Name: Arun Subramaniyan discusses probabilistic modeling (TensorFlow Meets)
Uploaded: 2020-03-31T07:34:40.000Z
Duration: 8 min 19 s
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And I'm delighted today to meet with Arun Subramaniyan

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

ARUN SUBRAMANIYAN: First of all, good morning.

And so probabilistic modeling and probabilistic theory

that have a combination of very complex phenomena coupled

with things that we can't measure precisely.

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

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,

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

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

So for example, say, designing gas turbines or trying

to predict what a very large-scale system like

thousands, of variables interacting with each other.

or you can measure maybe a few hundreds, if not

So how do you predict behavior of such complex systems

and still have actionable, meaningful outcomes

from your models without knowing all the information

That's really where we use probabilistic modeling.

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

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.

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

And that's where probabilistic modeling comes in.

And that is what I would call known unknowns.

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

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

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

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Arun Subramaniyan discusses probabilistic modeling (TensorFlow Meets)

stuff

absolutely

extremely

critical