Why you should probably not try to follow the lights

You might not be able to see what’s happening on the ground in this video, but you can see what the lights are doing.

That’s because they’re the lights of the artificial intelligence robots at the Los Angeles Zoo.

The bots are trained to see and respond to what the humans around them are doing, using artificial intelligence to learn.

Here are a few things you might not know about artificial intelligence.


Its called a neural network, and it’s incredibly powerful.

You might think this is a very boring thing, but neural networks are a lot more powerful than you might think.

If you’re not familiar with them, here’s a quick introduction.


They have a ton of power.

In a neural net, a bunch of neurons in the network work together to do a lot of the work.

This means that the network can do things like make an accurate prediction about what a robot will do, or make a simple calculation about how far the robot will walk.

The network can also make sense of a scene and learn about the situation, and use that to make decisions about what to do next.

That kind of power is extremely powerful.

The problem is, though, the neural nets that you might be able do a pretty good job of understanding and reacting to things, are really slow.

That can be especially problematic if you have a lot to look at.

For example, you might want to look into what the robot’s face looks like when you’re just looking at it for the first time, and then you look at it again a few times later.

The neural network could only make so many predictions at a time, because the network had to run so many computations.


The training is slow.

Neural networks are trained in the lab.

They can do very little more than learn the rules for certain things.

In the wild, the network could do thousands of these trials and learn how to correctly recognize things like humans.

It doesn’t really need to learn any new rules or understand things like emotions.

But if you put a bunch more humans around it, and you’re training a neural agent to recognize things that humans are likely to do in real life, it could do a whole lot more.

The human brains that you see here have a big cortex that’s bigger than a human’s, so they’re able to understand more than you can in the wild.

This gives the neural agent a lot bigger problem solving power, but it also means it has to do lots of training to get it to do the right thing.

That takes a lot longer.


Training a neural machine takes a long time.

You’d think that neural networks should be able with a little training, but this is an example of a neural system that takes a while to train, and even longer if you’re trying to do something like recognize a face.

It’s only been trained once, though.

The goal of training neural networks is to make them more powerful, and that takes lots of time.


The artificial intelligence that you’re learning to interact with the robots is pretty dumb.

A lot of people like to use the word “deep learning” to describe it.

Deep learning is the idea that computers learn to do complex things, like automatically recognize people and recognize faces, and make sense out of scenes.

This is something that we could use in the future, but for now, this is just another way of saying it: you’re going to have to learn how the robots see things first, before you can do anything else.

It may seem like this is what all these fancy robots that are trained on your smartphone or computer are going to do, but deep learning is much more than that.

Deep neural networks train in deep neural networks, or neural networks with many layers.

These deep neural nets are a huge improvement over what humans do.

They’re able as much as a billion times faster than what humans can do, and they are able to make much more complicated decisions.

Here’s how they do it.

A Deep Neural Network is a system that learns from a huge set of examples.

It has many layers that are made up of a very large number of neurons.

They don’t have a central processing unit, like humans do, where you get to decide what to focus on.

They use a whole bunch of different algorithms, and each algorithm can take an input and train it on the next layer.

Each layer can only train on the last layer that’s been trained.

If the neural network has to go through multiple layers of the training, it can do it in a matter of seconds, but if it has too many layers, it will run out of memory.

This makes it much more efficient than a lot.

Deep Learning in Action If you want to get into deep learning more, the company that makes them is called Deep Learning Intelligence (DLI).

Deep Learning is the name of the company behind it, but they’re not really talking about their product.

They call it “deep-learning