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  • LAURENCE MORONEY: Hi, everybody.

  • And welcome to "TensorFlow Meets."

  • I'm Laurence Moroney.

  • And I'm delighted today to meet with Arun Subramaniyan

  • from BHGE.

  • And Arun, I know you've been doing lots of great stuff

  • with probabilistic modeling.

  • So for those of us who don't really

  • understand probabilistic modeling,

  • could you tell us all about it?

  • ARUN SUBRAMANIYAN: First of all, good morning.

  • And thank you for having me here.

  • And so probabilistic modeling and probabilistic theory

  • generally is something that we've been

  • using for several years now.

  • And it's mostly for modeling systems

  • that have a combination of very complex phenomena coupled

  • with things that we can't measure precisely.

  • And just to give you a simple example,

  • if I were to ask you to predict where the stone would land

  • if you throw it, then any high school student

  • would tell you that they can calculate it precisely

  • based on how fast you threw it and at what angle

  • you threw the stone.

  • Now, if I were to add a little bit of uncertainty

  • to it, saying I don't know exactly at what angle

  • you threw the stone at or what velocity you threw it at,

  • then--

  • LAURENCE MORONEY: Maybe like wind shear and stuff like that?

  • ARUN SUBRAMANIYAN: --and wind shear and stuff

  • like that, then all of a sudden, your predictions

  • are no longer as precise.

  • Now, in a simple system like that,

  • you can already see things starting to get complex.

  • Imagine a complicated system in the real world.

  • Things can get much more complex if you're trying

  • to predict something precisely.

  • LAURENCE MORONEY: So you're working

  • on a lot of complex systems like this.

  • Could you share some examples?

  • ARUN SUBRAMANIYAN: Absolutely.

  • So at BHGE and broader GE, we work

  • on a lot of complex systems.

  • So for example, say, designing gas turbines or trying

  • to predict what a very large-scale system like

  • an offshore oil platform would do--

  • we're talking about hundreds, if not

  • thousands, of variables interacting with each other.

  • And most of the time, you can predict

  • or you can measure maybe a few hundreds, if not

  • a few tens of those variables.

  • So how do you predict behavior of such complex systems

  • and still have actionable, meaningful outcomes

  • from your models without knowing all the information

  • about the system?

  • That's really where we use probabilistic modeling.

  • LAURENCE MORONEY: I see.

  • It sounds complex.

  • So what is your approach to this?

  • How do you get started?

  • ARUN SUBRAMANIYAN: [INAUDIBLE] So we get started with--

  • starting with the domain.

  • So we understand the domain.

  • So what I mean by "domain" is you

  • might be a mechanical engineer.

  • You might be an aerospace engineer.

  • You might be a petrophysics engineer.

  • You start with the understanding of the domain

  • and marry that with traditional machine learning techniques.

  • And that's been going on for several decades.

  • That gives you a very good understanding

  • of how to predict things precisely.

  • And that's what we call known knowns.

  • And so we can predict the known things very precisely.

  • LAURENCE MORONEY: So known knowns, right?

  • ARUN SUBRAMANIYAN: Exactly, known knowns.

  • LAURENCE MORONEY: A core area you can work from.

  • ARUN SUBRAMANIYAN: Core area we can start from.

  • And then we add a layer of probabilistics on top of it

  • to say, what are the things that we cannot measure precisely

  • or measure at all?

  • And that's where probabilistic modeling comes in.

  • And that is what I would call known unknowns.

  • An example of that would be, say,

  • if I am trying to predict how a crack is going to propagate

  • in a particular component.

  • Then I need to know what is the temperature

  • of that particular component, for example.

  • I measure it to within plus or minus 10 degrees.

  • But I don't know what that variation in temperature

  • is going to do to my crack propagating.

  • LAURENCE MORONEY: I see.

  • ARUN SUBRAMANIYAN: So that work, that's

  • what I would call known unknowns.

  • And once I know what are the unknowns that I'm not

  • entirely sure about, I can go say, OK, this

  • is what is the impact of that on something [? real. ?]

  • There is another level of complexity, where things

  • that I don't know I don't know.

  • LAURENCE MORONEY: Got it.

  • ARUN SUBRAMANIYAN: And that's what

  • I would call unknown unknowns.

  • LAURENCE MORONEY: Unknown unknowns, right?

  • ARUN SUBRAMANIYAN: I know it's a mouthful.

  • But an example of that would be, say, you

  • have designed a system.

  • You have put it out in the real world.

  • You know some of the things that is going to affect that system.

  • But you're not entirely sure of everything that's

  • going to affect the system.

  • And that is that other everything.

  • That is what we would call unknown unknowns.

  • LAURENCE MORONEY: I see.

  • ARUN SUBRAMANIYAN: And most of the time, in real world,

  • you can predict something up to, say, 90% or 95% of the time.

  • The last 5% is what surprises us.

  • And in systems which are safety-critical systems that

  • are critical to keep up the infrastructure of the world,

  • you can't necessarily have even a 1% chance

  • of something going down.

  • So for example, if power goes down,

  • you need to be able to bring that back up very quickly.

  • So those are the kinds of things where unknown unknowns come in.

  • LAURENCE MORONEY: Got it.

  • So starting from the known knowns,

  • then going to the known or knowable unknowns, and then

  • there's the unknown--

  • ARUN SUBRAMANIYAN: Unknowns.

  • LAURENCE MORONEY: Unknown unknowns.

  • ARUN SUBRAMANIYAN: Exactly.

  • LAURENCE MORONEY: I see.

  • So you've gone from known knowns to knowable unknowns, and then

  • unknown unknowns.

  • ARUN SUBRAMANIYAN: Unknown unknowns, right?

  • And when you're trying to model systems that are highly complex

  • and are extremely critical, you need

  • to be able to predict things at all of those levels.

  • And even if you're not able to predict unknown unknowns,

  • you need to know how much are you missing.

  • If an event like that happens, how would you respond to it?

  • That is really where unknown unknown comes.

  • LAURENCE MORONEY: So now, bringing this, then,

  • into just developing these things,

  • you use TensorFlow Probability.

  • ARUN SUBRAMANIYAN: Yes.

  • And we started with TensorFlow and then combined that

  • with TensorFlow Probability quite a bit.

  • LAURENCE MORONEY: So could you tell us a little bit

  • about how you use all that?

  • ARUN SUBRAMANIYAN: Absolutely.

  • So we started with TensorFlow for deep learning, precisely.

  • And when we got introduced to the TensorFlow Probability

  • team, and Josh Dillon specifically, what we realized

  • was they were bringing extremely deep research concepts

  • from the probabilistics world into a production world

  • that is not generally common.

  • And we were able to mix the deep learning community

  • with the probabilistics community we

  • had within our own teams.

  • So running a reasonably large data science

  • team, what you have to do is mix teams that are not necessarily

  • talking the same language.

  • And TensorFlow allows us to do that

  • very effectively because now, a deep learning expert who

  • doesn't understand probabilistics well

  • can talk to a probabilistics expert who

  • doesn't understand deep learning in the same language.

  • LAURENCE MORONEY: Nice.

  • So having that framework that they could work together

  • was very powerful for them.

  • ARUN SUBRAMANIYAN: Absolutely.

  • And it helped scale both our teams

  • as well as our deployments very quickly.

  • LAURENCE MORONEY: Wow, so a lot of complex

  • stuff that you've been working on.

  • There must be somehow you got started to figure out this.

  • And how did you learn all this?

  • ARUN SUBRAMANIYAN: Absolutely.

  • So I'm not a trained data scientist by training.

  • So I got into data science by accident.

  • So I'm an aerospace engineer who had

  • to solve very complex problems by mixing

  • these concepts together.

  • LAURENCE MORONEY: It's a very common story, by the way.

  • ARUN SUBRAMANIYAN: Absolutely.

  • So one of the things that helped me a lot

  • was trying to mix practical aspects with the deeply

  • theoretical aspects.

  • So for practical aspects, a book that I really love

  • is called "Doing Bayesian Data Analysis."

  • And that gives-- at least it gave me--

  • quite a bit of understanding of how these

  • are applied in the real world.

  • But at the same time, thinking about probability requires

  • people to think about solving problems

  • in a very, very fundamentally different way

  • because we are good at being trained at saying,

  • here are the bunch of inputs.

  • How is that going to get me one outcome?

  • But if the same inputs give you multiple outcomes,

  • that's a very different paradigm to think about.

  • So a set of books that helped me were

  • from E.T. Jaynes, which is at a much more philosophical level

  • of understanding probabilistics.

  • I would urge folks to at least dabble

  • in both the practical aspects as well as

  • some bit of the philosophical aspects together.

  • And if you look at the recent blogs from the TensorFlow team

  • as well as the broader community in doing

  • probabilistic deep learning, there's

  • a lot of fantastic blogs out there

  • that'll help people get started as well.

  • LAURENCE MORONEY: And I'd say one thing.

  • I know you've written a couple of blogs yourself.

  • There's another one on the way the way

  • where you've gone into a little bit more detail

  • than what you've been talking about today.

  • ARUN SUBRAMANIYAN: Absolutely.

  • And we walked through the three blogs

  • because we wanted to walk through known knowns, known

  • unknowns, and unknown unknowns, and bring it all together.

  • LAURENCE MORONEY: So the one that you're still working on

  • is the unknown unknowns.

  • ARUN SUBRAMANIYAN: Yes, and we're

  • close to getting done with it.

  • And it's getting published in the next month or so.

  • LAURENCE MORONEY: So all that's on blog.tensorflow.org, right?

  • ARUN SUBRAMANIYAN: Absolutely.

  • LAURENCE MORONEY: So thanks so much, Arun.

  • And thanks, everybody, for watching this episode

  • of "TensorFlow Meets."

  • If you've any questions for me, or if you've

  • any questions for Arun, just please leave them

  • in the comments below.

  • And also, in the description for this video,

  • we'll put links to everything that we spoke about today.

  • So you can check him out for yourself.

  • Thanks, and see you next time.

  • ARUN SUBRAMANIYAN: Absolutely.

  • Thank you, and thanks for having me.

  • [MUSIC PLAYING]

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Arun Subramaniyan discusses probabilistic modeling (TensorFlow Meets)

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    林宜悉 posted on 2020/03/31
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