Okay, so here what I mean concretely by not doing this thing
over here any more. So here what I'm proposing we do now. We
take all the data, all of the training data we had, the
xy1, xy2, dot, dot, dot, xyn and we put it in a database
>> Ah-ha.
>> And then next time we get some new x to look
at, we just look it up in the database. And we're done.
>> None of this fancy shmancy learning, none of this
producing an appropriate function like you know, wx+b, or what ever
the equation of a line is. None of that fancy stuff anymore.
We just stick in to the data base. People written data
base programs before. We'll look it up when we're done. We're done.Period
>> I feel like you've changed the paradigm.
>> Yes, I am a paradigm changer. So what do you think?
>> It's like, it's like disruptive. It's going to throw off the markets.
>> Yeah. It's going to change everything. So, what do you think?
>> well, I mean, so there's a bunch of
really cool things about this idea. Which is why
I'm excited. So one is it, you know, it
doesn't forget, right? So it actually is very reliable.
It's very ,um ,dependable, right, so if you put in an x, y pair you ask for the
x you're going to get that y back instead of
some kind, you know, crack potty, smooth version of it.
>> Right, so we don't, yeah that's a good point. So we look at this little
blue line over here, you'll notice that say,
for this little x over here, we're not going to
get back what we put in, so, it remembers, it's like an elephant. Good.
>> So that's kind of cool. Another thing is that
there's none of this wasted time, you know, doing learning [LAUGH].
It just takes the data and it, and it's, very
rapidly just puts it in the database. So [CROSSTALK] it's fast.
>> It's fast. It's like. I like it when you say nice things
about my algorithms. Okay. So it's fast. Anything else, you can think of?
>> Sh, yeah I cant think of
one more thing at least.
>> Mm-hm.
>> Which is ,that like you, it's simple.
>> [LAUGH] That's true. I am simple. I am simple and
straightforward. I just need a few things to make me happy.
>> Bacon.
>> Bacon, and
>> And
>> Chocolate.
>> Oh nice.
>> Have you ever had chocolate covered bacon?
>> That seems wrong.
>> You know, you would think so, but it turns out it's delicious. It's
like if you take fat, and sugar, and you put it together somehow you like
it [LAUGH] I'm not making this up you can buy
chocolate covered bacon, you're unsurprised to here in America. Okay, so
we've got three good things in remember stuff. So, you
know, none of this little noisy throwing away information. It's very
fast, you just stick it in a database. Using your
favorite data base. And looking up is going to be equally as
fast. And it's very simple. There's really no interesting learning
going on here. So it's the perfect algorithm when we're done.
>> Okay. I mean, I, it feels
like there's more that we need to say though.
>> Like what?
>> In particular the way that you wrote this, F of X equals look up of X. If
I give you one of the other points in
between ,then it will return no such point found.
Which means it's really quite conservative. It's not willing
to go out on a limb and say ,well
I haven't seen this before but. It ought to
be around here, instead it just says, I don't know.
>> Mm. So the down side of remembering is, no
generalization.
>> [LAUGH] And I guess a similar sort of issue is that when you, when
you call it memorization, it makes, it reminds me of the issues that we saw with
regard to overfitting So, it bottles the noise
exactly, it bottles exactly what it was given.
So it's going to be very sensitive to noise. So it's kind of a yes and no.
>> So that's a little scary and, and it can over fit in a couple of
ways, I think it can over fit ,um,
by believing the data too much that is literally
believing all of it and what do you do if you
have couple ,uh, speaking in noise, what if you have you know
a couple of examples that are all the same. I have got
an x, shows up multiple times but each with a different y.
>> Oh,the same x,ah, yeah, and so the look up
would return two different things and this algorithm or whatever
that you have described so far, wouldn't commit to either
of them and it would just say, hey, here is both.
>> Yeah, that seems problematic.
>> Okay, alright. But I feel like,
you know, you are going to to tell me, how
to fix those things. So I wasn't too worried.
>> Yeah, well, there is gotta be a nice way
of fixing it. I think There's sort of a basic problem
here, which is that we're taking this remembering and then
looking up a little too literally, right? So I stick in
the data, and I can get back exactly the data
that I got, but I can't get back anything that I
don't have, and that seems like something that we might
be able to overcome if we're just a little bit clever.
>> Mm.
>> So let's see if we can be a little bit clever.