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  • Yes.

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

  • It just has some unusual things.

  • 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.

  • It's disturbing.

  • 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.

  • And so I want you to be here for it.

  • 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.

  • We're watching these machines begin.

  • 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 research.

  • 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.

  • So let's begin part one hallucinations.

  • 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.

  • So the probability.

  • But this image is of a cat.

  • 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.

  • Each neuron in a convolution.

  • 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.

  • Specifically, it applies an image.

  • 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.

  • And then we math.

  • 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.

  • S.

  • 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.

  • So this is pretty trippy.

  • Yeah, why is that?

  • What does it mean for something to be trippy?

  • To figure that out?

  • Let's take a look inside ourselves.

  • Meet Scully's.

  • Scully doesn't need all this craft.

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

  • Now Scalia's retina.

  • You're right now, our retinas.

  • They're actually pretty weird.

  • 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.

  • I don't know when.

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

  • Our cameras air about 100 mega pixels.

  • It's not gonna work.

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

  • They compress the data.

  • 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.

  • And if you think about what this does, creates an edge detection filter.

  • So we're doing processing even in our eyeballs in order to down sample the image coming in from our photo receptors 100 times while retaining vitally important information, namely, where the boundaries of objects are.

  • Okay, so then the signal is gonna go into the brain.

  • It's gonna hit the optic hi, Asma, where the data streams from our left and right eyes cross where we extract stereo vision.

  • It's gonna get processed by the thalamus, which is a switching center for all kind of signals in your brain.

  • It's responsible amongst other things for running our eyes.

  • Auto focus.

  • Each step in the signal pathway is doing a little bit of processing for extracting a little bit of information.

  • And that's all.

  • Before we even get to here, the visual cortex all the way around back.

  • Our visual cortex was arranged into a sack of neuronal layers.

  • The signal stay is actually pretty especially coherent throughout the visual cortex, so there's some slice of tissue in the back of your brain that's responsible for pulling faces out of this particular chunk of your visual field.

  • I mean, more or less.

  • Your brain is very squishy.

  • Each neuron in a layer of our visual cortex has a receptive field.

  • Some chunk of the entire visual field that it's looking at, and neurons in a given layer tend to respond to signals in the same way, and that operation distributed over a layer of neurons, it extracts futures from the visual signal.

  • Early layers extract simple features like lions and curves and edges, and then later, Blair's extract more complex ones like radiance and surfaces and objects, eyes, faces and movement.

  • It's no accident that we see very similar behavior and inception because convolution all neural networks like inception were inspired by the design of our visual cortex.

  • Of course, our visual cortex is different from inception.

  • In many ways, inception is a straight shot through it has branches, but no cycles.

  • Our visual cortex contains feedback loops, these pyramidal neurons that connect deeper layers to earlier ones.

  • Those feedback loops allow the results of deeper layers to inform the behavior of earlier layers so we might turn up the gain for edge detection along the edge of what is later detected to be an object.

  • This lets our visual system adapt and focus not optically, but intentionally.

  • It gives us the ability to ruminate on visual input well, because before we become consciously aware if it improving our predictions over time, you know this feeling.

  • I think you think you see one thing and then you realize that something else and these loop back pyramidal cells in our visual cortex are covered in serotonin receptors.

  • Different kinds of pre middle cells respond to certain and differently, but generally they find it exciting.

  • And don't we all.

  • You might be familiar with serotonin in its starring role as the target of typical antidepressants, which our serotonin re uptake inhibitors.

  • When serotonin gets released into your brain, they make it stick around longer there.

  • By treating depression, some side effects may occur.

  • Most serotonin in your body is actually located in your gut, where it controls bowel movement.

  • It signals to your gut that it's got food in it, and it should go on and do whatever it does to food.

  • And that seems to be with the molecule signals throughout your body.

  • Resource availability.

  • And for animals like us with complex societies, resource is could be very abstract.

  • Social resource is as well as energetic ones that your pyramidal cells respond excitedly to.

  • Serotonin suggests that we focus on that which we believe will nourish us now.

  • It's not correct as a blanket statement to say that pyramidal cells are excited by serotonin.

  • They're different kinds of serotonin receptors, and their binding produces different effects.

  • So five ht one day receptors tend to be inhibitory, somewhat drowsiness inducing five HT.

  • Three receptors in the brain, their associative sensations of queasiness and anxiety.

  • And in the cut, they make it run backwards.

  • So anti nausea drugs are frequently five ht three antagonised, so there's another serotonin receptor one that the pyramidal cells in your visual cortex find particularly exciting.

  • This is the five ht to a receptor.

  • This is the primary target of every known psychedelic drug.

  • This is what enables our brains to create psychedelic experiences.

  • So you go to a show, you eat a little piece of paper, and that piece of paper makes its way down into her stomach, where dissolves releasing molecules of lysergic acid.

  • Ayatollah my into your gun.

  • Now LSD doesn't bind to five HT three receptors, particularly.

  • So if you feel butterflies in your stomach, it's likely just because you're excited because you know what's gonna happen.

  • And what's gonna happen is this.

  • LSD will diffuse into your blood.

  • It has no trouble crossing the blood brain barrier because it's tiny but powerful.