Latest / From First Principles / Dream Engineering, the Proton Radius Puzzle, and an ALS Breakthrough (EP. 27)

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Dream Engineering, the Proton Radius Puzzle, and an ALS Breakthrough (EP. 27) · From First Principles

• 0:00
  Hello, Internet.
• 0:00
  This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
• 0:08
  We have an action-packed full episode this week with three main stories.
• 0:13
  We're going to start off with the neuroscience of dreams.
• 0:16
  We're going to follow that up with the physics of protons and
• 0:19
  And then we're going to wrap up with some of the latest advancements in the fight against ALS.
• 0:24
  We are going to learn about science from the ground up today because, as you know, this is From First Principles.
• 0:46
  For our first story, we are going to get a little bit into what feels like science fiction here with interactive dream engineering as the subject.
• 0:57
  Apparently, we now have the ability to hack into people's dreams, influence and interact with them and help them solve problems later.
• 1:06
  This is from Northwestern University in the neuroscience of consciousness.
• 1:12
  And this story is fascinating to me.
• 1:15
  Yeah, this one's like pretty, pretty crazy, okay?
• 1:18
  I mean, we've all heard of the idea that, you know, if you're trying to solve a problem and you get stuck, sleep on it, right?
• 1:25
  That's the advice that we get a lot of times.
• 1:27
  There's been a lot of problem solving that's happened during sleep.
• 1:32
  Inception is in the public zeitgeist about like, you know, influencing dreams.
• 1:39
  And it turns out there's more truth to that
• 1:43
  than previously thought.
• 1:45
  That's so crazy.
• 1:45
  Like, we're getting into the science fiction realm where we are able to now influence with dreams, interact with them, and then help...
• 1:53
  the people who we interacted with in those dreams solve problems that they previously didn't solve before.
• 2:00
  It's unbelievable.
• 2:00
  It's, it's pretty insane, dude.
• 2:02
  And the scientific evidence for like a lot of this, like dreams influencing your reality the next day, it's been elusive and it's been challenging because, you know, we can't really systematically manipulate dreams.
• 2:15
  Until now.
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  That's the idea.
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  There's this new study by neuroscientists at Northwestern University.
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  And what it does is validate the possibility of influencing dreams.
• 2:26
  And it's really supporting this theory that REM sleep, which is when dreams occur, that stage of sleep when dreams occur, it might actually be especially conducive to helping individuals come up with creative solutions to problems.
• 2:40
  This is so fascinating.
• 2:42
  I mean, even the idea of saying the phrase dream engineering.
• 2:46
  Yeah.
• 2:46
  No, that seems like something out of a Chris Nolan movie.
• 2:49
  Yeah, exactly.
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  But now it's like actually real.
• 2:50
  Exactly.
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  There's a science paper that's out, right?
• 2:54
  So as always, let's start with some of the history.
• 2:56
  Dreams have always been viewed as sacred, as this kind of psychological gateway into the subconscious mind.
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  The Egyptians literally had sleep temples for sleep and incubation rituals.
• 3:09
  And they used to practice these in the ancient temples with the Egyptian deity Imhotep.
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  Do you remember the mummy?
• 3:16
  Yeah, exactly.
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  And then, I mean, it's been it's been a thing of research for psychoanalysis for quite a while.
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  Sidman Freud was really into it.
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  Carl Jung was really into it.
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  But the modern birth really came in 1953 when Aserinsky and Kleitman discovered something called rapid eye movement sleep.
• 3:36
  OK, and they linked that sleep stage to vivid dreams.
• 3:41
  What was really the breakthrough here is we didn't have a subjective experience.
• 3:47
  telling of, oh, I dreamed, and that was the stage that I dreamt.
• 3:50
  Because what they could do is they could hook up electrodes onto the skull, and they could actually see signatures of
• 4:01
  dreaming that were not based on subjective reporting, right?
• 4:05
  I see.
• 4:05
  You don't have to wake up and be like, hey, I was dreaming.
• 4:08
  You could look at the eye movement during sleep.
• 4:10
  There's a certain part of sleep called REM sleep, rapid eye movement sleep, where your eyes move very rapidly.
• 4:16
  During that part, the electrophysiological signature from the EEG electrodes is actually very distinct compared to other parts of sleep.
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  So it was this real actual stage in the sleep cycle.
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  It happens about 25% of total sleep, happens about 90 minutes after you fall asleep.
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  And then during REM, what we've realized over the years of doing research on it, the brain is highly active.
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  It actually resembles the waking state.
• 4:42
  If you look at the EFIS signature, like the electrode signature of what these little electrodes on our skull picks up, and then you look at other stages of sleep versus when we're awake and we're looking at stuff and thinking about stuff...
• 4:59
  The electrophysiological signature during REM sleep is more similar to wakefulness than it is to other stages of sleep.
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  Right?
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  So when we're dreaming, it's almost like the brain is kind of awake.
• 5:12
  But obviously, we're still asleep.
• 5:14
  And it's been this really cool enigma to try to figure out.
• 5:19
  So the starting point was you had this objective experience from people saying, while I was sleeping, I saw stuff.
• 5:28
  Yeah.
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  Yeah.
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  But we did not at the time have a way to quantitatively track with instruments that state that people were describing that they were having until in this historical context we used electrodes and tracking the brain activity to see and mapping it with this rapid eye movement sleep that –
• 5:49
  There is a state while you are asleep where the brain activity looks very akin to a waking state.
• 5:56
  Yeah.
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  And that maps to when people are now happy.
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  You can now map the data with the subjective reporting from those sleeping and kind of map those two together.
• 6:06
  Exactly.
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  And then let's go even further, right?
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  Okay.
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  So now we figured out that there is definitely such a thing as REM sleep.
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  Yes.
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  And that's probably when you're dreaming.
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  Yes.
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  Now...
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  What if we wanted to talk about dream recall and how that affects how you solve problems the next day or day to day, right?
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  Right.
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  That's still correlational still, right?
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  Because the subject has to recall the dream.
• 6:28
  And then there's some correlation between him recalling certain dreams and the creativity that he has the next day.
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  There's some correlation between sleep deprivation and insight.
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  Like there's a decorrelation really.
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  It's like because like the more you're sleep deprived, the less you're really going to do novel stuff the next day.
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  You need your sleep in order to actually fully function.
• 6:52
  But these are all correlational, right?
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  You can't do a causal –
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  Meaning like this caused that.
• 6:59
  Exactly.
• 6:59
  Yeah.
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  It's always just observational studies.
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  And this breakthrough, which was in the neuroscience of consciousness, this is a causation study between your dreams and your ability to solve problems.
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  So before we were just saying these two things are happening at the same time.
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  And so we think they're related.
• 7:18
  Yeah.
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  But we didn't really see the bridge between the two things.
• 7:21
  Yeah.
• 7:21
  Yeah.
• 7:21
  Yeah.
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  We weren't able to literally like influence the dreams.
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  And now this is what's happening.
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  That's fascinating.
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  Okay.
• 7:26
  Yeah.
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  Okay.
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  This is getting good.
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  It's pretty insane.
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  Yes.
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  So let's talk about a little bit about cognitive psychology.
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  Okay.
• 7:32
  OK, it's divided into two domains when when it comes to creative problem solving.
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  There's really like two ways of thinking about it.
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  There's the convergent thinking, which is the idea that it's like super brute force and logical.
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  If I want to solve a problem, I start with all of my parameters and then I sort of logically deduce how to get to the solution.
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  look at relationships and try to like creatively figure out how to solve a problem.
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  Right now with the divergent thinking, there's something called a spreading activation model.
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  The idea is that your memory is represented like a giant graph of like a graph, like, you know, the internet is a graph.
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  We've talked about this before on the podcast, the idea that like on the internet, you're,
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  Each web page is linked to other web pages, so you have web pages and you have links between them.
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  That's what defines a graph.
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  Stuff and then links between that stuff.
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  Now, with the spreading activation model, our semantic memory can be thought of as a complex graph because there's nodes, which are the concepts.
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  Like, for example, dog.
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  Dog can be furry.
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  It can be friend.
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  It can be...
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  foe if you're out hiking and you see a wolf like there's all these different relationships that you can go from dog all the way to is it an enemy or is it a friend is it my own dog and then from there you can start connecting nodes so you can imagine you start with a dog and then you have that idea of a dog and from a dog you can now go to friend you can go to does he want does he want to play does like all the stuff that i would think about
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  when a dog comes up in my memory, right?
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  There's all these connections.
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  And the idea of the spreading activation model is that you start with some connection.
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  And then you, if you want to solve a problem around that connection, you start exploring all the stuff that's related to that.
• 9:29
  And you try to figure out what's the best path to get to your solution.
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  So as an example, let's say I'm afraid of dogs, which I'm not because I have three of them.
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  But let's say in this example, I'm afraid of dogs.
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  So when I then see a dog,
• 9:44
  I'm immediately now making nearby connections, which in this context might be fear, I need to run, defensive MMA, how to kick.
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  Again, only because in my head, in this specific context, I'm afraid of dogs.
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  And so I will be thinking of these associated concepts in my own head that are related to dogs.
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  a dog, and then most adjacently fear, and then all the things that come from the... And all the stuff that comes around that, right?
• 10:12
  Exactly.
• 10:13
  And so now let's try to take this network architecture and apply it to problem solving, right?
• 10:20
  When you're in the waking state, what ends up happening is, suppose you want to solve a problem...
• 10:26
  Based on the brain state, what ends up happening is you start with the problem statement and you try to get to the solution.
• 10:32
  During the waking state, my activation of all these different concepts is tightly regulated.
• 10:38
  Okay?
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  Because there's a lot of inhibition that goes on.
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  There's these inhibitory neurons that are firing a lot during my wake state.
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  And so only strong, obvious connections are actually the ones that are going to pop up.
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  Okay?
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  And so I might get stuck.
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  In like a fixation where I'm trying to like solve a problem and I get stuck while trying to find the solution.
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  And it's really hard for me to get out of that.
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  And I've had this personally in my research.
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  Like when I'm trying to solve something during my work or during my research, it's like.
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  I work on it for like two hours and I'm stuck and I don't find a path forward.
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  And that's when a lot of people stay.
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  Why don't you just sleep on it?
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  The idea is you have a problem.
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  Imagine it like traffic.
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  Or you're trying to drive through Los Angeles.
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  Yeah.
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  But 50% of the roads are closed.
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  Yeah.
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  In your waking state.
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  Yeah.
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  And so you can't take the 405.
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  You can't take the 101.
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  That's a really good analogy.
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  And so it's going to – you can't really – it's hard to get to where you need to go.
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  You try to go the back roads.
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  And you end up somewhere in El Segundo.
• 11:43
  Yeah.
• 11:45
  Okay.
• 11:45
  Exactly.
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  So maybe dreaming could fix something like this, right?
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  Yes.
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  And the analogy that I want to make is actually a physics analogy, something called annealing.
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  Okay?
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  Whenever we have a material that is in a certain state and we want to get it to a new thermodynamic equilibrium, what you can do is you can raise the temperature.
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  Okay.
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  Okay.
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  When you raise the temperature, you're raising the noise, right?
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  And so in a physics thing, suppose I've got a potential landscape, right, which is hills and valleys.
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  My state is stuck in a local valley, but it's not like the global minimum.
• 12:20
  Yes.
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  Right?
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  It's stuck in some local solution, and I want to get to the global minimum, which is the actual solution.
• 12:26
  Yes.
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  Yes.
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  If the temperature is too small, my state is only going to really jiggle around that local solution.
• 12:33
  But if I raise the temperature, then my state is able to explore a lot of different avenues because the jiggle has increased.
• 12:44
  That's the idea of annealing, right?
• 12:47
  And...
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  What people have started to figure out is perhaps the biological function of sleep and specifically dreaming is to raise the parameter of your neural network such that you start exploring different spaces in your brain.
• 13:05
  Yes.
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  Okay?
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  This happens a lot of times in, like, artificial neural networks.
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  Yes.
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  Like, when you're trying to code up an LLM or, like, some kind of AI, a lot of times it's going to get stuck in a local minima.
• 13:19
  And what you kind of do is you like increase – there's like various tricks that you can do to make it not get stuck in that local minima.
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  One of the ways is to increase the temperature.
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  So you increase the jump size in your gradient descent.
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  The other thing you can do is introduce momentum so that if it's going in one direction, even if that's local minima, it's going to have enough to like jump over that energy barrier.
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  And so the same things is sort of –
• 13:45
  a theory that's happening in brains.
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  Now, is there like a neurological idea?
• 13:51
  Like, is there a mechanistic philosophy in our brain that we can actually point to here, right?
• 13:56
  It turns out there is.
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  There's a lot of research on how this annealing phenomenon, this raising of temperature, is actually happening in our brain.
• 14:03
  So the REM sleep initiation... And just briefly, and just because I want to make sure I'm tracking, the way you described it earlier was when you're in the waking state, there are these tight...
• 14:15
  parameters around where to explore exactly the temperature is low the temperature is low and so we're basically trying to ideally we're trying to understand if you know is does dreaming loosen yes parameters the process we made we made the analogy that's akin to annealing in physics but the simple way of saying it is is turning off the guardrails a little bit to expand the ability to explore into other areas yes that's exactly right okay yeah
• 14:43
  That's effectively what's happening.
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  And there is actually neurological studies that show that this is happening at a cellular level.
• 14:50
  Okay.
• 14:51
  At like the biochemistry level.
• 14:52
  Okay.
• 14:52
  So REM sleep initiation is driven by the activation of these REM-ON cells in our brain, okay?
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  And they're located in like the LDT and the PPT.
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  That's the lateral dorsal and...
• 15:06
  tegmental nuclei in the brainstem.
• 15:08
  Okay, the brainstem is the part that connects our brain to the spinal cord and makes sure our brain can talk to the rest of the body.
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  Now, these cells, what they do is they flood the cortex and the hippocampus with massive amounts of acetylcholine.
• 15:24
  And the norepinephrine goes to zero.
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  Norepinephrine is kind of an inhibition.
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  Acetylcholine is an activation.
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  Okay.
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  During wakefulness, there's high amounts of norepinephrine.
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  So you have a lot of inhibition.
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  That's those guardrails that you were talking about.
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  But during dreaming...
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  That's gone.
• 15:41
  So now without that constraint, now these cortical networks have a really high temperature.
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  They have a lot of noise.
• 15:46
  They're able to explore all the different possibilities, right?
• 15:51
  And then when we think about like dream phenomenology, right?
• 15:54
  All those like bizarre hallucinatory nature of the dreams, like when you're like flying or like random crap happens during dreaming.
• 16:03
  Yes.
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  That might be a subjective experience that...
• 16:07
  Of this noise.
• 16:08
  I see.
• 16:09
  Of all of the brain sort of like getting really activated.
• 16:13
  So we're sort of seeing there's like the chemistry balance of what's happening in the brain changes in between waking and sleeping state with this acetylcholine.
• 16:23
  And what was the second one?
• 16:24
  It was norepinephrine.
• 16:26
  Norepinephrine.
• 16:27
  Yeah.
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  The norepinephrine is the guardrails.
• 16:30
  Yeah, that's the guardrails.
• 16:30
  It's the guardrails.
• 16:31
  And the acetylcholine is what allows you to kind of explore a little bit more crazy.
• 16:35
  Yeah, and because that balance is off during REM sleep.
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  Then it's like, whoa, what's over here?
• 16:40
  Yeah.
• 16:40
  Okay, very interesting.
• 16:41
  Oh, I'm flying.
• 16:42
  Oh, that's a monster.
• 16:43
  Like, crazy stuff during your dreams.
• 16:46
  And then it tracks because it makes sense for anyone who's had vivid dreams before, right?
• 16:50
  This idea that...
• 16:52
  you know, they do kind of exist in this weird realm a little bit beyond what is realistic.
• 16:57
  Yeah.
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  But still feel grounded in realism.
• 17:00
  Exactly.
• 17:00
  Yeah.
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  Because it's like synthesizing all the memories that you previously have, but now it's just on overdrive.
• 17:05
  It's like trying to connect to all this other kind of stuff.
• 17:08
  Right.
• 17:08
  Makes total sense.
• 17:09
  It's, it's, it's, it's pretty cool that we can go all the way down to the biochemistry level.
• 17:14
  Yeah.
• 17:14
  And like still see subjective effects all the way up.
• 17:19
  Neuroscience is like crazy and there's just so much more that we don't know about the brain.
• 17:23
  Right.
• 17:23
  And the fact that we're getting this far is already, I think, really, really cool.
• 17:27
  So now let's talk a little bit about information theory.
• 17:30
  Yes.
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  And the brain.
• 17:32
  Okay, because the brain is an information chugging machine.
• 17:36
  There's this idea of entropy, which is the same kind of idea that we have in physics, the idea of randomness.
• 17:42
  How structured is the data in our brain?
• 17:45
  If we look at the ECG signals...
• 17:49
  which is the electrical signal that we would get if we were to put electrodes on our scalp, right?
• 17:55
  You can see that the wakefulness and the REM sleep look very, very similar.
• 17:59
  This is what I was alluding to before.
• 18:01
  Okay, got it.
• 18:01
  Slow wave sleep, which is deep sleep, that's the part where we're not dreaming.
• 18:06
  That's like super deep, nothing...
• 18:10
  There's no subjective experience so far as we can tell of that.
• 18:13
  And light sleep look very different compared to REM and wakefulness.
• 18:17
  REM is almost like we are wakeful, but our body is completely paralyzed.
• 18:23
  And the way that our body does that is our brain, our brainstem, which is the part that connects our brain to the spinal cord.
• 18:33
  completely blocks all the signal.
• 18:36
  Oh, okay.
• 18:37
  That's why we don't act out our dreams.
• 18:38
  Well, I mean, some people do, and they've got a little problem.
• 18:42
  We're acting out our dreams right now.
• 18:45
  I think we are, actually, on this show.
• 18:47
  We've talked about this for a very long time.
• 18:50
  Yeah.
• 18:50
  But you know what I mean, right?
• 18:52
  Like when we're dreaming and like we're doing all the crazy stuff, our brainstem is actually paralyzing the rest of our body.
• 19:00
  Okay.
• 19:01
  But the dream state itself looks very much like the wakeful state.
• 19:06
  That's something that I...
• 19:07
  Really want you to like imagine.
• 19:10
  And I think that's kind of crazy to think about.
• 19:11
  Yeah, it is.
• 19:12
  That like during dreaming, the brain is basically simulating reality of being awake.
• 19:18
  It's just we're not acting it out because the brainstem, which is the part in the back of our brain connecting our spinal cord to the brain, is just like, no, none of that signal is getting through.
• 19:27
  It's sort of like the brain is putting on a biological VR headset, utilizing your memory as the context by which it then projects imagery into your mind's eye while paralyzing your body movement.
• 19:40
  So you don't have to worry about running into your TV.
• 19:43
  Yeah, dude, that's exactly right.
• 19:45
  That's, I mean, it's exactly what it's doing.
• 19:47
  It's like a little VR.
• 19:48
  Yeah.
• 19:49
  That's, that's in our head that we're making up as we go along.
• 19:52
  So given all this, given all these constraints, right?
• 19:55
  Yes.
• 19:56
  Now let's talk about how do we do sleep engineering?
• 20:00
  Right.
• 20:00
  Cause now we've set the basis of why we can actually look at dreaming, uh, from a quantitative perspective and understand the numbers and see it and track it.
• 20:13
  Yeah.
• 20:14
  But now the question is how can we actually be more targeted, not just read.
• 20:20
  How do we read and write?
• 20:21
  Yes.
• 20:22
  Yes.
• 20:22
  How do we now write into the sleep itself, right?
• 20:26
  We want a deliberate manipulation process.
• 20:30
  Of our sleep content, right?
• 20:33
  And it's funny because in the waking state, we have a variety of things that manipulate our conscious waking experience.
• 20:38
  Yeah, yeah.
• 20:39
  So theoretically, I mean, just like loosely speaking, if there's such a matchy-matchy.
• 20:44
  Yeah, then it should be possible, right?
• 20:48
  That's where we get into something called targeted memory reactivation.
• 20:52
  Okay.
• 20:52
  Okay?
• 20:52
  This is something that's used not just in dream practice.
• 20:57
  but just sleep research in general.
• 20:59
  Okay.
• 20:59
  Here's the idea.
• 21:00
  Okay.
• 21:01
  So we pair learning.
• 21:03
  Let's say we're awake and we're learning something.
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  We pair that learning with some kind of sensory signal.
• 21:10
  Usually it's a sound.
• 21:11
  So I'm reading something.
• 21:13
  I'm playing the piano.
• 21:14
  There's a sound that's playing.
• 21:16
  Okay.
• 21:16
  Now, when we go and sleep, that same sound is going to play while we sleep.
• 21:23
  Okay.
• 21:26
  Because that sound is associated with the thing that we were learning, and now we heard it while we slept, it's going to sort of give that extra oomph to consolidate that into the greater brain machinery.
• 21:43
  Does that make sense?
• 21:43
  It does.
• 21:44
  It's a signal that sort of triggers hardening of the concept of...
• 21:50
  From your waking state in your sleeping state.
• 21:53
  Exactly.
• 21:53
  Yeah.
• 21:53
  There's this external stimulus that's associated with the thing that you're trying to learn, either the task that you're trying to learn or the memory that you're trying to remember.
• 22:02
  And then when you trigger it during your sleep, it like doubles down.
• 22:07
  The neurons double down because they're getting a coincidence signal.
• 22:11
  You know?
• 22:12
  I wish I knew this when we were in school.
• 22:14
  Dude, I mean, there's a lot of cool research now that's doing this exactly right.
• 22:19
  Now, historically, this TMR, targeted memory reactivation, has focused on non-REM sleep.
• 22:25
  Okay, non-REM sleep is the slow wave sleep that we get.
• 22:28
  That's the deep sleep.
• 22:29
  And that is usually involved in memory consolidation.
• 22:31
  That's the idea of whatever short-term memory that I'm getting today is.
• 22:35
  whatever is salient and I need to store into long-term memory, that's what happens during this deep sleep.
• 22:42
  And so if I want to store certain things into long-term memory, what I can do is, or if I want to like have a skill that I really want to store into long-term memory, I can have this targeted memory reactivation and it's actually highly effective in 90 plus studies, this particular one by rash and other researchers,
• 23:00
  Authors have shown that this is like very much works.
• 23:05
  Okay.
• 23:05
  Like those who learn piano, for example, with an auditory stimulus, and then you reactivate the auditory stimulus during sleep, they're going to remember that piano sequence better.
• 23:15
  Like their motor neurons literally are going to be better the next day.
• 23:18
  So for all the coaches who used to say don't play music while we were training for soccer, have it be known that had you let us play our rap music and then we played it during our sleep.
• 23:32
  During the sleep, we would have won more games.
• 23:35
  Yeah, yeah, yeah.
• 23:36
  That's the big one, right?
• 23:37
  I'm being facetious a little bit here.
• 23:38
  Yeah.
• 23:40
  And so here in this particular study, what they're trying to do is break that barrier and not just go into non-REM sleep.
• 23:46
  They're trying to do targeted memory reactivation during dreaming.
• 23:52
  Okay?
• 23:53
  It gets a little tricky.
• 23:54
  Okay.
• 23:55
  Okay?
• 23:55
  When we get into dreaming, right?
• 23:57
  Because what we want to do is we want to prove that the targeted memory reactivation during dreaming can bias dream content.
• 24:07
  Oh, okay.
• 24:07
  Right?
• 24:07
  Right.
• 24:08
  So that's one thing where like you're going to dream of certain things.
• 24:13
  And then the second thing is when you dream of those certain things, you're actually better at the task later on.
• 24:19
  Interesting.
• 24:20
  So we're not just trying to harden it so that it can be reactivated when there's external stimulus.
• 24:27
  That was just a grounding.
• 24:28
  What we're saying now is can we actually influence what someone sees and feels?
• 24:34
  when they are actively dreaming.
• 24:36
  Yeah.
• 24:37
  And then not only that, can we then also have it so that the thing we're influencing them to see and feel while they are dreaming is functionally beneficial to like a reactivation or some functional process.
• 24:50
  Yes.
• 24:51
  For when they're now returning to the waking state.
• 24:53
  Yes, exactly.
• 24:54
  And in order to really make this foolproof, they actually selected their participants.
• 24:59
  Okay.
• 24:59
  to be 20 subjects with high lucid dreaming propensity.
• 25:05
  Do you know about lucid dreaming?
• 25:07
  I am from, we do live in LA.
• 25:08
  So, you know, there's a lot of people who love to talk about the woo-woo.
• 25:12
  Yeah.
• 25:13
  And lucid dreaming is very, it's the idea basically that you can have agency, like you have awareness.
• 25:19
  During dreams.
• 25:19
  During dreams.
• 25:20
  And then you can make decisions.
• 25:21
  Yeah.
• 25:22
  Not just be on a rollercoaster ride through it.
• 25:24
  Yeah, I mean, I think it's one of the coolest things that we can do as human beings is be conscious of the fact that we are in our brains simulation.
• 25:33
  Yep.
• 25:33
  Right?
• 25:34
  And then have agency and control over that dream.
• 25:37
  Right?
• 25:38
  This is central to the plot point of Inception.
• 25:40
  Yes.
• 25:40
  And so on and so forth.
• 25:41
  Yes.
• 25:41
  Right?
• 25:42
  So here's what they did.
• 25:44
  They got 20 subjects with really high lucid dreaming propensity, okay, 39 overnight sessions.
• 25:50
  And what these participants did was they tried to solve tasks, like little puzzles.
• 25:57
  This is like a matchstick puzzle, which is like you want to move exactly five matches such that this random scale that's made out of matches is exactly balanced.
• 26:08
  Mm-hmm.
• 26:10
  And these are all insight-driven puzzles.
• 26:12
  That's the key.
• 26:14
  They're not brute force puzzles.
• 26:15
  They're not like math algebra problems where there's a systematic algorithm that you can use to solve it.
• 26:22
  They're really like a creativity type of puzzle.
• 26:25
  Okay?
• 26:26
  And participants usually used to have some amount of puzzles that were not solved.
• 26:30
  Okay?
• 26:31
  Here's the key.
• 26:32
  Here's what they're doing.
• 26:34
  While they're solving the puzzles during the normal day, there is an auditory cue for each puzzle.
• 26:42
  Okay.
• 26:42
  Each puzzle has a unique sound associated with it.
• 26:46
  So as they're solving it, there's a unique sound that's being played in the background.
• 26:50
  So they're associating subconsciously that sound to this particular puzzle.
• 26:55
  Mm-hmm.
• 26:56
  Okay.
• 26:57
  Now we get into their sleeping.
• 26:58
  Okay.
• 26:59
  Now they're sleeping.
• 27:00
  We have something called targeted lucidity reactivation.
• 27:04
  Okay.
• 27:04
  They're able to target lucid dreaming to happen in these patients.
• 27:10
  No way.
• 27:11
  Yeah.
• 27:11
  This is what's crazy.
• 27:12
  So at 4 a.m.
• 27:13
  They wake up.
• 27:14
  Okay.
• 27:15
  And they spend 20 minutes training their mind to associate another set of auditory clues with
• 27:23
  a critical state of mind that will provoke lucidity.
• 27:26
  So as they're falling asleep, so they wake up at 4 a.m., and as they're falling asleep, they get played these three-tone beeps that are in ascending frequency.
• 27:35
  It's like beep, beep, beep.
• 27:37
  Beep, beep, beep.
• 27:38
  You hear that pitch, guys?
• 27:40
  You hear that pitch perfect over here?
• 27:42
  But...
• 27:43
  I mean, that's what they're doing.
• 27:44
  No, no, no, no.
• 27:45
  I get you.
• 27:45
  It's kind of crazy.
• 27:46
  And what they're doing is as they're lightly falling asleep, they're aware of that.
• 27:52
  Right.
• 27:52
  And they're like, oh, that's the sound.
• 27:54
  That's the sound.
• 27:55
  And they're trying to keep that lucidity.
• 27:56
  So they're trying to keep the conscious awareness of the sound.
• 27:59
  Yes.
• 27:59
  While they're falling asleep.
• 28:01
  The reason I'm kind of like giving all these facial expressions is, have you ever heard of this concept of like binaural beats?
• 28:09
  No.
• 28:09
  So there's this whole community, like, you know, Monroe Institute and a variety of these entities have done this research into this concept of binaural beats.
• 28:18
  And –
• 28:19
  There are two aspects to it.
• 28:21
  One is that it helps you – the way the frequencies are tuned, it helps you like relax.
• 28:26
  But in particular, people use it for lucid dreaming as like the thing to fall asleep to and then like – Oh, okay.
• 28:34
  Yeah, that's very much – It puts you in the right – Kind of in line.
• 28:38
  And state that enables or makes it more likely that you lucid dream.
• 28:42
  It's just interesting that there is an overlap because it's very much sort of in the –
• 28:47
  people who are quantitative self self-improvement kind of universe um so it's fascinating that there's a direct overlap yeah i mean it's pretty crazy yes that like i mean dude this stuff is just yeah i mean so here okay so getting back to this right so so they have this like ascending three-tone beep yes that then sort of like makes them sort of aware and try to lucidly dream more
• 29:13
  And then here's the track.
• 29:14
  Here's the key, right?
• 29:16
  They still have those TMR cues, the same targeted memory reactivation cue of the puzzle.
• 29:22
  Yes.
• 29:23
  The puzzle sound is different from this lucid sound.
• 29:27
  Okay?
• 29:28
  If they become lucid,
• 29:30
  Right?
• 29:31
  They're going to... And we can monitor their brain state because we have electrodes on them.
• 29:35
  So we can see when they enter REM sleep.
• 29:38
  Right.
• 29:38
  Right?
• 29:39
  And when they enter REM sleep, I can start playing now the sound from the puzzle.
• 29:43
  And if they're lucid and they play the sound from the puzzle...
• 29:46
  and they hear the sound from the puzzle, they're going to seek out that puzzle in their hallucinated dream state.
• 29:52
  So imagine you're dreaming, and you're out in Yosemite or something.
• 29:56
  This is so crazy.
• 29:57
  And now you hear the dun-dun-dun, which is like, oh, I'm lucid.
• 30:03
  And now whatever sound was associated with a certain puzzle that they couldn't solve, they're going to try to hallucinate that puzzle onto the granite walls of Yosemite.
• 30:12
  This is unbelievable.
• 30:14
  I'm so – I'm like – we need to do a follow-up on this because I'm so excited about this.
• 30:18
  It's insane.
• 30:19
  So just to say it back to you from the beginning, the idea is – And I haven't even gotten to the best part, by the way, but – We start – I don't know if I can take which one.
• 30:27
  Yeah.
• 30:28
  We basically have an audio cue when the participants are trying to solve a puzzle.
• 30:34
  That's an insight-based puzzle.
• 30:36
  Yeah.
• 30:36
  And the idea is to connect the puzzle to the sound for the participants.
• 30:41
  Yep.
• 30:41
  The participants then go to sleep.
• 30:43
  Yeah.
• 30:43
  While they're sleeping...
• 30:45
  separately from that set of sounds we have a different set of sounds they wake up at 4 a.m and we're trying to induce lucidity while dreaming yeah and the second set of sounds helps to facilitate that inducement of lucidity while dreaming that lucid state while dreaming that we've now induced with our second set of sounds means we're now incepting the first set of sounds into the dream yeah literally like the movie
• 31:12
  Literally, yeah.
• 31:32
  The brain has more space to explore and is potentially more likely to come up with the novel solution.
• 31:37
  Yes.
• 31:37
  That is one of the craziest, most unbelievable things that we've talked about on this pod.
• 31:43
  I think this is absolutely insane.
• 31:47
  Here's the part that I thought was crazy.
• 31:50
  We need a cherry on top.
• 31:51
  Okay.
• 31:52
  This is the cherry on top.
• 31:53
  Okay.
• 31:54
  There was...
• 31:55
  So we've gotten to the part where it's like interactive dreaming, right?
• 31:59
  Right.
• 32:00
  But at the end of the day, I still want to be sure that I am making this happen.
• 32:05
  Right.
• 32:05
  Okay?
• 32:06
  I don't want subjective reporting after the patient wakes up.
• 32:13
  I want during the dream to know that this is happening.
• 32:16
  Yes.
• 32:16
  Okay?
• 32:17
  But there's a caveat, right?
• 32:19
  This is going to be kind of difficult because as I told you, the brainstem-mediated paralysis is paralyzing the entire body.
• 32:26
  Yes.
• 32:27
  But it spares the eye muscles because it's REM sleep, right?
• 32:31
  So the eye is definitely moving.
• 32:33
  That's why it's called rapid eye movement.
• 32:35
  And it's also sparing the diaphragm because you're still breathing.
• 32:38
  Oh, right.
• 32:39
  Yeah.
• 32:40
  These subjects were trained, if they were lucid dreaming,
• 32:46
  They would move their eye around in the dream and they would breathe.
• 32:52
  They would huff and puff during the dream, like in their dream.
• 32:55
  Right.
• 32:55
  Like, so I'm in Yosemite and I'm like in my dreamscape.
• 32:59
  I start huffing and puffing in my dreamscape.
• 33:01
  Well, that's going to transfer into the physical body, huffing and puffing.
• 33:05
  And I could monitor that.
• 33:07
  This is so crazy.
• 33:09
  So I have the EEG signal.
• 33:11
  Yes.
• 33:11
  I have the signal from my...
• 33:15
  breathing stuff.
• 33:17
  I have the signal from the eyes.
• 33:19
  And I can now get a signal of the subject reporting back to me during dreaming that he is actually doing this.
• 33:28
  Y'all, I want you to understand something.
• 33:31
  I want you to understand something.
• 33:33
  What you're saying is we have now experimentally shown that a patient can communicate with
• 33:44
  While in a dream state, particularly when they're lucid dreaming, back to the researchers that are monitoring them across at least two modalities, eye movement and breathing.
• 33:59
  And breathing.
• 34:00
  Yeah.
• 34:02
  While we're trying to incept an idea to help them problem.
• 34:07
  On top of all of that.
• 34:10
  Mate.
• 34:12
  I'm at a loss for words because that is so – it's kind of weird because literally the movie Inception.
• 34:18
  It's kind of happening.
• 34:19
  It's kind of happening.
• 34:19
  Yeah.
• 34:20
  It's insane.
• 34:21
  There's no sharing of dreams.
• 34:22
  Fine.
• 34:22
  Sure.
• 34:23
  But like a lot of the stuff is there.
• 34:25
  Is there.
• 34:26
  Right?
• 34:26
  And the key to this two-way communication is that there's now real-time irrefutable proof.
• 34:32
  Right.
• 34:32
  Proof.
• 34:33
  Right.
• 34:33
  That the cue was heard.
• 34:35
  Right.
• 34:35
  It was integrated into the dream.
• 34:37
  Right.
• 34:37
  And there is a conscious response that came back.
• 34:39
  Right.
• 34:40
  Because in theory, you could make the conscious response different things.
• 34:43
  Yeah.
• 34:43
  You could say breathe like this or breathe like that or move your eyes like this or move.
• 34:46
  In order to then track over multiple different iteration types to be like, yeah, no, this is definitely happening.
• 34:51
  It's not just like, oh, it's the same single type of eye movement or breathing.
• 34:56
  Anyway.
• 34:56
  Yeah.
• 34:56
  Yeah.
• 34:57
  Okay, so now let's see if there's actually an outcome.
• 35:00
  Were they able to solve these problems better?
• 35:02
  So for one, 75% of participants reported the cued puzzle elements in their dreams.
• 35:08
  Okay.
• 35:08
  Okay, which is way greater than control.
• 35:10
  Meaning, like, I played the sound from the puzzle and the puzzle was in their dream.
• 35:14
  They were, like, solving it or doing whatever.
• 35:16
  Right.
• 35:16
  Right?
• 35:17
  The targeted dream efficacy, right, the subjects reported 40% more...
• 35:27
  Success on the queued puzzles versus the not queued puzzles.
• 35:30
  That's so crazy.
• 35:31
  So people who got the inception to solve the puzzle 40% of the time.
• 35:38
  were able to do so after the fact, greater than those who did not have the inception.
• 35:42
  Yeah.
• 35:43
  So if you didn't get incepted, you didn't do well.
• 35:46
  Yeah.
• 35:47
  If you did get incepted... You did a little bit better because sleep always... Sure.
• 35:50
  Like natural sleep... Sure.
• 35:51
  ...will get you a little bit farther.
• 35:54
  But this is like engineered dreaming...
• 35:57
  Yes.
• 35:57
  ...is making you superhuman at solving these puzzles.
• 36:00
  This is...
• 36:01
  This is...
• 36:01
  This is...
• 36:02
  I can't...
• 36:03
  I just...
• 36:05
  It's insane, dude.
• 36:06
  For those who are listening on audio, my mouth has been mostly open, slack-jawed, for a lot of this story.
• 36:12
  Dude, yeah.
• 36:13
  It's pretty – This is quite nice.
• 36:14
  This is quite nice.
• 36:15
  This is quite nice.
• 36:15
  And these studies are not in a vacuum, right?
• 36:18
  There's actually a lot of broad effort to do dream engineering and sleep engineering in general.
• 36:23
  It reminded me of this study from MIT called Dormio.
• 36:27
  They were actually targeting non-REM sleep.
• 36:29
  So this is like the edge of sleep, N1, which is called hypnagogia.
• 36:34
  Mm-hmm.
• 36:35
  It's like right when you're falling asleep.
• 36:37
  It's this transitional state between wakefulness and sleep.
• 36:40
  And they had a sensor.
• 36:41
  They had a glove that people would put on with sensors.
• 36:44
  And then it would give a cue.
• 36:46
  Like, think of a tree right when you were going to sleep.
• 36:50
  Okay?
• 36:51
  This thing was targeting divergent creativity, which is that the –
• 36:57
  Sorry, this thing was actually targeting convergent creativity, which is like the – Going down the rabbit hole, right?
• 37:05
  Yes, yes.
• 37:06
  And so we're doing like separate different things, and this is part of that.
• 37:11
  So I'm just saying there's like a huge modality of scientific research that is going into specifically sleep engineering.
• 37:20
  Right.
• 37:21
  And this is like one of the coolest examples that I've seen of like,
• 37:27
  Yeah.
• 37:28
  No, this is very good stuff.
• 37:29
  I mean, have you ever had a lucid dream before?
• 37:32
  I've had it once.
• 37:33
  Yeah, but I haven't been able to, like, control it, you know?
• 37:39
  I want to say twice, but it was only awareness.
• 37:41
  It was not... Yeah, I wasn't able to, like, literally be like, all right, I'm going to fly now.
• 37:44
  Yeah, yeah.
• 37:44
  You know, I wish I could do that.
• 37:45
  If you've ever had a lucid dream before, it is one of the coolest things.
• 37:51
  It can be obviously very scary, like, depending on whatever is in your subconscious and stuff like that.
• 37:56
  Yeah, yeah, yeah.
• 37:57
  It is such a fascinating mind bending thing because it's so bizarre, obviously.
• 38:05
  But you're there.
• 38:08
  It's like movies like Inception always may be like, well, like, you know, what is like, do you have an opposite life in opposite world in your dream that is consistent because it just kind of mirrors your own life and has different parameters and relationships that transcend time and stuff like that.
• 38:22
  But you just don't remember it.
• 38:25
  obviously we will find out soon because now we can incept our dreams.
• 38:28
  Yeah, no, no, I think this is, I think this is opening up doors for all kinds of really cool research.
• 38:34
  And there is clinical potential for this, right?
• 38:37
  For example, if you can do like nighttime neuro prosthetics,
• 38:41
  That's a new and emerging field.
• 38:43
  People who have PTSD, right?
• 38:44
  PTSD can be modeled as a pathological local minimum in some landscape.
• 38:50
  It's like a fear network and you're trapped in this local minima, right?
• 38:53
  And you can't get out.
• 38:54
  Well, if you do...
• 38:57
  targeted memory reactivation during REM sleep and you can cue stuff.
• 39:02
  You could theoretically restructure traumatic memories.
• 39:05
  That's a huge deal to not be traumatic anymore.
• 39:08
  Yeah.
• 39:08
  That's a huge deal, right?
• 39:09
  That's a huge deal.
• 39:10
  Yeah.
• 39:11
  No, I mean for veterans, for victims of abuse.
• 39:14
  I mean, there's a whole swath of people who could majorly benefit from that.
• 39:19
  Yeah.
• 39:20
  And of course, with all of this comes the ethical concern.
• 39:23
  Of course.
• 39:23
  Right.
• 39:24
  Because you're, you're,
• 39:28
  You're beating this sensory gate to get into dreams.
• 39:32
  Now imagine...
• 39:34
  if researchers can cue puzzle solutions, what if like commercial or malicious actors attempt to embed like branded content or, you know, alter preferences.
• 39:45
  This is, I, I get remembered of Tom Hardy's role in inception as like the defense guy.
• 39:51
  He was like dream, dream a little bigger.
• 39:53
  And he like came up with a giant gun.
• 39:55
  Yep.
• 39:55
  Um, it's like, it's like, so is dream sovereignty, um,
• 39:59
  going to now be the premier neuroethical challenge.
• 40:02
  I mean, this might...
• 40:05
  I didn't even think about this nexus, but while this might sound like, oh, this is early stage research and there's a ways to go, which is true, if you just look at, in the US, the recent...
• 40:19
  uh disclosures around uh what has been colloquially called havana syndrome but is uh what is also known as anomalous health incidents there is real legitimate congressional hearings and restitution being provided to veterans who've been victims of what are being called microwave weapon attacks from some foreign actor there are these diplomats who are started in congressman
• 40:45
  Havana, Cuba, which is why it's called Havana syndrome, where, you know, what the theory of the case is that there's this sort of small form factor microwave weapon, you point it at the target, and it creates all kinds of nausea and, you know, neurological issues.
• 40:58
  Allegedly, the director of national intelligence and some of their people just went to review one of these Havana syndrome weapons recently in order to bring it into the...
• 41:08
  U.S. inventory, it's been an issue that the government's very hesitant to discuss or address.
• 41:15
  I think because it touches on the same neurological sovereignty question that this issue brings up, which is not an area we've really spent a lot of time thinking about.
• 41:26
  But these technologies are getting us to a place where we can have impact on individuals
• 41:36
  external to their brain, but impacts their brain.
• 41:41
  And that gets...
• 41:42
  It gets tricky.
• 41:43
  That gets very dicey.
• 41:43
  Yeah, it gets very, very tricky.
• 41:45
  It's a frontier in neuroscience.
• 41:47
  I think it's going to be incredibly...
• 41:51
  exciting and a little bit weird.
• 41:54
  I just want to also briefly note that it, you know, we talk about, this is a science show.
• 41:58
  So we talk about the research and like how it, you know, what the implications are and what people are doing just because that's the focus of the show does not mean that we don't identify with and understand the,
• 42:09
  risks and challenges that are related to these in a whole number of different ways and situations.
• 42:14
  There are a lot of people who spend a lot of time thinking about that area and do content around that.
• 42:20
  We just try to talk about the science from first principles to give everyone a frame of reference to understand the,
• 42:26
  Where is technology going and how is it going to impact our everyday life?
• 42:29
  Yeah, and how does it work?
• 42:30
  And how does it work?
• 42:31
  Because we need to know how these things work to know, to even have the discussion about neurological sovereignty, which sounds so ridiculous.
• 42:37
  Yeah.
• 42:38
  But once you know how it works, you get why that's important to discuss.
• 42:42
  Yes.
• 42:42
  But we just don't yet always know how it works.
• 42:44
  Exactly.
• 42:44
  Yeah, and I just wanted to end this story on a lighter note.
• 42:47
  Yes.
• 42:48
  There's a movie, a little indie film that I was involved in.
• 42:52
  I was the scientific advisor on this film called Goldmine from a mutual friend of ours.
• 42:57
  He was the producer, director, and writer of the film.
• 43:00
  It is being distributed on Amazon and other digital platforms starting February 24th, 2026.
• 43:05
  I was the scientific advisor on the film.
• 43:10
  Lester was involved in some of the promotion of it.
• 43:12
  It's about...
• 43:14
  It's a really nice story about a father-daughter relationship.
• 43:18
  It involves virtual reality, getting into the neural landscape of virtual reality, maybe some potential...
• 43:28
  therapeutic advantages of virtual reality in the future.
• 43:31
  I think you guys should go check it out.
• 43:32
  It's available on Amazon starting pretty soon.
• 43:35
  I think this week.
• 43:37
  Yes, please go watch Goldmine.
• 43:39
  Big shout out to writer-director Matt Marder, who was one of the original co-hosts on one of your past podcast endeavors.
• 43:47
  That's right.
• 43:47
  Dark Matters.
• 43:48
  Yeah.
• 43:49
  So friend of the pod.
• 43:50
  Friend of the pod.
• 43:51
  Really, really helpful.
• 43:54
  Anyone is interested, it's fantastic.
• 43:56
  We went to the premiere.
• 43:57
  It's definitely worth a watch, so definitely check that out.
• 44:00
  One of our first friends to make it to streaming.
• 44:02
  Yeah.
• 44:02
  It's fantastic.
• 44:04
  Great story number one, neuroscience story, dream engineering.
• 44:09
  Again, you're not going to get these kind of stories anywhere else, folks.
• 44:12
  It's the best of the best.
• 44:14
  Yeah, that was crazy, bro.
• 44:14
  The best.
• 44:15
  Sir.
• 44:16
  And with that, we will move into our rundown.
• 44:20
  Again, we cannot cover every breaking science research story every week in the level of detail of our main stories.
• 44:27
  So we use the rundown as a way to talk about other stories we found fascinating that we want to share with you all.
• 44:32
  You can go in and dig in on your own.
• 44:34
  But we'll just cover them topically a little bit at first.
• 44:39
  Yeah.
• 45:03
  And if you're having an issue trying to grok the subjects we're talking about, check out the video podcast version.
• 45:09
  It's a much more robust way to learn these concepts and have them sink in so we can get it reactivated when we start manipulating your dreams later.
• 45:18
  And...
• 45:20
  When you're on said Spotify and YouTube, we are in our fight for our lives in the billionaire algorithm.
• 45:27
  So a five star on either is super helpful for us to get this show out to more people.
• 45:33
  Leave a comment if you want to say you have a different idea or you want to cover something, anything.
• 45:39
  Put it in the comments.
• 45:40
  Or even just like random words.
• 45:41
  Just anything.
• 45:42
  It's super helpful for us.
• 45:45
  Share it with a friend.
• 45:46
  If you're at a research lab, bring it up during your lunch.
• 45:49
  I know you guys have coffees and lunch and learns we talk about.
• 45:51
  This is the best show for the science research community.
• 45:55
  Trying to get it out to more people.
• 45:56
  And.
• 45:57
  If you would like to support the show, we have a new donation portal at ffppod.com backslash donate.
• 46:04
  Become a monthly patron.
• 46:06
  It helps us run this show, which, again, is just the two of us.
• 46:11
  I'm going to get better at singing so I can sing that better.
• 46:13
  Last note, we do have a new update to the website.
• 46:17
  We now have chaptering on all of our videos as well as all of the past both research papers and news articles related to each episode with a little bit of a more robust research papers page.
• 46:31
  We are now set up for some of our more fun features we're looking to do in the future.
• 46:35
  So please go check that out at ffppod.com.
• 46:40
  Our first story in the rundown is a possible pulsar at the Milky Way Center.
• 46:47
  This is an astrophysics story.
• 46:48
  There's a supermassive black hole at the center of our galaxy.
• 46:53
  What's going on here?
• 46:54
  Yes.
• 46:55
  What they're looking for with the supermassive black hole, which is a very famous supermassive black hole, it's the closest supermassive black hole to Earth.
• 47:03
  It's at the center of our Milky Way galaxy.
• 47:06
  And...
• 47:08
  We finally found a pulsar, or we think at least, that we found a pulsar near that supermassive black hole.
• 47:15
  A pulsar is a neutron star that's extremely, extremely dense.
• 47:20
  It's about – neutron stars are like the size of the sun.
• 47:23
  Sorry.
• 47:24
  No, I should say this clearly.
• 47:26
  A neutron star is about the mass of the sun, if not bigger, but it's about the size of a city.
• 47:32
  Okay.
• 47:33
  Okay?
• 47:33
  Extremely dense star where it's called a neutron star because the pressure from gravity is so high that the electrons and the protons fuse together, and it's a giant ball of neutrons.
• 47:46
  Like, not even atoms can exist at this amount of gravitational pressure.
• 47:50
  Okay?
• 47:51
  Okay.
• 47:52
  What's cool about pulsars are they are specific types of neutron stars that have a really high spin.
• 47:59
  And they shoot out radio waves.
• 48:03
  And the radio waves are like a lighthouse as they're spinning.
• 48:08
  And pulsars are such that the lighthouse beam is exactly aligned with Earth.
• 48:13
  Okay.
• 48:14
  Okay?
• 48:14
  There's plenty of neutron stars where the lighthouse beam is not aligned with Earth.
• 48:17
  Mm-hmm.
• 48:18
  But for pulsars, we just happen to be lucky enough that the lighthouse beam is aligned with Earth.
• 48:23
  So when we point our radio dish...
• 48:25
  at that pulsar, we're going to get beep, beep, beep, beep, beep, metronomic radio pulses from that point in the sky.
• 48:33
  Okay?
• 48:34
  This particular pulsar that they think they found is about 8 milliseconds.
• 48:38
  This is a millisecond pulsar.
• 48:39
  So every 8 milliseconds, this thing is turning around.
• 48:42
  Imagine that.
• 48:43
  Something the size of the sun, if not bigger, about the size of a city.
• 48:47
  The mass of the sun is the size of a city.
• 48:48
  Yeah, sorry.
• 48:49
  I keep saying that.
• 48:49
  The mass of the sun, but the size of a city rotating around, right?
• 48:53
  And given the conservation of angular momentum, this thing is not slowing down, okay?
• 48:59
  This thing is going to keep going at 8 milliseconds.
• 49:02
  Mm-hmm.
• 49:04
  That makes this a really nice clock.
• 49:07
  Right.
• 49:07
  Okay?
• 49:08
  And if we find such a really nice clock near the galactic center where there's a giant black hole that is warping space and time, we can test Einstein's general relativity to unprecedented degree.
• 49:23
  That's why everyone's excited about this.
• 49:25
  is the idea that the pulsar becomes an instrument in and of itself.
• 49:29
  Yes.
• 49:29
  Yes.
• 49:30
  That's exactly right.
• 49:30
  Yeah.
• 49:31
  Because a lot of things can happen.
• 49:33
  The gravitational field of the black hole itself can start influencing the pulsar.
• 49:38
  Right.
• 49:38
  Right.
• 49:39
  And maybe if it wobbles a little bit less or if the timing is delayed, we can test Einstein's prediction that way.
• 49:46
  On the other hand, the radio signal that's coming from the pulsar is light.
• 49:52
  And light is itself...
• 49:54
  you know, affected by space and time.
• 49:56
  Right.
• 49:57
  And if there's a lot of warping by that black hole, that light is going to get affected.
• 50:01
  Yes.
• 50:01
  So monitoring this thing over years is going to give us extremely nice test
• 50:08
  For Einstein's general theory of relativity.
• 50:10
  That's why everyone's excited.
• 50:11
  It's not entirely foolproof, the analysis.
• 50:16
  And so, you know, there's still more stuff that needs to be done to confirm it.
• 50:19
  Yes.
• 50:20
  But it's tantalizingly close.
• 50:22
  This was out of Columbia University and the Breakthrough Listen project, which I don't know if you know, that's the like new SETI.
• 50:30
  Yeah, yeah, yeah.
• 50:30
  Right?
• 50:30
  Yeah, yeah.
• 50:31
  And they have this giant like breakthrough listen galactic center survey where they conducted like the most sensitive radio search for pulsars near the galactic center.
• 50:42
  It was out in the Astrophysical Journal.
• 50:44
  It's something a lot of astrophysicists are very excited about.
• 50:47
  That is very exciting.
• 50:48
  Again, it's early, as is almost every story we talk about on this podcast.
• 50:53
  So it goes without saying that we're waiting for continued further analysis, replication, etc.,
• 51:00
  And I think it's going to be soon.
• 51:01
  So, you know, when we get a confirmation, maybe we'll do a deep dive into it.
• 51:05
  This is very exciting.
• 51:06
  That's actually a huge deal.
• 51:08
  That was our story number one, Columbia to Astrophysical Journey.
• 51:11
  For number two, decoding an ancient Roman board game, an archaeology story about how AI has now helped us figure out the rules for...
• 51:23
  For this unknown board game from the Roman period.
• 51:27
  Yeah, this one was pretty cool.
• 51:28
  Okay, so there's a 2000 year old limestone slab that was found in the Netherlands.
• 51:34
  And it was identified as an ancient Roman board game.
• 51:37
  It was sitting in a museum.
• 51:41
  And some people who are really into...
• 51:45
  board games from antiquity and from like human civilization saw it and wanted to analyze it.
• 51:51
  The rules are lost to history.
• 51:53
  Obviously, there's no like, you know, you know, the manual that comes with the board game about what the rules are.
• 51:58
  OK, there's nothing that we can do that.
• 51:59
  But what we can do is look at that slab of limestone and figure out where the wear and tear is.
• 52:05
  And then we can use AI to play thousands of simulated games with various rules.
• 52:12
  Right.
• 52:12
  Right.
• 52:13
  So an AI plays with itself.
• 52:14
  This is kind of like how Google DeepMind trained AlphaGo.
• 52:19
  Yep.
• 52:19
  You know?
• 52:20
  Yep.
• 52:20
  So we've got all of these AIs that are playing thousands of different rules.
• 52:27
  And from those thousands of different games, tens of thousands of different games, we can figure out which games required the movement of pieces.
• 52:35
  Right.
• 52:35
  Such that it would match the wear and tear on the limestone slab.
• 52:40
  And they figured out about, like, I think there were, like, four...
• 52:46
  four to nine different games.
• 52:50
  And they're all of a particular type that are still kind of played in Scandinavia.
• 52:55
  And they match the wear and tear of this thing.
• 52:57
  And it turns out it's like an asymmetric blockade game.
• 53:01
  You can actually go and play it.
• 53:03
  They made an online version of it.
• 53:04
  It's called Ludus Coriovali.
• 53:08
  It's played with four hounds trying to trap...
• 53:10
  Two hairs.
• 53:12
  That's what they called it.
• 53:13
  Okay.
• 53:13
  But it's very cool because it's like a use of AI to something that I literally never thought about.
• 53:19
  Yeah, yeah, yeah.
• 53:21
  And it's a clever use case because you have some kind of physical indicator, which is the where.
• 53:27
  Yeah.
• 53:27
  And tear.
• 53:28
  And ultimately, there's a finite number of options for what the rule set was for the game that would result in, especially on a limestone board, that kind of wear and tear.
• 53:37
  So it makes logical sense, but I never would have thought of that.
• 53:40
  I never would have thought of that.
• 53:41
  Yeah.
• 53:41
  And I mean, it's really cool because this discovery pushes the known history of these types of board games back several centuries now.
• 53:49
  You know?
• 53:50
  Fantastic.
• 53:50
  That's a good one.
• 53:51
  Very cool.
• 53:52
  We love our archaeology.
• 53:53
  We've touched on Rome a couple times.
• 53:55
  And this is out of Leiden University in the journal Antiquity.
• 53:59
  Yes, this is good, good, good.
• 54:01
  We are going to keep it hot with our story number three.
• 54:04
  This one is a genetics and human evolution story.
• 54:08
  How we became human might be related to, unsurprisingly, our fingers.
• 54:14
  But what about this one is particularly interesting?
• 54:16
  Yeah.
• 54:16
  Yeah, this one was really cool because usually when we think about human evolution, we always think about brain size.
• 54:21
  Yes.
• 54:22
  Bigger brain size means we're smarter, means we're more human-like to modern-day humans.
• 54:27
  This new research points to the fact that prenatal hormones— Mm-hmm.
• 54:31
  that we are subject to when we are in our mother's womb, that's actually a surprising driving factor to brain size and finger size.
• 54:41
  The clue lies in 2D to 4D finger ratio.
• 54:45
  That's the ratio between your ring finger and your index finger.
• 54:50
  And if you've got a longer index finger, that usually means that you've had higher prenatal estrogen exposure during the first trimester.
• 55:02
  larger brain size.
• 55:03
  Boo.
• 55:06
  For boys and not girls, too.
• 55:08
  Oh, no.
• 55:09
  I have a small brain.
• 55:10
  Now, I think this is an overarching evolution story rather than individual... Fair, fair, fair.
• 55:17
  Because I think what they're saying is the effect is something where these two genes are correlated.
• 55:23
  Okay.
• 55:23
  Right?
• 55:23
  Okay.
• 55:24
  And it's in line with a lot of recent...
• 55:30
  research that shows that there's like this this thing called the feminization of the skeleton and this is not like anything political or anything like that it's not a culture war i think no this is not a culture war at all it's relevant because in human evolution increases in brain size are found alongside something called the feminization of the skeleton
• 55:52
  For example, high values of this 2D to 4D in males, the ring finger to the index finger, they've been found with elevated rates of heart problems, poor sperm counts, predisposition to schizophrenia in males.
• 56:08
  I see.
• 56:09
  Okay.
• 56:09
  And it seems that this stuff is coming from prenatal hormones from the fact that we are—
• 56:15
  we were subjected to estrogen in our first trimester.
• 56:19
  That led to heightened brain size, but it also led to all of these problems.
• 56:23
  I see.
• 56:24
  And in the course of evolution, I guess the human species decided, I'll go with the large brain size, even though it comes with all of these problems.
• 56:35
  That's fascinating.
• 56:36
  You know?
• 56:36
  No, that isn't...
• 56:38
  The... You know, the...
• 56:40
  Let me not.
• 56:41
  I'll leave it.
• 56:42
  I won't.
• 56:43
  Let me leave.
• 56:43
  This is out of the Swansea University and Istanbul University in the journal Early Human Development.
• 56:49
  I thought it was quite interesting because we always think about how genes are related to each other when they go through evolution.
• 56:57
  So this is something that's very important.
• 56:58
  I mean, you can also map this onto the site –
• 57:03
  When people talk about quote-unquote masculinity and the change in body, the body stature of, like, men generally over time, if there's a correlation to higher brain size equals to the skeletal structure of us being slightly different, that is an interesting... That is an interesting angle, yeah.
• 57:19
  That, like, we kind of see manifesting, unfortunately, in the culture war, but has sort of this...
• 57:24
  genetic basis for it.
• 57:27
  Fascinating story number three.
• 57:28
  Our last story of the rundown combines AI with bioengineering.
• 57:35
  So no risk here at all.
• 57:37
  A new machine learning pipeline that is streamlining protein engineering.
• 57:41
  We've talked about AlphaFold God knows how many times.
• 57:45
  Yes.
• 57:45
  We've gotten comments.
• 57:46
  Yes, we understand.
• 57:48
  It's not perfect.
• 57:49
  Nothing we're talking about on the podcast is perfect.
• 57:50
  Yeah.
• 57:51
  No, this is science.
• 57:52
  We get that.
• 57:54
  That being said... That being said...
• 57:57
  Alpha fold is not everything.
• 57:59
  Right.
• 58:00
  Right?
• 58:00
  Alpha fold means you give me a protein sequence, which is a bunch of amino acids and a line.
• 58:05
  I'll probably be able to tell you what it looks like in 3D.
• 58:09
  Okay?
• 58:09
  And how it changes shape.
• 58:11
  Okay.
• 58:12
  What if we wanted to design proteins?
• 58:15
  If we want to design proteins, the search space is incredibly big because if I have 100 amino acids...
• 58:21
  that I want to fit into a protein.
• 58:22
  There's 20 choices for each amino acid.
• 58:25
  That's 20 to the 100th power.
• 58:27
  There's more atoms in the universe.
• 58:29
  So I'm not going to...
• 58:30
  I need ways to hone down if I want to design a new protein.
• 58:36
  How to actually...
• 58:39
  Reduce that search space.
• 58:41
  Okay?
• 58:42
  Right.
• 58:42
  So nowadays what we can do with various techniques is get down to like tens of thousands of protein candidates.
• 58:49
  If we get down to tens of thousands of protein candidates, the bottleneck then becomes the lab.
• 58:54
  Right.
• 58:54
  Because I can only really efficiently build and test around 100 different variants.
• 59:00
  Right.
• 59:00
  Right.
• 59:02
  So what's the best way to choose which hundred I want to actually test and make in the lab and then figure out?
• 59:10
  That is what these guys at the University of California at Berkeley and the ARC Institute are doing with this particular story that came out in science.
• 59:18
  They created something called Multi-Evolve.
• 59:21
  The idea is they've created an ensemble of protein language models.
• 59:26
  And what these protein language models do is not just care about the fitness landscape of like, what is the stuff that evolution constrains me for?
• 59:34
  These models also care about stuff like, is the enzyme going to catalyze faster?
• 59:40
  Is the antibody going to bind tighter?
• 59:44
  Is the CRISPR tool going to edit better?
• 59:47
  These are things that aren't really captured by evolution.
• 59:49
  Yeah.
• 59:50
  But this particular multi-evolve
• 59:54
  paradigm is actually going to do and what what's very cool about this is now we've put the lab and the ai right in the loop right okay like it's one big loop yes of discovery where the ai is interacting with people in the lab telling you what to what to do in the lab the lab is going back to the ai and you can have this iteration happen even faster right and the ai framework was able to accurately predict how proteins will function
• 1:00:22
  Even when several of their amino acids...
• 1:00:25
  get mutated.
• 1:00:26
  Because what you want to do is if you have an amino acid, you want to figure out, oh, if I replace this amino acid with a different one, is it going to make it better or is it going to make it worse?
• 1:00:34
  What's more is if I replace these two, is that going to make it better?
• 1:00:38
  Because if I just replace this one and it's good, and I replace this one and it's good, that doesn't mean if I replace both of them, it's good.
• 1:00:44
  There's some synergistic landscape.
• 1:00:47
  Maybe they're antagonists.
• 1:00:48
  Yes.
• 1:00:49
  Right?
• 1:00:49
  And so all of these relationships are now...
• 1:00:53
  Something that I can parameterize with this multi-evolved model, right?
• 1:00:56
  And bioengineers can now develop this new machine learning framework that condenses that problem of protein engineering into a single round of testing.
• 1:01:06
  And in their test, the model was successfully able to find combinations of mutations that outperformed the original proteins.
• 1:01:14
  Yep, showing that.
• 1:01:15
  that having it in the loop like that actually has fundamental improvement value.
• 1:01:21
  Yeah, yeah.
• 1:01:21
  And it's very cool because the Ark Institute, which is in Silicon Valley, the whole point of it is to have frontier AI capabilities and experimental biologists under the same physical roof.
• 1:01:33
  That's how they operate.
• 1:01:34
  And...
• 1:01:35
  Closing that loop between computation and the wet lab.
• 1:01:38
  Yep.
• 1:01:39
  This is like their first iteration.
• 1:01:40
  It's very exciting to see what they're going to come up with next.
• 1:01:43
  This is fascinating because this is something similar that, you know, my mom works in clinical trials.
• 1:01:49
  And there's this similar desire to find ways to incorporate tools like AI into the process to create that feedback, that virtuous cycle and feedback loop.
• 1:02:01
  But you have to start somewhere.
• 1:02:02
  And so it's interesting to hear in a variety of these spaces across –
• 1:02:07
  both fundamental research and application, for example, in pharmaceuticals, um, that people are finding ways to integrate it.
• 1:02:16
  That is changing outcomes.
• 1:02:18
  Yeah.
• 1:02:18
  That is straight up changing.
• 1:02:20
  Yeah.
• 1:02:20
  Influencing outcomes for the positive.
• 1:02:22
  I get that.
• 1:02:23
  Everyone wants to say it's wrong because they tried it one time.
• 1:02:26
  And that means that all of it is wrong, but, um, that's just not how it works.
• 1:02:30
  Uh, it's not perfect, but it is, it is filling in gaps that are significantly meaningful.
• 1:02:35
  Yeah.
• 1:02:35
  Yeah.
• 1:02:36
  Great rundown this week.
• 1:02:38
  I mean, it's going to be the first story, the rundown.
• 1:02:40
  These are some fantastic stories.
• 1:02:44
  We're going to move on to our main story number two.
• 1:02:47
  This one is a fundamental physics story.
• 1:02:50
  It's about trying to measure one of the smallest things in the universe.
• 1:02:54
  We talked about it a lot on this pod, the proton.
• 1:02:57
  It's one of the fundamental particles inside the atomic nucleus.
• 1:03:00
  Yes.
• 1:03:01
  And we've done it to super high accuracy.
• 1:03:03
  Yes, extremely high accuracy.
• 1:03:05
  This is from the Max Planck Institute for Quantum Optics, and it was published in Nature.
• 1:03:10
  I'm excited for this one.
• 1:03:12
  Let's dig in.
• 1:03:13
  Yeah.
• 1:03:13
  We'll start with the standard model.
• 1:03:15
  Yes.
• 1:03:15
  The standard model of particle physics is the most successful model that we have to date about our universe.
• 1:03:23
  Okay?
• 1:03:23
  It's the model that...
• 1:03:26
  The working constituents are the fundamental particles, or I should really say the fundamental quantum fields.
• 1:03:33
  The Higgs boson comes out of it.
• 1:03:34
  The electron, the proton is actually not in the standard model.
• 1:03:38
  You have up and down quarks.
• 1:03:40
  Those are the constituents of the protons and the neutrons.
• 1:03:43
  And it's incredible.
• 1:03:45
  It is incredible, but it is incomplete.
• 1:03:48
  There's no dark matter.
• 1:03:49
  There's no dark energy.
• 1:03:51
  And there's no gravity.
• 1:03:53
  So we've always been in the search for new physics.
• 1:03:56
  How do we break the standard model so we can find out what is missing?
• 1:04:01
  And what is the path forward to find out what is missing?
• 1:04:04
  This is the idea of like unification.
• 1:04:06
  Yeah.
• 1:04:06
  A grand unified theory.
• 1:04:08
  Yeah.
• 1:04:08
  Yeah.
• 1:04:08
  Not just unification, but also these new particles like is dark matter a particle?
• 1:04:12
  I understood.
• 1:04:13
  That's like not even has anything to do with unifying with gravity.
• 1:04:16
  It's like there's just a whole set of...
• 1:04:18
  other particles that we don't know anything about.
• 1:04:20
  Most of the universe, we have no idea what it is.
• 1:04:21
  Yeah, yeah, yeah, exactly.
• 1:04:23
  So there's got to be a way for us to find out, right?
• 1:04:25
  Right.
• 1:04:25
  And usually when we think about probing that frontier of particle physics, we think about particle accelerators, right?
• 1:04:33
  The most famous one is the one at CERN.
• 1:04:35
  The Large Hadron Collider is, you know, tens of kilometers long.
• 1:04:41
  They want to build like a giant future collider that's hundreds of kilometers long.
• 1:04:48
  They want a lot of money because they want to build these large, large colliders.
• 1:04:54
  Higher energy means we can probe the depths of these particles in these fields.
• 1:05:00
  But there's another frontier that physicists talk about when they're looking to test the standard model, and that is the precision frontier.
• 1:05:10
  And it might offer an equally vital search for new physics.
• 1:05:15
  What you can do is instead of building giant particle colliders, you build extremely precise experiments in your lab, in a room, in a physics department.
• 1:05:26
  And you're able to now measure stuff so precisely that perhaps you can discern between what is known physics and what is new.
• 1:05:35
  This particular photograph is from the Max Planck Institute.
• 1:05:42
  And I saw this actually on LinkedIn from the author of the study that we're going to talk about, Lothar Meissenbacher.
• 1:05:51
  Okay.
• 1:05:52
  He posted this photo of his lab that actually made this measurement of the size of the proton.
• 1:05:57
  Okay.
• 1:05:58
  And what it reminds me of is a colloquium that I had attended at UCLA when I was a PhD student there.
• 1:06:05
  It was a colloquium given by Dave DeMille, who's now at University of Chicago.
• 1:06:09
  He used to be at Yale.
• 1:06:11
  He was one of these precision physicists, AMO, atomic molecular optical physicists.
• 1:06:17
  And he was showing the lab setup, and he was trying to measure the dipole moment of the electron.
• 1:06:24
  Effectively, how round is the electron?
• 1:06:27
  We think the electron is the roundest object in the universe.
• 1:06:30
  Okay?
• 1:06:31
  But—
• 1:06:32
  there's a possibility that it's like got a tiny bump on the North pole.
• 1:06:36
  Okay.
• 1:06:37
  Right.
• 1:06:37
  And the, and the not tiny bump on the South pole.
• 1:06:40
  So there, there might be a little bit of not roundedness in the electron.
• 1:06:45
  And he wanted to measure that.
• 1:06:46
  And what he was showing was, he showed a photo of his lab and he would,
• 1:06:53
  The path of the laser light is a few meters, things like that.
• 1:06:56
  But he would put that as .001 kilometers, and he would do a dig at the particle accelerator guys and be like, I'm going to put this in a unit of length that these particle accelerator guys can understand.
• 1:07:10
  So this is about .001 kilometers.
• 1:07:15
  The size of my lens is about .0.
• 1:07:17
  And it was just so funny, dude, because it's just like...
• 1:07:20
  He's just like pulling their leg.
• 1:07:22
  That's quite nice.
• 1:07:22
  I think it was so funny.
• 1:07:24
  Look, it's okay to take the piss every once in a while.
• 1:07:27
  Yeah, yeah, yeah.
• 1:07:28
  He was a really funny guy.
• 1:07:30
  Really great speaker as well.
• 1:07:32
  So what these guys are trying to do at the Max Planck Institute –
• 1:07:36
  when they came out with this landmark nature paper in February 2026, so just this month, is try to solve the proton radius puzzle.
• 1:07:47
  Okay?
• 1:07:47
  There's been a decade-long discrepancy in how big the proton is.
• 1:07:54
  Okay?
• 1:07:54
  And this particular paper in Nature is trying to put that to rest.
• 1:07:59
  Yes.
• 1:07:59
  Okay?
• 1:08:00
  Subpart per trillion test results.
• 1:08:03
  Of the standard model with atomic hydrogen.
• 1:08:06
  What a great name.
• 1:08:08
  You know?
• 1:08:08
  They're testing the standard model with just a normal hydrogen atom.
• 1:08:13
  Okay.
• 1:08:13
  Okay?
• 1:08:14
  This is out of the Max Planck Institute for Quantum Optics.
• 1:08:17
  Yes.
• 1:08:17
  And the precision is 0.7 parts per trillion.
• 1:08:22
  That's pretty small.
• 1:08:23
  Yeah.
• 1:08:24
  That's pretty small.
• 1:08:24
  That's pretty small.
• 1:08:25
  And I want to give you a sense of just how small that is and why that's, like, ridiculous.
• 1:08:29
  Okay.
• 1:08:30
  Okay?
• 1:08:31
  Um...
• 1:08:32
  We're going to start with the hydrogen atom.
• 1:08:35
  Okay.
• 1:08:35
  This is our favorite atom.
• 1:08:36
  Every physicist's favorite atom.
• 1:08:38
  If you don't understand the hydrogen atom, there's no hope in understanding anything else.
• 1:08:43
  And it's something that we do on our first...
• 1:08:47
  First semester, quantum mechanics.
• 1:08:49
  You do the Schrodinger equation and you solve the hydrogen atom because it is the only atom that you can perfectly solve for.
• 1:08:56
  Okay.
• 1:08:57
  The idea with quantum mechanics is the following, right?
• 1:09:00
  We can never observe the electron.
• 1:09:02
  This is something that Heisenberg said when he actually came up with quantum mechanics 100 years ago in 1925.
• 1:09:08
  We can never observe the electron moving around a hydrogen atom, but what we can observe...
• 1:09:15
  is electrons jumping from one energy state to another energy state, because that's when light comes out.
• 1:09:22
  When we make hydrogen atoms glow, certain colors are emitted by that hydrogen atom.
• 1:09:29
  Different atoms emit different colors.
• 1:09:31
  And by observing those colors of light, we can discern the structure of the atom inside.
• 1:09:38
  That's always the game when it comes to quantum optics.
• 1:09:42
  Okay?
• 1:09:45
  These shells are not just like simple bore shells.
• 1:09:49
  They're actually very complicated.
• 1:09:50
  Okay.
• 1:09:50
  The hydrogen wave function...
• 1:09:53
  looks like very cool clouds of electrons around the central proton.
• 1:09:59
  This looks like some sick app icons right here.
• 1:10:02
  Yeah, but that's actually what the electrons are doing around a hydrogen atom.
• 1:10:06
  If you were to put them in certain orbitals, in certain states of angular momentum and energy, they would occupy those clouds.
• 1:10:15
  Those are the probability clouds that the electrons occupy.
• 1:10:18
  Okay?
• 1:10:18
  Got it.
• 1:10:19
  And so...
• 1:10:22
  What we want to do when we want to probe something like what is the size of the proton is we want to probe...
• 1:10:30
  what the colors of light are and how those colors of light shift when the proton is yay big versus yay big.
• 1:10:37
  Right.
• 1:10:37
  Okay.
• 1:10:38
  Because if the proton is some size or some other size, what that is going to do is change the electron orbitals, which will then change the light that comes out when electrons transition from one orbital to the other.
• 1:10:52
  Yes.
• 1:10:53
  Yes.
• 1:10:53
  Is that perfectly clear?
• 1:10:54
  Yeah.
• 1:10:54
  So different sizes...
• 1:10:56
  Of the proton, the way in which we can look at the way in which we understand the probabilities of where the electron will be, which is this electron cloud concept, is going to be fundamentally different.
• 1:11:09
  Yes.
• 1:11:10
  And we use, like, the color spectra, the energy, and how it appears as a means by which to identify what those cloud configurations will look like?
• 1:11:20
  Yes.
• 1:11:21
  If the proton is a certain size, it's a certain size.
• 1:11:23
  The cloud will look a tiny bit different, which means that the light that comes out when the electron transitions from one cloud, one energy state to another, is going to be slightly different frequency.
• 1:11:37
  Yes.
• 1:11:38
  And if we can measure that very precisely, then we can tell, back calculate what the size of the proton is.
• 1:11:45
  I see what you're saying.
• 1:11:46
  And so, okay.
• 1:11:47
  Yes.
• 1:11:47
  Makes sense.
• 1:11:48
  So what we're getting at is we're not measuring the proton directly.
• 1:11:52
  Yeah, you're not taking a ruler.
• 1:11:54
  Right, and looking like, hey, here's the, it's, you know, three angstrom.
• 1:11:58
  What we're saying is there is an emission, which is this electron changing energy states.
• 1:12:06
  That emission, if we measure that emission with a level of precision,
• 1:12:12
  we can derive the size of the proton from that quote.
• 1:12:16
  I'm using emission as a loose term here.
• 1:12:18
  Exactly.
• 1:12:19
  And if we look at photo number eight, right?
• 1:12:20
  Yes.
• 1:12:22
  The electrons are in these different clouds.
• 1:12:24
  Yes.
• 1:12:25
  There's different orbitals.
• 1:12:26
  For those in chemistry, you guys will remember something called 1s, 1s2, 2s2, 2p6, 3s2, 3p6.
• 1:12:33
  It's like a rap game that we used to memorize about all the electron configurations of each of the elements on the periodic table.
• 1:12:40
  Yeah.
• 1:12:41
  So each of these orbitals are shapes of the electron clouds.
• 1:12:46
  The 1s and the 2s are spherical.
• 1:12:49
  Okay.
• 1:12:49
  There's no net angular momentum.
• 1:12:51
  But like the 2ps, those are when electrons are lobed in particular axes.
• 1:12:57
  Like there's the x-axis, the y-axis, and the z-axis, right?
• 1:13:00
  And that's that second energy level.
• 1:13:01
  The first energy level, there's just the sphere.
• 1:13:03
  Yeah.
• 1:13:04
  The second energy level, the electron can be in the sphere, or it can be in these like dungbell shapes along the three axes, right?
• 1:13:11
  Yes.
• 1:13:11
  Now notice, the 1s and the 2s, those are spheres.
• 1:13:15
  Yes.
• 1:13:15
  Those will actually interact.
• 1:13:17
  With the proton, if there's a proton in the middle of that sphere.
• 1:13:21
  Okay.
• 1:13:21
  Okay?
• 1:13:22
  The 2S and the 2P, most of the time, the electron is hanging out on the outside.
• 1:13:27
  It's not hanging out near the nucleus of the atom.
• 1:13:30
  Which in this context is where the XYZ intersects.
• 1:13:32
  Yeah, the center.
• 1:13:33
  Yes.
• 1:13:34
  Right?
• 1:13:34
  Yes.
• 1:13:34
  And so...
• 1:13:36
  By looking at transitions between these different energies, we can figure out what is the contribution of the proton that's in the center and what is not.
• 1:13:45
  Because we're looking at basically the difference.
• 1:13:46
  Yes.
• 1:13:47
  Yes.
• 1:13:47
  Because the difference in these energies tells us what the frequency of the light that will come out.
• 1:13:52
  Higher frequency means higher energy difference.
• 1:13:54
  Okay?
• 1:13:55
  But to give you a sense of the scale of what we're talking about, right?
• 1:14:00
  Because we're – let's not lose sight of the fact –
• 1:14:04
  That we're talking about a hydrogen atom, and on top of that, we're talking about a proton at the center of the hydrogen atom.
• 1:14:09
  Right.
• 1:14:11
  The proton at the center of the hydrogen atom is measured in femtometers, which is 10 to the minus 15 meters.
• 1:14:18
  Okay.
• 1:14:18
  The hydrogen atom itself is about an angstrom, which is 10 to the minus 10.
• 1:14:23
  Okay, so there's 100,000 meters.
• 1:14:27
  difference between the size of a hydrogen atom and the size of a proton at the center of the hydrogen atom and to visualize this unimaginable scale i wanted to consider an analogy okay okay if a single hydrogen atom is expanded to the size of a professional sports stadium then the proton that's at its center is like the size of a p oh my god at the 50 yard line oh my god that's not that's not the worst part though okay in order to discern the size of the p
• 1:14:56
  We are looking at an electron that is in the grandstands.
• 1:15:00
  Oh, my God.
• 1:15:03
  You see what I'm saying?
• 1:15:03
  Yeah, no, that's ridiculous.
• 1:15:04
  Because the electron is hanging out all the way out here.
• 1:15:06
  Over there.
• 1:15:07
  We're looking at the behavior of these electrons that are hanging out at the grandstands to figure out how big is the P on the 50-yard line.
• 1:15:16
  I just – I have no – I have nothing.
• 1:15:17
  I just don't even – I don't even know what to say.
• 1:15:20
  It is insane that we can do these kinds of calculations.
• 1:15:22
  That's what I'm saying.
• 1:15:23
  Like, that's –
• 1:15:25
  Yeah, because I'm trying to even think about how you would do that at macro scale.
• 1:15:29
  Yeah, yeah, yeah.
• 1:15:29
  If I had a P. Yeah, yeah.
• 1:15:31
  I can't have a P. What kind of telephoto lens would I need at the grandstand?
• 1:15:38
  To be able to.
• 1:15:39
  Yeah.
• 1:15:39
  Okay.
• 1:15:40
  So this is something that is so infinitesimally small.
• 1:15:44
  And meaning the way in which we do all, like the engineering we do to even be able to do this stuff has to be able to operate at these impossibly small.
• 1:15:55
  Yes.
• 1:15:55
  Scales.
• 1:15:56
  Yeah.
• 1:15:57
  And at impossibly small error.
• 1:16:00
  Right.
• 1:16:00
  That's the main part.
• 1:16:01
  Right.
• 1:16:01
  Right.
• 1:16:02
  You're chasing down like 12 decimal places.
• 1:16:05
  Right.
• 1:16:06
  Okay.
• 1:16:07
  Right.
• 1:16:07
  That's a really important point.
• 1:16:09
  Yeah.
• 1:16:09
  Because it's not just like, oh, we got no.
• 1:16:10
  It's like to a level of specificity where 12 decimals is crazy.
• 1:16:14
  Which it's insane.
• 1:16:15
  It's like, no, it's not that.
• 1:16:16
  It's actually...
• 1:16:18
  This.
• 1:16:18
  Yeah.
• 1:16:19
  Okay.
• 1:16:19
  Oh, my God.
• 1:16:20
  My brain.
• 1:16:20
  Yeah.
• 1:16:21
  It's absolutely nuts.
• 1:16:22
  Okay.
• 1:16:23
  So here's how we're going to do it.
• 1:16:25
  Okay.
• 1:16:25
  Okay.
• 1:16:26
  We are going to calculate the transition frequency between different...
• 1:16:31
  orbitals.
• 1:16:32
  Okay.
• 1:16:32
  Okay.
• 1:16:33
  When we do that, this is the equation of what an electron sees when it's on the outside.
• 1:16:39
  This is the binding energy of atomic hydrogen.
• 1:16:41
  So what is the energy of an electron that's bound to the proton in the center?
• 1:16:46
  What I love about this is this is the first line of their paper.
• 1:16:49
  The binding energy of atomic hydrogen can be expressed as a giant equation.
• 1:16:55
  For those listening, there's like, I don't know, 23 different characters.
• 1:16:59
  Yeah.
• 1:16:59
  It's just, it is.
• 1:17:00
  It's just,
• 1:17:01
  And it depends on – crucially, it depends on two things, okay?
• 1:17:04
  It depends on something called the Rydberg constant, which is like a fundamental energy scale.
• 1:17:08
  Okay.
• 1:17:08
  And it depends on the proton charge radius, okay?
• 1:17:12
  Now, those are two different unknowns, right?
• 1:17:16
  And in order to figure out what those two unknowns are, you need two different observations.
• 1:17:20
  You can't just rely on one.
• 1:17:22
  So you can't just rely on a single atomic –
• 1:17:25
  energy spacing, you need to observe two different transition frequencies.
• 1:17:30
  I see.
• 1:17:30
  You need to observe two different colors that are coming out of my atomic hydrogen.
• 1:17:34
  Okay.
• 1:17:35
  That makes sense.
• 1:17:35
  Now the first one
• 1:17:38
  is the anchor measurement.
• 1:17:39
  That's already been done before.
• 1:17:40
  Okay.
• 1:17:41
  This is the ultra precise 1s to 2s transition.
• 1:17:44
  What they're doing in this paper is the 6p to 2s transition.
• 1:17:48
  The 6p orbital looks like that.
• 1:17:50
  It's dumbbells on top of, it's like a Russian nesting doll of dumbbells.
• 1:17:54
  Okay.
• 1:17:55
  What they're looking for is an electron in this particular energy state going down to the 2s state.
• 1:18:01
  Okay.
• 1:18:02
  What they want to do here is observe that transition frequency.
• 1:18:06
  And the 2S state was the larger spherical state that we talked about earlier.
• 1:18:09
  Yes, that we had talked about earlier.
• 1:18:11
  This is even bigger than that.
• 1:18:12
  Okay.
• 1:18:12
  This is, like, way out in the grandstands to, let's say, like, on the sidelines.
• 1:18:18
  Yep.
• 1:18:18
  Right?
• 1:18:18
  The electron is going from the grandstand all the way to, like, the really nice seats.
• 1:18:22
  Yeah, yeah, yeah.
• 1:18:23
  And that transition is what they're trying to look for.
• 1:18:26
  Got it.
• 1:18:26
  Got it.
• 1:18:27
  Okay.
• 1:18:27
  Yep.
• 1:18:28
  Yep.
• 1:18:28
  Let's talk about why this is even an issue.
• 1:18:31
  Right.
• 1:18:32
  Okay.
• 1:18:32
  Okay.
• 1:18:33
  Yeah, yeah.
• 1:18:33
  Proton size.
• 1:18:34
  Right.
• 1:18:35
  Because part of what I said earlier, which may or may not be true, it's like, you know, why can't we just measure it directly?
• 1:18:44
  Yeah.
• 1:18:45
  Well, I mean...
• 1:18:46
  How would you measure something that is a femtometer?
• 1:18:49
  Right.
• 1:18:50
  But so like for someone who might not understand the scale of the problem set, it's like, well, why don't you just look at it?
• 1:18:55
  Yeah.
• 1:18:56
  And it's like, well, the problem is it's what are we going to use to do that?
• 1:19:00
  Exactly.
• 1:19:01
  I mean, you could think, right?
• 1:19:02
  Like what if we just like set light?
• 1:19:04
  Okay.
• 1:19:05
  Okay.
• 1:19:05
  What if we just had like...
• 1:19:08
  Light at a femtometer.
• 1:19:10
  Mm-hmm.
• 1:19:11
  How...
• 1:19:12
  I think that kind of light is extremely high frequency.
• 1:19:15
  Okay.
• 1:19:15
  Okay?
• 1:19:16
  Like a light at a wavelength of a femtometer.
• 1:19:18
  Yeah.
• 1:19:19
  Anything larger than the obstacle.
• 1:19:22
  If I were to shine, like, visible light.
• 1:19:24
  Yes.
• 1:19:24
  Which is, like, 400 nanometers.
• 1:19:26
  Right.
• 1:19:26
  Right.
• 1:19:27
  On something that is a femtometer.
• 1:19:28
  400 nanometers is 10 to the minus 7 meters.
• 1:19:32
  This thing is 10 to the minus 15.
• 1:19:34
  Imagine a giant ocean wave.
• 1:19:37
  And then there's a pebble in the way.
• 1:19:39
  Is the ocean wave going to care?
• 1:19:41
  No.
• 1:19:42
  The way we image things is that the wavelength of light needs to be way smaller than the thing we're measuring.
• 1:19:48
  Because then the light bends and refracts and bounces off.
• 1:19:51
  This is important.
• 1:19:52
  But if it's like, you know...
• 1:19:55
  The object we're trying to measure is literally half the size of the wavelength of light we would send at it to even measure it.
• 1:20:02
  Yeah, not even half.
• 1:20:02
  No, it's a hundred thousandth.
• 1:20:05
  A hundred thousandth.
• 1:20:06
  Or a millionth the size.
• 1:20:07
  So it literally is.
• 1:20:08
  It's a pebble.
• 1:20:09
  With a tsunami.
• 1:20:09
  With a tsunami.
• 1:20:10
  Like, no, a grain of sand versus, like, the Nazare, you know, the Portugal wave.
• 1:20:17
  Yeah, yeah, yeah, yeah.
• 1:20:18
  The 100-foot wave in Nazare, Portugal.
• 1:20:20
  Yeah, in Nazare, Portugal.
• 1:20:21
  Yeah, yeah, yeah.
• 1:20:21
  Do you think the Nazare, Portugal wave cares about a tiny bit of grain of sand misplaced one way or the other?
• 1:20:28
  No, it really doesn't.
• 1:20:29
  Right.
• 1:20:30
  So we got to get really clever with this kind of stuff.
• 1:20:32
  So we can't just image it the way we normally do optical imaging.
• 1:20:36
  No.
• 1:20:36
  It's just the scale is not – it doesn't even make sense.
• 1:20:39
  It doesn't even make sense.
• 1:20:40
  Yeah, exactly.
• 1:20:41
  Okay.
• 1:20:41
  And so people have done it before.
• 1:20:43
  People have measured the size of a proton before.
• 1:20:46
  Yes.
• 1:20:46
  And it came out to about 0.8758 according to this giant consortium where they had like electrons that would scatter off nuclei.
• 1:20:56
  Yeah, yeah.
• 1:20:56
  And then they would try to figure out, okay, like what is the –
• 1:20:59
  what is the size of the proton in there.
• 1:21:01
  They also had something called electronic hydrogen spectroscopy.
• 1:21:04
  It's a traditional laser measurement.
• 1:21:06
  Kind of similar, but not as insane as the one that we're going to talk about.
• 1:21:10
  And they got it to about 0.8758 femtometers.
• 1:21:14
  Then in 2010, there was a bombshell.
• 1:21:17
  Because...
• 1:21:18
  Some of the same guys in this current paper made an exotic hydrogen atom.
• 1:21:24
  They made a hydrogen atom out of muons.
• 1:21:27
  Okay.
• 1:21:28
  And they published a paper in Nature called The Size of the Proton.
• 1:21:32
  This was almost 16 years ago.
• 1:21:35
  Almost 16 years ago.
• 1:21:35
  These guys published...
• 1:21:39
  a hydrogen atom, where instead of an electron moving around it, they've got a muon that moves around it.
• 1:21:45
  A muon is the close cousin of the electron, but it's 200 times more massive.
• 1:21:51
  And because it's 200 times more massive...
• 1:21:56
  The atom that you create with a proton and a muon is going to be 200 times smaller.
• 1:22:01
  And we've got a little sort of like thing to show for that, right?
• 1:22:05
  The atom is going to be – the muonic hydrogen is going to be 200 times smaller.
• 1:22:10
  If it's 200 times smaller, that muon – effectively what we've done is take that giant stadium and turn it into like a high school stadium.
• 1:22:20
  Now –
• 1:22:21
  The relationship between my muon and the proton is going to be a lot closer, and my error bar is going to be smaller.
• 1:22:29
  The measurement we're trying to make is the distance between the electron and the grandstands and the proton at the 50-yard line.
• 1:22:34
  Yeah, effectively, we're trying to get, like, the light that comes out of this atom.
• 1:22:39
  The smaller the atom is, like, the more we can sort of...
• 1:22:43
  nail down exactly what that frequency is.
• 1:22:45
  Right.
• 1:22:47
  The delta for error is much smaller.
• 1:22:49
  Yeah, because we've effectively made the atom smaller.
• 1:22:52
  Right.
• 1:22:52
  Right.
• 1:22:53
  And the key idea here was the muon as the orbiting part, as opposed to a standard electron.
• 1:23:01
  It was much more massive, which meant the proton necessarily needed to be smaller, which is what creates...
• 1:23:05
  The atom needs to be smaller.
• 1:23:06
  Excuse me.
• 1:23:07
  The atom needs to be smaller, which is what collapsed the surface area of the measurement to be this more...
• 1:23:13
  Deal with a bowl.
• 1:23:14
  Yes.
• 1:23:14
  Size.
• 1:23:15
  Yes.
• 1:23:16
  And we got a whole new number for the size of the proton there.
• 1:23:20
  0.84 instead of 0.87.
• 1:23:21
  Okay.
• 1:23:23
  Already on the second significant figure, we're off.
• 1:23:27
  Okay.
• 1:23:28
  That is unheard of for physicists who are doing precision measurement.
• 1:23:31
  They're like, this is absolutely awful.
• 1:23:34
  Okay.
• 1:23:35
  Okay.
• 1:23:35
  That's like almost 5% difference.
• 1:23:40
  Yeah.
• 1:23:40
  That's unacceptable.
• 1:23:41
  That's unacceptable.
• 1:23:43
  Okay.
• 1:23:43
  In every sense of the word.
• 1:23:45
  There's two...
• 1:23:46
  There's two possibilities.
• 1:23:48
  Okay.
• 1:23:48
  Okay?
• 1:23:49
  Either our theory is wrong, meaning leptons are not universal.
• 1:23:53
  We used to think that the muon and the electron, the only thing that's different between them is the mass.
• 1:23:58
  But what if there's new physics and the muon is actually behaving differently around my proton, which is why I'm getting this different...
• 1:24:06
  measurement okay yeah either that or okay there's some undetected systematic error in the earlier measurement of the electron hydrogen basically the first time we did it something was wrong something was wrong no or no one caught it at the time yeah but now we get it or there's literal new physics right that's one of the two it's one of the two and now you can see why this is such a big deal yeah we need to really figure out if that first measurement was was actually wrong correct because that's actually a okay yes right that would be a big deal
• 1:24:34
  That would be a big deal because if the first measurement was correct, then this is a really big deal because then that means there is actually new physics.
• 1:24:41
  The muon and the electron are actually different somehow.
• 1:24:44
  Yes.
• 1:24:44
  And there's like now the theorists are super happy.
• 1:24:47
  Right.
• 1:24:47
  Right.
• 1:24:48
  Right.
• 1:24:48
  Or the experimentalists are not happy.
• 1:24:51
  Yeah.
• 1:24:51
  And these new experimentalists are happy because they like their muonic hydrogen measurement was actually correct.
• 1:24:57
  Yes.
• 1:24:58
  And it's like everybody else.
• 1:24:59
  Theorists go back to the chalkboard.
• 1:25:01
  Yeah.
• 1:25:01
  Yeah.
• 1:25:02
  It's not a field day for you guys.
• 1:25:03
  Right.
• 1:25:03
  Right.
• 1:25:04
  And so that's where this current experiment comes in.
• 1:25:08
  Oh, this is fascinating.
• 1:25:09
  They replicated that muon experiment.
• 1:25:12
  Okay.
• 1:25:12
  From 2010.
• 1:25:13
  From 2010.
• 1:25:14
  They've been working on it for 10 years to try and nail down this measurement using just normal...
• 1:25:21
  Store grade hydrogen.
• 1:25:23
  Okay.
• 1:25:23
  Okay.
• 1:25:24
  And it's incredibly difficult.
• 1:25:26
  This is going back to our bigger stadium instead of our muonic smaller stadium.
• 1:25:29
  Now we're trying to do the same thing.
• 1:25:31
  Yes.
• 1:25:31
  But with our bigger stadium.
• 1:25:32
  And with a bigger stadium, there's a lot more problems.
• 1:25:35
  Yes.
• 1:25:35
  Yes.
• 1:25:35
  Okay.
• 1:25:36
  And it's just incredible some of the stuff that they were dealing with, dude.
• 1:25:40
  It's actually insane.
• 1:25:42
  Okay?
• 1:25:43
  The transition that they were trying to figure out was this 6P orbital to 2S.
• 1:25:49
  So all the way up there to, like, down here.
• 1:25:52
  There's that transition.
• 1:25:53
  That transition releases...
• 1:25:56
  Violet light at 410 nanometers.
• 1:26:00
  Okay.
• 1:26:00
  That's the first issue.
• 1:26:01
  Okay.
• 1:26:02
  Violet light at 410 nanometers.
• 1:26:04
  That's really short wavelength.
• 1:26:06
  We've got lasers at, like, blue and green and red.
• 1:26:12
  We don't have lasers in violet.
• 1:26:13
  Okay.
• 1:26:14
  You can't just buy that off the shelf.
• 1:26:15
  You've got to make a laser at 410 nanometers.
• 1:26:19
  Okay.
• 1:26:19
  Okay?
• 1:26:19
  Okay.
• 1:26:20
  So...
• 1:26:21
  That's something that they had to do.
• 1:26:22
  They actually took like a titanium sapphire laser, which operates at the infrared at 820 nanometers.
• 1:26:30
  And they effectively did like a trick where they stuck that inside a nonlinear optical crystal.
• 1:26:38
  And what this crystal did was, you know how when you like play guitar?
• 1:26:41
  And you like strum on one of the strings.
• 1:26:45
  But then if you like clamp down in the middle, then you're going to get the octave higher.
• 1:26:49
  Yes.
• 1:26:50
  Right.
• 1:26:50
  That's what this crystal is doing.
• 1:26:52
  Okay.
• 1:26:52
  It takes in...
• 1:26:54
  a laser light at 820 nanometers, but then effectively squishes the wavelength by half.
• 1:27:00
  So the frequency goes up by two, and then I get a 410 nanometer.
• 1:27:04
  For music producers who use keyboards, it's the jog wheel on the left is the same idea.
• 1:27:08
  When you jog the wheel up.
• 1:27:10
  Yeah, the octave just goes up.
• 1:27:12
  This is just like, it's going from, you know, a C to the C that's the dodo.
• 1:27:17
  Yeah, yeah.
• 1:27:17
  You know, it's just the octave is now up.
• 1:27:20
  Yes.
• 1:27:20
  Okay.
• 1:27:20
  Makes sense.
• 1:27:21
  So now we've got a laser.
• 1:27:23
  Right.
• 1:27:23
  Right.
• 1:27:24
  Now we've got to cool our hydrogen atoms down to 5 Kelvin.
• 1:27:27
  Trivial, you know.
• 1:27:28
  Trivial.
• 1:27:29
  Honestly, at this rate, this is pretty trivial.
• 1:27:31
  Yeah, yeah, yeah.
• 1:27:32
  Okay, cooling them down to 5 Kelvin.
• 1:27:34
  But even at 5 Kelvin, these hydrogen atoms in your cloud of hydrogen atoms that you're trying to, like...
• 1:27:39
  probe, those hydrogen atoms are moving at hundreds of meters per second, which means there's going to be some moving towards you, some moving away from you.
• 1:27:47
  So there's going to be Doppler shifting, right?
• 1:27:50
  And so if you're trying to measure the frequency of light, well, the guys that are coming towards you...
• 1:27:56
  They're going to be sensitive to shorter wavelengths.
• 1:27:58
  The guys that are going away from you are going to be sensitive to the longer wavelengths.
• 1:28:02
  And so you're going to have a Doppler broadening of your transition line.
• 1:28:05
  And the whole point is I really need to measure what frequency this transition line is at.
• 1:28:10
  Yes.
• 1:28:10
  Okay?
• 1:28:11
  So that's going to be a huge problem.
• 1:28:13
  We don't want a range.
• 1:28:14
  We want an explicit.
• 1:28:15
  No, yeah.
• 1:28:16
  I want, like, this is the frequency.
• 1:28:18
  Yes.
• 1:28:18
  Right?
• 1:28:19
  Yes.
• 1:28:19
  And so here's what they...
• 1:28:21
  Very cool.
• 1:28:21
  What they did was Doppler-free one-photon spectroscopy.
• 1:28:25
  Effectively, the idea is they custom-built, like, these active fiber-based retro reflectors.
• 1:28:31
  Okay?
• 1:28:32
  And what it's going to do is it's going to fire a laser at the atoms.
• 1:28:35
  The atoms are going to capture it.
• 1:28:37
  And then it's going to... And then the atoms come back.
• 1:28:40
  It's going to reflect that perfectly back into the atoms.
• 1:28:44
  Okay?
• 1:28:44
  Okay.
• 1:28:45
  The other thing that you want is you want this beam to be perfectly straight.
• 1:28:49
  The beam of the lasers, it can't be like spreading out as it goes into my apparatus.
• 1:28:54
  And the usual optics that's built for 486 nanometer wavelengths, which is blue-green, you can buy that like kind of off the shelf.
• 1:29:04
  Okay.
• 1:29:04
  There's like specialized science companies where you can buy that stuff.
• 1:29:07
  Sure.
• 1:29:07
  Sure.
• 1:29:08
  If it works for 486, it's not going to work for 410.
• 1:29:11
  Okay.
• 1:29:12
  At that frequency, it's the opposite of diminishing returns.
• 1:29:17
  Every nanometer is a headache.
• 1:29:19
  So you need to build custom optics such that my beam is completely straight.
• 1:29:25
  My new purple laser that we also had to custom make.
• 1:29:28
  Yes.
• 1:29:28
  We have to keep it in line.
• 1:29:30
  And so we need these custom, like, literally, like, optics glass lenses that keep it in a line.
• 1:29:37
  Yeah.
• 1:29:37
  And because it's a frequency where most optics are not built for it, that purple laser, it's fully, it has to be fully custom.
• 1:29:44
  Yeah, yeah.
• 1:29:45
  So the whole thing is, like, a custom apparatus, right?
• 1:29:47
  We love it.
• 1:29:47
  And then on top of that, there's, like...
• 1:29:51
  The quantum hurdle, which is you're putting in light in here, right?
• 1:29:55
  And the light is like bouncing back because of the earlier thing that I said.
• 1:29:58
  Yes.
• 1:29:58
  Well, if the light is going there and bouncing back, now you've created a standing wave.
• 1:30:02
  Same thing with the guitar.
• 1:30:03
  When you like pluck a string, it's fastened at both ends.
• 1:30:06
  So the wave is going to go back and forth.
• 1:30:08
  It's going to create a standing wave, right?
• 1:30:10
  There's going to be nodes and anti-nodes.
• 1:30:12
  Yes.
• 1:30:12
  Nodes are going to be where the light piles up.
• 1:30:14
  Yep.
• 1:30:15
  Anti-nodes are going to be where there's nothing.
• 1:30:16
  Yes.
• 1:30:17
  Yes.
• 1:30:17
  Now, usually you don't care.
• 1:30:19
  Yes.
• 1:30:19
  But when we're doing a precision measurement where we've put hydrogen into the 6p state, the electron orbital is a little bit like it's not spherical.
• 1:30:28
  Right.
• 1:30:29
  Right.
• 1:30:29
  And so because it's got these nodes, that electron is going to start caring about where the antinodes and the nodes are because the electric field is going to be higher here, lower here, higher here.
• 1:30:39
  And so the hydrogen atom that you're trying to...
• 1:30:43
  understand is now getting perturbed by the system itself right and so they had to make so many like monte carlo supercomputer simulations to model what would the electron do right in this space yes and and then correct for that right right because because it's
• 1:31:03
  There's so much headache, which is why this took like 10 years.
• 1:31:06
  That makes sense.
• 1:31:08
  It's effectively you have to remove the noise.
• 1:31:11
  Yeah, it's called the Stark shift.
• 1:31:13
  Okay, okay, right.
• 1:31:15
  And in order to do that, you kind of have to simulate first.
• 1:31:19
  Yeah.
• 1:31:19
  Because it's too expensive to do it experimentally all these times.
• 1:31:22
  No, but yeah, it's like the experiment has this artifact –
• 1:31:25
  In itself.
• 1:31:27
  I got you.
• 1:31:29
  It's like when you have Google Photos, and it's the feature where you take a photo of you and your loved one in a crowd, and then they have the Google Magic Eraser, and you just select someone's head behind your head, and then it's gone.
• 1:31:40
  And you shoot up.
• 1:31:41
  That's exactly what they're doing.
• 1:31:43
  Okay.
• 1:31:43
  But they need to build it and do the Monte Carlo simulation completely.
• 1:31:48
  In order to then be able to even have the thingy that will remove the noise.
• 1:31:52
  So they have to...
• 1:31:54
  it's nuts people i love the i love the uh the just the drive to not be beaten yeah by mother nature yeah no dude no it's like i will figure out what the size of the proton is you are not going to stop me okay it might take me 10 years but i want to know how big the proton is
• 1:32:18
  That's so good.
• 1:32:19
  Okay, that makes sense.
• 1:32:20
  Yeah, it's absolutely nuts.
• 1:32:22
  And finally, after all of this, they publish their measurement.
• 1:32:27
  They publish their measurement.
• 1:32:28
  Okay.
• 1:32:30
  The blue little starfighter that you see there.
• 1:32:33
  Yeah, in the middle.
• 1:32:34
  In the middle?
• 1:32:35
  Yeah.
• 1:32:36
  That is their measurement.
• 1:32:38
  0.84.
• 1:32:39
  Okay.
• 1:32:40
  Okay.
• 1:32:41
  It is in line with their Muon measurement that they took.
• 1:32:46
  16 years ago.
• 1:32:47
  So the 0.87, I think it was.
• 1:32:49
  The 0.87?
• 1:32:50
  Yeah, yeah.
• 1:32:51
  That's all these other...
• 1:32:53
  Things over to the right.
• 1:32:54
  Yeah.
• 1:32:54
  Yeah.
• 1:32:56
  That is wrong.
• 1:32:57
  You are the weakest link.
• 1:32:58
  Goodbye.
• 1:32:59
  Yeah.
• 1:33:00
  Yeah.
• 1:33:00
  So they corroborated that measurement.
• 1:33:02
  The muonic hydrogen is the same as the normal hydrogen.
• 1:33:05
  The true size of the proton is 0.84.
• 1:33:09
  And there is no new physics, at least in terms of the difference between the muon and the electron.
• 1:33:16
  They are indeed the same up to this significance.
• 1:33:19
  Mm-hmm.
• 1:33:21
  For all intents and purposes, the only thing that is different between the muon and the electron is their mass.
• 1:33:25
  Is the mass.
• 1:33:26
  That's it.
• 1:33:27
  That's it.
• 1:33:28
  And what this also means then is that the theorists have to go back to the woodshed and work on it.
• 1:33:34
  Yeah, they need to figure out what else to try.
• 1:33:37
  Right.
• 1:33:37
  Right.
• 1:33:38
  Right.
• 1:33:38
  Because they were – I bet you there were tons of theorists that were really hoping that this measurement was not going to fall within that error bar.
• 1:33:46
  Yeah, yeah, yeah.
• 1:33:46
  Right?
• 1:33:47
  That the muon was going to be here.
• 1:33:49
  The electron was going to be here.
• 1:33:50
  And they'd be like, oh, I got a theory for why they're different.
• 1:33:54
  Turns out they're not.
• 1:33:56
  This is actually a really great story around understanding the refinement that goes through the scientific process on something like the size of a proton.
• 1:34:06
  Like why it's incredibly difficult.
• 1:34:07
  Yeah.
• 1:34:08
  Like fundamentally because it's so small like we can't just image it the way we normally would.
• 1:34:13
  And so we have to go through this process of understanding like how these things interact because how they interact creates these derivative things.
• 1:34:24
  variables that we can measure exactly that electron piece that we just talked about and then you can sort of now back calculate what you're looking for because you actually have a strong understanding of these other uh aspects of physics and the relationship between these different moving parts and again this tension between theory and experiment uh and the back and forth and sort of it's it's sort of like a it's sort of like a
• 1:34:53
  it's like a dance, right?
• 1:34:56
  Where both influence and counter-argue each other at different periods in time.
• 1:35:03
  However, the theorists did not win this one.
• 1:35:05
  No.
• 1:35:06
  I'm sorry to say.
• 1:35:07
  Yeah.
• 1:35:08
  Maybe next time.
• 1:35:09
  Maybe next time.
• 1:35:09
  Probably not.
• 1:35:10
  I mean, is this why they're always looking at string theory?
• 1:35:12
  Because then the experimentalists can't...
• 1:35:14
  Yeah, because on string theory, they're just like, oh, well, I need like 10 more decimals on your measurement.
• 1:35:20
  And then just keep going.
• 1:35:21
  Just keep going.
• 1:35:23
  To really validate my theory or not.
• 1:35:25
  But jokes aside, I mean, it seems like a relatively boring thing to talk about, just the size of the proton.
• 1:35:34
  But the techniques and the fact that this closed that door on there being some new physics.
• 1:35:43
  Mm-hmm.
• 1:35:44
  We don't have to think about that anymore.
• 1:35:45
  Right, right, right.
• 1:35:46
  Like lepton universality is a thing.
• 1:35:48
  The muon and the electron are the same except for mass.
• 1:35:51
  Yes.
• 1:35:51
  Now we can worry about other stuff.
• 1:35:53
  Yes.
• 1:35:53
  Right?
• 1:35:54
  We can put constraints on exotic particles.
• 1:35:56
  Like there's zero anomalous deviation from quantum electrodynamics rules.
• 1:36:02
  Right?
• 1:36:02
  So like a bunch of other stuff about dark matter.
• 1:36:05
  Like, oh, there's this dark sector that maybe does this.
• 1:36:07
  Well, if that particular way of thinking about dark matter has a different measurement,
• 1:36:14
  It's wrong.
• 1:36:14
  Right.
• 1:36:15
  Right.
• 1:36:15
  Falsifiability is everything when it comes to physics, right?
• 1:36:18
  That's actually – I didn't think about that.
• 1:36:19
  What you're saying is this experimental result can now inform research areas that are not about measurements of these subatomic particles and things like that.
• 1:36:32
  Yeah.
• 1:36:33
  There's overlap conceptually or architecturally that if you were basing your dark matter model off of the original 0.087 theoretical framework.
• 1:36:46
  Sorry.
• 1:36:46
  Sorry.
• 1:36:47
  Yeah.
• 1:36:47
  Yeah.
• 1:36:51
  Gotta love science.
• 1:36:52
  That's a good – that is a very good, very technical story.
• 1:36:55
  Yeah.
• 1:36:56
  But really, again, the fun to me is in the journey and the process.
• 1:37:02
  Yeah.
• 1:37:03
  Not simply the results.
• 1:37:04
  Like you said, the result – okay, the size of the proton.
• 1:37:06
  Great.
• 1:37:06
  Yeah.
• 1:37:08
  But all of those new techniques, again, also are not isolated to just this specific research question.
• 1:37:13
  Fantastic.
• 1:37:15
  That was –
• 1:37:16
  So the sort of physics story on the proton size, we have this out of Max Planck Institute, and it was for quantum optics in nature.
• 1:37:25
  I'll ask you questions.
• 1:37:29
  I really like that one.
• 1:37:30
  I'll ask you questions off air about that one because there's a couple more I have.
• 1:37:33
  We are going to go ahead and move into our last story, which is a medicine story about ALS.
• 1:37:42
  It's been in the news recently because of the recent death of Eric Dane.
• 1:37:51
  Many of you have maybe saw the...
• 1:37:53
  Netflix documentary that came out.
• 1:37:56
  For those who may not know, Eric Dane is quite a famous and popular actor and father and husband.
• 1:38:04
  He was Dr. McSteamy in Grey's Anatomy.
• 1:38:09
  And he, while having been diagnosed with LAS, I think it was just over 10 months ago, excuse me, when he had found out about his diagnosis, he was just wrapping up
• 1:38:21
  the shooting of season three of euphoria um unfortunately accelerated quite quickly and so we just wanted to um you know take this opportunity to i think cover you know what is the latest in the the fight against als
• 1:38:39
  understanding it a little bit, and then we'll end with a funding piece that's related to this research area.
• 1:38:46
  So, you know, we know it's something that a lot of folks have been not only talking about, but have been personally impacted by because of the way in which...
• 1:38:56
  Eric communicated just his worldview, what it's like to be a father, his message to his kids, the stories that he had to tell about life, I think really resonated with a lot of people.
• 1:39:10
  And so with that, we're going to kind of sort of look at the current landscape.
• 1:39:16
  Yes.
• 1:39:17
  I wanted to take this opportunity to talk about ALS, the disease, and one particular scientific paper that came out
• 1:39:25
  last year that is just, you know, one of the latest in a series of breakthroughs.
• 1:39:32
  Obviously, it's still very much a crisis in medicine.
• 1:39:37
  Amyotrophic lateral sclerosis, okay, it's a catastrophic motor neuron degradation that happens with motor neurons specifically.
• 1:39:48
  These are the
• 1:39:55
  ALS in its most advanced phase gets so bad that even the contraction of the muscles like the diaphragm that allow you to breathe.
• 1:40:06
  Because everything, every bit of movement in our body is from muscular contraction, right?
• 1:40:11
  And like...
• 1:40:13
  Even the act of breathing becomes something that the brain cannot sustain because these motor neurons have degraded so much.
• 1:40:19
  The survival is really quite bad.
• 1:40:23
  2.3 to 5 years post-diagnosis is on average.
• 1:40:27
  One of the most famous cases is Stephen Hawking.
• 1:40:30
  He survived for like 50 years, but it's extremely rare to do that.
• 1:40:35
  Now he's no longer the scientific hero that he once was, but he's one of these rare, rare cases.
• 1:40:43
  Annual patient care is about 250,000 plus per patient, right?
• 1:40:49
  And the current incidence, it's about one to two per 100,000.
• 1:40:56
  By 2040, there's approximately going to be a 25% increase in the global prevalence.
• 1:41:03
  And...
• 1:41:04
  If we talk about like, there's really two types of ALS.
• 1:41:08
  There's familial ALS, which comes from genetic mutations that you inherit from your family.
• 1:41:14
  That's only about 10% of cases.
• 1:41:16
  90% of cases are sporadic ALS.
• 1:41:19
  There's all these polygenetic...
• 1:41:22
  landscapes in our genome that causes sporadic ALS.
• 1:41:26
  And that happens just, there's no familial history of it.
• 1:41:30
  You know, you didn't carry a mutation from your parents.
• 1:41:32
  It just happened.
• 1:41:34
  So let's get into like, what exactly is going on in our nervous system when ALS happens.
• 1:41:41
  So ALS, as I described, it's motor neurons, right?
• 1:41:44
  Yes.
• 1:41:45
  Motor neurons are extreme in scale.
• 1:41:48
  Okay.
• 1:41:49
  They can be about a meter long, a needle.
• 1:41:51
  That's three feet.
• 1:41:53
  Oh, wow.
• 1:41:53
  A single cell is the size of like three feet.
• 1:41:56
  Okay.
• 1:41:57
  If you think about it, it kind of makes sense, right?
• 1:41:59
  Because think about your spinal cord, like the lumbar spinal cord.
• 1:42:02
  Sure, sure, sure.
• 1:42:03
  A single cell body is going to be located in the spinal cord, but it's going to relay signal all the way down to the foot.
• 1:42:11
  Yep.
• 1:42:11
  That makes sense.
• 1:42:12
  Okay.
• 1:42:12
  That's a single motor neuron that's doing it.
• 1:42:14
  Okay.
• 1:42:14
  Okay.
• 1:42:14
  Okay.
• 1:42:16
  Now, maintaining this kind of architecture over an extremely long distance requires moving a massive amount of physical cargo from one end of the cell to the other, right?
• 1:42:26
  You're going to have to move proteins.
• 1:42:28
  You're going to have to move mRNA from your nucleus if you want to, like, you know, make it all the way out there.
• 1:42:34
  You have to move mitochondria.
• 1:42:36
  You have to move synaptic vesicles, which are like the acetylcholine and the neurotransmitters.
• 1:42:41
  That's got to move.
• 1:42:42
  All of that stuff has to move across...
• 1:42:46
  A meter, a yard worth of stuff.
• 1:42:48
  Right.
• 1:42:49
  That requires a lot of energy, and it requires a lot of motion from these molecular motors.
• 1:42:55
  They're called kinesin.
• 1:42:58
  These kinesin are like little tiny molecular motors that actually step in eight nanometer steps.
• 1:43:04
  Right.
• 1:43:05
  On our microtubules.
• 1:43:06
  They're incredible machines that literally take up ATP, which is like the energy currency of our cell.
• 1:43:13
  And each ATP they take up gets them eight nanometers across.
• 1:43:19
  Now, eight nanometers, that's eight times ten to the negative nine meters.
• 1:43:23
  If I want to traverse a meter worth of stuff for a single molecule, that's on the order of ten to the eight.
• 1:43:29
  Mm-hmm.
• 1:43:30
  ATP molecules that I need to hydrolyze.
• 1:43:33
  Yep.
• 1:43:33
  Right?
• 1:43:33
  Yep.
• 1:43:34
  It requires a lot of energy.
• 1:43:37
  Okay?
• 1:43:37
  And when you have a lot of ATP that's being built up...
• 1:43:41
  and something is wrong in the genetics of that neuron, you're going to get an accumulation of misfolded proteins.
• 1:43:47
  Specifically, one of the main ones is TDP43.
• 1:43:50
  It's a misfolded protein that causes physical blockage and traffic jams on these highways of transport.
• 1:43:58
  And when you have that traffic jam, you're going to get into an energy crisis because now your mitochondria are unhappy.
• 1:44:04
  They don't have the actual raw materials.
• 1:44:07
  They're the factories that create the powerhouse of the cell or whatever.
• 1:44:11
  They're the factories that actually create this ATP.
• 1:44:14
  You're going to not have them being happy.
• 1:44:17
  Those guys are going to create reactive oxygen species, which are just like byproducts with oxygen.
• 1:44:22
  Oxygen is just like something that wants to react with everything.
• 1:44:25
  And so it's going to cause effectively like rust in your neurons.
• 1:44:30
  The same way the iron rusts in atmosphere.
• 1:44:32
  If you don't have the correct machinery to regulate the reactive oxygen, that reactive oxygen is going to go and go haywire in your neurons.
• 1:44:40
  Right.
• 1:44:42
  The other analogy for this would sort of be on that highway.
• 1:44:44
  It's just like you're getting potholes over time that never get fixed.
• 1:44:48
  You don't have Department of Transportation coming through and fixing it.
• 1:44:51
  And then it just deteriorates over time to a point where you can no longer traverse.
• 1:44:56
  Exactly.
• 1:44:56
  It's this thermodynamic breakdown, right?
• 1:44:59
  And ALS is like a macroscopic manifestation.
• 1:45:02
  of that thermodynamic breakdown.
• 1:45:04
  You're right.
• 1:45:04
  The neuron can literally not maintain its highly ordered state because the mitochondria is doing all sorts of crap.
• 1:45:11
  The, the genetics is not being good enough to keep that low entropy state alive.
• 1:45:17
  Right.
• 1:45:18
  Right.
• 1:45:18
  Right.
• 1:45:18
  And so, and so you're getting just massive amounts of failure everywhere for these motor neurons.
• 1:45:25
  And the point is, this is something that's, it's like, it's across your, your whole system.
• 1:45:29
  Yeah.
• 1:45:29
  The entire motor neuron, like, it's just like, right.
• 1:45:32
  Yeah.
• 1:45:32
  Right?
• 1:45:33
  Yeah.
• 1:45:33
  Now, the muscle's not so much, but the motor neuron is like the wire that is sending the muscle the signal.
• 1:45:39
  Yeah.
• 1:45:40
  Right?
• 1:45:40
  Yeah.
• 1:45:41
  It's like in your car when your computer goes down.
• 1:45:45
  Yeah.
• 1:45:46
  Turn it however much you want.
• 1:45:48
  The engine could be fine.
• 1:45:49
  The engine's still fine.
• 1:45:51
  It may not be, but the engine's still fine.
• 1:45:52
  Yeah.
• 1:45:53
  But the problem is you don't have no...
• 1:45:54
  But like your spark plug or whatever.
• 1:45:55
  Yeah, yeah, yeah.
• 1:45:56
  It's like there's no electricity going there.
• 1:45:57
  Yeah, that makes sense.
• 1:45:58
  To do anything.
• 1:45:59
  Yeah, that makes sense.
• 1:45:59
  You know?
• 1:46:00
  Yeah.
• 1:46:00
  So that's the problem.
• 1:46:02
  It's the motor neurons that are really just...
• 1:46:07
  from overuse and from degradation are not working properly.
• 1:46:11
  And there's been a translational chasm in drug development.
• 1:46:16
  It's not been good.
• 1:46:18
  We've been, as humanity, incredible at solving problems.
• 1:46:22
  medical problems.
• 1:46:24
  ALS is one where historically it's just been beating us.
• 1:46:28
  And it's been crazy, dude.
• 1:46:29
  Like there's the example of failed drugs.
• 1:46:31
  Like there's so many failed drugs because they all show preclinical promise in like rat models and mouse models.
• 1:46:38
  But then when you get to humans, it's all failed.
• 1:46:40
  These are just some of the many.
• 1:46:43
  There's, I think something like more than a hundred
• 1:46:53
  But then when it comes to humans, it's just all failure, failure, failure.
• 1:46:57
  Okay?
• 1:46:57
  And what does that say?
• 1:46:58
  What that says is we don't have a good model in the lab to test drugs for ALS.
• 1:47:08
  Right.
• 1:47:08
  Because the point here is for a variety of things, we can use these rodent, mouse, rat model.
• 1:47:15
  We can use them as the test bed.
• 1:47:17
  Yeah.
• 1:47:17
  And there's so many where it works.
• 1:47:19
  It works.
• 1:47:19
  That's why we do research on them.
• 1:47:20
  It works fine and it's fantastic.
• 1:47:22
  Yeah.
• 1:47:22
  It does not work for this.
• 1:47:23
  It does not work for ALS.
• 1:47:25
  There's something fundamentally different.
• 1:47:26
  And what we need is in lab a model that we can actually test drugs on.
• 1:47:31
  Right.
• 1:47:31
  Right.
• 1:47:32
  Right.
• 1:47:32
  That translates post-preclinical into actually the clinical trials.
• 1:47:38
  And this is where the – now we can start talking about the paper that I want to talk about.
• 1:47:43
  Okay.
• 1:47:43
  The paper has to do with iPSCs.
• 1:47:46
  These are pluripotent stem cells.
• 1:47:48
  Okay.
• 1:47:50
  These are induced pluripotent stem cells.
• 1:47:54
  In 2012, the Nobel Prize went to John Gurdon and Shinya Yamanaka for the discovery that mature cells can be reprogrammed
• 1:48:04
  to become stem cells.
• 1:48:06
  Stem cells are...
• 1:48:07
  So stem cells are cells that have not differentiated into skin cells or blood cells or bone cells or whatever.
• 1:48:15
  They are a clean slate.
• 1:48:17
  They don't have a specialization.
• 1:48:18
  They're like you in high school.
• 1:48:20
  Before you went into college and then got a major and now you're stuck at whatever path of life.
• 1:48:26
  Yes.
• 1:48:26
  Right?
• 1:48:27
  And what induced pluripotent stem cells do...
• 1:48:31
  is now we're able to reprogram someone who's, let's say, been through college and a PhD, pre-program their brain so that now their brain is back in high school and they can be something totally different.
• 1:48:42
  Just to quickly note, that's like a phenomenal – like in and of itself, this which is from the 2012 Nobel Prize, that concept is really powerful because effectively what you're saying is I'm in the college – high school college analogy.
• 1:48:58
  If I get to age 45 and the world has changed and my industry has been replaced by insert whatever –
• 1:49:06
  I can just be almost reset to being 19, 20 and retrained again without like – with a substrate that is not my 45-year-old mind, but it's my 20-year-old mind.
• 1:49:18
  Yeah, yeah, yeah.
• 1:49:19
  And this is really amazing for biomedical research because, one, we don't have to rely on embryonic stem cells.
• 1:49:25
  Yeah, that's a good point.
• 1:49:26
  point because embryonic stem cells are really where the stem cells are right because as an embryo you can imagine from an egg and a sperm that egg is just like a single cell right that single cell is now becoming my nails my my lungs my skin my muscles my neurons right so somewhere that single cell becomes a ball yes
• 1:49:46
  And that ball has a bunch of embryonic stem cells.
• 1:49:49
  Each cell now defines, I'm going to go become the eye.
• 1:49:51
  I'm going to go become a hair cell, so on and so forth.
• 1:49:54
  With this technology, we don't need embryonic stem cells.
• 1:49:57
  We can take skin cells, graft them, put them with a genetic cocktail, and then they are going to become stem cells on their own.
• 1:50:05
  It completely changes the landscape.
• 1:50:06
  And that's why they won the Nobel Prize in 2012.
• 1:50:08
  That makes sense.
• 1:50:09
  Very much deserved because it's changed biomedical research, not just for ALS, but like countless other diseases.
• 1:50:17
  Okay.
• 1:50:19
  Now, as you can imagine, 2012, this is a pretty old technology.
• 1:50:23
  Sure.
• 1:50:23
  Right?
• 1:50:24
  We have tried to use it for ALS.
• 1:50:26
  There was this consortium called Answer ALS.
• 1:50:29
  They got 1,000 lines from ALS patients, and they tried to make lines of stem cells.
• 1:50:35
  But they observed the same problem, which is that the drugs—
• 1:50:42
  Like the same drugs that don't work in clinical trials, they'll work in pluripotent stem cells.
• 1:50:49
  They will.
• 1:50:49
  They will.
• 1:50:50
  So they were still missing something.
• 1:50:53
  Right.
• 1:50:53
  Right?
• 1:50:54
  It's like these stem cells were not mimicking ALS motor neurons.
• 1:50:59
  They were trying, right?
• 1:51:01
  We got the stem cells from ALS patients that have the disease, but when we make...
• 1:51:07
  them into stem cells.
• 1:51:08
  We get the donor skin cells from the ALS patients.
• 1:51:10
  When we try to make them into stem cells, they don't mimic the disease that the patient has.
• 1:51:15
  And that's the whole point.
• 1:51:16
  That's the whole point.
• 1:51:17
  So what's going on?
• 1:51:19
  Oh, which is interesting because...
• 1:51:23
  Anyway.
• 1:51:24
  You understand what I'm saying though, right?
• 1:51:25
  Yeah, because in my head, intuitively, I'd be like, oh, well, if you're taking my skin – let's say I have ALS in this example.
• 1:51:32
  You take my skin cells and then you try to reprogram it.
• 1:51:35
  Yeah, and I try to make that into a motor neuron.
• 1:51:37
  Right.
• 1:51:37
  The problem is the motor neuron is not my body's stuff, which has it.
• 1:51:41
  Yeah, it's just like a normal motor neuron.
• 1:51:43
  Right, because it's been reprogrammed.
• 1:51:45
  But the whole point is I want to make –
• 1:51:48
  Right.
• 1:51:48
  A model of ALS in a Petri dish.
• 1:51:51
  Correct.
• 1:51:51
  That I can then mess around with.
• 1:51:53
  Which currently we just can't get from the boilerplate out of the box induced pluripotent stem cell process.
• 1:51:59
  And that is where we get to our study in 2025.
• 1:52:03
  Okay.
• 1:52:03
  It's an in vitro model of sporadic ALS.
• 1:52:06
  It was a nature neuroscience, large scale drug screening.
• 1:52:09
  And what they did was actually figure out how to make this happen.
• 1:52:12
  Okay.
• 1:52:13
  How to make Petri dish...
• 1:52:15
  level, ALS, that I can mess around with in a lab and really try to understand what drugs would work and what drugs wouldn't, right?
• 1:52:25
  It's out of the University of Melbourne.
• 1:52:27
  and the University of Queensland, a lot of Australian universities.
• 1:52:31
  Yeah, there's quite a few institutions that were collaborating on this.
• 1:52:34
  That's right.
• 1:52:35
  So what they did different is the following.
• 1:52:37
  So they got 100 patients with sporadic ALS.
• 1:52:41
  They got 11 patients with familial ALS.
• 1:52:44
  They got 25 healthy controls.
• 1:52:46
  They got skin cells from the dermal fibroblasts.
• 1:52:50
  They extracted from skin biopsies from these patients and from these 25 controls.
• 1:52:57
  They used something called non-integrating epizomal vectors.
• 1:53:01
  So usually when we try to make stem cells, like pluripotent stem cells from whatever donor skin cells that we got, I need to change the genetics of that skin cell to now forget everything about being a skin cell and go back to being a stem cell.
• 1:53:17
  Right.
• 1:53:18
  Usually how we do that is we use a retrovirus.
• 1:53:22
  Okay.
• 1:53:22
  So a virus that has a piece of RNA, the RNA goes into the cell that becomes reverse transcribed into DNA.
• 1:53:29
  The DNA then goes and gets mixed in with the native DNA of the cell.
• 1:53:35
  With these specific non-integrating episomal vectors, this is DNA that just goes into the nucleus and hangs out.
• 1:53:42
  Hmm.
• 1:53:42
  It doesn't get inserted into the chromosome of the cell.
• 1:53:46
  Okay.
• 1:53:46
  And what that does is prevent any random, you know, like off-target stuff from happening.
• 1:53:53
  It's exactly what we want.
• 1:53:54
  Yeah.
• 1:53:54
  It's just it's not messing with anything.
• 1:53:57
  Right.
• 1:53:57
  That's already there because the cell is already in this sort of, it's from an ALS patient.
• 1:54:02
  So it already has the mutations that are causing ALS.
• 1:54:05
  We don't want to mess with that because we want to replicate the ALS in my Petri dish.
• 1:54:10
  So if I want to reprogram the cell to become a motor neuron, I just get my DNA, I put it in the nucleus, and I just have you hang out.
• 1:54:17
  Do not go inside the house.
• 1:54:19
  Just hang out.
• 1:54:21
  Okay.
• 1:54:21
  Right?
• 1:54:22
  Okay.
• 1:54:22
  Because previously the retrovirus insertion,
• 1:54:25
  Yeah, it would insert.
• 1:54:26
  And then that changes whatever's happening inside the house.
• 1:54:28
  And because this is such a combinatorial problem, we have no idea.
• 1:54:31
  You don't know.
• 1:54:32
  We don't know.
• 1:54:32
  Yeah, we don't know what happened.
• 1:54:34
  But now we have total control.
• 1:54:35
  We're trying to have total control over each of the sort of second and third order steps that happen after we introduce it into the environment.
• 1:54:43
  Exactly, yeah.
• 1:54:43
  And they did a lot of other stuff that I'm not going to totally get into.
• 1:54:47
  Sure.
• 1:54:47
  But the way that they...
• 1:54:50
  grew the stem cells they actually withdrew a bunch of growth factors that would prevent artificial masking of this phenotype of als because we want that phenotype to actually happen yes they also like cultured it under strict five percent oxygen which is this hypoxic condition that happens in the spinal cord so it's something that you want to mimic when you want to make
• 1:55:11
  modern neurons that are like ALS.
• 1:55:14
  So we removed stuff that would potentially make it so that the thing would, like the system would make it go away.
• 1:55:19
  And we also created the environment that is almost identical to the environment that exists to remove, again, any possibility that we're missing the mark in terms of the, like the variables that need to be present in order to generate the results we're looking for with this new lab, to be able to basically replicate it in a Petri dish.
• 1:55:39
  Yes, we want ALS motor neurons in a petri dish.
• 1:55:42
  Right?
• 1:55:43
  And finally, the resulting purity that they got, it was exceptionally pure.
• 1:55:47
  Motor neurons, 90% were co-expressing the gene that we wanted.
• 1:55:52
  Okay.
• 1:55:52
  Very low contamination, so there's not that many astrocytes.
• 1:55:55
  There's not that many microglia.
• 1:55:57
  They're all motor neurons, and they all look great.
• 1:56:00
  Okay, so this is a very big deal.
• 1:56:04
  Yes, so finally, we might have...
• 1:56:07
  Our ALS in a Petri dish.
• 1:56:09
  Yes.
• 1:56:09
  And now we can start doing preclinical trials in our Petri dish with some confidence that it might work.
• 1:56:19
  At the clinical stage.
• 1:56:20
  The point here is we're saying this ALS in a Petri dish is going to increase the conversion rate of preclinical targets to those that actually can make it into clinical trials.
• 1:56:31
  Because we are basically saying – we're basically recreating the environment that would be an actual human in the trials, not using the rodent, mouse, rat model, which can work in a lot of other cases.
• 1:56:44
  Yeah.
• 1:56:45
  But just in this one, it does not.
• 1:56:46
  Exactly.
• 1:56:47
  Okay.
• 1:56:47
  Yes.
• 1:56:47
  Yeah.
• 1:56:48
  And when they created these motor neurons in the Petri dish, those motor neurons would exhibit the same kind of damage that we got in ALS.
• 1:56:59
  They came up with this metric called the lethal day 50%.
• 1:57:01
  So it's the exact day.
• 1:57:04
  when the total length of the neurites degrades by 50%, the maximum peak length.
• 1:57:09
  So it's like imagine you made the motor neuron, and then you press play, and then because of degradation, it's going to get shorter and shorter.
• 1:57:16
  They came up with this metric.
• 1:57:18
  That metric was correlated, and this is kind of the nail in the coffin that we have replicated in the Petri dish.
• 1:57:24
  It's kind of an unfortunate metric, but at the same time,
• 1:57:29
  It's a lot of hope for the future because here's what they did.
• 1:57:32
  That metric is correlated with the actual clinical patient survival time.
• 1:57:39
  Oh, interesting.
• 1:57:40
  Do you see what I'm saying?
• 1:57:41
  Yes.
• 1:57:41
  So the patient that I got my donation from of the skin cells that I made my pluripotent stem cells from to make into a motor neuron, the survival of that culture is correlated with the patient, him or herself.
• 1:57:57
  The unfortunate point being here is that the real world outcome for the patient in terms of time.
• 1:58:03
  Yeah.
• 1:58:03
  Yeah.
• 1:58:04
  Is correlated with our ALS in a Petri dish.
• 1:58:08
  Yes.
• 1:58:08
  Meaning it is replicating the problem that the patient had to a degree that it's matching.
• 1:58:16
  It's matching.
• 1:58:17
  One to one.
• 1:58:17
  Exactly, yeah.
• 1:58:18
  And it's unfortunate, but at the same time, very encouraging.
• 1:58:20
  Because now we have the model in our Petri dish.
• 1:58:24
  Now we can finally start...
• 1:58:27
  getting to the bottom of how do we fix this?
• 1:58:29
  Yes.
• 1:58:29
  Right.
• 1:58:30
  That's actually a really, really big deal.
• 1:58:32
  Yeah.
• 1:58:33
  And we didn't have this before.
• 1:58:34
  Right.
• 1:58:35
  Right.
• 1:58:35
  Right.
• 1:58:35
  This is like we were working with like rats and mice that were just like awful.
• 1:58:40
  Right.
• 1:58:40
  Just didn't work.
• 1:58:41
  Right.
• 1:58:41
  Right.
• 1:58:41
  And now we finally got a model.
• 1:58:43
  We were throwing darts at the board, but the board was behind us.
• 1:58:45
  Yeah, exactly.
• 1:58:47
  And it's like, what are we doing?
• 1:58:48
  What are we doing?
• 1:58:49
  Yeah.
• 1:58:50
  But the challenge here is, again, to your point, you know,
• 1:58:53
  The model, the sort of rodent model works in so many other arenas.
• 1:58:57
  It's fair to have extrapolated it to this one.
• 1:58:59
  Yeah, yeah, yeah.
• 1:59:00
  Totally.
• 1:59:00
  But after some time.
• 1:59:01
  Yeah, you're just like, okay, we need something else.
• 1:59:03
  We need something else.
• 1:59:04
  Yeah.
• 1:59:05
  This is totally not working.
• 1:59:06
  Yes.
• 1:59:06
  Right?
• 1:59:06
  Yes.
• 1:59:07
  So here's what they did.
• 1:59:09
  They made a screening library.
• 1:59:10
  Yeah.
• 1:59:11
  107 commercially available drugs from phase one to three ALS trials.
• 1:59:16
  So this is preclinical.
• 1:59:17
  Yes.
• 1:59:17
  Right.
• 1:59:18
  Pretty sure.
• 1:59:19
  Right.
• 1:59:19
  Phase one to three is, is I think preclinical.
• 1:59:21
  And then 97% of the tested drugs failed.
• 1:59:25
  Yeah.
• 1:59:25
  Yeah.
• 1:59:26
  And this is sort of what we want.
• 1:59:30
  Because that's what we're seeing.
• 1:59:31
  We're seeing them pass the preclinical.
• 1:59:33
  And then only when they get to clinical stage in a human being, they're failing.
• 1:59:37
  Now they're failing in the Petri dish.
• 1:59:39
  I don't have to waste time and resources like actually doing a clinical trial if it's failing in the Petri dish.
• 1:59:45
  That's the whole point.
• 1:59:45
  Just to say it back to you.
• 1:59:47
  We took what was working in preclinical before but failing in humans.
• 1:59:51
  We took our Petri dish platform.
• 1:59:53
  We applied the already commercially available options and 97% of them failed in our petri dish when they were passing in the preclinical in the prior regime.
• 2:00:04
  And so the point here is, again, not only are we seeing in patients the one-to-one on survival time, we are also seeing a one-to-one on efficacy in testing patients.
• 2:00:19
  in this sort of pre-trial environment, in this in-lab environment.
• 2:00:25
  Yeah, yeah.
• 2:00:25
  Not even in the in-lab.
• 2:00:27
  Yeah.
• 2:00:27
  Meaning we can feel, we can have some level of confidence in the world.
• 2:00:32
  that we are actually in a new 2.0 foundation.
• 2:00:37
  For lab testing.
• 2:00:38
  For lab testing.
• 2:00:40
  Yeah.
• 2:00:40
  For lab testing.
• 2:00:41
  Exactly.
• 2:00:41
  And out of the 107, three actually did pretty good.
• 2:00:46
  Riluzol, memantine, and baricitinib.
• 2:00:52
  I think that's how you pronounce it.
• 2:00:53
  Okay.
• 2:00:54
  So let's get into what each of these things are.
• 2:00:59
  Riluzol is actually something that...
• 2:01:02
  People are already testing nowadays.
• 2:01:04
  It was dose-dependent.
• 2:01:05
  There was significant rescue of this lethal day 50% metric that I was telling you about.
• 2:01:12
  And if we look at the physical effect on these neurons, there was restored electrical coherence.
• 2:01:18
  So on the top row, you see...
• 2:01:21
  Just control.
• 2:01:23
  Yes.
• 2:01:23
  On the bottom row, they're treated with riluzole.
• 2:01:26
  Yes.
• 2:01:26
  The neurons are healthier.
• 2:01:27
  Yeah, yeah, yeah.
• 2:01:28
  Right?
• 2:01:28
  They just look better.
• 2:01:29
  Yep.
• 2:01:29
  And it inverted the disease vector.
• 2:01:31
  So there was a negative correlation with, like, the effect and, like, how much time the neurons actually stayed alive.
• 2:01:39
  Yeah, yeah, yeah.
• 2:01:39
  So it was all a good thing, right?
• 2:01:40
  Yeah, yeah, yeah.
• 2:01:41
  The other two...
• 2:01:43
  They did not confer statistically significant population-level benefit.
• 2:01:48
  Okay.
• 2:01:48
  So you think, okay, maybe they're a dud, right?
• 2:01:50
  Not so fast.
• 2:01:51
  Okay.
• 2:01:51
  What if we combine...
• 2:01:53
  The drugs, the three drugs that we had a positive effect on, what if we combine them?
• 2:01:58
  Is it a synergistic multiplication effect?
• 2:02:02
  Right?
• 2:02:02
  And that is what they found.
• 2:02:04
  They found that each of these columns now is individual drugs, and then you add the two, right?
• 2:02:11
  Like there's Riluzol and Mimantin.
• 2:02:14
  There's Baracitinib and Mimantin.
• 2:02:16
  And then there's all three.
• 2:02:18
  The more you added, the three were somehow working together to create a much better lasting effect.
• 2:02:25
  I mean, the visuals here are just astonishing.
• 2:02:28
  As you go from left to right, right?
• 2:02:30
  It's just like healthier and healthier neurons.
• 2:02:32
  Yeah, yeah, yeah.
• 2:02:33
  And then basically on the one with all three, at all levels, it basically maintains its structure.
• 2:02:40
  Yes.
• 2:02:41
  Whereas when we were just having Untreated...
• 2:02:45
  By the time we get to that.
• 2:02:46
  Yeah, but by the time...
• 2:02:47
  I mean, it's just like the time axis is on the Y axis.
• 2:02:49
  On the Y axis, yep.
• 2:02:50
  So it's like the earliest.
• 2:02:52
  And then as time goes on, they just start dying, right?
• 2:02:55
  I mean, this fish is astonishing.
• 2:02:56
  But at the rightmost with all three of them, it's like they're still alive.
• 2:02:59
  That's astonishing.
• 2:03:00
  Yeah.
• 2:03:00
  I mean, the level of impact...
• 2:03:02
  Yeah, the level of impact is crazy.
• 2:03:04
  On the combinatorial piece.
• 2:03:06
  Yes.
• 2:03:06
  And then if you wanted to do, okay, so what is the ultimate zenith, right?
• 2:03:09
  Right.
• 2:03:10
  Is it all three?
• 2:03:10
  Is it just two?
• 2:03:12
  You can actually test.
• 2:03:13
  Now that we've got a Petri dish, we can do A-B testing.
• 2:03:16
  Right.
• 2:03:17
  Right?
• 2:03:17
  Yeah, yeah.
• 2:03:18
  On all the different patient donors, like all the different profiles, is it only for sporadic?
• 2:03:23
  Is it only for familial?
• 2:03:25
  We can do all of these different categorical tests and figure out that the triple combination is actually the best.
• 2:03:32
  Right?
• 2:03:32
  Each of those cells actually lasted for 30 more days.
• 2:03:36
  30 might not seem like a lot, but it's actually a 6.5 times greater than just Riluzole alone.
• 2:03:42
  Right.
• 2:03:42
  Right?
• 2:03:43
  Right.
• 2:03:43
  Right.
• 2:03:44
  It's, it's, I mean, this is something that we can now take to clinical trials, right?
• 2:03:48
  Like, why don't we just try all three at the same time?
• 2:03:51
  Right, right.
• 2:03:51
  Right.
• 2:03:52
  Let alone the fact that you have the new in-lab test bed.
• 2:03:55
  Yeah, that's the proof of concept now.
• 2:03:57
  To then do whatever.
• 2:03:58
  Yeah.
• 2:03:58
  To do, even if you say, let's not use these three, let's now try something totally different.
• 2:04:02
  Yeah.
• 2:04:03
  The fact that we can now try something totally different, right?
• 2:04:05
  It creates a scalable, robust, and like pharmacologically predictive model.
• 2:04:10
  Yes.
• 2:04:10
  Yes.
• 2:04:11
  Of ALS That's a really big deal Yeah, I think it's a really big deal I thought it was a really cool paper And the clinical advantage is clearly there, right?
• 2:04:19
  Like all three drugs You do a cocktail of all three drugs These drugs are already FDA approved For other indications With known safety profiles So they've got known safety profiles Now we can actively start prescribing these To patients The next step is to rapidly move to Human clinical trials
• 2:04:39
  But I think it's got a potential to fundamentally alter ALS therapy and projections, right?
• 2:04:46
  I thought it was very cool.
• 2:04:48
  It's very clearly a step function change in what can be done in therapeutics for ALS and research in this area.
• 2:04:58
  Yeah.
• 2:05:00
  The area we wanted to just end on here, just given Congress actually just approved new funding for this month, is related to federal funding for ALS research.
• 2:05:14
  So for those who might not be super familiar with who pays for fundamental science and how do we actually get the money to do a lot of this stuff, there are a variety of sources, but the federal government –
• 2:05:27
  has been a huge funder of fundamental science research since the Constitution.
• 2:05:36
  Let me – I don't know if you know this.
• 2:05:37
  Did you know this?
• 2:05:38
  In the Constitution, the word science is used, and I think like in Article 1, which is establishing the powers of the legislative branch –
• 2:05:48
  I think it's section eight, clause eight, is that Congress must basically promote the investment and work in the sciences and related arts.
• 2:06:03
  Wow.
• 2:06:04
  And the word science –
• 2:06:06
  is an Article I of the Constitution.
• 2:06:09
  That's really cool.
• 2:06:10
  That's got to be either Benjamin Franklin or Thomas Jefferson.
• 2:06:13
  Yeah, 100%.
• 2:06:13
  One of those two.
• 2:06:14
  100%.
• 2:06:14
  Yeah.
• 2:06:15
  And so it is a part of the ethos of even the founding document for our nation.
• 2:06:23
  It's a great document, by the way.
• 2:06:25
  It is.
• 2:06:25
  They were so ahead of the time.
• 2:06:26
  Yeah, the Constitution is an amazing document.
• 2:06:28
  It's quite nice.
• 2:06:29
  If you have not read it, I encourage you because we now need to understand it now more than ever.
• 2:06:33
  Yeah.
• 2:06:34
  But on February 3rd of this year, Congress approved new ALS funding for the fiscal year of 2026.
• 2:06:40
  So that means for the year that we're in, fiscal year of 2026, every year all these federal agencies and projects, they need to go –
• 2:06:49
  They need to be appropriated new funding by Congress.
• 2:06:53
  This is what Congress is always fighting about, among other things, is where should money be allocated?
• 2:06:58
  So for fiscal year 2026, meaning next year they're going to have to, you know, all of the folks who are supporting and doing the nonprofit work to provide.
• 2:07:08
  make sure Congress does continue to appropriate money, are consistently advocating for this to be true.
• 2:07:14
  This year was a total of $315 million, which is the highest level of funding ever for ALS research.
• 2:07:20
  It's a lot of money.
• 2:07:22
  And I just briefly want to break down what the different categories of where this is going is.
• 2:07:27
  Right.
• 2:07:28
  $90 million is going for accelerating access to critical therapies for ALS.
• 2:07:33
  It's sort of called ACT for ALS at the NIH, National Institutes of Health, which is a $15 million increase over fiscal year 2025.
• 2:07:41
  They're going to do $30 million for Advanced Research Projects Agency for Health.
• 2:07:45
  This is like ARPA-H.
• 2:07:46
  It's like DARPA, but for health.
• 2:07:49
  And so this is...
• 2:07:50
  Net new funding.
• 2:07:52
  This $30 million is net new that didn't exist previously.
• 2:07:55
  $145 million for NIH general ALS research, not specific to critical therapies, which is sort of a subsidiary under NIH.
• 2:08:03
  $40 million for Department of Defense ALS research through their directed medical research programs.
• 2:08:09
  And then $10 million for the Center for Disease Control, CDC, and Prevention of National ALS Registry.
• 2:08:18
  So, you know...
• 2:08:21
  This funding is super important to generate outcomes.
• 2:08:27
  It is not just private pharmaceutical companies and all these things that are kind of working in these areas, especially in areas that are not perceived to be financially lucrative.
• 2:08:38
  Like my dad used to work in neglected diseases.
• 2:08:43
  So this is a lot of things that impact the economy.
• 2:08:46
  you know, sub-Saharan African population, et cetera.
• 2:08:49
  There is no market for leishmania or African sleeping sickness, et cetera, et cetera.
• 2:08:55
  And so it can be difficult to actually get the funding to do this work.
• 2:09:02
  That does impact.
• 2:09:02
  So kudos to Congress for being able to not only continue funding ALS research, but increasing it, and I think applying it in ways that are going to be impactful to,
• 2:09:11
  And again, we are seeing the results of – because through NIH, et cetera, they give grants out to universities and research labs.
• 2:09:19
  And so this money flows through the system in order to reach the experts like the folks we just talked about in the previous story.
• 2:09:25
  Yeah.
• 2:09:26
  Yeah, incredibly important work.
• 2:09:29
  The U.S. has always been at the forefront.
• 2:09:32
  Not always, but since post-war, we have been at the forefront of scientific research because our government has used our incredible economy to bankroll amazing scientific research.
• 2:09:45
  And that shouldn't stop.
• 2:09:47
  It should not.
• 2:09:48
  It's incredibly important.
• 2:09:49
  We had a fantastic, big, chunky, over two hours.
• 2:09:55
  I think we're just over two hours.
• 2:09:57
  We got a little excited.
• 2:09:58
  We had some pretty good stories.
• 2:10:00
  I mean, we started with the dream engineering, which...
• 2:10:02
  I still want to follow up on that was out of Northwestern in the Neuroscience of Consciousness.
• 2:10:07
  We followed that up with Understanding Measuring Protons, where we actually confirmed the previous experimental measurement.
• 2:10:16
  This was from Max Planck Institute for Quantum Optics.
• 2:10:19
  That was in Nature.
• 2:10:20
  We ended with a sort of high-level overview of ALS research generally.
• 2:10:26
  And the rundown, we had a bunch of stories in there as well.
• 2:10:28
  We are going to go ahead and wrap this up.
• 2:10:31
  Again, if you are still here listening, you are one of the core FFP nation.
• 2:10:39
  You are so important to us.
• 2:10:40
  We really appreciate those of you who find this interesting and valuable in your busy day.
• 2:10:46
  There's so many ways you could spend your time.
• 2:10:48
  There's so much infinite amounts of content.
• 2:10:51
  You're choosing to spend your time with us, and we are grateful to you.
• 2:10:54
  We must do our comment.
• 2:10:56
  For our late show listeners.
• 2:10:58
  Yes.
• 2:10:59
  I think you said you had an idea.
• 2:11:00
  Did I?
• 2:11:01
  I'm sorry.
• 2:11:03
  Brain fried.
• 2:11:03
  No, look.
• 2:11:04
  It's been two hours.
• 2:11:06
  Hey, what would you...
• 2:11:07
  If you could dream engineer... Oh, yeah.
• 2:11:11
  Yeah.
• 2:11:11
  What would you want to dream engineer in your dreams?
• 2:11:13
  Yes, that's a good one.
• 2:11:15
  Because we're going to get some interesting answers.
• 2:11:17
  So let us know in the comments.
• 2:11:19
  below.
• 2:11:20
  Again, if you're listening, make sure you check us on video on Spotify and YouTube.
• 2:11:26
  We're on all the socials where we put out all of our clips.
• 2:11:29
  The clips don't always have the full context, so it's always encouraged to listen to the full podcast.
• 2:11:36
  If you would like to support the show, you can become a patron by going to ffppod.com slash donate.
• 2:11:43
  Follow, like, subscribe as always.
• 2:11:46
  My name is Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
• 2:11:52
  We are just so grateful for you all.
• 2:11:55
  We will see you all next week.
• 2:11:59
  And I now forget what I'm supposed to say with this is from First Principles at the end of the episode.
• 2:12:03
  Something like that, yeah.
• 2:12:04
  Something, something.
• 2:12:06
  This is from First Principles.
• 2:12:08
  Cue exit music.