What is deep learning and what do researchers who do research in deep learning actually do ?
In: Engineering
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So if you have a very basic neural network, you have three layers. An input, output, and a hidden layer. The hidden layer is the one that has the artificial neurons and actually makes the neural net work. So you go input ->neurons->output.
In deep learning, you have multiple hidden layers. So you go input->neurons->neurons->output. You technically don’t need this, but it’s easier and performs better. Deep learning is used in pretty much every AI you see these days. At an ELI5 level, you can consider them synonymous. Chat gpt uses deep learning. All the image recognition and generation AIs use deep learning. Pretty much any AI that uses a neural network is doing deep learning.
To understand deep learning, you need to understand a little bit about what deep learning and AI are *trying* to simulate. They’re trying to simulate how the human brain solves problems.
When we normally program computers, what we do is we write down a series of steps you have to follow to achieve a result. But what happens when your problem is so complicated, we don’t really have a series of steps to solve it?
Imagine I hand you a picture, could you determine if the picture is of a cat? You could probably do that pretty easily, it’d probably take you less than a second to determine if there’s a cat in the picture, right? But amazingly, it doesn’t matter if I hand you a cartoon image of Garfield, or if I hand you a black and white photo, or if I hand you a 3D model of a cat, or if I hand you a stone slab with a cat carved into it; it doesn’t matter if it’s a lion, or a puma, or a house cat, you could easily identify all of these things as cats.
How did you do it? What are the steps you followed?
There isn’t any easy answer to this, and these types of problems that don’t have obvious steps to follow to solve them are what we use AI to solve.
When you learned what a cat is, and learned how to identify a cat, you learned by seeing cats and seeing images of cats, having people tell you that it’s a cat, and then having people show you things and ask you if that thing is a cat. Each time you got it wrong (or right), your brain responded by reinforcing or decreasing certain connections between the neurons inside your brain. Your brain built specific pathways for the signals to travel in response to particular images that lead to you understanding it’s a cat.
While we don’t understand exactly *how* you’re identifying a cat, we can still reproduce the *process* by which you identified the cat by building the entire structure that did it – the pathway in your brain.
To do that, we make something called a “neural network.” Essentially, we’re building a mathematical model that represents a bunch of different neurons – like a slice of your brain, but just the part that identifies cats.
Then, we have to teach this slice of your brain, just like you had to be taught. We teach it by showing it a picture and saying “is this a cat?” It gives an answer, “no, this is not a cat.”
Then, we provide feedback to the model. “Actually, this was a cat.” The model takes that feedback, and then it determines which connections it needs to strengthen and weaken so that the next time it sees this picture, it identifies it as a cat. This process is “deep learning.”
We use the term “deep” because that “slice” of your brain (the neural network we built on a computer) contains many layers, and the learning has to go “deep” through those layers.
Deep learning is a type of machine learning that uses specific algorithms called “neural networks”. Most AI models these days use some kind of deep learning.
At a fundamental level, it isn’t terribly different from other machine learning methods. The goal is to generate a computer model that lets you tell it some information about a lot of things (the “training set”). For example, you might input a bunch of pictures and tell the computer which ones are real people’s faces and which ones are AI generated.
Then the model uses some fancy algorithms and math to find patterns in those things that you have told it. This is where the real “magic” happens. Deep learning is a specific way that the computer learns patterns using multiple steps that all happen sequentially and automatically (called “hidden layers” in this context). Compared to a non-deep learning model, a deep learning model is almost always more complex and therefore requires more computing resources to make and may even require more training data to be particularly worthwhile, but the result usually makes more accurate predictions because it can “learn” more subtle or complex patterns. Another trade off of deep learning is that, without some special additions (like “attention layers”), it can be pretty much impossible to actually figure out what specific patterns the model learned. You just know it “learned” *something*, and you then “validate” or “test” it on new data (that you still already know the correct answer to) to see how useful or accurate that “something” is. Repeat until you’re satisfied.
Finally, once you’ve “trained” your model, you then give it a new picture and ask it “Is this real or AI?” The deep learning model then breaks down this image into parts and looks for the patterns it learned from the training data to give you an answer, which might be a “Yes/No” or something more granular like “75% likely to be AI generated”.
TL;DR a specific type of machine learning/AI model that lets you detect more complex patterns to make better predictions than a simpler model.
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