- I'm hoping that I'm gonna tell you something

that's interesting and, of course,

I have this very biased view,

which is I look at things from my computational lens

and are there any computer scientists in the room?

I was anticipating not, but okay, there are,

so there's one, maybe every now

and then I'll ask you a question,

no, no, no, I'm just kidding, but, so,

and then so my goal here is gonna be to basically,

actually just give you a flavor of what is machine learning,

this is my expertise, and so just, actually,

again, to get a sense of who's in the room,

like, if I picked on someone here,

like raise your hand if you would be able to answer

that question, like, what is machine learning?

Okay, a handful, no, actually one, or two.

Great, okay, so I just want to give you a sense

of that, and I'm gonna, you know,

most of this is gonna be pretty intuitive,

I'll try to make little bits of it concrete

that I think will be helpful,

and then I'll tell you how we use machine learning

to improve guide designs, specifically

for knockdown experiments, but I think a lot

of it is probably useful for more than that,

but we haven't sort of gone down that route,

and so I can't say very much about that.

And please interrupt me if something doesn't make sense

or you have a question, I'd rather do

that so everybody can kind of stay on board rather

than some, you know, it makes less

and less sense the longer I go.

Alright, so machine learning, actually, during my PhD,

the big, one of the big flagship conferences was peaking

at around 700 attendees, and when I go now,

it actually is capped, like, it's sold out at 8,000 like,

months in advance, 'cause this field is just like,

taken off, basically it's now lucrative for companies,

and it's become a really central part of Google,

Microsoft, Facebook, and all the big tech companies,

so this field has changed a lot,

and kind of similar to CRISPR,

there's an incredible amount of hype and buzz

and ridiculous media coverage and

so it's a little bit funny, in fact,

that I'm not working at these two kind of,

very hyped up areas.

But anyway, so, you know,

people in just the mainstream press now,

you're always hearing about artificial intelligence

and deep neural networks, and so these are like,

so I would say machine learning is a sub-branch

of artificial intelligence,

and a deep neural network is sort

of an instance of machine learning, and so like,

what really is this, this thing?

So it kind of overlaps sometimes

with traditional statistics, but the,

like, in terms of the machinery,

but the goals are very different and,

but, really like the core, fundamental concept here is

that we're gonna sort of pause at some model, so maybe like,

think linear regression is a super simple model,

and you can like, expose it to data, it has some parameters,

right, the weights, and then we essentially want

to fit those weights, and that's the training,

that's literally the machine learning.

So I'm sorry if that sounds super simple

and not like, God-like, like machine learning

and everything working magically,

but that really is what it is,

and, right, and so let me just also give you like,

sort of drive home that point.

So we're gonna pause at some sort of model,

and so here I'm giving you the simplest example

because I think most people here work

with linear regression at some point in their life,

and so you can think of this as a predictive model

in the sense that if I give it a bunch

of examples of Y and X, and I learn the parameter of beta,

then for future examples where I don't have Y

but I only have X, I can just compute,

X times beta, and I get a prediction of why.

So that's the sense in which I call this a predictive model,

and that's very much how machine learning people tend

to think of it, where statisticians are often very focused

on what is beta, what are the confidence intervals

around beta and things like this.

So like, there's, that's the sense

in which there's a lot of overlap,

but the goals are kind of quite different.

We want to like, use real data

and make predictions, so here it's gonna be predictions

about guides, and which guides are effective

at cutting and at knockout.

Right, and so it has these free parameters,

and we call these things that we put in here features,

and so in the case of guide design,

the question is gonna be, what features are we gonna put

in there that allow us to make these kinds of predictions,

and, so I'm gonna get into that in a little bit,

but just as an example to make this concrete,

it might be how many GCs are in this 30mer guide,

or guide plus context.

Right, and like I said, we're gonna call,

we're gonna give it some data,

and so in this case, the data for guide design is gonna be

data from (mumbles), there's a community

that's now publicly available where there are examples,

for example, what the guide was

and how effective the knockout was,

or what the cutting frequency was.

For example, I get a good, a bunch of these examples,

and then that's gonna enable me

to somehow find a good beta, and of course we're not,

actually, we do sometimes use linear regression,

but I'll tell you a little bit more about,

more sort of complex and richer models

that let us do a lot more, and then the goal is going

to be to fit this beta in a good way,

and like, I'm not gonna do some deep dive on that here,

but in the one way that you are publicly familiar

with is just means squared error,

and when you find the beta that minimizes this

for your example training data,

then you get some estimate of beta

and you hope that on unseen examples

when you do X times beta, it gives you a good prediction.

So is that sort of make it somewhat concrete,

what I mean by a predictive model

and how you could view linear regression

as a predictive model in how you might use this

for guide design?

Okay, so obviously I'll tell you a lot more.

So, right, but linear regression is just sort

of the simplest possible example,

and so in our work we actually use,

some of the time, what are called classification

or regression trees, and so in contrast

to here where you might have, say,

this, you might have a bunch of these features,

right, like how many GCs were in my guide,

and then another feature might be,

was there an A in position three,

and you can put in as many as you want,

and then you get all these betas estimated.

So it's very simple, because in that case,

none of these features can interact with each other,

right, you just, you know you just add X times beta one

plus X times beta two, so we call this like,

a linear additive model.

In contrast, these trees allow very sort

of deep interactions among the features,

so this might be how many GCs,

so, of course, this is just, I didn't,

this is not suited to the features I just described,

but this might be some feature like,

I don't know, proportion of GCs,

'cause now it's fractional, and then it,

this algorithm, which is gonna train the betas,

so find a good value beta, well, sort of

through a procedure that I'm not gonna go into detail

for all these models, how it works,

but it's going to somehow look at the data

and determine that it should first split

on the second feature at this value,

and then it will sort of keep going down that.

It says, "Now partition the examples

"in my training data like this."

And then on the second feature in this way,

until you end up at the sort of leaves of this tree,

and these leaves are the predictions.

And so when you do it for the training data,

whichever training examples here end up at this leaf,

you basically take their mean,

and that's now the prediction for that leaf,

and if you take a new example,

you basically just pipe it through this,

these sort of rules, and you end up

with that kind of prediction.

This, simplified, but I think it's a good conceptual view,

and this is just another way of thinking

about it is if you only had two features

and you drew them, like, one against the other,

then effectively, every time you make a branch here,

you're kind of cutting up this space.

So that's also just another way to think about it.

And so, also, so this is, now all over the press nowadays,

and whenever I give these talks,

there's a bunch of young, hungry grad students who say,

"Did you do deep neural networks?"

'Cause that's what everybody wants to do now,

and so deep neural networks, they're kind

of like a really fancy linear regression.

So you could think of these as the Xs in linear regression,

you can think of this as,

imagine there's only one thing out here,

I should have done a different picture, but that's just Y.

And so this again is a mapping where you give it the Xs,

you do a bunch of stuff, and out you get a Y here,

except linear regression, you know,

is this very simple thing,

and now you can see, there's all these other kinds

of, we call these like, hidden nodes,

and so there's this complicated mess now of parameters,

beta, and again, I'm not gonna go into it,

I just want to give you a sense that like,

linear regression is this very simple thing,

and there's a lot of other models that let you do much,

much better prediction, and these are typically the kinds

of models that we use, because they're more powerful

if we care about prediction.

But the flip side is they're actually very hard

to interpret, and so if you want to ask a question,

like, was it the GC feature that is most important

in your guide design, which is what I always,

you know, get a question like this,

and we can do our best to kind

of poke and prod at this machine,

but it's always a little bit ad hoc,

it's hard, the more complicated the model,

then the, you know, the better we might predict

and the less interpretable it is,

and so there's always this kind of tension.

So right, and so what are some of the challenges?

So I've sort of shown you sort of some,

like, increasing amount of complexity in some models,

and so one of the big difficulties is,

if I pause at a very complex model

with a lot of parameters, then I need a lot

of data in order to actually fit those parameters,

and if I don't have enough data,

what's gonna happen, is the parameters,

you're gonna find this very specific setting

of the parameters that effectively memorize

the training data, and the problem then is you

give it a new example that you really care about,

you say, I want to knock out that gene,