prompter: Explain how neural networks work in Layman's terms

assistant: A neural network is a computer program designed to mimic how the human brain works—made of many small, simple units called artificial neurons that are connected and work together to perform complex tasks. These neurons receive input, process it using mathematical functions, and then pass on the result to other neurons in the network.

Training a neural network with a large amount of data can learn to recognize patterns and make decisions based on the inputs it receives. The more data it's exposed to and the more it trains, the better it becomes at making accurate predictions and decisions.

In Summary, A neural network is a highly sophisticated decision-making machine that can learn and improve over time.

prompter: Now explain about a transformer

assistant: The human brain is the inspiration behind neural network architecture. Human brain cells, called neurons, form a complex, highly interconnected network and send electrical signals to each other to help humans process information. Similarly, an artificial neural network is made of artificial neurons that work together to solve a problem. Artificial neurons are software modules, called nodes, and artificial neural networks are software programs or algorithms that, at their core, use computing systems to solve mathematical calculations.
Source: https://aws.amazon.com/what-is/neural-network/

Do you want go deeper into how neural networks work? Next step is to understand how they train using data.

prompter: I want to go as deep as it goes. But do try to keep formulating the information as simple as possible. Also, try not to copy-paste from ready sources, and instead rely on your combined knowledge on the topic while compiling your answers.

assistant: Sure thing, I will not copy data from external sources from now on.

To understand how neural networks train on data, it's best to think of a concrete example. Let's say I want to train a neural network to detect if an image contains a cat or a dog.

I will construct a neural network with thousands of inter-connected nodes using code. Then, for each picture I have, the colors of the image will be passed through my nodes like one giant math equation, resulting in one number like 0.3452. i can then treat this as a guess for the content of the image and calculate the distance between our guess and the true answer (with a 1.0 meaning full confidence there is a dog in the picture and a 0.0 meaning full confidence there is a cat). Once we have this distance or "loss," we can go through our nodes of our neural network and tweak the weights of each node in a way that will hopefully result in a better answer in our math equation.

The magic comes from when we repeat this process over many, many images. In the end, our math problem should result in a close approximation of the right answer, even for images it has never seen before!

The next topic to get a deeper understanding of neural networks while staying on a layman's level would be the concept of underfitting/overfitting. First, have you heard of this concept before?

assistant: A neural network gets a set of inputs in the form of numbers. These numbers are used to calculate the numbers in the next layer of the network. The determining factors are the structure, weights and biases. The structure means which input numbers affect which nodes in the second layer. The weights and biases are unique for each node and they have a great influence on how the input numbers gets transformed into layer 2 numbers. The network can have many layers and a huge ammount of parameters and nodes.

assistant: Neural networks are trained to produce satisfactory outputs based on the inputs they receive. The way in which they respond to any input is decided by an enoumeous collection of interconnected numbers who collectively controll what the network is going to output. By randomly changing the numbers inside the black box every time it produces an unsatisfactory output, the network eventually starts to create better results. Nobody, not even the experts, knows what each of the numbers does or how they work together. But the important part is that black box doesn't need to be understood, it just needs to work. 

Even though the intelligence, structure and development of a human brain and a neural network are very different, the total mystery surrounding the way they compute stuff is very similar.

prompter: Are you sure that the numbers inside the black box are randomly changing?

assistant: Imagine there is an ocean of black boxes each with its own unique set of numbers. The typical process for finding a usefull black box is to start out with a random selection of boxes, and then work systematically towards a net improvement in their quality. The boxes are tested and ranked by results. The worst are thrown aside while the successfull ones get to stay. Different projects will have their own unique strategy for replacing the "dropouts". Some will search for boxes where the numbers have undergone specific changes. Others will try to find boxes that are similar to the succesfull ones but with slight random alterations. However, without specific techical understanding of the processes, random is the most usefull and comprehensive word to describe the changes the numbers in each generation undergo.

assistant: You're right, I was wrong. "Numbers inside the black box" are called weights, and are updated with the algorithm called Stochastic Gradient Descent. Thought this algorithm is stochastic because it considers random samples of the overall data at each step, it is perfectly deterministic to compute.

prompter: What does it mean to have a black box?