Name: 機械からの学習 by Ashi Krishnan｜JSConf.Asia 2019 (Learning From Machines by Ashi Krishnan | JSConf.Asia 2019)
Uploaded: 2021-01-14T10:25:18.000Z
Duration: 34 min 48 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

So this talk is that it's not actually that scary.

Some flickering moments, a few unsettling pictures, nothing graphic, nothing where you'll be looking at it and you'll say, like, Wow, this is disturbing I and I know why.

No, it's it'll be more like I don't know why, but this looks.

This is weird to me and we'll be asking why, Um and we'll also be talking about some things some of the kinds of things that we don't usually talk about, and all sorts of stuff kind of wants to pour down river when you open locks like that.

But if you can't be here for it than take care of yourselves Otherwise, hello, my name is Sashi, and today I want to talk with you about a hobby.

I want to share some of the things that I've learned at the intersection of computational neuroscience and machine learning.

For years, I've been fascinated by how we think, how we perceive how the machinery of our bodies results in qualitative experiences and why we have the kinds of experiences we d'oh why they're shaped like this and not like that, Why we suffer for years I've also been fascinated by a I and I think we all have.

Thio approximate the tasks of her cognition, sometimes in unsettling ways.

And so today I want to share with you some of what I've learned.

Some of it is solid speculation and all if it speaks to a truth that I have come to believe, which is that we are computations, our worlds created on an ancient computer, powerful beyond imagining.

This person is Michaela Perry Oh Nieto and he has something to show us.

It starts with the simple patterns and splashes of light and dark.

I kind of like images from the first eyes.

These give way to lines and colors and then curves and more complex shapes.

What's happening is that we're diving through the layers of the inception image classifier, and it seems that there are whole worlds in here shaded, multi chromatic hatches, the crystalline farm fields of an alien world.

The cells of plants toe understand where these visuals air coming from Let's take a look inside the job of an image classified areas to reshape its input, which is the square of pixels into its output, which is a probability distribution.

The probability of a dog, a person, a banana, a toaster.

It performs this reshaping through a series of convolution, all filters convolution.

All filters are basically Photoshopped filters.

A layer has a receptive field, some small patch of the previous layer that it's looking at, and each convolution a layer applies a filter.

Colonel, A colonel is a matrix of numbers.

Were each number represents the weight of the corresponding input neuron.

So each pixel in a neurons receptive field is multiplied by its weight, and then we some, all of them to produce the output neurons value.

We apply that same filter across every neuron in a layer, and the values, not filter, are learned during training.

So we feed the classifier labeled image that something where we know what's in it.

It outputs predictions, and then we math to figure out how wrong those predictions were, and then we math again to figure out how to change the values in this filter to produce a better result.

So the term for that is greedy int descend.

The deep dream process, which is what's creating these visuals, inverts that So this visualization is recursive.

I didn't feel the next frame we feed the previous week.

We feed the current frame into the network.

We run it through the networks many layers until we find the letter that we're interested in.

What could we do to the input layer to make that layer activate more?

And then we had just the input image in that direction.

So the term for this process is great and sent.

Finally, we scale the image up very slightly before feeding it back into the network for the next frame.

That kind of keeps the network from fixating on the same details in the same places, and it also creates this really wild zooming off it.

Every 100 frames or so we moved to a deeper layer or layer that's off to the side.

Inception has ah whole lot of players, and they're not all arranged in a neat linear stack and that gives us this.

So we started with these rudiments of light and shadow.

And now, down here, we kind of have, ah City of Cagamas situation happening.

But then we're about to enter the spider observation Area in which spiders observed you.

But it's okay because soon the spiders will become Courtney's and the core G's are gonna become the seventies.

Later, we've got this, ah, space of nearly human eyes, which will transform into dogs, slugs and then dog bird slugs deeper.

Unfortunately, we had a saxophonist teleporter accident and finally, the flesh zones with a side of lizard.

So when I first saw, this was like, should I tell the story?

When I told the story when I first saw this, I I thought it looked like nothing so much as U.

President Donald Trump and and I resolved to never tell anyone that certainly not on stage until my best friend was watching the same video.

And she said, You know, this is just kind of reminds me of, and I think that actually says more about the state of our neural networks than this when I think the lizard juxtaposition has something to do with it.

But I do want you to notice and think about what it means that all of the flesh in this network is so very pale.

What does it mean for something to be trippy?

Were just looking at Scalia's visual A system which starts here in the retina.

Light comes into them, and it immediately hits a membrane.

There's a layer of ganglia ions, which are not actually particularly photo receptive that they are a little.

Then there's a layer of more stuff that does important things, presumably and at the back.

Or the photo receptors, the rods and cones, which sends luminous and color.

So when light comes in, it has to go through these four layers of tissue hit a photo receptor.

That photo receptor is going to get excited.

It'll send out a signal to its ganglia NHS, which then have to send it to the brain.

Somehow, through the optic nerve, which has drilled through the center of your eye, which means the scent, the sensors and our eyes are mounted backwards, and there's a hole in the center of them, and it's all okay because we patch it up later in software, a couple of other problems with our eyes to one.

We have about 120 million photo receptors, and there's 10 times you're gangly aunts than that, so it can't be a 1 to 1.

Mapping and to the band with of our optic nerve is 10 megabits, which is not, you know, like a lot.

The last time you tried toe watch video over a 10 megabit connection is but much slower than WiFi.

And so our retinas do what you would do if you were given those design constraints.

Each gang Leon is connected to a receptive field.

That's 100 or so photo receptors that is that are divided into a central disk and the surrounding region.

So when there's no light on any of them, the gang Leon doesn't fire.

When the whole field is illuminated, the gangly on fires very weakly.

When on Lee the strand is eliminated and the center is dark.

Half the gang liens in your retina fire release strongly, and the other half don't fire it all in the same situation.

But those gangly ons behave the opposite way.

They fire when the center is bright and the surround a star.

So these different species of gang liens, they're not actually different species.

They're just kindly owns that naturally behaved this way.

They're scattered, they're distributed evenly throughout your retina.

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機械からの学習 by Ashi Krishnan｜JSConf.Asia 2019 (Learning From Machines by Ashi Krishnan | JSConf.Asia 2019)

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