EKABA BISONG: Good afternoon.
My name is Ekaba Bisong, and I'm a data science lead at T4G.
So T4G is an editor consulting company up north, in Canada.
I came here from Calgary earlier, as of this morning.
And so today, briefly, we'll be talking
about using machine learning to assist
in detecting toxic language on online forums.
And I think when I came in, I heard someone also
from the Google Brain team talk about a jigsaw data set.
So that is the exact type of data set
that was used in this project.
Or what I call it--
this is a weekend project anyway.
But the cool thing here is to kind of promote
how we can leverage AutoML on GCP
for building actual data products that
can make an impact in society.
So there is a challenge of pruning online communities.
And one is that it can be time consuming.
And looking at charts, or images, or videos, or whatnot.
And also there is an issue of consistency in judgment.
As far as we have two people, even two tweens,
who would make two different judgments.
[INAUDIBLE] of having 1,000 people or tens of thousands
of people having to make a judgment of what is toxic
and what is not toxic.
And another challenge is the psychological harm
to the moderators.
Now, imagine for a second that you're
looking at tons of beheading videos every day,
or tons of obscene photographs.
It does a lot of damage to the mind.
I mean, even taking a look a jigsaw--
which jigsaw data set is a text data set--
just looking at some of what was labeled as toxic words,
reading some of them, my mind took a beating.
It was not-- some of those sentences are a bit intense.
Have to do a mental cleaning after.
So these are some of the challenges
in this sort of moderating.
So then I'll go and talk about the case for AutoML.
Now what they call AutoML, it encapsulates three concepts,
broadly speaking.
And the first is the concept of transfer learning.
Now, as we all know, building machine learning models,
large-scale models can be extremely computationally--
can be computationally intensive.
And so it makes a lot of sense to leverage pre-built models,
and then use it in solving your own domain use case.
So that's the moonshot idea of transfer learning.
So how it works is it takes just the layer
before the output layer, which is called the bottleneck.
And then you train that layer or your own data set.
So you're leveraging the weights,
or the already-understood parameters
of a large-scale network in your own data set.
And that's how that works from a high level.
And the other is what is called Neural Architectural Search.
It's a fancy word, but it's really very interesting.
And that has been around for a couple of years now, probably
two or three.
I can't remember when.
But the idea is how can you build--
because part of the challenges of building a large-scale
network in production is--
one is to find out what's the best architecture for my use
case.
In line with the idea of no free lunch,
there is no silver bullet ML architecture.
So Neural Architecture Search is using
the idea of a reinforcement learning agent
where the action it's taking is proposing a certain set
of layers for the network.
And then the idea is you want to optimize some utility.
And that could be the performance index of the model
to decide which is the best architecture for your problem
space or for your use case.
So that's what a Neural Architecture Search is.
And then the other is hyper-parameter tuning.
So take any method.
So it's a neural network, or let's
say linear regression method.
They're a set of parameters that you may need to tune.
A common one would be [INAUDIBLE]
or some metric, some method, some parameter
you want to tune.
How can we tune this at scale?
So you're handed three things, you're
trying to find the best hyper-parameter,
you're trying to find the best architecture, a lot of things
going on.
So that's where AutoML comes in.
And then GCP-- that's Google Cloud Platform--
they have a product called GCP AutoML for NFP,
for Natural Language Processing.
So it packages these concerns into a product and trains
on scale.
So you have Google's massively--
I mean, parallel distributed machines available
for you to load your own data set,
process it first, load it, and then train.
So that's what we did with the jigsaw data set.
So there's a method for preparing it.
I will just gloss over that, and waive that for this talk.
So I'll just say that we upload the data set
to AutoML for natural language.
But it has to be processed or developed--
arranged in a particular format.
And as you see in the screen, the next thing
is that you train it.
And the longer you train, of course, you
could have it better, depending on the size
of your data set anyway.
So you do the training, and then the model is deployed.
Now, in the deployed model, you have a user interface
where you can kind of validate/evaluate your model.
And I can't see what I put there,
but there is an example of a toxic or a clean set.
And it did pretty well.
But of course, there are obvious limitations.
It's one thing you're asking when
you talk about toxic and clean.
It doesn't understand things.
There's a lot of issues when we talk about context.
And when we play around with a funny context--
like someone can use the F-word on the current president--
many people would think it's toxic.
Others would think-- a lot of others
would say that's not toxic.
Yeah.
[AUDIENCE CHUCKLING]
Now, whichever divide you lie, that's up for the jury.
That's part of the issues of this kind of stuff.
[INAUDIBLE] used the F-word on the previous president.
A lot of people would think that's toxic, and a lot more
say, that's not toxic.
But that's where you are, and that's the world, and it's OK.
It's OK.
There's differences of opinion.
It cannot be otherwise.
But this is strange.
I would say there's mixed sense in moderating
what is clear and obvious.
And I think this is part of the thin line we need to toe as AI
researchers and developers.
I think my friend Wally talked about AI for social good.
These are some of the things that--
or AI ethics-- some of the things
that we should look at in terms of how
we draw the line between having our online community
safe for us and then going to another length of what we will
call intellectual or speech fascism, groupthink, of how
you should think or see.
So freedom is important, but being cordial,
congenial-- and these are some of the trade-offs
that I think that this is more in the arena of wisdom.
So those are some of the limitations of these.
But what is clear and obvious, I think it does pretty well.
Now, the goal of this is to see that there are opportunities
for products, leveraging these kind of opportunities
for downstream software products or applications that
leverage this infrastructure on Google Cloud Platform.
There's an object detection--
AutoML for object detection that just came out,
I think, the early part of this year.
I'm not too sure.
But the image specification one has been there.
These things can tie together.
And it exposes endpoints which you
can infuse into your software pipeline-- product pipeline.
So there are a lot of opportunities here.
And we could talk.
And these things can be leveraged to create products
that improve the society.
And the meat and bones of how I built this,
it's part of the chapter in that book you see up there.
So it's the book that it is available for preordering.
It will come out October, I guess, mid-October,
so "Building Machine Learning and Deep
Learning Models on GCP."
And thank you so much for your time,
and it's a pleasure to be here.
[APPLAUSE]
[TITLE MUSIC]