YouSaid · the spoken record

Jeremiah Lowin

lines on the record: 80
first: 2017-01-17
most recent: 2017-01-17
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Yeah, or perhaps it makes you weigh the different routes you could take a bit differently. So you have a whole bunch of ways you're considering to solve a problem and maybe stress makes you value the faster one as opposed to the more complete one. Whereas if you had all the time in the world, you might take the scenic route and”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“So, a little bit of stress turns out to be a healthy thing. If you just give perfectly clean data to a machine learning algorithm, you had to learn your data, but it has a difficult time generalizing because it's never had to in its training process. Whereas if you add a little bit of noise to that data, there's a popular technique right now, which is called dropout, which means that randomly you essentially break your model at random, and you force it to solve the problem maybe a slightly different way or a slightly suboptimal way. The net result is a much more robust, much more generalizable model. And I think that They're a little bit easier to diagnose when you're a machine.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“So unfortunately for better or for worse, so maybe not so unfortunately, we don't learn like most machine learning algorithms learn explicitly. But in a reverse kind of way, the machine learning researchers have looked at how we do learn and tried to encode that into the algorithms they build. That's an example.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Would be very disappointed to learn that all I do is Take NX's and spit out Y's with some well known well defined optimization system. Perhaps the fact that my brain is a little bit random makes me intelligent or makes me think I'm intelligent. I don't know. It's a very scary road to go down because I think the conclusion that we ultimately draw is either we don't know or what we think we know can ultimately be boiled down to a system of rules which in theory any computer can replicate.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I think that we're very focused on intelligence and especially artificial intelligence as measured by what it does. And perhaps really the metric we should be looking at is how it does it. So this optimization as opposed to brute force, as opposed to evolution, or rather evolutionary algorithms I don't think that's how we do it, but I don't know how we do it, right? We know at a very physical, tangible level, we know how the brain works in the sense of neurons that spike and communicate with other neurons, but we don't know how that leads to consciousness or the thing we call consciousness if we really want to get sort of philosophical about completely off tracks. I mean, this conversation can go deep. But I think at the end of the day, it comes down to a process.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, what if we said that that machine came up with that by brute force, by trying the infinite number of possible theories until it found the one that worked?”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Species of cat it is. Those are things that would blow anyone's mind very recently. And now we take them for granted. That's how we automatically tag images and videos, caption them. So it used to, the goalposts are moving not only for the AIs themselves, but for our own definition of intelligence. It's not experience. Is it decision making? I'm not sure if it's decision making. I want to tell you that it has something to do with these leaps of intuition, absolutely jumping from one area to another, but I'm sure if we had a machine learning researcher here with us, he would say, well, no, no, no, I train a model on cats and with some very small tweaks it works wonderfully for dogs, or it works wonderfully for driving cars. I don't know.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“If you Gosh, I guess the answers you have. I don't know. I think we like to believe that there are things that we do that are the consequence of intelligence. So gathering experience, let's go back to doctors and lawyers a second. We believe that those jobs are protected from the automation revolution because they require massive and deeply intellectual experience gathering, knowing how to relate one thing to another. Well, along comes machine learning, which can gather experience faster than any person and at scale and benefit from the experience of its neighbor, right? It doesn't have to do it itself. We have these distributed networks. So all of a sudden gathering experience is no longer a qualification for intelligence, or at least not as I define it, because I can set up a set of system of rules and have it go out and gather experience and tell me when a cat is in a picture as opposed to a dog and maybe what's”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“What does it mean to be intelligent so that I can even start to say what does it mean to be artificially intelligent? How do I bestow intelligence upon something? I have no idea.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think the word intelligence is a dangerous one. I don't think anything that we call an AI today is intelligent. I don't think it's even close. It's a useful construct because it's a historically relevant construct. We've been calling things AI's. Machine learning systems that do it for every language. Google recently announced that they are now able to translate between two languages without the Rosetta Stone that links the two by going through a third language or fourth even, and they still have these remarkably accurate, realistic interpretations of the source text. I think that intelligence, I don't know what intelligence is. I keep coming back to that. I always ask myself,”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“So, it's hard to imagine somebody who lives in a world where nothing they do can be, one might say, helped, one might say replaced by a tool designed to do exactly that thing. But it's a big, big, big leap to then say that that tool is intelligent, any more than it is to say that a loom is intelligent, just because it does something with greater speed, efficiency, and potentially accuracy than the person who used to do it.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Is in some way based on the gathering of experience and from that experience a fairly rule based answer, even if they're not entirely sure what those rules are. That's where the machine learning tools come into play, where what you do is repetitive but not easy to define. Translating is a great example of that. Translating in theory is extremely straightforward. I have English, I have French, they mean the same thing, therefore translating is going from one to the other. But if you sit down and try and write the rules for it, it's very hard. So perfect for these machine learning tools that we don't need that closed-form solution, that magical just catch-all answer, but we know how to define this problem, we know how to take steps towards the right answer. And as Google has demonstrated, you can do a very, very good job.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Rhesbles this characteristic that I'm describing. Will people be absolutely replaced by these AIs to give them this glorified term that I'm not even sure I agree with? Yes, of course. Productivity and technological advancement are always going to replace certain elements of our working structure. Will they in turn create more jobs? Yes, on par with the number they replace? No, probably not. I don't know. I guess that's the most honest answer. I think it's very hard for me to imagine someone who lives in such a special place that nothing they do”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“And a role that therefore can be replaced easily historically. You mentioned a loom. It's a great example. You were doing something that was somewhat repetitive, needed some ingenuity, some creativity, but at the end of the day was taking X and turning it into Y. I think at some level everyone's job looks a little bit like that. And for that reason, I think that it's very easy for everyone to imagine how these tools will help them to the degree that their job”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Tough to say, isn't it? I could take the cop out answer, but I'll try and give you a real one. For a long time, I've had this mantra that models are a tool. In fact, models are just a tool. And machine learning models are no different. They're magical to some degree. They're a little bit harder to interpret and understand, but they are still just tools for people to use. So the real question is, to what degree is a person replaceable by a tool? table for a moment the discussion of some artificially intelligent robot walking up and just you know willy-nilly going off and taking people's jobs let's talk about tools because that's what we know we can build and we can use so if you're a financial advisor and most of what you do is based on experience that you've gathered and essentially mapping people in their utility curves to portfolios that are appropriate for them you are sort of fulfilling a role that looks an awful lot like a tool”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Machine. Yeah, exactly. We don't know what it is. Arthur C. Clark said, right, any technology sufficiently advanced will be indistinguishable from magic. And maybe it is the exercise of exposing these magic tricks that, I don't know, cheapens to some degree what we view as intelligent.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“John Searle. I'll say it for your listeners, but they may know if you put someone in a room and you give them all the rules of translating from Chinese English and vice versa, and you start passing pieces of paper into that room and having this person execute whatever algorithm they have designed to perfectly translate, and then they pass the paper out, can it be said that the room itself and the person in it is intelligent? Does it understand what it's doing? And by the way, does that understanding even matter for them for deciding that the room is intelligent? I think that to a degree, as soon as we strip any of these algorithms to their bare essentials, which is to say take the x's, take the y's, apply a bunch of rules, known or not, fuzzy or not, it strips some of that magic away. Intelligence almost is defined in some ways as this special magic.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“What does Google Maps do? Well, we give it a problem and it goes out with an infinite number of solutions and it finds an answer. Is it the best answer? We have no way of knowing, but it is a very good answer from an enormous set of possible answers. And I would argue that that is an example of machine learning. Now, I don't work on Google Maps. I don't know behind the scenes how they've chosen to solve this problem, and it may not bear any of the hallmarks of what today we call machine learning. But to an outsider, it does the same thing. It takes a bunch of X's, it takes a bunch of Y's, it finds a relationship between the two, and it presents that model. We take a step past Google Maps and we look at Google Translate, is Google Translate intelligent? Well, there's a famous thought puzzle called the Chinese Room.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, Let's see, how do we tackle that? I don't know the answer. I don't think anybody knows the answer. I think it's much easier to say what isn't intelligent than what is. I think it's much safer to say what isn't intelligent. I think the New York Times article that we talked about a moment ago spends a lot of time saying what isn't an example of artificial intelligence in an effort to demonstrate perhaps that certain things are in fact artificially intelligent. Google Maps is a great example. The article talks about going back in time with an iPhone and having Google Maps on it and showing it to somebody to whom it would no doubt represent the pinnacle of artificial intelligence, right? And I think the article says something about it does something that a person could do in theory, but it does it so much faster, so much better with such efficiency you could never hope to match it as a person. Therefore, it is artificially intelligent. Today, I don't know anyone who would think of Google Maps as being an example of artificial intelligence. And yet, if we take a step back”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Going to start working with ordered data. And so for forks in finance, this is critical stuff. As soon as the order matters, everything changes. So if I give you a book, But all the pages are out of order. It probably means nothing to you, even though it's the same text, or a movie with all the frames scrambled. It means nothing to you, even though it's the exact same information as if you sat down and watched a movie. So the insight here at a very high level is that the order is really important because order equates to context. And so as Google goes from just looking at a series of words and trying to relate them to another series of words to actually saying, no, no, no, let's step through these words and build up context for this sentence, just as you and I, as we're talking, are building up a context for this conversation, for this question that you've asked me, for the last word I just said, Google systems are doing that. And in doing so, build up a much more powerful representation of the information they're trying to model than if they just try in one shot to take this series of words and just figure out what it means.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“One to the other. Now, it has to look over an enormous number of such pairs to be able to do that because of homonyms and synonyms and all kinds of weird linguistic hurdles that it has to overcome. But in the aggregate, as it looks at English text and French text, it will start to find statistically things that seem to go together or patterns that seem to go together. That's the approach that Google started, I don't know, 10, 15 years ago with their first version of translate. What the article references that has led to this incredible leap in accuracy is the layering of time into these models, which is a particular interest of mine. So you use what's called a recurrent neural network. A recurrent neural network essentially means that rather than just taking a batch of X's and a batch of Y's, where the relationship of the X's to the Y's is all we care about.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Without telling the system that the word and has such and such a grammatical form, and this is how prepositions work and verbs have to end in such and such a way, they want to let the computer discover these patterns on its own by presenting it with a piece of text in one language and a piece of text in the other language where you're telling the system these two pieces of text are the same. They have the same semantic underlying meaning. The challenge for the computer is to take these characters, which it doesn't know what they are, letter A, letter T, whatever it is, and find some representation that applies to both of them. So that when you say to the computer, I'm giving you some string of characters in English, it builds this representation, and then it can use that representation to generate the same in French. I realize I haven't actually explained how it does that, and I'm just trying to think of an easy way of getting into it. Let's say the English is the X and the French is the Y. And we're asking the computer to map from”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Sure. So Google, it's hard to think of a company that's done more than Google to effectively communicate the benefits of everything we're talking about to consumers. Use a Google product, you are taking advantage of very concrete implementations of what we're talking about. That said, the examples in the article specifically translate are using really, really, really cutting-edge stuff. And the reasons that it works necessarily better than, for example, what they were using a generation ago is still very much out in the tail of the details of this implementation. But if we want to characterize it as a high level, what Google's doing with Translate is statistical learning as opposed to rule-based learning. So it's a little more like a Rosetta Stone kind of approach, right? So you dig up this Rosetta Stone, it's got two languages on it, or three, I think, historically. And for the first time, you look at them side by side, and you're looking for patterns. At the end of the day, machine learning is just pattern-seeking algorithm.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Know when I run the model how to improve it. The evolutionary approach doesn't, we don't know how it's going to improve. There's a lot of randomness in how these things breed, and the brute force approach is just going to try everything. So this is sort of a middle ground, which is appealing because to some degree it's interpretable, where the other two approaches are not.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“I'm vastly over simplifying here, but you literally breed your good answers together to hopefully create yet more good answers. And you can evolve the answer to a machine learning problem or to any problem. What has happened in the machine learning world rather than that which has a very, it's still very computationally intensive, you may end up with answers that are awfully weird just because they evolved in a strange way. The machine learning community has evolved no pun intended toward optimization driven approaches where the derivative of this error term is well known. So if I know if I have a function that tells me how wrong I am and I know the derivative of that function with respect to my input parameters, that's how I know how to tweak my model. And so intuitively we like that approach because we know at every step what it's doing. Given a set of X's, a set of Y's and an error function,”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“So typically, machine learning will actually take a third option, which is a more optimization driven approach. On the brute force side of things, brute force works, but it tends to waste a lot of time, right? You have a giant search space. Your answer is somewhere in it, but you're going to spend an awful lot of time looking at the places your answer isn't. And that's wasted time, that's wasted processing power. Generally, we don't like that, right? Evolution, there are many, many evolutionarily driven approaches. So you can evolve an answer. You start with, for example, 100 answers and you see which ones were good, and you take certain characteristics of those potential, we call them candidates, and you throw away, let's say, 90% of them, you keep 10%, and now you breed them. What you literally do is you say, okay, well, this answer was doing a lot of addition and this one was doing a lot of multiplication, and the multiplication answer was more effective. So in our next generation, let's do a lot of multiplication.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“But it'll analyze its own result and it'll say, well, if I tweak this parameter and raise it and I lower this one over here, I get a better answer as measured by whatever this metric we've chosen to quantify our error with. And it'll do that. And it'll do that a million times. It'll do that a billion times. You can overfit the hell out of these things.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Some of which retain this sort of nice easy, closed form nature, meaning has a solution. We don't have to do much work, and some of which start to get into what are called iterative models. Iterative model means we know what we're trying to do, and we have a way of quantifying how good we are at doing it. We usually call that the error, or the error term. And we're going to take steps towards that. Machine learning as a catch-all term basically relates to a class of iterative models, meaning we don't really know this closed-form solution for them, but we have well-defined error terms, and we have well-defined methods of over time decreasing the error. So I line up my x's, I line up my y's, but now instead of linear regression, I apply the flavor of the day from the machine learning toolkit, and it'll run through the data, and it'll come up with an answer. And the answer is probably wrong. It's almost certainly wrong because we haven't built a model yet.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so at the risk of grossly oversimplifying the whole thing, but in order to give a flavor of, I think the answer you're looking for, linear regression is a great place to start. We have x's, we have y's, or rather we have inputs, and we have outputs or desired outputs. And we're seeking to build a model that relates them, that turns x into y. So linear regression is just such a great utility tool for basically every statistician to keep on the shelf, you reach for it whenever you need it. It's a close-form solution, which means you don't really have to do much work. It's well understood. You line up your X's, you line up your Y's, and boom, the relationship between them falls out. And the reason it's so easy is because it is explicitly defined as a linear relationship, which just happens to be very easy to work with. So we can take steps past that. We can start to introduce nonlinear relationships. We can start to introduce a more complex correlation structures. As many steps we can take.”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source
“Who knows how much of my time just trying to find better, better ways of building models without going too far down that rabbit hole and dangerously over modeling the world?”
2017-01-17 · Invest Like the Best · Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20] · IDENTIFIED FROM THE TRANSCRIPT · source