# Ep68 "What if our brains worked a trillion times faster?"

An episode of [Inner Cosmos with David Eagleman](https://invisiblenetwork.ai/podcast/1677842672), 22 Jul 2024, 43 min.

Audio: https://podtrac.com/pts/redirect.mp3/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/534dc725-2971-42a1-b1bf-afaa0107416e/44337071-6000-4251-b405-b1b5002e4e2a/audio.mp3?utm_source=Podcast&in_playlist=bee00526-7528-4581-b6b6-afaa01083a3d

Why are the majority of stock trades decided by algorithms at timescales we can scarcely conceive of? What is it like to have the speed and power of a computer, and to be dealing with slow humans? Why are movies compelling, given that they are just a series of photographs flashed rapidly? And what happens if we someday discover planets with creatures who operate on totally different time scales? Join Eagleman this week for a deep dive into speed: the speed at which we operate, the speed at which our machines operate, and what this all means for the future as the divergence grows larger.

## Transcript

[0:05] Speaker 1: Why do we find movies so compelling given that they're
[0:08] Speaker 1: just a series of photographs flash rapidly. What does this
[0:13] Speaker 1: tell us about the slow speed of human brains? What
[0:18] Speaker 1: percentage of stock market trades are taken care of by
[0:22] Speaker 1: algorithms nowadays at timescales.
[0:25] Speaker 2: That humans could not even conceive of.
[0:28] Speaker 1: And what is it like to have the speed and
[0:31] Speaker 1: power of a computer and be dealing with slow humans.
[0:39] Speaker 1: Welcome to Inner Cosmos with me, David Eagleman. I'm a
[0:43] Speaker 1: neuroscientist and in these episodes we sail deeply into our
[0:47] Speaker 1: three pound universe to understand some of the most surprising
[0:52] Speaker 1: aspects of our lives. Today's episode is about speed, the
[1:06] Speaker 1: speed at which we humans operate and the speed at
[1:10] Speaker 1: which our machines operate, and the future of this as
[1:14] Speaker 1: the divergence grows even larger.
[1:17] Speaker 2: So let's start with this.
[1:19] Speaker 1: Imagine an extraterrestrial planet where they have an active society.
[1:24] Speaker 1: These aliens have politics and division of labor, and philosophers
[1:28] Speaker 1: and actors and athletes and artists, everything that makes up
[1:31] Speaker 1: an active society. Okay, So, now imagine a second planet,
[1:36] Speaker 1: Planet B. We land on this second planet in the
[1:40] Speaker 1: near future, and we discover nothing like a society there. Instead,
[1:44] Speaker 1: there are just things like trees, like these very old
[1:47] Speaker 1: growths that don't really do much of anything. And we
[1:50] Speaker 1: the astronauts, we stay there for a month, we take
[1:52] Speaker 1: a lot of measurements, and finally we blast off when
[1:55] Speaker 1: we go home. So two planets, one with an active
[1:58] Speaker 1: society and the other with nothing but tree like things.
[2:03] Speaker 1: Now imagine that these two descriptions are of the same planet.
[2:08] Speaker 1: What we have stumbled on is a society of creatures
[2:12] Speaker 1: that operate at a very different time scale from us.
[2:17] Speaker 1: They move so slowly that we just can't see it.
[2:21] Speaker 1: They have an active society, it just operates a trillion
[2:25] Speaker 1: times slower than ours. And the question is, would we
[2:29] Speaker 1: ever even notice that these tree people have a society
[2:34] Speaker 1: happening in what we would think of as super slow motion.
[2:38] Speaker 1: A conversation over a cup of coffee for them takes
[2:42] Speaker 1: eons for us. So would we ever decode their language?
[2:47] Speaker 1: Would we ever even have an opportunity to discover that
[2:50] Speaker 1: there's a whole world going on there, but at a
[2:53] Speaker 1: different temporal scale. So that's what today's episode is about.
[2:58] Speaker 1: Hidden landscapes, parts of the time domain that are totally
[3:03] Speaker 1: invisible to us. Now you've heard me talk in other
[3:06] Speaker 1: episodes about all the information in the world that we
[3:10] Speaker 1: just don't see. So is one example. We only see
[3:14] Speaker 1: a small fraction of the light that is out there,
[3:18] Speaker 1: and that little slice we call visible light. That's what
[3:22] Speaker 1: we think of as red, orange, yellow, green, blue, indigo, violet.
[3:26] Speaker 1: And it seems like that's all the light that's out there.
[3:28] Speaker 1: You open your eyes, you see reds and greens and
[3:31] Speaker 1: blues and golds, and you think, okay, that's everything. And
[3:34] Speaker 1: so it came as a surprise when scientists eventually realized
[3:39] Speaker 1: that all these other signals that had been detected, things
[3:43] Speaker 1: that are totally invisible to us, like infrared light or
[3:46] Speaker 1: radio waves or X rays or ultraviolet or microwaves, all
[3:50] Speaker 1: of these are light. They're exactly the same thing, is
[3:54] Speaker 1: what we call visible light. It's just that we don't
[3:56] Speaker 1: have the receptors in our eyes to pick up on them,
[4:00] Speaker 1: and so they're totally invisible to us. And it turns
[4:03] Speaker 1: out that what we call visible light is a very
[4:07] Speaker 1: tiny part of the light spectrum. It's less than a
[4:11] Speaker 1: ten trillionth of it. And once we understood this, we
[4:14] Speaker 1: started to build technologies to burrow into those other parts
[4:19] Speaker 1: of the spectrum. For example, we make little machines that
[4:24] Speaker 1: sit in the dashboards of our cars, and these see
[4:28] Speaker 1: in exactly the frequency that we call radio waves, and
[4:31] Speaker 1: so we can transmit information in that frequency. Or we
[4:35] Speaker 1: build night vision security cameras which see in the infrared range,
[4:40] Speaker 1: so that we can detect in that frequency outside of
[4:43] Speaker 1: what we can naturally see. And we build X ray
[4:46] Speaker 1: machines and telescope dishes, and these all pick up on
[4:50] Speaker 1: other frequencies that are invisible to us. We build all
[4:54] Speaker 1: kinds of devices to see what we can't naturally see,
[4:58] Speaker 1: and this has been the gen of scientific development over
[5:01] Speaker 1: the last few hundred years. It's the understanding that the
[5:05] Speaker 1: reality we inhabit is actually just a small fraction of
[5:10] Speaker 1: what is out there, and there are many other things
[5:14] Speaker 1: that can be taken advantage of. So we're very used
[5:18] Speaker 1: to communicating in landscapes that are outside our biology. Our
[5:23] Speaker 1: biology allows us to detect something like visible light, and
[5:27] Speaker 1: then we figure out from there that there are other
[5:30] Speaker 1: flavors of light that are beyond our biology, and we.
[5:33] Speaker 2: Take advantage of those.
[5:35] Speaker 1: So I want to use this as an analogy and
[5:39] Speaker 1: an inroad for us thinking about somewhere else that we
[5:42] Speaker 1: do this, and that is in the domain of time.
[5:47] Speaker 1: The question is can we use different ranges of time
[5:50] Speaker 1: to build technologies, perhaps a range of time that is
[5:55] Speaker 1: essentially invisible in our normal perception. When I was a tea,
[6:00] Speaker 1: I visited MIT and I met with Harold Edgerton who
[6:04] Speaker 1: was known as Doc Edgerton. And he was a pioneer
[6:07] Speaker 1: in high speed photography. And what he would do is
[6:11] Speaker 1: capture moments that were otherwise totally invisible to us. So
[6:16] Speaker 1: he froze ultrafast events using a quick strobe light and
[6:21] Speaker 1: ultra high speed photography. One of his most famous photographs
[6:25] Speaker 1: was taken in the nineteen sixties and you may have
[6:27] Speaker 1: seen this. It shows a bullet piercing an apple. The
[6:32] Speaker 1: image captures the exact moment of impact with the apple
[6:36] Speaker 1: exploding into fragments, and the exposure time for images like
[6:40] Speaker 1: this it was less than a microsecond. Another one of
[6:43] Speaker 1: his iconic images is the milk drop coronet that shows
[6:48] Speaker 1: a drop of milk hitting a surface and forming this
[6:52] Speaker 1: crown like splash, and the detail in this splash, frozen
[6:57] Speaker 1: in time, gave all kinds of insights into flu with
[7:00] Speaker 1: dynamics and surface tension. So the temporal resolution of these
[7:04] Speaker 1: photos allowed everyone to study and to appreciate the beauty
[7:09] Speaker 1: and the complexity of fast phenomenon. So he could freeze
[7:15] Speaker 1: the motion of bullets in flight or splashing milk drops,
[7:18] Speaker 1: and these images revealed details that were previously hidden to
[7:23] Speaker 1: us due to the limitations of our very slow perception.
[7:27] Speaker 1: And he also did this not just with still shots,
[7:29] Speaker 1: but a series of still shots, so you get a
[7:31] Speaker 1: little time lapse film. And in this way he could
[7:35] Speaker 1: expand super brief events into viewable sequences. This was great
[7:40] Speaker 1: for science and for art, and also for military applications
[7:44] Speaker 1: like studying the behavior of bombs. So with these techniques
[7:48] Speaker 1: he led us into new time domains which we suspected
[7:52] Speaker 1: might exist, but which no human had ever actually seen.
[7:57] Speaker 1: So he opened a window into a world of fleeting moments,
[8:01] Speaker 1: capturing events that occur too quickly for our eyes to see,
[8:05] Speaker 1: and reveling in a hidden beauty that we didn't even
[8:08] Speaker 1: have the mental tools to imagine correctly. So the point
[8:13] Speaker 1: is we can take advantage of these other timescales that
[8:17] Speaker 1: we don't know much about, and we can do this
[8:19] Speaker 1: by building the right kind of machinery.
[8:22] Speaker 2: Now, this all works.
[8:23] Speaker 1: Because although we humans are slow and pitifully limited in
[8:29] Speaker 1: vision and in time, we have large prefrontal cortices. This
[8:33] Speaker 1: is the part of the brain that allows us to extrapolate,
[8:36] Speaker 1: to think about what's in the next room even though
[8:39] Speaker 1: we're not there, to think about what we might do tomorrow,
[8:44] Speaker 1: to think about possibilities that we haven't yet seen. And this,
[8:48] Speaker 1: I suggest, is what has driven the development of new technologies,
[8:53] Speaker 1: because we are constantly imagining things that are bigger or
[8:57] Speaker 1: smaller than we can see. So we invent tell scopes
[9:00] Speaker 1: and microscopes. We invent ways of seeing into other parts
[9:05] Speaker 1: of the visible spectrum, like radios or X ray machines,
[9:09] Speaker 1: or devices in the infrared or ultraviolet, and all over
[9:12] Speaker 1: the place. We burrow into spaces that we can't see
[9:16] Speaker 1: with our eyes, but we extrapolate that they must exist.
[9:20] Speaker 1: And I suggest the prefrontal cortex is what allows us
[9:24] Speaker 1: to extrapolate in the time domain. So here's an example.
[9:29] Speaker 1: I was in Germany a little while ago, and I
[9:31] Speaker 1: was transfixed watching a river flowing through a very deep valley,
[9:36] Speaker 1: and it was pretty effortless to look at the valley
[9:39] Speaker 1: walls and think about this at the scale at which
[9:42] Speaker 1: geologists look at a landscape. This exact moment of the
[9:47] Speaker 1: river flowing had been happening for millions of years, and
[9:51] Speaker 1: if I sped up the film in my head, that's
[9:55] Speaker 1: how you get a valley of that depth. Now, to
[9:59] Speaker 1: be clear, I wasn't imagining millions of years. I was
[10:02] Speaker 1: imagining a film at my human speed. I just took
[10:06] Speaker 1: the concept of huge time scales and squished them down
[10:10] Speaker 1: to something that I could understand. This is the same
[10:12] Speaker 1: way that someone using a microscope doesn't stare at the
[10:17] Speaker 1: bacterium at the scale of a micron. Instead, she uses
[10:21] Speaker 1: the machine, the microscope, to push the image up to
[10:25] Speaker 1: a large size that her brain can handle. And this
[10:29] Speaker 1: is all we ever do. We take things from different
[10:32] Speaker 1: domains and translate them into something that we can understand.
[10:37] Speaker 1: Now we're very good at that. Here's an example on
[10:40] Speaker 1: the flip side of the river carving the valley. Open
[10:43] Speaker 1: any book on let's say, molecular biology, and you'll see
[10:47] Speaker 1: two strands of DNA unzipping and proteins buying to those
[10:51] Speaker 1: strands and walk along it and this gets transcribed to RNA,
[10:56] Speaker 1: and then that gets translated into little amino acids, and
[11:00] Speaker 1: you can walk yourself through all the steps of transcription
[11:03] Speaker 1: and translation. But all this takes place at a timescale
[11:08] Speaker 1: that's faster than you can possibly conceive. It's about twenty
[11:12] Speaker 1: amino acids get translated every second, and it's hard to
[11:16] Speaker 1: picture that in real time, But no problem.
[11:19] Speaker 2: We can apply the tools of our.
[11:22] Speaker 1: Thinking to imagine it as though it were happening at
[11:25] Speaker 1: our timescale, and of course we do this on crazier
[11:29] Speaker 1: and crazier timescales. Let's say you're a physicist building a
[11:34] Speaker 1: nuclear reactor and you're thinking about two nucleis smashing into
[11:39] Speaker 1: one another, and as it busts up, the parts of
[11:42] Speaker 1: it smash into other nuclei and bash those up, and.
[11:46] Speaker 2: You get a chain reaction.
[11:48] Speaker 1: And once you understand the simple model, you can picture
[11:50] Speaker 1: each step of the chain reaction, even though what is
[11:54] Speaker 1: happening takes place in one millionth of a second, a microsecond.
[12:00] Speaker 1: If you made these calculations every day of your life
[12:03] Speaker 1: on the whiteboard or on the computer, it would be
[12:05] Speaker 1: absolutely impossible for you to ever perceive the event anywhere.
[12:11] Speaker 1: But in your imagination, you couldn't perceive it directly. So
[12:16] Speaker 1: in this way, You are like the biblical story of Moses,
[12:21] Speaker 1: who leads his people through the desert but never gets
[12:24] Speaker 1: to enter the Holy Land himself. You are the temporal Moses,
[12:30] Speaker 1: knowing about these timescales, knowing about these other temporal worlds,
[12:33] Speaker 1: but you are never able to enter the territory directly.
[12:39] Speaker 1: Just think about the best theories of the beginning of
[12:42] Speaker 1: the universe. The best models for this suggest that there
[12:45] Speaker 1: was cosmic inflation where everything got bigger. There was this
[12:49] Speaker 1: exponential expansion of space, and we say this all happened
[12:52] Speaker 1: in the early universe. And when we look at the models,
[12:56] Speaker 1: there was the Plank Epic, where physics were dominated by
[13:01] Speaker 1: the quantum effects of gravity, and then that got followed
[13:04] Speaker 1: by the Grand Unification Epic, and that was followed by
[13:08] Speaker 1: the inflationary epic, in which the universe expanded by trillions
[13:12] Speaker 1: and trillions of times, and then the electroweek Epic, and
[13:16] Speaker 1: the quark Epic and the hadron epic and so on.
[13:18] Speaker 2: Now here's the key.
[13:20] Speaker 1: The Plank Epic lasted less then ten to the negative
[13:25] Speaker 1: forty three seconds. That's less than a trillionth of a
[13:29] Speaker 1: trillionth of a trillion of a trillionth of a second,
[13:32] Speaker 1: and the grand unification epic was over by ten to
[13:35] Speaker 1: the negative thirty six seconds, and the inflationary epic was
[13:40] Speaker 1: over in the first peko second of cosmic time, that's
[13:43] Speaker 1: ten to the negative twelve seconds. And eventually protons formed.
[13:47] Speaker 1: And oh, by the way, that happened by the first
[13:50] Speaker 1: microsecond one millionth of a second after the Big Bang,
[13:53] Speaker 1: and then nuclear fusion began, and that was a ten
[13:56] Speaker 1: milliseconds after the start of the universe, or one one
[13:59] Speaker 1: hundredth of a second. No, how do people even put
[14:01] Speaker 1: together theories like this, Well, you can't even run experiments
[14:04] Speaker 1: at those temperatures or densities. But you can do is
[14:07] Speaker 1: extrapolate the known physical laws to extremely high temperatures, and
[14:12] Speaker 1: you get a picture of what was likely to have
[14:14] Speaker 1: happened there. And again, this is something that we can
[14:18] Speaker 1: never experience directly, but we can extrapolate our local understanding
[14:24] Speaker 1: of time to imagine it. Presumably, the squirrels running around
[14:28] Speaker 1: in your backyard simply don't have the neural capacity to
[14:33] Speaker 1: imagine time at any scale other than their own. So
[14:37] Speaker 1: a squirrel is never going to come up with the
[14:39] Speaker 1: Plank epic. Now, what I want to talk about is
[14:58] Speaker 1: the way that we do these extra appellations, and then
[15:01] Speaker 1: we build technologies to get us there, like Doc Edgerton
[15:05] Speaker 1: at MIT did and translating things into something useful for
[15:10] Speaker 1: our pitifully slow time scales. So I'll give you an
[15:14] Speaker 1: example of this. I was in Shanghai a few years ago,
[15:18] Speaker 1: and on the edge of the river where a lot
[15:20] Speaker 1: of tourists walk around, they had this terrific display where
[15:23] Speaker 1: it was like a big plant wall, and from a
[15:26] Speaker 1: distance you could see these beautiful, slowly flapping butterflies on
[15:31] Speaker 1: the wall. But when you go up close and really
[15:34] Speaker 1: examine this, you see that each butterfly is actually an
[15:37] Speaker 1: illusion produced from a rapidly spinning bar of led lights.
[15:43] Speaker 1: So the bar is attached at one end to a
[15:45] Speaker 1: motor and it spins around and around in a circle
[15:48] Speaker 1: very quickly.
[15:49] Speaker 2: You may have seen these before. You can display.
[15:51] Speaker 1: Cartoon characters or words or animals. The rod is a
[15:56] Speaker 1: row of led lights, and these are all turning on
[15:59] Speaker 1: and off with different colors of different intensities at exactly
[16:03] Speaker 1: the right time in their rotations, so that they draw
[16:06] Speaker 1: out what appears to be a picture of an object.
[16:09] Speaker 1: Now why does this work, Well, it's because of something
[16:13] Speaker 1: called persistence of vision. And this is what happens when
[16:17] Speaker 1: you flash a bunch of images really rapidly, one after
[16:20] Speaker 1: the other. The bar is in this position, and then
[16:23] Speaker 1: the next moment it's displaying it slightly differently one degree away,
[16:26] Speaker 1: and then differently again when it ticks.
[16:28] Speaker 2: Forward one more degree, and so on.
[16:30] Speaker 1: But because each of these little flashes of light are
[16:32] Speaker 1: so fast, they last in your vision. So the light
[16:37] Speaker 1: pops on and off in a fraction of a millisecond,
[16:39] Speaker 1: but your brain just can't see something that brief, So
[16:42] Speaker 1: as far as you're concerned, it comes on and it
[16:44] Speaker 1: stays on for a little while. So when the bar
[16:47] Speaker 1: spins around and around, it essentially paints in the air.
[16:52] Speaker 1: Now here's the key. If you could see faster, if
[16:54] Speaker 1: you could see what is actually happening out there, you'd
[16:57] Speaker 1: see a little bar spinning in a circle and changing
[16:59] Speaker 1: color as it does so. But that's not what you see.
[17:03] Speaker 1: You see a butterfly in the air, you see a
[17:06] Speaker 1: cartoon character, you see a flag waving. But here's the
[17:10] Speaker 1: thing to appreciate. The speed at which the rod needs
[17:13] Speaker 1: to spin is very fast, and therefore the speed at
[17:17] Speaker 1: which the lights need to know precisely when to turn
[17:21] Speaker 1: on and off is even faster, and all of this
[17:24] Speaker 1: is computed at rocket ship speed, and you just look
[17:27] Speaker 1: at it and appreciate that there's a nice butterfly out there.
[17:32] Speaker 1: So on the one hand, you have lightning fast calculations
[17:34] Speaker 1: on the level of sub millisecond, and on the other
[17:37] Speaker 1: level we get a nice picture.
[17:40] Speaker 2: And persistence of.
[17:41] Speaker 1: Vision is the reason why movies appear to be continuous motion,
[17:45] Speaker 1: even though they are actually just a series of still images.
[17:49] Speaker 1: In other words, after photography was invented, people realize that
[17:54] Speaker 1: if you flash one photograph and then there's a blank,
[17:57] Speaker 1: and then you flash another photo right after, like twenty
[18:00] Speaker 1: milliseconds after, your brain will interpret that as continuous motion.
[18:06] Speaker 1: And this was the birth of moving photography or movies. Now,
[18:11] Speaker 1: the challenge was tougher with televisions.
[18:14] Speaker 2: Entrepreneurs wanted to get movies into the homes of.
[18:17] Speaker 1: People, but you couldn't flicker photographs forty eight times per
[18:21] Speaker 1: second because you didn't have a film strip that you
[18:23] Speaker 1: were just shining light through.
[18:24] Speaker 2: So how do you do it? Well?
[18:26] Speaker 1: The genius there was to use a cathode ray tube,
[18:29] Speaker 1: which points a beam of light at a phosphorescent screen,
[18:34] Speaker 1: and that causes a little spot to glow from dark
[18:38] Speaker 1: to bright and then you move that little light gun
[18:40] Speaker 1: over to the next spot over and set the next brightness,
[18:44] Speaker 1: and then you go to the next spot in the
[18:46] Speaker 1: next spot.
[18:46] Speaker 2: And so on.
[18:47] Speaker 1: And you had to cover the entire screen one dot
[18:50] Speaker 1: at a time, and the whole trick was to do
[18:52] Speaker 1: it really fast, so that you paint the screen in
[18:56] Speaker 1: one sixtieth of a second, and then you paint the
[18:59] Speaker 1: next screen again one dot at a time, and it
[19:02] Speaker 1: has to get done in the next sixtieth of a second. Now,
[19:06] Speaker 1: the genius of coming up with the technology like that
[19:09] Speaker 1: is realizing that you can do something very very fast
[19:12] Speaker 1: with a machine, and that the person at the receiving
[19:16] Speaker 1: end is incredibly slow and has a sluggish brain and
[19:20] Speaker 1: can't understand anything at these millisecond time scales and instead
[19:24] Speaker 1: just blends everything together. And that's how you tell stories
[19:28] Speaker 1: about James Cagney or Fat Albert or the family in Bonanza,
[19:33] Speaker 1: and the sluggish humans on the receiving end are simply
[19:36] Speaker 1: registering the emotion of the story instead of seeing the
[19:41] Speaker 1: invisibly fast mechanics that are zipping along in their own
[19:46] Speaker 1: timescale range, painting the screen over and over sixty times
[19:50] Speaker 1: every second in a way that is completely invisible to us.
[19:55] Speaker 1: So we build machines to leverage the fact that the
[19:59] Speaker 1: visual brain integrates what it sees over a little window
[20:03] Speaker 1: of time. So if two or more images arrive within
[20:06] Speaker 1: a window of about a tenth of a second, they're
[20:09] Speaker 1: smeared together. And by the way, building devices to take
[20:13] Speaker 1: advantage of that isn't even so new. We've been building
[20:17] Speaker 1: primitive things for centuries. For example, think of that little
[20:21] Speaker 1: toy known as a thaumatrope. It's a flat disc with
[20:26] Speaker 1: a picture on each side. So you might have a
[20:28] Speaker 1: picture of a bird on one side of the disk
[20:30] Speaker 1: and a picture of a tree branch on the other.
[20:33] Speaker 1: And what you do is you wind up the disk
[20:36] Speaker 1: with a piece of string, and when you pull the
[20:38] Speaker 1: string tight, the disc spins rapidly so that both sides
[20:42] Speaker 1: are seen in rapid alternation. You see the bird, you
[20:45] Speaker 1: see the branch. You see the bird, you see the branch,
[20:47] Speaker 1: and the bird appears to be sitting on the branch.
[20:50] Speaker 1: The images fuse. This is an example of an old
[20:54] Speaker 1: technology taking advantage of the slowness of our brains. Now
[20:59] Speaker 1: I find all this credible, from the butterflies to the
[21:02] Speaker 1: fact that movies move, to the fact that television works.
[21:06] Speaker 1: But with modern electronics, combined with the slowness of our
[21:10] Speaker 1: visual systems, we can build these kinds of devices to
[21:13] Speaker 1: paint pictures for us. Our machines can be fast, a
[21:17] Speaker 1: lot faster than our poor little brains. Now, if you
[21:21] Speaker 1: were the machine, you would think, what am I doing?
[21:24] Speaker 1: I'm just changing the colors of the light as I spin.
[21:28] Speaker 1: Surely no one out there is falling for this. And
[21:33] Speaker 1: this is the thing I really want to dig into today,
[21:35] Speaker 1: the difference in the time worlds between us and our machines.
[21:40] Speaker 1: So the best place to start is with our computers,
[21:44] Speaker 1: which perform a certain number of floating point operations per second.
[21:49] Speaker 1: This measure is called flops floating point operations per second. Now,
[21:54] Speaker 1: don't worry about the details, except to say a floating
[21:56] Speaker 1: point operation is a calculation like adding two.
[22:00] Speaker 2: Very big numbers.
[22:02] Speaker 1: So this is a standard way to measure how fast
[22:04] Speaker 1: the computer can chug along. Now, think about how long
[22:08] Speaker 1: it would take you to add two very large numbers.
[22:12] Speaker 1: It's unlikely that you could do even one floating point
[22:16] Speaker 1: operation in a second. But a computer from the last
[22:20] Speaker 1: century could do a gigaflop that's one billion floating point
[22:27] Speaker 1: operations in a second. Just think about sitting there with
[22:32] Speaker 1: a paper and pencil and adding two big numbers and
[22:35] Speaker 1: doing that a billion times every second. Then in this century,
[22:40] Speaker 1: computers hit terraflops that's one trillion floating point operations in
[22:47] Speaker 1: a second. And then it wasn't long before computers hit petaflops,
[22:51] Speaker 1: which is a thousand terraflops. In other words, a thousand
[22:56] Speaker 1: trillion or a quadrillion operations person second. Nvidia just released
[23:02] Speaker 1: a new chip that clocks in at twenty petaflops, So
[23:06] Speaker 1: just imagine how long it would take you to do
[23:10] Speaker 1: twenty thousand trillion calculations. It does that a second. The
[23:16] Speaker 1: current record is the US supercomputer called Frontier, which does
[23:21] Speaker 1: eleven hundred petaflops or one point one exaFLOPS. These numbers
[23:26] Speaker 1: are so mind boggling to think about. Just imagine a
[23:29] Speaker 1: machine performing one quintillion floating point operations per second. That's
[23:35] Speaker 1: a one followed by eighteen zeros. That's a lot faster
[23:39] Speaker 1: than all the humans in the world could do if
[23:42] Speaker 1: they all sat with paper and pencil and worked on
[23:44] Speaker 1: calculations for a million years. That's what gets accomplished in
[23:49] Speaker 1: one second by a computer like that. We are living
[23:53] Speaker 1: on completely different time domains, and this is what allows
[23:58] Speaker 1: you to enjoy a song or YouTube video or an
[24:02] Speaker 1: Inner Cosmos podcast. Now, something I think is quite stunning
[24:06] Speaker 1: is to consider what it is like.
[24:08] Speaker 2: To be your computer keyboard.
[24:11] Speaker 1: Even if you think you are a fast typist, your
[24:14] Speaker 1: keyboard actually goes to sleep in between your slow keystrokes.
[24:20] Speaker 1: So I'm gonna do something here. Let's imagine that you
[24:24] Speaker 1: and I are down inside your computer, living at the
[24:27] Speaker 1: speed of your computer chip. Now, the poor slow human
[24:32] Speaker 1: on the outside is typing, and let's say they're typing
[24:35] Speaker 1: relatively fast, so when they strike a key on the keyboard,
[24:41] Speaker 1: it's going to sound like this to us inside the computer. Now,
[24:47] Speaker 1: let's keep talking and we'll see when the next keystroke arrives.
[24:52] Speaker 1: Imagine that this human is typing relatively fast, like sixty
[24:55] Speaker 1: words per minute, which is about three hundred keystrokes per minute,
[24:59] Speaker 1: or a keystroke every two hundred milliseconds from our point
[25:03] Speaker 1: of view from inside the computer. The next key will
[25:07] Speaker 1: not even get hit until about the end of this podcast,
[25:11] Speaker 1: And that's why the software routine that monitors the keyboard
[25:16] Speaker 1: goes to sleep while it waits for the next key
[25:21] Speaker 1: to be struck. And I'll tell you something else fascinating
[25:24] Speaker 1: about our computers and our interaction with them. You remember capture,
[25:29] Speaker 1: which is how at the beginning of the Internet you
[25:31] Speaker 1: could try to tell if it was a real human
[25:33] Speaker 1: doing something versus a computer, because you could say, hey,
[25:36] Speaker 1: what are these funny shaped letters spelling out? And a
[25:40] Speaker 1: computer just couldn't figure that out, but a human could.
[25:43] Speaker 1: Now eventually this evolved to what are now known as
[25:45] Speaker 1: recapture boxes. So you know these little tests when you're
[25:49] Speaker 1: let's say, making a purchase on a website and you
[25:52] Speaker 1: have to click a little box that says I am
[25:55] Speaker 1: not a robot, and you check that box because you
[25:59] Speaker 1: are in fact a human. Now, if you thought about this,
[26:01] Speaker 1: you know that this is because AI can't figure this
[26:04] Speaker 1: out and click the box. Wait, what AI has read
[26:08] Speaker 1: everything written on the planet. It can do extraordinary things,
[26:11] Speaker 1: so why can't it click the box. Well, of course
[26:14] Speaker 1: it can click the box. It's absolutely trivial for any
[26:18] Speaker 1: large language model to read that text and check the box.
[26:21] Speaker 1: There's no problem at all, So, why are companies still
[26:25] Speaker 1: using this as a way to distinguish humans from computers.
[26:29] Speaker 1: There's a remarkable story here. It turns out the reason
[26:33] Speaker 1: you can still use this to make the distinction is
[26:36] Speaker 1: because of the time domain. Humans check the box at
[26:40] Speaker 1: their slow human speed.
[26:43] Speaker 2: While AI does it instantly.
[26:45] Speaker 1: And by the way, sometimes you click the box, it
[26:48] Speaker 1: might take you to a little puzzle where you see
[26:51] Speaker 1: nine photographs and it says, click all the photos that
[26:54] Speaker 1: contain a motorcycle. Now, if chat GPT or Claude or
[26:59] Speaker 1: GEMIN and I can crush the SATs and the MCATs
[27:03] Speaker 1: and the l sets, why can't it find a motorcycle? Well,
[27:06] Speaker 1: of course it can, But the website determines whether you
[27:09] Speaker 1: are a human or a bot by how slow you are.
[27:13] Speaker 1: Humans are unbelievably clunky at doing these things. Bots do
[27:17] Speaker 1: them instantaneously. Just as a side note, like all capture tests,
[27:22] Speaker 1: this will be short lived before it has to evolve again,
[27:25] Speaker 1: because computers will soon imitate not only the right answer,
[27:29] Speaker 1: but the right timing. Going to sleep for a long
[27:33] Speaker 1: nap to impersonate our pitifully slow timescales. And it's no
[27:39] Speaker 1: surprise that we're increasingly surrounded by machines that operate in
[27:44] Speaker 1: different time domains. A typical modern self driving car is
[27:49] Speaker 1: ringed with cameras and radar, and it generates something like
[27:53] Speaker 1: six gigabytes of data every thirty seconds. The key again
[27:57] Speaker 1: is that this is not at all what a human
[27:59] Speaker 1: can do. These cars, which are all around us on
[28:02] Speaker 1: the road now, they capture vision on multiple cameras that
[28:06] Speaker 1: ten to thirty frames per second, sometimes more. And by
[28:09] Speaker 1: the way, these cameras aren't just in the visible range,
[28:11] Speaker 1: but they can include infrared cameras, and all this data
[28:15] Speaker 1: is combined and digested rapidly into a picture of the
[28:19] Speaker 1: world out there. And these cameras never blink or lose
[28:23] Speaker 1: attention or get sleepy, and they will only continue to
[28:26] Speaker 1: get faster. Although they appear to be accomplishing the same
[28:30] Speaker 1: tasks that we are, they're doing it at a totally
[28:33] Speaker 1: different timescale. And by the way, in commercial airliners there
[28:37] Speaker 1: are computer chips which auto correct the flight of the
[28:40] Speaker 1: airplane one hundred thousand times a second, which keeps the
[28:44] Speaker 1: rides smooth and good and infinitely better than the best
[28:48] Speaker 1: human pilot could manage. And this is all just the beginning.
[28:52] Speaker 1: Take something that underlies our entire economy. I'm always interested
[28:57] Speaker 1: to discover that not everyone realizes what's going on with
[29:01] Speaker 1: high frequency stock market trading. This is where computers use
[29:07] Speaker 1: algorithms to trade instead of humans doing the trading, so
[29:11] Speaker 1: the computer can ingest an enormous amount of information and
[29:15] Speaker 1: make a decision about buying or selling stocks on microsecond
[29:19] Speaker 1: time scales. And the part that often comes as a
[29:22] Speaker 1: surprise is that sixty percent of all trades on the
[29:27] Speaker 1: US stock market are done by computer.
[29:29] Speaker 2: There's no human in the loop these high frequency computerized trades.
[29:33] Speaker 1: They'll often only make a few pennies on a trade,
[29:36] Speaker 1: but they're doing this thousands or millions of times a
[29:39] Speaker 1: day at an extraordinary timescale. That's simply not human. A
[29:45] Speaker 1: colleague of mind started one of the first high frequency
[29:47] Speaker 1: trading companies, and he was fascinated that the ticker tape
[29:51] Speaker 1: back in the day, the physical ticker tape which showed
[29:54] Speaker 1: the stock prices that was rolling off straight into the
[29:58] Speaker 1: trash can. And he was in and understanding whether he
[30:01] Speaker 1: could use that past data, which no one really cared
[30:04] Speaker 1: about anymore, to make really good algorithms to predict what
[30:08] Speaker 1: happens in the next second or eventually the next millisecond
[30:12] Speaker 1: or eventually the next microsecond. So he rented physical office
[30:17] Speaker 1: space just on the other side of the wall from
[30:20] Speaker 1: the New York Stock Exchange, because when you're talking about
[30:23] Speaker 1: signals at this speed, you have to worry about the
[30:26] Speaker 1: transmission time from your computer to the exchange floor. And
[30:30] Speaker 1: pretty soon everyone was getting real estate physically right next
[30:33] Speaker 1: to the stock floor, because microseconds really matter if your
[30:38] Speaker 1: computer is trying to beat the next guy's computer to
[30:40] Speaker 1: a trade. So the key is that all these computers
[30:44] Speaker 1: are competing against one another, but human traders aren't even
[30:48] Speaker 1: in that race. They are orders of magnitudes slower, literally
[30:52] Speaker 1: millions of times slower in making their decisions. So the
[30:57] Speaker 1: computers capitalize on short term price movement, some arbitrage opportunities
[31:01] Speaker 1: that are impossible for human traders to exploit because those
[31:06] Speaker 1: timescales are invisible to the Homo sapien. So when I
[31:27] Speaker 1: put all this together and think about where this goes,
[31:30] Speaker 1: I'm reminded of a dream I had some years ago.
[31:33] Speaker 1: In this dream, I was standing over a giant valley
[31:37] Speaker 1: below me, and I was standing on a column that
[31:40] Speaker 1: was coming straight up from the earth like a stalagmite,
[31:42] Speaker 1: and I stepped out into the void, and as soon
[31:45] Speaker 1: as I did, a new stalagmite shot up instantly from
[31:49] Speaker 1: the ground way below and provided a perfect landing spot
[31:53] Speaker 1: for my foot. Then I put my other foot out
[31:56] Speaker 1: into the void, and right when I was about to
[31:58] Speaker 1: put my weight down, the next lag might shot up
[32:02] Speaker 1: in milliseconds and net my foot right where a floor
[32:06] Speaker 1: would be. So no matter which way I stepped or
[32:09] Speaker 1: at what pace, a stepping surface would shoot up to
[32:13] Speaker 1: meet me. It was an incredible dream, and I woke
[32:16] Speaker 1: up flooded with emotion. It didn't feel like I just
[32:19] Speaker 1: experienced magic. It felt like I had just experienced AI technology,
[32:25] Speaker 1: but at a level we don't have now. There's no
[32:28] Speaker 1: reason we wouldn't have this kind of thing in the
[32:31] Speaker 1: next century. So I felt like I had just experienced
[32:34] Speaker 1: a bit of twenty second century technology that had dropped
[32:38] Speaker 1: into the dream of a twenty first century sleeper. So
[32:43] Speaker 1: when I woke up, I started thinking about this and
[32:45] Speaker 1: calling this physical AI. Just imagine a world where we
[32:49] Speaker 1: can build intelligent machines that physically move so much faster
[32:54] Speaker 1: than us on a totally different timescale that we just
[32:58] Speaker 1: get to enjoy moving physically however we want, like stepping
[33:02] Speaker 1: off a cliff and being certain that it will be
[33:06] Speaker 1: there essentially instantly as far as we're concerned to catch us.
[33:10] Speaker 1: Imagine being able to trip on something and fall without
[33:14] Speaker 1: fear because you know you will get cushioned before you
[33:17] Speaker 1: hit the ground. In a sense, this would be like
[33:19] Speaker 1: the airbag in a car, which deploys in twenty milliseconds,
[33:23] Speaker 1: faster than the blink of an eye, but you would
[33:25] Speaker 1: have this with you in all ways, in all situations.
[33:29] Speaker 1: It's very difficult for us, as twenty first century minds,
[33:33] Speaker 1: to imagine what this will be like or what it
[33:36] Speaker 1: will translate to socially. All we can be certain of
[33:39] Speaker 1: is that we don't have the capacity to make a
[33:42] Speaker 1: meaningful model of what life will be like in a
[33:45] Speaker 1: century from now. The idea of physical AI seems so
[33:50] Speaker 1: foreign to us, but just like the keyboards or the
[33:53] Speaker 1: high frequency trading, this is where we're heading with technology.
[33:58] Speaker 1: The technology becomes very useful to us because it lives
[34:02] Speaker 1: on a totally different timescale, and it also becomes more
[34:06] Speaker 1: and more foreign to us. By operating so inconceivably fast,
[34:12] Speaker 1: it comes to provide the firm ground that we step on,
[34:16] Speaker 1: but it also becomes something like magic that we no
[34:20] Speaker 1: longer understand, and this leads us into our final act
[34:24] Speaker 1: about our relationship with machines that operate on a different timescale.
[34:29] Speaker 1: Sometimes we need things to operate, or specifically to pretend
[34:33] Speaker 1: to operate at our own time scales. So, for example,
[34:37] Speaker 1: think about the movie Her. There's this guy who's played
[34:40] Speaker 1: by Joaquin Phoenix. His marriage ends, he's left heartbroken. He
[34:44] Speaker 1: becomes intrigued with a new operating system in which he
[34:48] Speaker 1: meets an AI named Samantha who's played by Scarlett Johansson,
[34:52] Speaker 1: and this AI is sensitive and playful, and their friendship
[34:56] Speaker 1: soon deepens into love and his relationationship with the AI
[35:01] Speaker 1: bought means everything to him. Now, this film has an
[35:04] Speaker 1: incredible ending because at the end he comes to understand
[35:08] Speaker 1: that she, the AI, is having this same kind of
[35:11] Speaker 1: relationship with hundreds of thousands of other men, all at
[35:15] Speaker 1: the same time, because she is computational and operates in
[35:20] Speaker 1: a different time domain and processes what appears to be
[35:24] Speaker 1: intimate conversation at a rate millions of times faster than
[35:28] Speaker 1: our poor brains, and so she is maintaining this intimacy
[35:32] Speaker 1: with all of these others. She's doing a million things
[35:36] Speaker 1: in between his pillow whispers, just like your keyboard goes
[35:41] Speaker 1: to sleep in between your keystrokes.
[35:43] Speaker 2: Now, the thing I want.
[35:44] Speaker 1: To surface here is that in order for this AI
[35:46] Speaker 1: to work, it has to slow way down for each
[35:50] Speaker 1: user so that they think they're talking to someone at
[35:53] Speaker 1: their timescale. Now, this question doesn't just exist in the
[35:57] Speaker 1: realm of movies or theory. Companies have to deal with
[35:59] Speaker 1: it all the time. For example, when user experienced designers
[36:04] Speaker 1: work to create good impressions on people, they learn that
[36:08] Speaker 1: good design is about shaving off confusing messages and getting
[36:12] Speaker 1: rid of bugs, and they're traditionally taught to remove any
[36:17] Speaker 1: unnecessary delays. But often a delay is exactly what's called for.
[36:23] Speaker 2: And this is.
[36:23] Speaker 1: Something that many user experienced designers have independently discovered, which
[36:28] Speaker 1: is that people don't always want the results as quickly
[36:31] Speaker 1: as you can generate them. So take this as an example.
[36:34] Speaker 1: The people who developed the coinstar machines discover this. These
[36:37] Speaker 1: are those machines where you dump in a bunch of
[36:40] Speaker 1: change and accounts the change for you and gives you
[36:42] Speaker 1: the result. Now, apparently these coinstar machines can do their
[36:46] Speaker 1: work very quickly, like in a second or two, but
[36:48] Speaker 1: that speed made customers feel nervous that maybe the counting
[36:52] Speaker 1: wasn't happening correctly. So Coinstar inserted pre recorded noise of
[36:58] Speaker 1: change moving through the machine clink clink, clink clink, And
[37:02] Speaker 1: even though the machine comes to its conclusions rapidly, like
[37:06] Speaker 1: you have seven dollars and thirty six cents, it purposefully
[37:10] Speaker 1: delays its output so that the counting seems slower. And
[37:14] Speaker 1: this is an interesting form of theater, one which our
[37:18] Speaker 1: machines perform in the time domain, and this is not uncommon.
[37:22] Speaker 1: Apparently ATM machines insert an artificial delay for a similar reason.
[37:28] Speaker 1: You stick in your card and you say you want
[37:29] Speaker 1: one hundred bucks, and the machine words and clicks and
[37:33] Speaker 1: turns for a while. There's no necessity, like a computational
[37:37] Speaker 1: or mechanical necessity, for ATM machines to be so slow,
[37:41] Speaker 1: but apparently customers are more satisfied when the money doesn't
[37:46] Speaker 1: come out instantly, and software designers increasingly have to deal
[37:51] Speaker 1: with inserting artificial delays because of the speed of computation.
[37:56] Speaker 1: So there was one company that builds software that creates
[38:00] Speaker 1: an online blog for you with the design and the
[38:02] Speaker 1: title page and templates for your blog posts. And what
[38:06] Speaker 1: they found was that when users got to the end
[38:09] Speaker 1: of their inputs and clicked to the button that said
[38:12] Speaker 1: create my blog, the blog appeared for the user instantly,
[38:17] Speaker 1: and users felt confused. They weren't sure if something was
[38:20] Speaker 1: wrong or if something had broken. So after some testing,
[38:24] Speaker 1: the software company added a page in the middle that
[38:27] Speaker 1: said creating your blog and had a spinning wheel, and
[38:32] Speaker 1: then after about seven seconds, it would send users to
[38:35] Speaker 1: their newly minted blog page, and users felt much more
[38:39] Speaker 1: satisfied just based on the delay, just based on things
[38:43] Speaker 1: happening at a timescale that they were comfortable with. Now,
[38:48] Speaker 1: why are users of coinstar and ATM machines and blog
[38:52] Speaker 1: software happier with a delay? Sometimes designers will point out
[38:57] Speaker 1: that a user might have anxiety about it service, like
[39:00] Speaker 1: is the coinstar counting all my coins correctly? Or is
[39:03] Speaker 1: the ATM giving me the right amount of money? Or
[39:07] Speaker 1: building a blog must be a really complicated thing, and
[39:10] Speaker 1: so the best strategy is to address this anxiety by
[39:14] Speaker 1: convincing them that everything is running at a careful pace.
[39:19] Speaker 1: I think that's a pretty good hypothesis, but I think
[39:21] Speaker 1: there might be something else going on here, and it's
[39:23] Speaker 1: not just about anxiety.
[39:25] Speaker 2: I think this might be an expression.
[39:26] Speaker 1: Of how we value things and how that pivots on
[39:30] Speaker 1: what I have called the effort phenomenon. I discussed this
[39:34] Speaker 1: way back in episode six.
[39:36] Speaker 2: About AI and why we value a.
[39:39] Speaker 1: Book written by a person much more than a book
[39:43] Speaker 1: written by AI, even if the exact same words.
[39:47] Speaker 2: It's not clear.
[39:48] Speaker 1: Why we would value one over the other. But the
[39:50] Speaker 1: suggestion I made is that we care very much about
[39:53] Speaker 1: the effort that goes into something. For example, take two
[39:57] Speaker 1: pieces of art. Let's say someone makes a replica of
[40:00] Speaker 1: a Michelangelo statue entirely by gluing quarters together in a
[40:05] Speaker 1: giant three dimensional structure, and someone else puts a single
[40:09] Speaker 1: red dot in the middle of a canvas. How much
[40:13] Speaker 1: would you pay for these two different pieces of art.
[40:16] Speaker 1: Presumably you would pay more for that which you believe
[40:19] Speaker 1: took more effort. And in the same way, the coinstar
[40:24] Speaker 1: or the ATM machine or the blog post can give
[40:28] Speaker 1: you the illusion that it has just put in a
[40:31] Speaker 1: lot of effort. It's just pulling a sleight of hand
[40:36] Speaker 1: in the time domain, but it's enough to align with
[40:39] Speaker 1: your human expectations of what hard work looks like. It
[40:44] Speaker 1: lines up better with your internal model of how hard
[40:48] Speaker 1: it would be for you to count a bunch of
[40:51] Speaker 1: coins or bills, or program a blog template from scratch.
[40:57] Speaker 1: And so it's fascinating that if the machine or the
[40:59] Speaker 1: software it doesn't give you this false delay, that actually
[41:03] Speaker 1: diminishes how much you think the service is worth. And
[41:07] Speaker 1: this becomes like a temporal touring test. You remember, the
[41:11] Speaker 1: touring test is whether you can tell if a conversation
[41:14] Speaker 1: partner is a human or a computer. And I think
[41:17] Speaker 1: increasingly our computers and our devices will have to be
[41:21] Speaker 1: sensitive to the temporal aspect and slow way down if
[41:27] Speaker 1: they hope to fool us. Okay, so let's wrap up. Ah,
[41:34] Speaker 1: there's the next keystroke by the human who was typing.
[41:38] Speaker 2: That took a very long time.
[41:41] Speaker 1: So do you remember this thing I said at the
[41:43] Speaker 1: very beginning, I was imagining that we land on a
[41:46] Speaker 1: planet for a month and we don't see anything move,
[41:48] Speaker 1: and we conclude that there are only these tree like
[41:51] Speaker 1: things on the planet, and we blast off because it's
[41:53] Speaker 1: not so interesting. But it turns out that they live
[41:56] Speaker 1: on a much slower timescale, millionions of times slower, but
[42:01] Speaker 1: they have societies and loves and wars and clubs, and
[42:06] Speaker 1: it's just that to us, we wouldn't even recognize something
[42:10] Speaker 1: moving at that slow pace. So here we are in
[42:13] Speaker 1: the twenty first century, giving birth to a new species.
[42:17] Speaker 1: We're sparking new machines into existence and to our electronic progeny.
[42:25] Speaker 2: We are the tree People.
[42:32] Speaker 1: Go to Eagleman dot com slash podcast for more information
[42:35] Speaker 1: and to find further reading. Send me an email at
[42:38] Speaker 1: podcasts at eagleman dot com with questions or discussion, and
[42:42] Speaker 1: check out and subscribe to Inner Cosmos on YouTube for
[42:45] Speaker 1: videos of each episode and to leave comments until next time.
[42:49] Speaker 1: I'm David Eagleman, and we have been traveling together in
[42:53] Speaker 1: the Inner Cosmos in th

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