How to pronounce “neural” in English

can happen. Mhm. What we're now finding is a whole series of other types of apnea. Now there areneural causes to it. There are environmental causes. um there are positional causes. And so as a classicexample to round the story out, traditionally if you're told you have apnea, you're going to be given one of

AI is in fact getting uh a lot better and I can say more about why, you know, scaling laws, um deepneural nets bigger, trained on more data, become more efficient, more competent at those things.I also became a bit more disillusioned with the AI industry. So

That's very effective. It's called an ensemble model.Neural networks are sort of a kind of ensemble model.Anyway, when you go to neural networks and other kinds of more numerical methods, it becomes a soup of math, and it becomes more opaque.

So if you tried to assign a unique vector to each one of them, you'd have an ungodly mathematical mess on your hands.Neural networks need much more compact word embeddings.And in a 2013 paper led by a team of Google researchers, they discovered a brilliant way to create them.

The next field, educational field.Neural feedback, yeah. Something by yourself, and then you say, OK.

that finally helped relieve his pain pain just a little was when they gave him a nerve block blocking all pain andneural signaling from his hand so these guys are pretty awfully venomous and it might seem that a Venomlike that has no good purpose and that maybe we are justified in vilifying these

And this is actually-- it relates to the default mode network and other related-- there's some 24 or 25 so far--neural resting states that have been detected.And so all of these states altogether, I'll just call the diffuse mode.

So the fact that periodic flow states were calming my system back down is allowing me to form new neural nets.Neural nets that didn't lead immediately back to illness.And this is what kind of gave me a toehold and possibility to get better.

They're working with a drug company to bring that to the human market.neural net that the brain represents.

picture at the beginning okay so now let's step back and ask ourselves the question what is it about the pattern ofneural activation in the retina and the levant weakly a nucleus in the visual cortex that explains why something looks as if it's on your visual left ratherthan on your visual right you can't explain it if the only place you look

of Nobel Laureate François Jacob, Ricard has logged well over 10,000 hours of meditation and I couldn't avoid the sense that I was watching a powerfulneural engine power up, roar, and accelerate, like a race car, cognitively going from zero to 60 in the figurative snap of a finger. As Ricard's meditative statedeepened, the EEG readings scrawling across Andy Francis' computer screen visibly changed. At the beginning, they had been a series of thin,

Modern AI is not just built with math, it's forged from it.A neural network is just a monumental structure of applied mathematics.And when AIs learn, they're using the tools of calculus to navigate vast landscapes of possibilities with billions of dimensions.

and how their groundbreaking work laid the foundations for key technological innovations of the digital age, from streaming and data encryption to machine learningand neural networks. We'll be doing some audience Q&A at the end of the talk, so if you're joining us on the live stream,please feel free to submit your questions.

I think this is a game where we're using words in a worrying way, that we're saying that it's learning.This neural network's learning how to fit the data.Well, there's an algorithm there.

And it's just absolutely fascinating to watch them.So neural networks were originally inspired by the brain, but they took on a life of their own and have veered completely away from their initial source

And neural networks can do intelligence stuff.

And neural processing is not instantaneous.

The neural pathways we have in our brain, they're highways of information, and the more you think about something, the quicker it

of neural activation. Absorbing is about turning up the sensitivity, the gain, as it were in the internal memory making machinery

and neural electricity, which are three of the things you need to talk about to talk about brain stuff-- we did not talk about networks.

The neural nets-- with the neural nets, though, you don't know how it got to the answer, right?

Because neural pathways can turn on and turn off, which is the beauty of what I do.

Differential neural activity in frontal areas was absent, whereas activation in the back of the head and the middle of the head persisted.

So neural nets were great for a while, but they didn't get us very far.

The neural activity seems to leave something critical out, namely the feeling itself, the qualitative feeling.

The neural tube, the spinal cord in your back, will be represented by your wrist.

of neural cells in the brain and creates these patterns of impairments/gifts so that you can end up with someone who is visuo-spacialy gifted but verbally impaired, or vice versa.

sculpts neural structure, which gives us opportunities increasingly to intervene actually inside the black box of the brain.

of neural events the light comes into your eye goes back to your retina into the optic nerve there are chemical

In the neural networks in our brain, you have a few regions, like the prefrontal cortex that link hundreds of different functions.

uh you know electronic neural networks right uh which is which is part of the magic there right in terms of these transform models which came out of Google right so I think that's important for

which a neural network is.

But these are all neural networks we're talking about.

the recurrent neural networks, and I said, I don't know, what if you make them bigger and give them more layers?

What's a neural network?

In the world of neural networks and deep learning, being able to find patterns and learn from huge quantities of data is gonna work better than waiting for a human expert to code them in.

lots of nodes. And they're connected with these parameters that we vary until we get a good fit.But besides the neural network, we have some function that we're trying to optimize.When it outputs something, we want to know how good it is.

that you'd ask it a question, and it would respond.So the little neural network I have, where I go through in detail, I think has only got four nodes.

The sitters have more neural complexity than the rovers.

SegNet and other deep neural networks can segment photographs well, but they don't work that well on paintings.

So we use deep neural net style transfer.

for training deep neural networks and things like this.

And it's just absolutely fascinating to watch them.Another one is neural networks.

And it'll be neural.

And so there are neural networks that can mimic the surface of Bach's music pretty decently, actually.

First is at the neural level, which is a task dependent, experience dependent, in the moment

And these neural circuits can do all that.

all the engines are neural network based, like the Stockfish engine, the Leela, which is open source.

It's all neural network.