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[Assistant] Anyway, this is something, fairly simple trigonometric problem.


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[Assistant] Slightly more complicated problem, where I'm going to add two waves. And we'll do a lot more of this in the next term, but right now, you're supposed to have first pass at this topic. This is called the double-slit experiment. You have a source of waves here.


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[Assistant] The source is so far away you can treat this to be just parallel lines.


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[Assistant] And you hit a double slit, namely an impenetrable barrier, with two holes in it.


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[Assistant] If you want, think of it as water waves and you've erected a wall on your estate, but there are two holes.


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[Assistant] So you know what will happen, right? These two holes will themselves start generating their own waves.


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[Assistant] And you want to stand on a line here, go up and down and ask, what's the water doing where I am? So first take a point exactly midway between these two points.


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[Assistant] What are you getting? You're getting a signal from here, and you're getting a signal from here. One signal travels that way, other travels that way.


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[Assistant] Do you understand that because you're symmetrically located, the two signals will be in step when they come to you?


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[Assistant] When this front hit this double hole and started making his own wave, so these waves are emitted in step.


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[Assistant] They march along and come to you without, with the same time delay.


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[Assistant] So when a crest from this reaches you, a crest from that will reach you. Crop reaches, crop reaches. So you'll just double the answer. So here you will get


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[Assistant] Uh, psi will become 2psi, or A will become 2A. If you add an amplitude A from this and A from this, you will get 2A here. So the water will be very choppy there.


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[Assistant] If you're living on a little boat, the boat will rock up and down with amplitude 2a. If you shut off this hole,


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[Assistant] So amplitude will go back to just that from one hole, it'll be A. 2 gives you 2A. That's what makes a lot of common sense. But pick another point here. I will tell you how to pick that point.


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[Assistant] So that these two lengths are not equal. So let's call that length L1 and length L2. Suppose L1 minus L2 is half a wavelength.


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[Assistant] As you know, between every two crests there is a trough where the function is minus the amplitude, at the crest it's equal to plus the amplitude.


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[Assistant] So if you're half a wavelength apart, when a crest from here appears, a trough from here will appear.


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[Assistant] When a trough from here appears, a crest from here will appear. In fact, at every instant, whatever comes here, minus of that will come here.


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[Assistant] Let's understand why. The way, the way it travels is 2 pi x over lambda.


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[Assistant] Where x is the same measure from here. For the other guy, it is 2 pi times x prime over lambda, where x prime is that length.


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[Assistant] If that length is longer than this by lambda over 2,


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[Assistant] But cosine of pi added to it, if pi added to anything changes the cosine to minus itself.


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[Assistant] So the way to think about it is, when you're half a wavelength behind, coming from here, whatever this one does, this does minus of that. Because shifting by half a wavelength is adding a pi to the cosine. Adding a pi to cosine changes to minus its value at every instant.


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[Assistant] Not only does crest come with trough, and trough comes with crest, every possible displacement comes with minus of that from here, and you get nothing here. So this is called an interference minimum.


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[Assistant] Or we say here, the interference is constructive, and here we say the interference is destructive.


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[Assistant] How will you find the point of destructive interference? It's a matter of simple geometry. You measure that length.


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[Assistant] When you measure that length, take the difference and set it equal to λ over 2.


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[Assistant] That's the first destructive interference. That's a very interesting point to sit in. Look what it's telling you.


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[Assistant] It says if you sit here with your boat, the boat won't move at all.


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[Assistant] Even though there are two holes letting waves in, at that point, that's the principle behind noise cancellation. You can actually cancel one sound with another sound if you bring the second sound off by half a cycle.


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[Assistant] Miraculously, putting a second hole at the right place can actually calm the waters where you are.


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[Assistant] And the minute somebody blocks one of these holes, water will get choppy again where you are. This is the property of waves.


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[Assistant] The two waves can lead to nothing, but one wave cannot, because two waves have a chance of working against each other and cancel. This is something you gotta get very straight. Now if I go further up here,


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[Assistant] Till I reach a point where the difference in those two lengths is the full wavelength, then I'm back to being instead.


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[Assistant] Of course, full wavelength means that these are not doing the same length, but


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[Assistant] This is differing from that by whole wavelength. That means when the 13th wave from here arrives, the 14th or the 12th from here will arrive. But we don't care as long as the crest comes with the crest.


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[Assistant] We are happy where we are. So things get, start with constructive, destructive, constructive, destructive. It oscillates both above and below the midpoint. Yes? Yes?


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[Assistant] It all depends on the aperture and so on. But what is clear by symmetry is that these two have the same amplitude.


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[Assistant] In practice, we believe the amplitude will also be the same, because amplitude has to be continuous when you go through a slit. The water cannot be jumping up by two inches before the slit, and suddenly on the other side, jump to a different length. Amplitude is always continuous. So in fact, this amplitude and that amplitude will be equal.


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[Assistant] You can also see from the wave equation, if the ψ was not continuous, dψdx will be infinity.


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[Assistant] You're not able to read this equation because it's very badly written. Let me tell you what I tried to do. Cosine 2 pi x over lambda. Let's pick a certain time so that I don't worry about omega t. In fact, let me write it as L1.


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[Assistant] Suppose L2 is equal to L1 plus half a wavelength. Then this one becomes cosine 2 pi L1 over lambda plus pi.


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[Assistant] Write L2 as L1 plus λ over 2. This is the L1. If you put a λ over 2 times 2π over λ, you get a π.


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[Assistant] Then you've got to understand that whenever you add a π to a cosine, you get minus the cosine without the π.


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[Assistant] And you can see, this guy is producing a signal, which is exactly the minus of that guy. I did this at t equal to zero, but you can put an omega t to both, but nothing changes the fact.


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[Assistant] That when you subtract the same ωt from the 2, it doesn't change anything. So I didn't worry about the time. It's a spatial mismatch that's the problem here.


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[Assistant] Okay, now sometimes people write this formula for destructive and constructive interference as follows. There's an approximate formula that's very handy.


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[Assistant] The correct way to do this, of course, is simply measure that distance and that distance and take the difference. Okay?


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[Assistant] Is equal to m lambda, where m is an integer.


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[Assistant] If it's a half integer, which we like to denote as follows, m plus half lambda.


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[Assistant] Again, m equal to zero plus minus one, et cetera, is destructive.


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[Assistant] The way of writing M is usually the standard symbol people use for integers. M and N are common symbols for integers.


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[Assistant] So if the path difference is an integer multiple of lambda, it's harmless. If it's a half integer multiple of lambda, it's harmful, meaning destructive.


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[Assistant] The whole approximation has to do with what is L1 minus L2. One is to actually measure these lengths exactly, and using Pythagoras theorem, uh, given the distance between the slits, given the distance to the screen,


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[Assistant] If that distance is d, one can do the math and calculate it. But here's what people do. They say, imagine this screen is very, very far away.


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[Assistant] Very far away so that the rays leaving this are actually parallel because they meet at infinity and that's where you screen this. It's not really at infinity, but it's far enough for this purpose.


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[Assistant] Then you can tell very easily that the extra length traveled by this wave is just this part here. Do you see that?


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[Assistant] Because this part cancels out. So drop a perpendicular here. Those two are equal in length going out to infinity. That's the extra section. How long is the extra section? With that is d.


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[Assistant] Distance between the slits. If that angle is theta here, then we say d sine theta


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[Assistant] And that's what you want to be, either mλ or m½λ for constructive and destructive.


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[Assistant] But there's a way to relate this theta here to this direction. They are the same angle.


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[Assistant] You may want to go look at your book, where the graphics are better than mine. But I'm telling you, the same old trick we use from inclined planes.


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[Assistant] If you've got two lines, this line and this line, the angle between them is θ, then the perpendicular of the two lines have the same angle between them. Perpendicular to this line is this one, and perpendicular to the screen is the horizontal one. So θ is also that angle.


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[Assistant] Therefore, the way you will do any of these problems is, suppose someone says, find out when I get the first destructive interference.


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[Assistant] Given that length to the screen, and someone says how far should I move?


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[Assistant] What you do is you say d is the distance between the two slits, sine theta equal to say lambda over 2 for destructive, solve for theta.


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[Assistant] Once you get the angle, you're basically telling the person to go at that angle. And that's where the intensity will be a minimum.


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[Assistant] But then it's trigonometry to understand that d over l is just tanθ. So if you knew θ, you can find d over l.


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[Assistant] Lambda is given to you and theta has to be found. If theta is given to you, lambda has to be found. Somebody can say, in some experiment with waves, I find the first minimum at that angle. What's the wavelength? The distance between the slits is so and so.


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[Assistant] But the basic idea is really to find the path lengths, take the difference, set it equal to either integer multiple or half integer multiple of the wavelength.


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[Assistant] Interference problem has to do with all these musical instruments and it goes like this. Take a string. Now there's a wall here. And you start shaking it and ask what happens.


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[Assistant] So let's say I shake it just once and I send a pulse like this moving to the right.


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[Assistant] By the time it gets to this place where the string is un- is anchored and not free to move, it cannot vibrate here.


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[Assistant] So how is that going to happen? Well, some forces will be exerted on the string by the wall to make sure


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[Assistant] That the signal the wall sends out, along with the signal you send out, add up to zero at the point of contact because no vibration there is allowed.


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[Assistant] It comes out reflected in direction and inverted in its sign.


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[Assistant] For those of you who wanted more satisfactory proof of this, let me say the following. Suppose there was no wall.


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[Assistant] I saw you send a signal and I manufactured a mirrored image of that to the right traveling towards this point.


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[Assistant] The two are coming near each other, and when they meet, by design, I've arranged it so that this one and this one, when they overlap, are exactly opposite of each other, so that they cancel each other at this point.


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[Assistant] Then they go through each other, and this will come out here, that will go to the other side.


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[Assistant] But throughout this encounter, here's the punch line, throughout the encounter, this point was not asked to move. Because the two waves cancel.


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[Assistant] That means even if I put my finger there, it won't make a difference to this problem because that point is not planning to move anyway.


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[Assistant] It also means that if you put a barrier here that doesn't allow the string to move, doesn't change the outcome.


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[Assistant] That's the reason we convince ourselves that the incoming pulse this way comes out that way.


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[Assistant] Okay, now what I want to study is not a single one-shot pulse, but a pulse that looks like this. A cos kx minus omega t.


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[Assistant] A continuous signal goes in, how does it come out? It comes out as cos ωt kx. So, I've reversed the amplitude, and I've reversed the direction of motion.


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[Assistant] So this is a result of an incoming wave and the one that's sent back by the wall. Together they coexist. That goes in and that comes out and together they form what I will find in this region. I keep sending stuff to the wall, wall keeps sending that back to me, and both exist.


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[Assistant] Well, you've got to do another trigonometric identity to show you that this is really 2sin kx sinωt.


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[Assistant] I don't want to go through the trigonometric identity, but what I want you to notice then


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[Assistant] Is that the resulting physics looks like this. Here is a wall.


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[Assistant] At a given point x, the amplitude is 2a sin kx, and it oscillates with the frequency ωt, and if you draw sin kx, it's going to look like this.


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[Assistant] Kx is equal to π, where that distance is λ over 2.


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[Assistant] There is no vibration. There is no vibration. This is called a node. In between is an antinode. This kind of thing will be set up all the way back.


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[Assistant] Now you see what that means. So this point is not going to vibrate. If I put another barrier here, it won't make any difference. Because that point is


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[Assistant] These two waves add up to 0, identically at that point. So, if you put your finger there, it won't matter.


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[Assistant] Making no difference if you hold the string there, it also doesn't matter if you hold it here.


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[Assistant] If you turn the problem around and say, look, I give you the following problem. I give you a string which is clamped at these two ends. What are the possible vibrations of the string? They have to look


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[Assistant] Like the following, your string length has to be equal to that or that or any of these multiples.


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[Assistant] That means what can happen in your string is either that or it can do that.


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[Assistant] Or something with more and more lobes in it. These are the allowed modes of vibration of the string fixed at two ends.


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[Assistant] What I want to extract from this is what sound will you hear? What frequency will you hear?


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[Assistant] So you have, this is the family of problems that you guys had expected to do. So you have to think as follows. That is the length.


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[Assistant] How is the length related to the wavelength is all you gotta figure out. Is it clear to you guys that this is half a wavelength?


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[Assistant] If you got that, you're whom? Because you're saying half a wavelength is equal to L, or the wavelength is equal to 2L. The frequency is the velocity of sound divided by the wavelength, which is the velocity divided by 2L. That's the frequency you will hear from this guy.


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[Assistant] When it vibrates like this, I think it's clear to you that it's gone through one full cycle here. This is λ equal to L. Due to the whole thing, you will find frequency is now V over L.


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[Assistant] Which is 2 times the lowest frequency, which we denote by f0. It means the fundamental frequency.


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[Assistant] So the bottom line is very simple. A string clamped to two ends, and has length L, can only vibrate in


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[Assistant] Both ends. And there are some longitudinal sound waves going back and forth. These are transverse waves on a string. This is sound waves going back and forth.


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[Assistant] You realize the sound waves going back and forth cannot go back and forth at this wall or at this wall.


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[Assistant] Therefore, the motion of the air molecules has to have a node here and has to have a node here, so the possible modes of vibration for sound also look exactly like this.


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[Assistant] Here the picture really talks about the string moving perpendicular to the length. This is not indicated here.


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[Assistant] The sound, the air molecules are going up and down. This is the amplitude for longitudinal vibration.


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[Assistant] So we just, out of habit, plot everything in the y-axis. That doesn't mean the motion is in the y-direction. For a longitudinal wave, the motion is side to side. This is the extent of side to side motion.


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[Assistant] So the only problem where you may get stumped is the following one. I will tell you how to do that and then we're done. Take a tube, like this. It's closed here and it's open here.


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[Assistant] You know, take a Coke bottle or something and try to blow on it, you hear a certain sound. Why is it picking that one frequency? That's what I want to understand. Now, since I'm making the noise here,


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[Assistant] Here's where the amplitude will be the biggest, and of course there's nothing doing at this end, because that's a wall which cannot go back and forth. So what pattern can I draw? Take a minute and think about it. I want a maximum vibration here, nothing here. The lowest one I can draw looks like that.


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[Assistant] The next one I can draw is constrained to have a maximum here, and then a minimum at that end.


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[Assistant] Now, what is the relation of the length of the tube to the wavelength? I think you can see the length of the tube is now a quarter of a wavelength. You start from here to the top. Coming down is the other quarter, then the other half is missing.


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[Assistant] So what's the frequency? It's equal to V over λ. It's going to be 4V over L.


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[Assistant] I'm getting the frequency to be velocity divided by wavelength, and I bet you will get this. Oh, did I get it wrong? Uh, yes, I'm sorry. It's V over 4L. 4L is λ. Now what's the answer here?


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[Assistant] What is the relation of the length to wavelength? The length happens to be


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[Assistant] A half a cycle plus another quarter is three-fourth of λ.


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[Assistant] And the frequency, if you do this, will be 3b over 4L. And this is all I want you to notice.


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[Assistant] That in this peculiar problem where one end is open and one end is closed,


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[Assistant] The frequencies are odd multiples of the fundamental frequency. Three times that, five times that, seven times that.


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[Assistant] And in exams you can be asked a variety of questions, either this one or some other exam you take.


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[Assistant] Let me do one practice thing. Here's a tube open at both ends. Draw me the lowest frequency you can draw.


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[Assistant] I want an antinode here, an antinode here, but between them I got to have a node. So, it will look like that.


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[Assistant] Then you can draw more complicated patterns, provided the two ends look like this. And I think you can see here that this is half a wavelength.


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[Assistant] So L equal lambda over 2, and again you will find here all frequencies are integer multiples of the fundamental. So the story is this. As long as the two ends are the same, both open or both closed,


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[Assistant] All frequencies are integer multiples of the fundamental one. If you got a funny situation, one end is open, one end is closed, you will get odd multiples of the fundamental.


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[Assistant] I don't have a better way to explain this in the somewhat easier part of the course, and I think you should try to be on top of this particular topic.


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[Assistant] Oh shit, the big Oz himself! Is there a reason you're calling me on the weekend? Your sin-sinness? Sinfulness? Sin-


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[Assistant] The thing I would very much like to talk about today is the state of the art in deep learning. Here we stand in 2019.


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[Assistant] Really at the height of some of the greater causes that have happened, but also stand at the beginning.


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[Assistant] And it's up to us to define where this incredible data driven technology takes us. And so I'd like to talk a little bit about the breakthroughs that happened in 2017 and 2018 that take us to this point.


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[Assistant] Lecture is not on the state of the art results on main machine learning benchmarks.


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[Assistant] So the various, uh, image classification, object detection, or the NLP benchmarks, or the GAN benchmarks, this isn't about the cutting edge algorithm.


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[Assistant] That's available on GitHub that performs best on a particular benchmark. This is about ideas. Ideas and developments that are at the cutting edge of what defines this exciting field of deep learning.


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[Assistant] And so I'd like to go through a bunch of different areas that I think are really exciting. Now of course this is also not a lecture that's complete.


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[Assistant] There's other things that I may be totally missing that happened in 2017 and 18 that are particularly exciting to people here, people beyond.


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[Assistant] And protein folding and all kinds of applications that there has been some exciting developments from DeepMind and so on that don't touch on. So.


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[Assistant] Forgive me if your favorite developments are missing, but hopefully this encompasses some of the really


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[Assistant] Fundamental things that have happened, both on the theory side, on the application side, and on the community side of all of us being able to work together on this, on these kinds of technologies. I think 2018


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[Assistant] In terms of deep learning is the year of natural language processing. Many have described this year as the ImageNet moment in 2012 for computer vision when AlexNet was the first neural network that really gave that big jump in performance on computer vision. It started to inspire people what's possible.


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[Assistant] With deep learning, with purely learning based methods. In the same way, there's been


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[Assistant] A series of developments from 2016-17 led up to 2018 with the development of BERT that


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[Assistant] Has made on benchmarks and in our ability to apply NLP to solve various NLP tasks, natural language processing tasks, a total leap.


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[Assistant] So let's tell the story of what takes us there. There's a few developments. I've mentioned a little bit on Monday.


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[Assistant] About the encoder-decoder recurrent neural networks. So this idea of


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[Assistant] Recurrent neural networks encode sequences of data. And I'll put something.


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[Assistant] Output, either a single prediction or another sequence. When the input sequence and the output sequence are not the same, necessarily the same size,


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[Assistant] They're like a machine translation. We have to translate from one language to another. The encoder decoder architecture.


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[Assistant] Takes the following process. It takes in the sequence of words or the sequence of samples as the input.


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[Assistant] And uses the recurrent units, whether that's LSTM or GRUs or beyond, and encodes that sentence into a single vector.


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[Assistant] So forms an embedding of that sentence of what it represents, a representation of that sentence. And then feeds that representation in the decoder recurrent neural network that then generates the


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[Assistant] By taking a sequence and mapping it to a fixed size vector.


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[Assistant] representation and then you decode by taking that fixed size vector representation and unrolling it


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[Assistant] Into the sentence that can be of different length than the input sentence.


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[Assistant] Okay, that's the encoded decoder structure for recurrent neural networks. It's been very effective for


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[Assistant] It provides a mechanism that allows to look back at the input sequence. So as opposed to saying that you have a sequence that's the input sentence, and that all gets collapsed into a single vector representation, you're allowed to look back at the particular samples from the input sequence.


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[Assistant] As part of the decoding process. That's attention.


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[Assistant] And you can also learn which aspects are important for which aspects of the decoding process. Which aspects of the input sequence are important to, uh, the output sequence. Visualize another way.


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[Assistant] And this is, there's a few visualizations here that are quite incredible that are done by Jay Alomar.


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[Assistant] I highly recommend you follow the links and look at the further details of these visualizations of attention. So if we look at neural machine translation, the encoder RNN takes a sequence of words and throughout


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[Assistant] After every sequence forms a set of hidden representations.


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[Assistant] A hidden, a hidden state that captures the representation of the words that followed. And those sets of hidden representations...


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[Assistant] As opposed to being collapsed to a single fixed size vector are then all pushed forward to the decoder that are then used by the decoder to translate


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[Assistant] But in a selective way, where the decoder


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[Assistant] Here visualized on the y-axis the input language and on the x-axis the output language.


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[Assistant] The decoder weighs the different parts of the input sequence differently in order to determine how to best translate, generate the word that forms the translation in the full output sentence. Okay, that's attention.


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[Assistant] Allowing, uh, expanding the encoded decoder architecture to allow for


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[Assistant] Uh, selective attention to the input sequence as opposed to collapsing everything down into fixed representation. Okay, next step, self-attention. In the encoding process, allowing the


[User] Please transcribe this text

[Assistant] Informing the hidden representations at


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[Assistant] What are the important relevant aspects of the input sequence that can help you encode that word the best?


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[Assistant] So it improves the encoder process by allowing it to look at the entirety of the context. That's self-attention.


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[Assistant] Building on that, transformer. It's using the self-attention mechanism in the encoder.


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[Assistant] To form these sets of representations on the input sequence.


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[Assistant] And then, as part of the decoding process, follow the same but in reverse with a bunch of self-attention.


[User] Please transcribe this text

[Assistant] That's able to look back again, so it's self-attention on the encoder, attention on the decoder, and that's where the magic, that's where the entirety of the magic is that's able to capture the rich context of the input sequence in order to generate in a contextual way the output sequence. So let's take a step back then.


[User] Please transcribe this text

[Assistant] And look at what is critical to natural language.


[User] Please transcribe this text

[Assistant] In order to be able to reason about words, construct a language model and be able to reason about the words in order to classify a sentence or translate a sentence or


[User] Please transcribe this text

[Assistant] Compared to sentences and so on, they're, the sentences are


[User] Please transcribe this text

[Assistant] Collections of words or characters and those characters and words have to have an efficient representation that's meaningful for that kind of understanding. And that's what the process of embedding is. We talked a little bit about it on Monday. And so the traditional word to vec,


[User] Please transcribe this text

[Assistant] Process of embedding is you use some kind of trick and unsupervised way to map words into a compressed representation.


[User] Please transcribe this text

[Assistant] So language modeling is the process of determining which words follow each other usually. So one way you can use it in a skip gram model, taking a huge data sets of words, you know, there's writing all over the place, taking those data sets and feeding a neural network


[User] Please transcribe this text

[Assistant] Which is what Word2Vec does, if you throw away the output and the input.


[User] Please transcribe this text

[Assistant] In just taking the hidden representation form in the middle, that's how you form this compressed embedding. A meaningful representation that when two words are related in a language modeling sense, two words are related, they're going to be


[User] Please transcribe this text

[Assistant] So that is the approach of using bidirectional LSTMs to learn that representation. And what bidirectional, bidirectionally. So looking not just the sequence to let up to the word but in both directions. The sequence that followed and the sequence that before. And that allows you to learn


[User] Please transcribe this text

[Assistant] In learning the rich full context of the word, you're forming representations that are much better able to represent the statistical language model behind the kind of corpus of language that you're looking at. And this has taken a big leap


[User] Please transcribe this text

[Assistant] In ability to then that further, further algorithms then with the language model, a reasoning about doing things like sentence classification, sentence comparison, so on, translation, that representation is much more effective for working with language.


[User] Please transcribe this text

[Assistant] The idea of the OpenAI transformer


[User] Please transcribe this text

[Assistant] Is the next step forward, is taking the, the same transformer that I mentioned previously, the encoder with self-attention, decoder with attention looking back at the input sequence, and using it, uh,


[User] Please transcribe this text

[Assistant] Taking the le- taking the language learned by the decoder.


[User] Please transcribe this text

[Assistant] And using that as a language model and then chopping off layers and training on a specific, uh, on a specific language task like sentence classification. Now BERT is the thing that did the big leap in performance.


[User] Please transcribe this text

[Assistant] With the transformer formulation, there's always, there's no bi-directional element. There's, it's always moving forward, so the encoding step and the decoding step. With BERT, it's,


[User] Please transcribe this text

[Assistant] It's richly bidirectional. It takes in the full sequence of the sentence.


[User] Please transcribe this text

[Assistant] Some percentage of the words, 15% of the words, 15% of the samples, the tokens from the sequence.


[User] Please transcribe this text

[Assistant] And tasks the entire encoding.


[User] Please transcribe this text

[Assistant] Self-attention mechanism to predict the words that are missing.


[User] Please transcribe this text

[Assistant] That construct, and then you stack a ton of them together. A ton of those encoders, self-attention, feedforward network, self-attention, feedforward network together. And that allows you to learn the rich context of the language to then, at the end, perform all kinds of tasks.


[User] Please transcribe this text

[Assistant] You can create, first of all, like Elmo, and like Word2vec, create rich contextual embeddings.


[User] Please transcribe this text

[Assistant] Take a set of words and represent them in the space that's very efficient to reason with. You can do language classification, you can do


[User] Please transcribe this text

[Assistant] Uh, sentence pair classification, you could do the similarity of two sentences, multiple choice question answering, general question answering, uh, tagging of sentences.


[User] Please transcribe this text

[Assistant] Okay, I lingered on that one a little bit too long.


[User] Please transcribe this text

[Assistant] Is a implementation of the Nvidia Drive PX2 system which runs a ton of neural networks. There's eight cameras on the car and a variant of the inception network is now


[User] Please transcribe this text

[Assistant] Taking in all eight cameras at different resolutions as input and performing various tasks.


[User] Please transcribe this text

[Assistant] A huge fleet of vehicles where it's not engineers, some I'm sure are engineers, but it's really regular consumers.


[User] Please transcribe this text

[Assistant] That have purchased the car, have no understanding in many cases of what a neural network's limitations and capabilities are, so on. Now it has, a neural network is controlling the well-being.


[User] Please transcribe this text

[Assistant] Its decisions, its perceptions and the control decisions based on those perceptions are controlling the life of a human being.


[User] Please transcribe this text

[Assistant] And that to me is one of the great sort of breakthroughs of 17 and 18.


[User] Please transcribe this text

[Assistant] In terms of the development of what AI can do in a practical sense in impacting the world. And so,


[User] Please transcribe this text

[Assistant] Now, there's two types of systems currently operating in Teslas. There's hardware version one, hardware version two. Hardware version one was Intel Mobileye Monocular Camera Perception System, as far as we know that was not using a neural network, and it was a fixed system that wasn't learning, at least online learning in the Teslas. The other is hardware version two, and it's about half and half now in terms of the miles driven.


[User] Please transcribe this text

[Assistant] The dream of automating some aspects or all aspects or as many aspects as possible of the machine learning process where you can just drop in a data set that you're working on and


[User] Please transcribe this text

[Assistant] The, the system will automatically determine all the parameters from the details of the architectures, the size of the architecture, the different modules on that architecture, the hyperparameters.


[User] Please transcribe this text

[Assistant] The success of the neural architecture search is 16 and 17 and there's been a few ideas with Google AutoML that's really trying to almost create an API where you just drop in your data set and it's using reinforcement learning and recurrent neural networks to, to


[User] Please transcribe this text

[Assistant] Given a few modules, stitch them together in such a way where the objective function is optimizing the performance of the overall system. And they showed a lot of exciting results Google showed and others that outperform state AR systems both in terms of efficiency and in terms of accuracy.


[User] Please transcribe this text

[Assistant] Now in 18, there have been a few improvements on this direction, and one of them is Adonit, where it's now using


[User] Please transcribe this text

[Assistant] The same reinforcement learning AutoML formulation to build ensembles on your own network. So in many cases, state of the art performance can be achieved by, as opposed to taking a single architecture, is building up a multitude, an ensemble, a collection of architectures.


[User] Please transcribe this text

[Assistant] And that's what it's doing here is giving, uh, candidate architectures and stitching them together to form an ensemble to get state of the art performance.


[User] Please transcribe this text

[Assistant] A breakthrough leap forward, but it's nevertheless a step forward. And it's a very exciting field that's going to be receiving more and more attention.


[User] Please transcribe this text

[Assistant] The breakthrough performance of showing what deep learning networks are capable of.


[User] Please transcribe this text

[Assistant] From that point on, from 2012 to today, there's been non-stop, extremely active developments of different architectures that even on just ImageNet alone, on doing the image classification task, have


[User] Please transcribe this text

[Assistant] Improve performance over and over and over with totally new ideas. Now on the other side, on the data side,


[User] Please transcribe this text

[Assistant] There's been very few ideas about how to do data augmentation. So data augmentation,


[User] Please transcribe this text

[Assistant] Is the process of, you know, it's what...


[User] Please transcribe this text

[Assistant] Kids always do when you learn about an object, right? Is you look at an object and you kinda like twist it around. Is, is taking the, the, the raw data


[User] Please transcribe this text

[Assistant] And messing with such a way that it can give you much richer representation of what this can, this data can look like in other forms, in other, in, in other contexts in the real world.


[User] Please transcribe this text

[Assistant] There's been very few developments, I think still. And there's, this auto-augment is just a step, a tiny step into that direction that I hope


[User] Please transcribe this text

[Assistant] We as a community, uh, invest a lot of effort in.


[User] Please transcribe this text

[Assistant] As it says, okay, so there's these...


[User] Please transcribe this text

[Assistant] Data augmentation methods like translating the image, sharing the image, doing color manipulation like color inversion. Let's take those as basic actions that you can take and then use reinforcement learning and an RNN, again construct, to stitch those actions together in such a way that can augment data


[User] Please transcribe this text

[Assistant] Like on ImageNet, to when you train on that data, it gets state of the art performance. So mess with the data.


[User] Please transcribe this text

[Assistant] In a way that optimizes the way you mess with the data.


[User] Please transcribe this text

[Assistant] And then they've also showed that given that the set of


[User] Please transcribe this text

[Assistant] Data augmentation policies that are learned to optimize, for example, for ImageNet, given some kind of architecture, you can take that


[User] Please transcribe this text

[Assistant] A totally different data set. So there's the process of transfer learning.


[User] Please transcribe this text

[Assistant] So what is transfer learning? So we talked about transfer learning, you have a neural network that learns to do cat versus dog or no, learns to do a thousand class classification problem on ImageNet and then you transfer, you chop off a few layers and you transfer on the task of your own data set of cat versus dog. What you're transferring is the weights


[User] Please transcribe this text

[Assistant] That are learned on the ImageNet classification task, and now,


[User] Please transcribe this text

[Assistant] You're then fine-tuning those weights on the, uh, the specific personal cat versus dog data set you have.


[User] Please transcribe this text

[Assistant] Now, you can do the same thing here. You can transfer, as part of the transfer learning process, take the


[User] Please transcribe this text

[Assistant] Data augmentation policies learned on ImageNet and transfer those. You can transfer both the weights and the policies.


[User] Please transcribe this text

[Assistant] That's a really super exciting idea, I think. It wasn't quite demonstrated extremely well here.


[User] Please transcribe this text

[Assistant] In terms of performance, so it had gotten an improvement in performance and so on, but it kind of inspired an idea that's something that we need to really think about. How to augment data in an interesting way such that given just a few samples of data,


[User] Please transcribe this text

[Assistant] We can generate huge data sets in a way that you can then form


[User] Please transcribe this text

[Assistant] Meaningful, complex, rich representations from.


[User] Please transcribe this text

[Assistant] I think that's really exciting and one of the ways that you break open the problem of how do we learn a lot from a little. Training deep neural networks with synthetic data, this...


[User] Please transcribe this text

[Assistant] Also really an exciting topic that a few groups, but especially NVIDIA has invested a lot in. And here's a, from a CVPR 2018, probably my favorite work on this topic is they really went crazy and said, okay, let's mess with synthetic data.


[User] Please transcribe this text

[Assistant] In every way we possibly can. So, on the left there is showing a set of backgrounds, then there's also a set of artificial objects, and you have a car.


[User] Please transcribe this text

[Assistant] Or some kind of object that you're trying to classify. So let's take that car and mess with it with every way possible.


[User] Please transcribe this text

[Assistant] Apply lighting variation to it every way possible. Rotate everything as, as, as crazy as, so, so what NVIDIA


[User] Please transcribe this text

[Assistant] Is really good at is creating realistic scenes and they said, okay, let's create realistic scenes, but let's also go way above board and not do realistic at all. Do things that can't possibly happen in reality. And so generally these huge data sets I want you to train and again achieve quite interesting, uh, quite, uh, good performance.


[User] Please transcribe this text

[Assistant] On image classification. Of course, they're trying to apply to ImageNet and so on, these kinds of tasks. You're not going to outperform


[User] Please transcribe this text

[Assistant] Networks that were trained on ImageNet but they show that with just a small sample from


[User] Please transcribe this text

[Assistant] From those real images, they can fine tune this network train on synthetic images, totally fake images, to achieve steady AR performance.


[User] Please transcribe this text

[Assistant] Again, another way to generate, to get to learn a lot from very little.


[User] Please transcribe this text

[Assistant] By generating fake worlds, synthetically.


[User] Please transcribe this text

[Assistant] The process of annotation, which for supervised learning, is what you need to do in order to


[User] Please transcribe this text

[Assistant] Train the network. You need to be able to provide ground truth. You need to be able to label whatever the entity that is, uh, being learned. And so for image classification, that's saying what is going on in the image.


[User] Please transcribe this text

[Assistant] And part of that was done on ImageNet by doing a Google search for creating candidates. Now, saying what's going on in the image is a pretty easy task.


[User] Please transcribe this text

[Assistant] Then there is the object detection task of detecting the bounty box.


[User] Please transcribe this text

[Assistant] And so saying, drawing the actual bounding box is a little bit more difficult, but it's a couple of clicks and so on.


[User] Please transcribe this text

[Assistant] Then if we take the final, the, the probably one of the higher complexity tasks of perception of image understanding is segmentation, is actually drawing


[User] Please transcribe this text

[Assistant] So the work with, uh, polygon RNN is to use recurrent neural networks to make suggestions for polygons. It's really interesting. It's, uh, there's a few tricks to form these high resolution polygons. So the idea is it drops in a single point.


[User] Please transcribe this text

[Assistant] And the performance is really good. There's a few tricks and this tool is available online. It's a really interesting idea. Again,


[User] Please transcribe this text

[Assistant] The dream with AutoML is to remove the human from the picture as much as possible. With data augmentation, remove the human from the picture as much as possible for menial data. Automate the boring stuff. And in this case, the act of drawing a polygon tried to automate it as much as possible. The interesting


[User] Please transcribe this text

[Assistant] Along which deep learning is recently been trying to be optimized is how do we make deep learning accessible? Fast, cheap, accessible.


[User] Please transcribe this text

[Assistant] So the Dawnbench from Stanford, the benchmark, the Dawnbench benchmark from Stanford, asked, formulated an interesting competition.


[User] Please transcribe this text

[Assistant] Which got a lot of attention and a lot of progress. It's saying...


[User] Please transcribe this text

[Assistant] If we want to achieve 93% accuracy on ImageNet and 94% on CIFAR 10,


[User] Please transcribe this text

[Assistant] Let's now compete. That's like the requirement. Let's now compete how you can do it in the least amount of time and for the least amount of dollars.


[User] Please transcribe this text

[Assistant] You know, Renegade, awesome Renegade group of deep learning researchers have been able to train on ImageNet in three hours. So this is for training process for 25 bucks.


[User] Please transcribe this text

[Assistant] So training a network that achieves 93% accuracy for 25 bucks.


[User] Please transcribe this text

[Assistant] Really boils down to messing with the learning rate throughout the process of training.


[User] Please transcribe this text

[Assistant] So the learning rate is how much you, based on the loss, based on the error the neural network observed, how much do you adjust the weights?


[User] Please transcribe this text

[Assistant] So they found that if they crank up


[User] Please transcribe this text

[Assistant] The learning rate while decreasing the momentum, which is a parameter of the optimization process, where they do it that jointly, they're able to make the network learn really fast.


[User] Please transcribe this text

[Assistant] That's really exciting and the benchmark itself is also really exciting because that's exactly for people sitting in this room.


[User] Please transcribe this text

[Assistant] That, that opens up the door to doing all kinds of...


[User] Please transcribe this text

[Assistant] Or OpenAI or Facebook or so on, without computational resources.


[User] Please transcribe this text

[Assistant] That's important for academia, that's important for independent researchers and so on. So GANs.


[User] Please transcribe this text

[Assistant] There's been a lot of work on generative adversarial neural networks. And, uh, in some ways there's not been breakthrough ideas in GANs for quite a, quite a bit. And I think big GAN from


[User] Please transcribe this text

[Assistant] From Google DeepMind, the ability to generate incredibly high resolution images.


[User] Please transcribe this text

[Assistant] And it's the same GAN technique, so in terms of breakthroughs and innovations, but scaled.


[User] Please transcribe this text

[Assistant] So increase the model capacity and increase the batch size, the number of images that are felled, uh, that are fed.


[User] Please transcribe this text

[Assistant] To the network. It produces incredible images. I encourage you to go online and look at them. It's hard to believe that they're generated.


[User] Please transcribe this text

[Assistant] So that was the, so 2018 for GANs was a, uh, a year of scaling and parameter tuning, uh, as opposed to breakthrough new ideas.


[User] Please transcribe this text

[Assistant] Video to Video Synthesis, this work is from NVIDIA, is


[User] Please transcribe this text

[Assistant] Looking at the problem, so there's been a lot of work on generally going from image to image.


[User] Please transcribe this text

[Assistant] So, from a particular image generating another image.


[User] Please transcribe this text

[Assistant] So whether it's colorizing an image or just traditionally defined GANs.


[User] Please transcribe this text

[Assistant] The idea with video-to-video synthesis that a few people have been working on but Nivea took a good step forward is to


[User] Please transcribe this text

[Assistant] Make, uh, the video to make the temporal consistency, the temporal dynamics part of the optimization process. So make it look not jumpy.


[User] Please transcribe this text

[Assistant] So if you look here at the comparison for this particular


[User] Please transcribe this text

[Assistant] Of the, the NVIDIA approach is on the bottom right. See, it's temp, it's very temporally consistent. If you look at the image to image mapping, that's, that's state of the art, pics to pics, HD, it's very jumpy. It's not temporally consistent at all.


[User] Please transcribe this text

[Assistant] And, uh, there's some naive approaches for trying to maintain temporal consistency, uh, that's in the bottom left.


[User] Please transcribe this text

[Assistant] So you can apply this to all kinds of tasks, all kinds of video to video mapping. Here is mapping it to face edges, edge detection on faces, mapping it to faces, generating faces from just edges.


[User] Please transcribe this text

[Assistant] You can look at, uh, body pose to actual images. So you can, as input to the network, you can take the pose


[User] Please transcribe this text

[Assistant] of the person and generate the, uh, the video of the person.


[User] Please transcribe this text

[Assistant] The problem of perception sort of began with AlexNet and ImageNet has been further and further developments where the input, the problem is of basic image classification where the input is an image and the output is a classification of what's going on.


[User] Please transcribe this text

[Assistant] Uh, SCNet, DenseNet, all these networks are forming rich representation that can then be used for all kinds of tasks. Whether that task is object detection.


[User] Please transcribe this text

[Assistant] Bonnie Boxes and their classes are generated and there has been a tremendous amount of work in the space of object detection. Some are single, single shot methods, some are


[User] Please transcribe this text

[Assistant] Uh, region-based methods, and there's been a lot of exciting work, but not more, not, I would say, breakthrough ideas. Uh, and then we take it to the highest level of perception, which is semantic segmentation.


[User] Please transcribe this text

[Assistant] There's also been a lot of work there. The state of the art performance is, at least for the open source systems, is DeepLab V3 Plus.


[User] Please transcribe this text

[Assistant] On the Pascal VOC challenge. So, semantic segmentation to catch it all up. Started 2014 with fully convolutional neural networks. Chopping off the fully connected layers. And then,


[User] Please transcribe this text

[Assistant] Very low resolution. Then improving that with SegNet, performing, uh, max pooling, with, uh, a breakthrough idea that's reused in a lot of cases is dilated convolution, atrious convolutions.


[User] Please transcribe this text

[Assistant] Having some spacing which increases the, the field of view of the convolutional filter. Uh, the key idea behind DeepLab v3 that I


[User] Please transcribe this text

[Assistant] Without increasing the parameters, the multi-scales achieved by quote unquote, uh, the atrious rates. So taking those atrious convolutions and increasing the spacing. And you can think of the increasing that spacing.


[User] Please transcribe this text

[Assistant] But by enlarging the model's field of view. So you can consider all these different scales of processing and looking at the, at the layers of features.


[User] Please transcribe this text

[Assistant] So allowing you to be able to, uh, grasp the greater context is part of the, uh, op sampling deconvolutional step. And that's what's produced in the state of our performances. And that's where we have the notebook, uh, tutorial.


[User] Please transcribe this text

[Assistant] Architecture trained on cityscapes. So cityscapes is a driving segmentation data set.


[User] Please transcribe this text

[Assistant] Uh, that is, that is, uh, one of the most commonly used for the task of driving scene segmentation.


[User] Please transcribe this text

[Assistant] So this is touching a bit, a bit on the 2017.


[User] Please transcribe this text

[Assistant] But I think the excitement really settled in in 2018 as the work from Google and from OpenAI DeepMind. So it started in


[User] Please transcribe this text

[Assistant] DQN paper from Google DeepMind where they beat a bunch of, uh,


[User] Please transcribe this text

[Assistant] So the same kind of architecture is able to learn how to beat these, how to beat these games. Super exciting idea that kind of has echoes of what general intelligence is, taking in the raw


[User] Please transcribe this text

[Assistant] I was able to beat the top of the world champion at Go. And then 2017, AlphaGo Zero.


[User] Please transcribe this text

[Assistant] A specialized version of AlphaZero was able to beat


[User] Please transcribe this text

[Assistant] Uh, the, uh, alpha go with just a few days of training and zero supervision from expert games. So through the process of self play, again, this is kind of, uh,


[User] Please transcribe this text

[Assistant] Getting the human out of the picture more and more and more, which is why AlphaZero is probably, or this AlphaGo Zero is a demonstration.


[User] Please transcribe this text

[Assistant] Of the cleanest demonstration of all the nice progress in deep reinforcement learning. I think if you look at the history of AI, when you're sitting on a porch a hundred years from now, uh, sort of reminiscing back, Alpha Zero,


[User] Please transcribe this text

[Assistant] Will be a thing that people will remember as a interesting moment in time. As a key moment in time.


[User] Please transcribe this text

[Assistant] And AlphaZero was applied in 2017 to beat, uh, well AlphaZero paper was in 2017 and it was this year, played Stockfish in chess, uh, which is the best engine, chess playing engines.


[User] Please transcribe this text

[Assistant] It was able to self-play to very quickly learn to beat the state-of-the-art chess engine and learn to beat the state-of-the-art shogi engine, elmo. And the interesting thing here is


[User] Please transcribe this text

[Assistant] Presumably, the, the better you do, that's how deep blue beat, uh.


[User] Please transcribe this text

[Assistant] Kasparov in the 90s is you just look as far as possible down the tree to determine which is the action is the most optimal. If you look at the way


[User] Please transcribe this text

[Assistant] Human grandmasters think it certainly doesn't feel like they're like looking down a tree. There's something like...


[User] Please transcribe this text

[Assistant] Creative intuition. There's something like you can see the patterns in the board, you can do a few calculations, but really it's in the order of hundreds. It's not on the order of millions or billions. Which is kind of the...


[User] Please transcribe this text

[Assistant] The stockfish, the state-of-the-art chess engine approach. And AlphaZero is moving closer and closer and closer towards the human grandmaster concerning very few future moves.


[User] Please transcribe this text

[Assistant] It's able to do much, much less look ahead. So the neural network learns the fundamental information. Just like when a grandmaster looks at a board, they can tell how good that is. So that's, again, interesting. It's a step towards


[User] Please transcribe this text

[Assistant] At least echoes of what human intelligence is in this very structured formal constrained world of chess and Go and Shogi.


[User] Please transcribe this text

[Assistant] And then there's the other side of the world that's messy.


[User] Please transcribe this text

[Assistant] It's still games. It's still constrained in that way.


[User] Please transcribe this text

[Assistant] But OpenAI has taken on the challenge of playing games that are much messier, that have this, uh,


[User] Please transcribe this text

[Assistant] On the human side of that, there's the, the competition, the international, hosted every year, where, you know, in 2018, the winning team gets 11 million dollars. So it's a very popular, very active competition that's been going on for, for, uh, for a few years.


[User] Please transcribe this text

[Assistant] They've been improving and achieved a lot of interesting milestones. In 2017, their 1v1 bot beat the top professional Dota 2 player.


[User] Please transcribe this text

[Assistant] The way you achieve great things is, uh, is, is you try.


[User] Please transcribe this text

[Assistant] And in 2018, they tried to go 5v5. The Open AI 5 team lost two games against the top two, uh, the top Dota 2 players.


[User] Please transcribe this text

[Assistant] At the 2018 International. And of course, their ranking here, the MMR ranking in Dota 2 has been increasing over and over, but there's a lot of challenges here that make it extremely difficult to beat the human players and, uh,


[User] Please transcribe this text

[Assistant] This is, uh, you know, in, in every story, uh, Rocky or whatever you think about, losing is an essential element of, uh, of a story that leads to then, uh, a movie and a book and, uh, greatness. So, you better believe that they're coming back next year and there's going to be a lot of exciting developments there. It also, Dota 2 and this particular video game makes it currently, there's really two games


[User] Please transcribe this text

[Assistant] The, uh, associate with the best paper in Europe's, it was the, uh, heads up, Texas no limit hold'em. AI was able to...


[User] Please transcribe this text

[Assistant] And on the gaming side, this dream of Dota 2, now that's the benchmark that everybody's targeting. And it's actually an incredibly difficult one, and some people think it would be a long time before we can win. And on the more...


[User] Please transcribe this text

[Assistant] Starting 2017 has been a year of, of, uh, the frameworks growing up, of maturing and creating ecosystems around them, with TensorFlow, with the history there,


[User] Please transcribe this text

[Assistant] Dating back a few years has really, with uh, TensorFlow 1.0,


[User] Please transcribe this text

[Assistant] As, as come to be a sort of a mature framework. PyTorch 1.0 came out in 2018, it's matured as well. And now the really exciting developments in the TensorFlow with eager execution and beyond that's coming out in TensorFlow 2.0 in, in 2019. So really those,


[User] Please transcribe this text

[Assistant] Standardizing deep learning in, in, in


[User] Please transcribe this text

[Assistant] And the fact that a lot of the ideas I talked about today and Monday and will keep talking about are, all have a GitHub repository with implementations in TensorFlow and PyTorch, making it extremely accessible, and that's really exciting. It's probably best to quote Jeff Hinton, the quote unquote godfather of deep learning, one of the key people behind backpropagation,


[User] Please transcribe this text

[Assistant] Said recently of back propagation is my view is throw it all away and start again is believes back propagation is is totally broken and an idea that has ancient and needs to be completely revolutionized and the practical protocol for doing that.


[User] Please transcribe this text

[Assistant] As he said, the future depends on some graduate student who is deeply suspicious of everything I have said.


[User] Please transcribe this text

[Assistant] That's probably a good way to, to, uh, to end the discussion about what the state of the art in deep learning holds. Because everything we're doing is fundamentally based on


[User] Please transcribe this text

[Assistant] Ideas from the 60s and the 80s and really in terms of new ideas, there's not been many new ideas. Especially the state of the art results that I've mentioned are all based on, fundamentally, on stochastic gradient descent and back propagation.


[User] Please transcribe this text

[Assistant] It's ripe for totally new ideas. So it's up to us to define the real


[User] Please transcribe this text

[Assistant] Breakthroughs in the real state of the art 2019 and beyond. So that I'd like to...


[User] Please transcribe this text

[Assistant] Thank you, and, uh, the stuff is on the website, deepleary.nih.edu.


[User] Please transcribe this text

[Assistant] The word culture in business over the last 10, 20 years has become a catch-all.


[User] Please transcribe this text

[Assistant] And it actually disappoints me because I think most people...


[User] Please transcribe this text

[Assistant] Underestimate what it really means and how vitally important it is to succeed in building a great enduring company. So how would I define culture?


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[Assistant] Uhm, I would define it as understanding human behavior and understanding how to elevate a group of people to realize and believe and trust that they are part of something larger than themselves.


[User] Please transcribe this text

[Assistant] And each person individually and collectively has a responsibility


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[Assistant] To shaping the behavior of the organization and that behavior is the foundation for the culture of the company.


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[Assistant] It's set of values and it's guiding principles which will ultimately define what the core purpose and reason for being of the company is.


[User] Please transcribe this text

[Assistant] You may not realize it, but every single day in the startup phase, in the imprinting stage of a young business, you are defining the culture of the company.


[User] Please transcribe this text

[Assistant] And I've said many times, building a business and ultimately defining your culture is like raising children.


[User] Please transcribe this text

[Assistant] Because you are imprinting the child with a level of self-esteem, a set of values. What happens when the child falls? You don't yell at him or her. You pick them up, you care for them, you nurture them, and you give them the self-esteem to try again.


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[Assistant] It doesn't matter how many times a child falls, you never shame them, you never hit them, you're always picking them up.


[User] Please transcribe this text

[Assistant] The kind of company whose culture is defined by what people say around their dinner table at home at night about their company.


[User] Please transcribe this text

[Assistant] How many people who are working with and for you are so proud of the company, its values and what it stands for that at the dinner table at night they are sharing with their family how proud they are to be part of this enterprise, this company, how much they respect what this company stands for. The flip side of all this


[User] Please transcribe this text

[Assistant] Is ensuring the fact, especially in the early stages, that when anything comes up that is inconsistent,


[User] Please transcribe this text

[Assistant] With your core purpose, reason for being, and ultimately the culture of the company, you swiftly ensure that everyone understands this action


[User] Please transcribe this text

[Assistant] This strategy, this decision, whatever it might be, is not consistent with the values of our company. And as a result of that, we're not going to do it or we're going to stop doing it. So I think the structural issue, first and foremost, in building a brand and maintaining that equity,


[User] Please transcribe this text

[Assistant] Is establishing trust and confidence with the two constituencies, which are your people and your customer.


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[Assistant] And, uh, it can't be like 90% of the time. It's gotta be like 100% of the time. This is who we are. This is what we stand for. And this goes back to core purpose and reason for being. It all ties back.


[User] Please transcribe this text

[Assistant] Uh, it can't be something off to the side. It's not an adjacency. It has to be fully integrated. Where everything kind of matches up.


[User] Please transcribe this text

[Assistant] I think it's very healthy to do what I would loosely describe as a cultural audit, uh, where you're actually kind of institutionalizing an opportunity either once or twice a year to ask people to respond to a series of questions.


[User] Please transcribe this text

[Assistant] And, uh, people can sign their name where they don't have to and ask ten questions about the culture and values of the company and how they're feeling about the company, the business, the environment, their manager, uh, the senior leadership team, and you'll be surprised.


[User] Please transcribe this text

[Assistant] And then, you know, I talked about open forums and having town halls and establishing there'll be no retribution.


[User] Please transcribe this text

[Assistant] Let's have an honest conversation about who we are and how we are.


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[Assistant] I also think culturally it's really important to bring in outside resources into the business for continuing education.


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[Assistant] And bring people in from all walks of life.


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[Assistant] There's so many people who come to the city of Seattle on a book tour, and we know we're on that list, so whenever there's someone who we want to see, we say, come on in, we'll buy X number of books, and come talk to our people.


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[Assistant] We have outside people coming in a lot during lunch hour or morning or afternoon. Spend an hour, it's open seating and people love it.


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[Assistant] And I think you're, you're, you're adding to that rest before.


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[Assistant] You don't want to make too many, you don't want to have, you don't want to make too many withdrawals out of that reservoir. You want to keep making a deposit into the culture.


[User] Please transcribe this text

[Assistant] Uh, issue that I think is in the minds of everyone who works in a company, especially young people.


[User] Please transcribe this text

[Assistant] And I think culturally you have to define for the organization, what does that mean for this company?


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[Assistant] Not rules, but uhm, of standards in which we are going to try and create the balance that we all need. And, uh, I think every company has to establish that. I don't think it's something that can be ignored.


[User] Please transcribe this text

[Assistant] Uh, I think the role and responsibility of the leader needs to be very sensitive, uh, to the responsibility that women have, especially young mothers, and you want to be


[User] Please transcribe this text

[Assistant] Uh, and the right atmosphere for this. That is never punitive.


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[Assistant] Uh, but it's really deeply ingrained in the sensitivity that we need around a humane work environment that is performance driven.


[User] Please transcribe this text

[Assistant] Uh, then I think you can manage through this. And what I've said publicly, uh, for years is that Starbucks


[User] Please transcribe this text

[Assistant] It is a performance driven company through the lens of humanity.


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[Assistant] And that I think creates the framework for how we should do our work. We, we have to financially perform. At the same time, we have to create an environment that's very humane.


[User] Please transcribe this text

[Assistant] And I don't, I never believe the two are mutually exclusive.


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[Assistant] When a light passenger plane flew off course some time ago,


[User] Please transcribe this text

[Assistant] It crashed in the mountains and its pilot was killed. The only passengers, a young woman and her two baby daughters, were unhurt. It was the middle of winter. Snow lay thick on the ground.


[User] Please transcribe this text

[Assistant] The woman kept as near as she could to the children and even tried to get into the case herself, but it was too small. Early next morning she heard planes passing overhead and wondered how she could send a signal. Then she had an idea.


[User] Please transcribe this text

[Assistant] She stamped out the letters SOS in the snow. Fortunately, a pilot saw the signal.


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[Assistant] It was not long before a helicopter arrived on the scene to rescue the survivors of the plane crash.


[User] Please transcribe this text

[Assistant] It's a very exciting race, a great edition of Paris-Roubaix. Let's go back to Phil and Paul.


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[Assistant] Well, those of us who have watched the career of Peter Sagan over the last, uh, well, probably about 10 years now have been amazed by, uh, his performances. Uh, he's won so many races here on SBS Cycling, but, uh, well, today he just confirmed, uh, what we already knew. He is the greatest in the modern era, and I'm sure you'll agree too, Henk Virgel. That was phenomenal.


[User] Please transcribe this text

[Assistant] And Hank, at a time though, just on Sargon, at a time when perhaps he was in the middle of a stoush, particularly with Tom Bohnen as we spoke of earlier, this was D-Day for him, Day of Redemption perhaps. I don't know why he had so much to prove, given what he's done, but he silenced the critics, didn't he?


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[Assistant] Let's have a look at the results now. The, the top 10 as it stands. Peter Sargan wins his first Roubaix. He's second monument after collecting honours in Flanders in 16. Uh, Suvan Deleere as you say, best on ground. He is the man of the, of the race. Uh, and despite finishing second, he'll claim this as a victory. Terpstra.


[User] Please transcribe this text

[Assistant] That's been a good week for the man from the Netherlands, and there you have the rest. Van Avermaet, perhaps fully disappointed not to be able to defend it, but great result.


[User] Please transcribe this text

[Assistant] And I guess the guns, the big names that you expected to be among those chasing for overall honours in the top ten.


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[Assistant] Yeah, thank you to Peter Sarkin, uh, that's the face of a man who's just raced 267 hard kilometres over 29 cobbled section, sections, and he is actually the first man to wear the world championship jersey and win Roubaix since Bernhard Ino back in 1981. That's the face of a hard man.


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[Assistant] Let's have a look at the highlights now. It's been an intriguing edition of, uh, Paris-Roubaix. And as you say, uh, we saw a breakaway a very early on. Nine riders, that was reduced and Peter Sagan made his winning move with 54 kilometres to go. We thought at the time, Henk, it's not possible.


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[Assistant] Well, that roar when he crossed the finish line, uh, we rarely see that from a man who's won countless numbers of races. So uh, he desperately wanted to win this one today.


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[Assistant] I think you spoke about the legendary status, the legend that is Peter Sargan. Green jerseys at the Tour de France, triple world champions, countless number of stages in Grand Tours, countless number of one day races, not just monuments. He's the complete package. How do you think we will look back on someone like this in years to come?


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[Assistant] Well, it was a dry day in the north of France. It started out very sunny before the clouds rolled in. No rain to speak of. Despite that, we did see crashes.


[User] Please transcribe this text

[Assistant] Hank, uh, you were involved in some horrible accidents, uh, during your career. One particular accident kept you in hospital, uh, for quite a while. When you see images like that, what goes through your mind?


[User] Please transcribe this text

[Assistant] Uhm, I totally believe that and I asked that question earlier at the start of the coverage. Can Quickstep do what they did last week in Flanders given that the roads are narrower, they're uneven, it's a different sort of scenario. But you're right, this is a man versus man race and Bora wasn't the best team yet they came out on top.


[User] Please transcribe this text

[Assistant] Where to now for Sargan? He's won everything, almost. Does the appetite continue? Does the hunger to succeed continue? Seems to me like when he jumps on a bike, it's not because he's getting well paid, it's because he enjoys it.


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[Assistant] Well, tell me this just before we wrap up, uh, the Ardennes classics are coming up. I've got Amstel next week, uh, and Liège-Bastogne-Liège. Uh, you'll see them on SBS Live. But Henk, uh, is it possible for a cobbled, uh, rider, a rider who rides over cobbles like Peter Sagan to focus on the Ardennes, which is a totally different style of racing?


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[Assistant] So we have been told that Matt Heyman has finished, so that is another tick to the box, uh, of the career of one of Australia's greatest one-day races. His 17th tour, or 17th Roubaix, and he finished in 17th position.


[User] Please transcribe this text

[Assistant] What is it now, five or green jerseys? He was kicked out of last year's race and still getting over, still trying very hard to get over why the Commissaires pulled him out, but I'm sure they look back on the vision and think, well, why did we do it?


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[Assistant] He brings so much to the world of cycling, uh, he's a popular figure and uh, it's, it's, it's great to know that he's not going anywhere just yet.


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[Assistant] Remind us to stay up to date with all the latest news, social and video content with Cycling Central online, and you can get us on Facebook and Twitter. On behalf of Hank Boggles and the entire team, thanks for staying up late. We'll do it all again soon. From us to you, good night.


[User] Please transcribe this text

[Assistant] I mean, for startup business today, there's so many opportunities and ideas. At the same time, there's a lot of capital available. Uhm, there is no one single formula.


[User] Please transcribe this text

[Assistant] Uh, it's something you yourself have to be comfortable with. If you're raising money from an outside resource, whether it's an individual investor or a VC fund, the values that you hold so dear


[User] Please transcribe this text

[Assistant] Have to be applied to who you're taking your, taking money from or who you're trying to convince to become an investor in your company. And so their history,


[User] Please transcribe this text

[Assistant] Uh, should be the due diligence that you've done on the companies in your space. Do that same due diligence on the people that you're talking to who have provided money to other entrepreneurs. What has been their investment practice? Talk to the entrepreneurs and get a reference check on how they acted.


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[Assistant] Did they share your values, your long term approach? Can you rely on them when things get difficult? Once they give you money, they're in your company, they have a role to play, and you're gonna have to deal with them. You wanna know


[User] Please transcribe this text

[Assistant] How that fund and how that person


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[Assistant] And you've got a, a, you're reaching, you're close to reaching an agreement and they're going to become an investor in the company. I would just sit down with the investor and say, you know, one of the concerns I have is how, how are we going to act as partners?


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[Assistant] Uh, when things don't go exactly as planned. So let me set up a hypothetical.


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[Assistant] And set up a situation where something goes wrong, you don't hit your numbers.


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[Assistant] You get the market wrong, whatever it might be, and get a response, get a reaction.


[User] Please transcribe this text

[Assistant] How can I rely on you during this period? When there's pressure?


[User] Please transcribe this text

[Assistant] And, uh, can you give me some reference of how this is applied to your other investments in other companies? And then ask that person for specific references. You want to talk to the entrepreneurs.


[User] Please transcribe this text

[Assistant] And so before I'd sign any document with any investor.


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[Assistant] To give us permission to believe we know more than they do?


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[Assistant] Overly zealous and overly confident. And I think you gotta balance that. Uh, and I think most investors will respect this.


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[Assistant] We believe we've identified a massive opportunity. And I've spent the last two years studying the category, the opportunity, the competition. But I also believe, like any startup, uh, it's going to be difficult to crack the code. We haven't done this before.


[User] Please transcribe this text

[Assistant] But I've surrounded myself with like-minded people who share my values and have the entrepreneurial DNA I do to make history.


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[Assistant] To take the road less traveled and do something that has never been done before. And I want you to join with me.


[User] Please transcribe this text

[Assistant] I also want you to know something. I've done my homework about you. And I know you share my values. I've talked to other entrepreneurs you've invested in. And I know we'll be a better company and I'll be a better entrepreneur with you at my side.


[User] Please transcribe this text

[Assistant] In terms of how much equity to give up, that's an easy answer. As little as possible.


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[Assistant] Uh, but, I had a strong belief early on, that I wasn't gonna take on any debt, uh, that I was gonna raise equity, and I uhm,


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[Assistant] I've always believed you should raise more than you think you need. And I wouldn't haggle so much about price.


[User] Please transcribe this text

[Assistant] Uh, in the early stages. It's always hard for the entrepreneur to give up equity.


[User] Please transcribe this text

[Assistant] And I understand that so well. Uh, cause we raised so much capital in the early days and, and dilution happens quickly. But you gotta ask yourself,


[User] Please transcribe this text

[Assistant] What am I trying to do? And if you're trying to build a great, big, enduring business, and equity is going to be the way to fund it, uh, then the 100% ownership that you own as an idea is going to be significantly diluted down the road. And most entrepreneurs get concerned about control. Uh, better maintain 51%. Control is about performance. If you perform, uh, you can own 10% of the company, but


[User] Please transcribe this text

[Assistant] If you're the person who had the idea and is executing the strategy, uh, and is the right leader, uh, you're gonna maintain control of the company.


[User] Please transcribe this text

[Assistant] There is a large personal responsibility in taking money from another person or an institutional investor who's going to invest in your company. You have crossed the threshold.


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[Assistant] of a responsibility that is now bigger than yourself and everyone in the company especially the senior leadership team needs to understand


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[Assistant] Individually and collectively that we have a fiduciary responsibility. Fiduciary responsibility.


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[Assistant] What I believe, I believe for 40 years at Starbucks, from our earliest days to today, and that is we all are enthusiastic about our business and the opportunity. If we weren't, we wouldn't have jumped into the deep end of the pool.


[User] Please transcribe this text

[Assistant] But I think you will be served well if you under promise and over deliver. So there's nothing better than beating your projections. There's nothing worse than going to the investors and say, we've underperformed and we need more money. But in the imprinting stage of your relationship with the investors,


[User] Please transcribe this text

[Assistant] That first quarter, that first year, if you beat your numbers, you have become an anomaly. You've become one of the winners. You're heroic. So under-promised and over-delivered.


[User] Please transcribe this text

[Assistant] You know, I've sat through a number of, um, entrepreneurial ideas, uh, in which we have been pitched to become an investor.


[User] Please transcribe this text

[Assistant] I assume that everyone is coming with an economic business plan.


[User] Please transcribe this text

[Assistant] That makes sense because if it didn't make sense, there would be no opportunity for return on investment. So I, I, I have someone else looking at the numbers and it's not where I'm going to spend my time. So I'm looking for the character of the entrepreneur. I want to know their history, their personal history. Uh, where did you grow up? Um, and so I, so I think it's really important.


[User] Please transcribe this text

[Assistant] For the entrepreneur to convey to the investors, who am I? Before I start talking about my company, my product, an opportunity of a lifetime for you, uhm, I want you to know who I am. Why am I doing this?


[User] Please transcribe this text

[Assistant] What I'm sacrificing to do it and why I believe in this opportunity and why I believe you should invest in it. So I think it's got to come from a very personal place and that personal place also has to be threaded to the responsibility that you have.


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[Assistant] And asking for money and demonstrate the fact that I realized what the responsibility is in someone investing in my company. And I can promise you with a hundred percent conviction that I'm going to do everything in my power to make this business successful. So you have the rate of return that you deserve.


[User] Please transcribe this text

[Assistant] Alright, here comes four to the porch guard. That's going to be an easy tag. It goes to Void Swamp. RNG punishing the Flash's ball.


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[Assistant] Uh, Kingzone get two, it was only one for RNG. Khan may have stuck around far too long. He gets taken out. All the CC is layered. And that's an easy reply by RNG.


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[Assistant] It is insane how much action we have had. We're approaching 20 minutes. It feels like there have been skirmishes the entire time.


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[Assistant] Thank you, thank you. Riot Support lied to you. Gonna need a little bit more, uh, itemization by then, before that becomes plausible.


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[Assistant] The King's Zone is still gonna be getting this very interesting, very unique team composition to work. Use that question that we talked about from the fact that BDD and Prey are farming and doing so well.


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[Assistant] He's gonna wreck some kids. There's no doubt about that.


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[Assistant] Thank you very much and welcome back to what could be the last game of MSI 2018 to give you a brief update on the state of the game. Kingzone are marginally ahead thanks to some very good tower plays despite the fact that Khan has been


[User] Please transcribe this text

[Assistant] Look at the itemization. Two stopwatches in the inventory of King's Zone. Those next teamfights that I was talking about a little earlier, the next Baron or Dragon.


[User] Please transcribe this text

[Assistant] Well, first dragon of the fourth game in Faithful Coil, Never Give Up, that's an infernal piece.


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[Assistant] Maybe, maybe it all starts from the fact that Khan once again got shut down.


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[Assistant] RNG now trying to defend. Supers in two lanes. Uzi peeled away from top to defend middle. Time is running out for RNG. They're being swarmed in. They RNG. Gorilla was knocked up for a moment but there wasn't any follow up. No engage. Caster too far away.


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[Assistant] It didn't take too long for it to collapse. RNG cannot afford giving up that Baron, but they cannot afford running into Kingzone in the Baron pit either. It went so horribly wrong last time.


[User] Please transcribe this text

[Assistant] Oh, there's so much attention. Uh, obviously, guys, an update there it is. Uh, another technical-related pause, I believe, on the side of King's Zone, as I can see. Uh,


[User] Please transcribe this text

[Assistant] And they are, is he looking at his peanuts? Man on your screen right now.


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[Assistant] And this game is heating up. 2-1 and 0, we talked so much about Uzi, but the suppression on the sidelines, we've seen some engages by GorillA that have worked, and some where he has been shut down. That engage from Karsa a few minutes ago, well, 10 minutes ago, the flash catch. Game changer, keeping RNG alive.


[User] Please transcribe this text

[Assistant] Man, what an intense situation. And of course, you know, these pauses are difficult in the players. I mentioned the mental states a moment or two ago.


[User] Please transcribe this text

[Assistant] Kingzone received a lot of early game pressure. There's been a number of teams that have stepped up and challenged them. RNG, this series, stepped up and challenged them. Our expectation was that they were going to be this late game team. Now they find themselves in a late game hole. I'm hearing we're a few minutes away, so hopefully that will be the case. How does RNG survive? I assume they cannot give up Baron.


[User] Please transcribe this text

[Assistant] And I assume they don't really want to be the ones engaging in a choke point, like let's say around the steps of your inhibitor. That plays into King's Zone. So it's difficult.


[User] Please transcribe this text

[Assistant] LPL spring miracle run taking every single opponent down that China had to offer and now the world too.


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[Assistant] Karsa cannot believe it. After years and years of playing with the Flash Wolves 2018, he moved to the LPL. He moved to RNG.


[User] Please transcribe this text

[Assistant] I used to travel by air a great deal when I was a boy.


[User] Please transcribe this text

[Assistant] My parents used to live in South America.


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[Assistant] And I used to fly there from Europe in the holidays. A flight attendant would take charge of me and I never had an unpleasant experience. I'm used to travelling by air.


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[Assistant] And only on one occasion have I ever felt frightened. After taking off we were flying low over the city and slowly gaining height


[User] Please transcribe this text

[Assistant] When the plane suddenly turned round and flew back to the airport. While we were waiting to land, a flight attendant told us to keep calm and to get off the plane quietly as soon as it had touched down. Everybody on board was worried.


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[Assistant] Later we learned that there was a very important person on board.


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[Assistant] This is gonna be it. This is gonna be it. Highlight of this trip.


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[Assistant] I used to play this to Mummy Pig when we first met.


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[Assistant] Peppa's right. It is impossible to play.


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[Assistant] It's been a lifetime to say the least. Let's go ahead and relive those final moments of RNG's comeback victory here in Game 4 to claim the crown of Kings of the World.


[User] Please transcribe this text

[Assistant] Talk about the individual procl- uh, performance removing throughout the tournament. 26k damage in this game alone. It just boggles my mind how this guy stepped up onto stage every single time. Never faltered, or almost never faltered.


[User] Please transcribe this text

[Assistant] And we're getting a look in right now as, uh, Ren Dong gets to interview a couple of the RNG members, but you mentioned that counter that now resets to zero. I would almost want to flip the script at this point. Hey, the clock starts at zero for zero days since Korea has beaten the LPL in a best of five.


[User] Please transcribe this text

[Assistant] There were the other four members that we talked about throughout the tournament who had to come around him, who had to play that supportive style and allow him to be that true carry.


[User] Please transcribe this text

[Assistant] All three of their victories over that 40% mark for the damage share. Only two deaths across those three victories in this best of five. As you mentioned Japp, this performance will likely go down in MSI history as one of the best and probably not be matched for a very, very long time.


[User] Please transcribe this text

[Assistant] Always pull through in the end comes together and I think that's what makes it even that much more impactful that Uzi came up with the plays he did in those moments, right?


[User] Please transcribe this text

[Assistant] The quality of those four games was so incredible. We got cheated out of a game, but we did not get cheated out of a series. To hear more from the champions, let's send it back over to Shox and the victorious mid laner.


[User] Please transcribe this text

[Assistant] Thank you very much, Sharks. 2018 is the year of RNG. I mean, it definitely is right now, and that's the thing about only these two major international tournaments a year. For Korea, they're gonna have to wait that three, four months to have another shot of stealing the title back.


[User] Please transcribe this text

[Assistant] of coming in second to the LCK. They've completed step one in terms of shirking that identity here for the LPL. MSI 2018 was absolutely packed with big plays and you made sure, uh, we caught some of the best ones to close out the tournament for the final MSI big plays brought to you by Acer Predator.


[User] Please transcribe this text

[Assistant] Of course then we had MixingItUp who offered a friendly suggestion to RNG's opponents. May not want to give Uzi Ezreal if you want to win by the way.


[User] Please transcribe this text

[Assistant] I mean, and that just forces us to remember the tournament that Flash Wolves had. Again, unrecognized in a sense coming into the tournament. Most positioning them in 5th place, dooming them rather to 5th place. And then the 6-0 start through group stages really shook everything up and gave us quite a tournament.


[User] Please transcribe this text

[Assistant] Remember that next year when MSI rolls around. Next, the fine scope felt reckless as Penta versus RNG was fitting for the EU LCS MVP.


[User] Please transcribe this text

[Assistant] Yeah, what an exciting team that was to watch throughout the tournament. Finally, Christian believes that this is the year of Uzi. He is going to conquer the season.


[User] Please transcribe this text

[Assistant] There's still more. There is still more. Here it comes. Shox is standing by for another interview, this time with RNG's Top Laner.


[User] Please transcribe this text

[Assistant] I mean, one more time, again, another member of the supporting cast here behind Uzi coming up with some huge engages, especially on that Ornn, and being charged with dealing with the man that was said to be the best top laner in the world coming in.


[User] Please transcribe this text

[Assistant] The first international event of 2018. But if you're itching for a rematch between these powerhouses who slugged it out today, Kingzone and RNG will be facing off again in just a couple of months. As RIP Rivals returns the week of July 2nd, we ask for more international play, more top tier play, and we're gonna get it just then.


[User] Please transcribe this text

[Assistant] Papa Smithy, we already hit on it briefly, but kind of the idea that perhaps now Korea will have a chip on the shoulder. The idea that where maybe in the past they feel as the most dominant region that they can walk it. Now they got something to prove. They got to strike back as the MSI scoreline.


[User] Please transcribe this text

[Assistant] And as we put a cap around this tournament one more time, I want to recognize where we are, the venue, the crowds that we played to that these guys on stage again, remember these are two international, two foreign teams to the country that stepped up onto this stage and