one
Speaker 3: neuron raising a little flag saying move left.
Speaker 2: That would be too easy. If it were one neuron,
Speaker 2: we could just find that one guy and listen to them.
Speaker 3: It would be vastly easier. We'd have solved this decades ago.
Speaker 3: But the brain uses something called population coding. A population,
Speaker 3: so a crowd, it's a distributed chorus. It's thousands of
Speaker 3: neurons firing in a complex, coordinated pattern. And they aren't
Speaker 3: just saying left. They are simultaneously encoding direction and speed
Speaker 3: and force and the kinematics of the whole trajectory, all
Speaker 3: at once, all at once. It is a massive, noisy,
Speaker 3: high dimensional data stream. So if I'm a BCI engineer,
Speaker 3: I'm not looking for a single switch I can flip.
Speaker 3: I'm trying to to a choir of thousands of people
Speaker 3: screaming slightly different instructions at once, and I have to
Speaker 3: figure out what song they're singing.
Speaker 2: That is a very very good analogy, and the problem
Speaker 2: gets even worse. Oh great, because usually the brain is cheating, cheating.
Speaker 3: How does the brain cheat?
Speaker 2: Feedback a constant stream of feedback. When you move your
Speaker 2: hand to pick up that water glass, you weren't just
Speaker 2: sending a command down. You are receiving data up.
Speaker 3: Your eyes see the hand moving, Your skin feels the
Speaker 3: air moving past it. Your muscles and joints have sensors
Speaker 3: proprioception that tell you exactly where your arm is in
Speaker 3: space without you even looking right.
Speaker 2: I can touch my nose with my eyes closed.
Speaker 3: That's proprioception, and your brain is constantly, millisecond by millisecond
Speaker 3: adjusting the plan based on that rich stream of feedback.
Speaker 2: Oh, I am a little too far to the left.
Speaker 2: Correct course, Wait, the glass is heavier than I thought.
Speaker 2: Apply more force constantly.
Speaker 3: It's a closed loop system. When we build a BCI,
Speaker 3: we are often flying blind. We are taking the output command,
Speaker 3: the shout from the choir, but we usually can't give
Speaker 3: the brain the same rich sensory feedback it's used to.
Speaker 2: We're intercepting the shout, but the brain can't hear the echo.
Speaker 3: Exactly, and that makes it the control problem so much harder.
Speaker 2: So we know the brain is shouting these electrical commands,
Speaker 2: the next big question is how do we listen? And
Speaker 2: looking at the research, this seems to be the biggest
Speaker 2: debate in the field right now. It's this fundamental trade off.
Speaker 3: It is the trade off resolution versus invasiveness.
Speaker 2: To cut or not to cut.
Speaker 3: That's a good way to put it. We can really
Speaker 3: think of this as three different scales of listening, three
Speaker 3: ways to eavesdrop on the brain.
Speaker 2: Okay, let's start with the most intense one, the one
Speaker 2: that requires a sterile operating room and a neurosurgeon.
Speaker 3: The finest scale is single unit recording. This means we
Speaker 3: are putting electrodes directly inside the brain tissue. We are
Speaker 3: physically penetrating the cortex and getting right up next to
Speaker 3: the cell bodies of the neurons.
Speaker 2: This is open brain sugery. We are drilling a hole
Speaker 2: in the skull.
Speaker 3: We are There's no way around it. But the advantage
Speaker 3: is clarity. The signal to noise ratio is incredible.
Speaker 2: Going back to your acquire analogy, what does this get us?
Speaker 3: This is like putting a microphone right in front of
Speaker 3: the soloist's mouth. You hear every breath, every subtle change,
Speaker 3: and pitch every single note. The precision is extraordinary, and.
Speaker 2: With that precision you can decode much more complex movements precisely.
Speaker 3: You can start to pull out signals for individual finger movements.
Speaker 2: For example, you're stabbing the brain, I mean gently. I'm sure,
Speaker 2: but that can't be good for long term health.
Speaker 3: It's not ideal. The brain is a very hostile environment
Speaker 3: for electronics. It's salty, it pulsates with every heartbeat, and
Speaker 3: the immune system absolutely hates foreign objects.
Speaker 2: It treats the electrode like a splinter.
Speaker 3: Exactly like a splinter. Over time, you get scarring. It's
Speaker 3: called glial scarring, where the brain's immune cells build up
Speaker 3: a wall of tissue around the electrode.
Speaker 2: And that pushes the neurons away.
Speaker 3: It pushes the neurons away from the electrode, the microphone
Speaker 3: gets muffled, the signal degrades. This long term stability is
Speaker 3: one of the biggest challenges for ENVA.
Speaker 2: Of BCIs okay, So that's that high risk, high reward option,
Speaker 2: maximum data, but maximum danger, and it degrades over time.
Speaker 2: What's the middle ground?
Speaker 3: The middle ground is usually called local field potentials or LFPs.
Speaker 3: Another term you'll here is ECoG, which stands for electrocordicography.
Speaker 2: So what's the difference in approach here?
Speaker 3: Instead of poking the sharp electrodes inside the tissue, you
Speaker 3: lay a flexible grid or a mat of electrodes directly
Speaker 3: on the surface of the brain, under the skull, but
Speaker 3: on top of the gray matter. Ah, So you're still
Speaker 3: inside the skull, which is invasive, but you aren't penetrating
Speaker 3: the brain tissue itself.
Speaker 2: Correct, you aren't triggering that same aggressive immune response.
Speaker 3: So in our choir analogy, where are we now?
Speaker 2: You aren't hearing the soloist anymore. You're hearing the entire
Speaker 2: soprano section as a group. You know they're singing, you
Speaker 2: know the general melody, but you can't pick out an
Speaker 2: individual voice.
Speaker 3: So you're summing up the activity of thousands of neurons.
Speaker 3: Less detail, but maybe safer and more stable over time.
Speaker 2: Exactly. It's a compromise. You lose that fine grain specific
Speaker 2: data you might need for say, playing a piano with
Speaker 2: a robotic hand, but it's much more stable long term.
Speaker 3: Which brings us to option three, the one everyone wants
Speaker 3: to work because it doesn't involve.
Speaker 2: A drill, the Holy Grail, non.
Speaker 3: Invasive, the headset, the EEG cap that you just put on.
Speaker 2: Right EEG or electro and cephlography. You're just placing electrodes
Speaker 2: on the scalp with some conductive gel, no surgery, just
Speaker 2: a funny looking hat with a lot of wires.
Speaker 3: It sounds perfect, that's the catch. The catch is the skull.
Speaker 3: The skull is a massive electrical insulator. It smears and
Speaker 3: distorts the tiny electrical signals from the.
Speaker 2: Brain and the sources used a great analogy for this.
Speaker 2: They said, it's like standing in the parking lot of
Speaker 2: a football stadium and trying to listen to the conversation
Speaker 2: of two people on the fifty yard line.
Speaker 3: It's a perfect analogy. You can hear the roar of
Speaker 3: the crowd, you know when a touchdown happens, because the
Speaker 3: whole stadium erupts. You can tell if the crowd is excited.
Speaker 2: Or bored, big large scale signals.
Speaker 3: Exactly, you can detect large scale brain states, but you
Speaker 3: cannot hear the quarterback calling the play. The specific detailed
Speaker 3: commands are lost in the noise.
Speaker 2: So if I want to control a cursor on a
Speaker 2: screen to just lect yes or no, the stadium roar
Speaker 2: is enough for that.
Speaker 3: Yes, that's a big signal. We can detect that. But
Speaker 3: if you want to control a robotic hand to gently
Speaker 3: pick up a grape without squashing.
Speaker 2: It, you need to hear the quarterback.
Speaker 3: You need to hear the quarterback. You need to be
Speaker 3: inside the stadium.
Speaker 2: That is the cool irony of this whole field, isn't it.
Speaker 2: It feels like the central conflict. To get the magical
Speaker 2: sci fi dexterity that could change lives, you currently have
Speaker 2: to drill a hole in someone's skull, that's right. But
Speaker 2: to make the technology accessible and safe for everyone, you
Speaker 2: have to use the non invasive methods, which for now
Speaker 2: lose too much fidelity for those complex tasks.
Speaker 3: That is the central tension, The engineering dilemma that every
Speaker 3: single lab in company in this space is wrestling with
Speaker 3: is essentially how can we get the highest resolution signal
Speaker 3: with the absolute minimum amount of harm to the patient.
Speaker 2: So let's assume we've made that choice. We've gone for
Speaker 2: the high fidelity option. We've drilled the whole, we've got
Speaker 2: the wires in, we're listening to the soloist. Now comes
Speaker 2: the part that honestly breaks my brain a little bit.
Speaker 3: The decoding the machine learning problem. This is where neuroscience
Speaker 3: stops and computer science really begins.
Speaker 2: Because the brain isn't sending computer code, it's not sending English,
Speaker 2: it's sending what sounds like static. How on earth do
Speaker 2: we turn a storm of electrical noise into the command
Speaker 2: move robot arm left.
Speaker 3: This is where the progress in AI and machine learning
Speaker 3: over the last decade has completely changed the game. In
Speaker 3: the old days. By old days, I mean, you know,
Speaker 3: ten fifteen years ago, we used relatively simple linear models, simple.
Speaker 2: Math, like more firing in this area equals more speed
Speaker 2: in that direction.
Speaker 3: Relatively Yes, it was essentially drawing a straight line. If
Speaker 3: this neuron fires fast, move hand right, that one fires fast,
Speaker 3: move hand up, and surprisingly that works for basic stuff.
Speaker 3: Really yeah, the brain is robust enough that even a
Speaker 3: simple MAC like that captures something meaningful. But it hit
Speaker 3: a ceiling. You couldn't do complex, fluid, coordinated movements. It
Speaker 3: was always jerky and slow.
Speaker 2: Enter deep learning exactly.
Speaker 3: Now we use much more sophisticated tools. We use recurrent
Speaker 3: neural networks RNNs and transformers, the same kind of AI
Speaker 3: architecture behind things like chat GPT, but applied to neural
Speaker 3: signals instead of words.
Speaker 2: How does that work? How do you apply a language
Speaker 2: model to brainwaves?
Speaker 3: We stop thinking about individual neurons and start treating the
Speaker 3: neural activity as a trajectory through what we call high
Speaker 3: dimensional space.
Speaker 2: Okay, hold on, you're losing me. High dimensional space. Explain
Speaker 2: that to me like I'm five.
Speaker 3: Okay, Think of it this way. Instead of looking at
Speaker 3: one neuron at a time, the AI looks at the
Speaker 3: state of the entire population of neurons. We're recording from
Speaker 3: say one hundred of them, all at once, at a
Speaker 3: single snapshot in time.
Speaker 2: So it's looking at the whole choir, not just one singer.
Speaker 3: The whole choir, and it does this many times a second.
Speaker 3: Imagine a flock of birds turning in the sky. If
Speaker 3: you only watch one bird, its movement seems chaotic and unpredictable, right,
Speaker 3: But if you watch the shape of the entire flock,
Speaker 3: you can see clear patterns. You can predict where the
Speaker 3: group is going. The AI learns the shape of the intention.
Speaker 3: It recognizes that this specific swirl of activity, this shape
Speaker 3: in high dimensional space means prepared to grasp, and that
Speaker 3: swirl means accelery forward.
Speaker 2: So it's pattern recognition on a massive, massive scale.
Speaker 3: That's all it is. But it's incredibly powerful.
Speaker 2: But there's a catch. The sources all mentioned something they
Speaker 2: call the drift, which sounds like a horror movie title,
Speaker 2: by the way, or maybe a racing movie.
Speaker 3: It is a bit of a nightmare for engineers, so
Speaker 3: maybe horror movie is right. The problem is that the
Speaker 3: brain is not a static chip. It's plastic. It changes,
Speaker 3: it learns, it learns, Neurons die or they change their tuning.
Speaker 3: The way they fire in response to a certain intention,
Speaker 3: or on a purely mechanical level, the electro to array
Speaker 3: might shift by a few micrometers because the brain wobbles
Speaker 3: when you sneeze or turn your head quickly.
Speaker 2: Oh, I never even thought of that. So the algorithm
Speaker 2: I spent all morning training at nine zero zero.
Speaker 3: Am might be completely useless by two point zero pm.
Speaker 3: The map has changed the relationship between the neural signals
Speaker 3: and the intention has drifted.
Speaker 2: That sounds incredibly frustrating for the user. I mean, imagine
Speaker 2: your computer mounts working perfectly in the morning, but by lunchtime,
Speaker 2: moving your handwright makes the cursor go up. You'd go crazy.
Speaker 3: It happens, and traditionally the solution was clumsy. It was recalibration.
Speaker 2: What does that mean.
Speaker 3: It means you have to stop everything you're doing and
Speaker 3: you have to do a twenty minute training session. Okay,
Speaker 3: now think about moving left four minute. Now think right
Speaker 3: for a minute. Now think up. Every single day, sometimes
Speaker 3: multiple times a day.
Speaker 2: That's a job. That's not a tool. If I have
Speaker 2: to train my phone for twenty minutes before I can
Speaker 2: send a text message, I'm throwing the.
Speaker 3: Phone away exactly. It's a huge barrier to adoption. So
Speaker 3: the cutting edge right now is in adaptive algorithms.
Speaker 2: So they adapt on the fly.
Speaker 3: The AI continues to learn while you use it. It
Speaker 3: watches for errors. If it sees you correcting a mistake,
Speaker 3: like you clearly intended to go left, but the cursor
Speaker 3: went up and you immediately pulled it back down, the
Speaker 3: AI logs that and says, oops, my map was wrong
Speaker 3: for that movement. Let me update that in real time.
Speaker 2: So it learns from the user's frustration.
Speaker 3: In a sense. Yes, it's trying to dynamically realign the
Speaker 3: machines map with the brains map moment by moment so
Speaker 3: the user doesn't have to stop and recalibrate.
Speaker 2: Okay, there's one more piece to this puzzle that we
Speaker 2: touched on earlier, the missing.
Speaker 3: Feedback, the closed sleep problem.
Speaker 2: My biological hand feels the glass, it knows when it's
Speaker 2: made contact. A BCI controlled robot arm does not. So
Speaker 2: the user is relying entirely on vision right, just watching the.
Speaker 3: R move mostly Yes, and that's very unnatural and slow.
Speaker 3: You're constantly overcorrecting because you don't have that sense of
Speaker 3: touch or position.
Speaker 2: So what's the frontier there? How? Do you send signals
Speaker 2: back to the brain.
Speaker 3: This is truly bleeding edge research. The idea is to
Speaker 3: stimulate the sensory cortex, the part of the brain that
Speaker 3: processes touch, to send touch and position data back, so.
Speaker 2: You're not just reading from the brain, you're writing.
Speaker 3: To it exactly. You could, for example, have sensors in
Speaker 3: the fingertips of the robotic hand, and when it touches something,
Speaker 3: you send a small electrical pulse into the part of
Speaker 3: the brain that corresponds to the index finger, so.
Speaker 2: The person would actually feel a sensation of touch in
Speaker 2: their phantom fingers.
Speaker 3: That's the goal. It's incredibly difficult, but if we can
Speaker 3: close that loop and give the brain the feedback at craves,
Speaker 3: the level of control could increase exponentially.
Speaker 2: I want to move this from the abstract to the
Speaker 2: real because this isn't just theory anymore. People are actually
Speaker 2: using this stuff to regain function. This isn't just mice
Speaker 2: in a lab.
Speaker 3: No, The clinical history is much richer than I think
Speaker 3: people realize, and to talk about that, we have to
Speaker 3: talk about.
Speaker 2: Brain Gate, the brain Gate legacy. This is the big
Speaker 2: academic consortium Brown University, Stanford, mass General that's been doing
Speaker 2: this for over twenty years now.
Speaker 3: They are the absolute pioneers, and for most of that
Speaker 3: time they've been using a specific piece of hardware called
Speaker 3: the Utah array, which.
Speaker 2: Looks like describe it for us. It's an intimidating looking device.
Speaker 3: Imagine a very tiny, very scary looking hair brush. It's
Speaker 3: a four x four millimeters square grid of silicon spikes,
Speaker 3: one hundred tiny sharp.
Speaker 2: Electrodes, a bed of nails for neurons.
Speaker 3: A tiny bed of nails. Yes, it's small, about the
Speaker 3: size of a baby aspirin. That those spikes were designed
Speaker 3: to penetrate about a millimeter and a half into the cortex.
Speaker 2: And this is the device that gave us the coffee moment.
Speaker 2: I remember seeing the video of this years ago, but
Speaker 2: going back to the notes, it just it hit me
Speaker 2: so much harder this time. Tell us about that.
Speaker 3: This was a landmark case published in twenty twelve with
Speaker 3: a woman named Kathy Hutchinson. She had suffered a brainstem
Speaker 3: stroke years earlier.
Speaker 2: And she was what they call quadriplegic.
Speaker 3: And anarthrich, meaning she couldn't speak. She had been unable
Speaker 3: to move her limbs or speak for nearly fifteen years.
Speaker 3: They implanted the Utah ray in her motor cortex, and
Speaker 3: they hooked it up to a large industrial look of
Speaker 3: robotic arm. Not a prosthetic attached to her, but a
Speaker 3: big robot mounted on a table next to her.
Speaker 2: And the goal was so simple, so mundane. Just drink
Speaker 2: the coffee.
Speaker 3: A simple goal, but an immensely complex task. Reach out,
Speaker 3: grasp the bottle, bring it to the mouth, drink, and
Speaker 3: then place it back on the table.
Speaker 2: And when you watch the video, yeah, it's not smooth.
Speaker 2: It's not a sci fi movie. It's not Luke Skywalker's
Speaker 2: perfect robotic hand.
Speaker 3: No, not at all. It's a bit jerky. The arm
Speaker 3: hovers for a second. You can see the intense concentration
Speaker 3: on her face. The robot arm shakes a little as.
Speaker 2: It approaches the bottle, but she gets it.
Speaker 3: She gets it. The robot fingers close around the bottle,
Speaker 3: she lifts it, she brings the straw to her lips,
Speaker 3: and she takes a drink.
Speaker 2: And the smile, the smile on her face.
Speaker 3: The smile is everything. And this is the crucial insight.
Speaker 3: From that moment, it wasn't about the grace of the robot.
Speaker 3: It was about the restoration of agency the first time
Speaker 3: in fifteen years, she had a thought, I want to drink,
Speaker 3: and the world obeyed. The loop between intention and action
Speaker 3: was closed again.
Speaker 2: That's the miracle of the mundane we talked about right
Speaker 2: at the very beginning, restoring something we take for granted.
Speaker 2: That is profound.
Speaker 3: It is, and since that moment the technology has just accelerated.
Speaker 3: We aren't just doing robot arms anymore. We are seeing
Speaker 3: things like functional electrical stimulation or FEES.
Speaker 2: Okay, explain fees, because that's even wilder in a way.
Speaker 2: That's where they bypassed the robot entirely and wire the
Speaker 2: BCI back into the person's own arm.
Speaker 3: Right, Yes, this is incredible stuff. A participant, someone with
Speaker 3: paralysis from a spinal cord injury, will have the BCI implanted.
Speaker 3: Then they put a sleeve of electrodes on the outside
Speaker 3: of their paralyzed arm, right over the muscles.
Speaker 2: So you've got a BCI reading the brain and electrode
Speaker 2: sleeve ready to stimulate the muscles.
Speaker 3: Correct The brain signals for say, open hand are decoded
Speaker 3: by the computer, which then sends a precise patter of
Speaker 3: electrical shocks to the muscles in the forearm and.
Speaker 2: The muscles contract, the hand opens.
Speaker 3: The hand opens. The person is moving their own paralyzed
Speaker 3: limb with their own thoughts, using the computer as a
Speaker 3: digital spinal cord.
Speaker 2: That is mind blowing. It's reanimating the body.
Speaker 3: It's a biological bypass. It's really incredible to watch.
Speaker 2: But there's another area of recent breakthroughs that I think
Speaker 2: might be even more emotional, even more fundamental for people, and.
Speaker 3: That speech speech BCIs are arguably the most transformative thing
Speaker 3: happening in this field right now. To understand why, you
Speaker 3: have to imagine locked in syndrome, right.
Speaker 2: The mind is fully there, but you cannot move a
Speaker 2: single muscle, you can't speak. In some cases, you can't
Speaker 2: even blink. You are a ghost in the machine.
Speaker 3: It's the most extreme version of that imprisonment we talked about,
Speaker 3: solitary confinement in your own body.
Speaker 2: So how are researchers tackling this.
Speaker 3: Teams at places like UCSF and Stanford have been implanting
Speaker 3: ECoG arrays, those surface grids over the speech centers of
Speaker 3: the brain. The art that moves the hand, but the
Speaker 3: part that coordinates the hundreds of muscles in the lips, tongue, jaw,
Speaker 3: and larynx.
Speaker 2: And they aren't just decoding letters right. This isn't like
Speaker 2: a slow letter by letter spelling device.
Speaker 3: No, that's the old way. The new way is to
Speaker 3: decode the intention to speak whole words. The AI learns
Speaker 3: the neural patterns for entire words or even phonemes, the
Speaker 3: building blocks of.
Speaker 2: Speech, and it reconstructs them into audio.
Speaker 3: It reconstructs them into text on a screen or audible
Speaker 3: speech through a digital avatar. And the speed, this is
Speaker 3: the key. We are now approaching conversational speeds.
Speaker 2: The sources mentioned a qualitative change. That's the term they used,
Speaker 2: going from slowly typing with your eyes, which is painstaking,
Speaker 2: maybe ten words a minute on a good day, to
Speaker 2: actually talking through a synthesizer at over sixty or seventy
Speaker 2: words per minute.
Speaker 3: It's the difference between being a patient who needs to
Speaker 3: be cared for and being a person who can participate
Speaker 3: in a conversation, who can express complex ideas, tell jokes, argue,
Speaker 3: express love, all in real time.
Speaker 2: Doors the self, Okay, we can't put it off any longer.
Speaker 2: We have to address the big, shiny, celebrity backed elephant
Speaker 2: in the room.
Speaker 3: Neuralink.
Speaker 2: You can't have a conversation about BCIs in the twenty
Speaker 2: twenties without talking about Elon Musk's company. But I want
Speaker 2: to stick to our rules here. We aren't interested in
Speaker 2: the hype or the tweets. We're interested in the technology.
Speaker 2: From an engineering perspective. How is neuralink different from that
Speaker 2: Utah array hairbrush we just talked about.
Speaker 3: From an engineering perspective, it's a significant leap in a
Speaker 3: few key areas. The first is the electrodes themselves. The
Speaker 3: UTAH array is rigid. It's stiff silicon spikes going into
Speaker 3: soft jello like brain tissue.
Speaker 2: And that mismatch causes problems.
Speaker 3: That mechanical mismatch causes damage and scarring over time. It's
Speaker 3: like sticking a fork in tofu and then wiggling it around.
Speaker 2: A gruesome image, but it makes the point.
Speaker 3: Neuralink's primary innovation is what they call the threads. Instead
Speaker 3: of rigid spikes, they use flexible hair like polyamide probes.
Speaker 3: They're designed to be able to move and flex with
Speaker 3: the brain as it pulsates, so.
Speaker 2: Less DAMA image, which should mean better signal quality over
Speaker 2: a longer period of time.
Speaker 3: That's the hypothesis. Yes. The second big difference is the
Speaker 3: sheer number of channels. The UTAH array has one hundred electrodes.
Speaker 3: Neuralink's first generation device has over one thousand.
Speaker 2: An order of magnitude more more microphones in the choir.
Speaker 3: Exactly, more microphones in the choir means you can potentially
Speaker 3: get a much richer, higher fidelity signal, which in turn
Speaker 3: allows for better decoding.
Speaker 2: But the thing that really stood out to me in
Speaker 2: the technical breakdown wasn't just the threads. It was the robot.
Speaker 2: They built a custom sewing machine robot for the surgery.
Speaker 3: They had to a human surgeon can't insert these threads.
Speaker 3: They're thinner than a human hair, and they're too flexible
Speaker 3: to be manipulated with tweezers. You need a robot with
Speaker 3: microscopic vision and micron level precision to weave them into
Speaker 3: the cortex, specifically dodging blood vessels to prevent bleeding.
Speaker 2: It's an industrial approach to neurosurgery.
Speaker 3: That's a perfect way to describe it.
Speaker 2: And they've now started here trials. The sources all point
Speaker 2: to the first patient in early twenty twenty four, a
Speaker 2: man named Nolan Arbaugh who is quadriplegic, and the initial
Speaker 2: results showed him controlling a cursor playing video games like
Speaker 2: Civilization the sixth of his mind right, and.
Speaker 3: It's important to put this in context. Strictly speaking, the
Speaker 3: scientific result a person with paralysis controlling a cursor is
Speaker 3: something the brain Gate Consortium demonstrated fifteen, maybe twenty years ago.
Speaker 3: The result itself isn't the.
Speaker 2: Novel part, So why is it a big deal? Why
Speaker 2: did it get so much attention.
Speaker 3: Because of the packaging, the productization. The brain date device,
Speaker 3: as incredible as it is, requires a massive metal pedestal
Speaker 3: screwed into your head with thick wires coming out to
Speaker 3: a literal rack of servers.
Speaker 2: You're tethered. You can't leave the lab.
Speaker 3: You can't leave the lab. The Neuralink device is fully implantable.
Speaker 3: It's a small disc that sits flush with the skull
Speaker 3: invisible under the skin. It charges wirelessly. It transmits its
Speaker 3: data wirelessly via bluetooth to a phone.
Speaker 2: It's the difference between a nineteen sixties main frame computer
Speaker 2: that fills a room in an iPhone.
Speaker 3: That is the perfect analogy. It's the shift from a
Speaker 3: science experiment to the prototype of a scalable consumer.
Speaker 2: Product, and that shift changes everything.
Speaker 3: It changes the incentives. It brings massive private capital into
Speaker 3: a field that has historically been funded by slow moving
Speaker 3: government grants. It brings an intense focus on manufacturing and
Speaker 3: reliability and user experience. Academic labs prove what's possible. Companies
Speaker 3: like this are trying to figure out how to make
Speaker 3: it a product. It accelerates the timeline for everyone.
Speaker 2: But let's be real, and I think this is a
Speaker 2: crucial point. Not everyone wants a chip in their brain.
Speaker 2: I mean, I love technology, but I'm not signing up
Speaker 2: for elective brain surgery tomorrow.
Speaker 3: And you absolutely shouldn't. And that's why the non invasive
Speaker 3: field is still so huge and so important.
Speaker 2: We talked about the stadium analogy for EEG. Is there
Speaker 2: any hope for the non invasive stuff getting better or
Speaker 2: are we just kind of stuck in the parking lot forever.
Speaker 3: There is hope, and there are some clever tricks people
Speaker 3: use to get more out of EG. We touched on it,
Speaker 3: but one of the most reliable is something called ssvep.
Speaker 2: Okay that's steady state visually evoked potentials. Say that three
Speaker 2: times fast.
Speaker 3: It's a mouthful, but the concept is brilliant. It relies
Speaker 3: on a known quirk of the brain's visual system. If
Speaker 3: you look at a light that is flickering at a
Speaker 3: specific frequency, let's say ten hurts, so ten times a second,
Speaker 3: your visual cortex will start firing at exactly ten hurts.
Speaker 2: It sinks up with the flicker.
Speaker 3: It sinks up. It's a phenomenon called entrainment. So if
Speaker 3: I build a user interface with a yes button on
Speaker 3: a screen that's flickering at ten hurts and a no
Speaker 3: button next to it flickering at a different frequency, say
Speaker 3: fifteen herds, you.
Speaker 2: Don't have to decode my intention. You just have to
Speaker 2: look at the brain waves coming from my visual cortex
Speaker 2: and see if they are humming at ten herts or
Speaker 2: fifteen herts exactly.
Speaker 3: The EEG can pick that up clearly. It effectively turns
Speaker 3: your gaze into a mouse click. It's very reliable, it
Speaker 3: requires almost no training, and it works right through the skull.
Speaker 2: What's the downside It sounds a little a to look
Speaker 2: at flickering lights all day.
Speaker 3: It can be very tiring on the eyes. Yes, it's
Speaker 3: not a perfect solution for.
Speaker 2: All day use. So what's the next big thing and
Speaker 2: non invasive, the one that doesn't feel like I'm at
Speaker 2: a rave.
Speaker 3: The big hope on the horizon is probably magneto in cepholography.
Speaker 2: Or meg magnets. So we're moving from electricity to magnetism.
Speaker 3: We are because every electrical current creates a corresponding magnetic field.
Speaker 3: That's just basic physics. So the brain's electric currents create tiny,
Speaker 3: tiny magnetic fields.
Speaker 2: And the skull is a problem for those two.
Speaker 3: Ah, this is the key. The skull distorts electricity, but
Speaker 3: it's basically transparent to magnetism. The magnetic fields pass through
Speaker 3: the skull and scalp almost completely undistorted.
Speaker 2: So less distortion equals a much clearer signal.
Speaker 3: On the other side, much clearer you get better spatial
Speaker 3: resolution than e g. The problem has always been the hardware.
Speaker 3: To detect these magnetic fields, which are about a billion
Speaker 3: times weaker than the Earth's magnetic field. You traditionally needed
Speaker 3: a room size machine called a squid array. A squid
Speaker 3: it stands for superconducting quantum interference device. It had to
Speaker 3: be cooled to near absolute zero with liquid helium.
Speaker 2: Not exactly a wearable headset.
Speaker 3: No, you had to put your head inside this giant
Speaker 3: stationary helmet. But the new technology and this is very exciting.
Speaker 3: Is something called optically pumped magnetometers or opms.
Speaker 2: And what's different about them.
Speaker 3: They can work at room temperature, they use quantum sensing
Speaker 3: with lasers and vapor cells, and they are getting small
Speaker 3: enough and light enough to be built into a wearable helmet.
Speaker 3: This could be the sweet spot. We've been looking for
Speaker 3: much better resolution than EEG, but with no surgery required.
Speaker 2: I want to pivot now. We've talked so much about
Speaker 2: healing the sick, restoring speech, moving paralyzed limbs. That's the
Speaker 2: medical necessity chapter of this story. But let's open the
Speaker 2: human two point zero chapter, the augmentation chapter.
Speaker 3: This is where it gets really philosophically tricky and wild.
Speaker 2: The sources talk about industrial applications, controlling a hasmat robot
Speaker 2: or a search and rest you drone. My first question is,
Speaker 2: why would I use my brain for that instead of
Speaker 2: a jaystick. Joysticks are pretty good.
Speaker 3: Joysticks are great for two or three dimensions of control up, down, left, right,
Speaker 3: But think about the limitations of a joystick. It's sequential.
Speaker 3: If you are controlling a complex bomb disposal robot, you
Speaker 3: have to toggle a switch to move the arm, then
Speaker 3: coggle another switch to rotate the wrist, then another one
Speaker 3: to close the gripper.
Speaker 2: It's cereal, it's clunky.
Speaker 3: It's very clunky, but your brain doesn't work like that.
Speaker 3: Your brain thinks, grasp that object gently and it sends
Speaker 3: a parallel signal containing position, rotation for speed, gripper posture.
Speaker 2: All at once, the high dimensional signal we talked about.
Speaker 3: Exactly, if we can decode that rich high dimensional signal,
Speaker 3: a worker could control a robot arm as intuitively and
Speaker 3: fluidly as their own. It dramatically increases the control bandwidth
Speaker 3: between human and machine.
Speaker 2: And then there's the idea of the third arm, which
Speaker 2: sounds like something out of a comic book.
Speaker 3: It's a fascinating area of neuroscience research. Can the brain's
Speaker 3: motor map handle a third non biological limb If you
Speaker 3: plug a robotic arm into the cortex, does the brain
Speaker 3: say error, does not compute? Or does it say cool
Speaker 3: new tool, let's learn how to use it?
Speaker 2: And what's the answer.
Speaker 3: The early research suggests the brain is surprisingly adaptable. The
Speaker 3: motor map is plastic enough that it can learn to
Speaker 3: control an extra effector an extra arm without losing control
Speaker 3: of the biological ones.
Speaker 2: So I could theoretically be typing with my two hands
Speaker 2: and using my mind to hold a coffee cup with
Speaker 2: my third robotic arm.
Speaker 3: Theoretically someday, yes, But this is the moment where we
Speaker 3: come crashing right into the ethical wall.
Speaker 2: Yeah, the dark side. Let's start with the most obvious one, privacy,
Speaker 2: Because if you are reading my motor intentions from my brain,
Speaker 2: what else are you reading.
Speaker 3: The brain is not neatly compartmentalized. The motor cortex is
Speaker 3: deeply interconnected with everything else, the prefrontal cortex, the limbic.
Speaker 2: System, so the signals are all mixed together.
Speaker 3: The signals contain more than just movement. They encode attention,
Speaker 3: code cognitive effort, they encode frustration, They encode emotional states.
Speaker 2: So if I'm using a BCI to control my computer
Speaker 2: at my job in.
Speaker 3: The future, your boss could theoretically know not just what
Speaker 3: you are typing, but how hard you are concentrating. They
Speaker 3: could know if you were bored. They could know if
Speaker 3: you are getting drowsy, or if you are feeling angry
Speaker 3: about an email you just read.
Speaker 2: That is that's dystopian employee hashtag four twenty four, Your
Speaker 2: engagement levels dropped by twelve percent at three zero pm,
Speaker 2: please report to HR.
Speaker 3: It's a concept that ethicists are calling data intimacy. We
Speaker 3: have never had a technology that sits inside the processing
Speaker 3: center of the self, reading the raw data of thought
Speaker 3: and feeling.
Speaker 2: And we have no laws for this.
Speaker 3: Who owns that data, the user, the company that made
Speaker 3: the BCI, your employer, the government. We have no regulatory
Speaker 3: framework for this. You're starting to see neuro rights movements
Speaker 3: springing up now for this exact reason.
Speaker 2: And then there's the other big ethical can of worms,
Speaker 2: the inequality aspect.
Speaker 3: Right, if these devices eventually move from restoration, which everyone
Speaker 3: agrees is good, to enhancement. If a BCI allows you
Speaker 3: to think faster, or learn a skill instantly, or interface
Speaker 3: directly with an AI.
Speaker 2: Who gets in the wealthy it's always the wealthy first.
Speaker 3: It will be incredibly expensive, yes, And if the wealthy
Speaker 3: are not just richer, but are now cognitively superior because
Speaker 3: of their neural hardware, we aren't just talking about a
Speaker 3: wealth gap anymore.
Speaker 2: We're talking about a capability gap, a biological one.
Speaker 3: All the people have called it a speciation event, the
Speaker 3: enhanced versus the naturals. Wow, it fundamentally challenges the ideal
Speaker 3: of a level playing field, the very fairness of the
Speaker 3: human experience. And we're absolutely not ready for that conversation
Speaker 3: as a society.
Speaker 2: We really aren't, but we have to be because, as
Speaker 2: you said earlier, the trajectory is set. This isn't a
Speaker 2: what if anymore.
Speaker 3: The trajectory is clear. The technology gets smaller and more powerful,
Speaker 3: the decoding gets smarter, the surgery gets safer. We are
Speaker 3: moving from the era of can we do this? To
Speaker 3: the era of how do we live with this?
Speaker 2: So let's try to wrap this up. We started with
Speaker 2: a simple act, reaching for a glass of water, the
Speaker 2: miracle of the mundane. And we've traveled through the broken
Speaker 2: biological circuits, the silicon bridges being built to cross them,
Speaker 2: and in this very strange, very exciting, and frankly very
Speaker 2: scary future of human augmentation.
Speaker 3: And here's the thought that I think I want to
Speaker 3: leave everyone with. For the entire history of life on
Speaker 3: this planet, billions of years, there was only one way
Speaker 3: for an organism to affect the world. Howso, you had
Speaker 3: to move a muscle. That's it.
Speaker 2: If you couldn't move, you couldn't act, You couldn't change
Speaker 2: your environment.
Speaker 3: Exactly every thought, every intention, every plan had to pass
Speaker 3: through the physical bottleneck of the body. We are now,
Speaker 3: for the very first time in history, breaking that fundamental rule.
Speaker 3: We are creating a direct, unmediated link between the mind
Speaker 3: and the physical world.
Speaker 2: The evolution did not plan for this. This is not
Speaker 2: in the manual.
Speaker 3: No, this is a new chapter. And so the final provocation,
Speaker 3: the final question for you to think about is this.
Speaker 3: As we merge more and more seamlessly with these machines,
Speaker 3: as the latency drops to zero and the feedback loops
Speaker 3: get richer and close, where does the user end and
Speaker 3: the tool begin.
Speaker 2: If the machine acts on my thought before I'm even
Speaker 2: fully conscious of the decision.
Speaker 3: Are you controlling it? Or has it just become an
Speaker 3: extension of you? Is that third robotic arm you in
Speaker 3: the same way your biological arm is you? What does
Speaker 3: it mean to be human when your mind is no
Speaker 3: longer confined to your body?
Speaker 2: That is going to keep me up tonight?
Speaker 3: It should? It's the question of our time.
Speaker 2: Thanks for listening to this exploration into the world of
Speaker 2: brain computer interfaces. We'll catch you on the next one.