From: nikhil.kamath
Neural Networks are a specific approach within the broader field of Machine Learning (ML) [03:38]. The term “Machine Learning” refers to the process of training a computer program to make intelligent predictions on new, unseen data based on recorded inputs [03:44].
What is a Neural Network?
A neural network is essentially a network of artificial neurons connected layer by layer [03:52]. While inspired by the biological neural network of the human brain, it is not designed to work in exactly the same way [03:11].
Each artificial neuron functions as a computational unit, taking an input number and producing an output number [03:01]. Conceptually, a neural network can be thought of as a massive circuit that processes numerical inputs and generates new numerical outputs [03:30]. The outputs are based on patterns the network recognizes within the input data [03:42].
How They Work
To visualize a neural network:
- Input Layer: You feed numbers into an input layer [03:20].
- Transformation: The first layer takes these numbers and transforms them by applying a mathematical function [03:26]. This often involves multiplying the inputs by matrices of random numbers, which are then modified by a non-linear function to introduce higher-order dependencies [03:50].
- Layers: This process is repeated across several layers (e.g., four or five different layers) [04:16].
- Output and Optimization: The network produces outputs. During training, these outputs are compared to a “target output” from a large dataset, potentially millions of examples [04:28]. The difference, known as the “loss,” is calculated, and the network’s internal parameters (the matrices at each layer) are updated through a process called backpropagation to minimize this loss [04:30].
- A neural network implicitly captures useful patterns required to reliably predict an output [04:11]. For example, to predict the next word, it learns grammar, sentence construction, and common sense [04:45].
A model can only learn actual patterns that exist in the data; anything else is considered irreducible noise that cannot be captured by any loss function [04:48].
Rise of Neural Networks in AI
The significant change in AI from 2010 to the 2020s has been the realization that neural networks “actually work” [03:55]. Key figures like Yann LeCun, Geoffrey Hinton, and Yoshua Bengio laid the foundations, but Ilia Sutskever is credited with making them truly work by throwing vast amounts of data and compute at the problem [03:10]. This approach, while seemingly simple, was driven by “blind faith” [03:40].
Neural networks are particularly effective when leveraging scale; their prediction accuracy improves with more data or more compute power [04:58]. This contrasts with other machine learning algorithms like support vector machines, linear regression, or logistic regression, which might perform well with smaller datasets (e.g., 100-200 examples) [04:46].
Neural Networks and Large Language Models
Large Language Models (LLMs), such as ChatGPT, are essentially giant neural networks [04:29]. They are trained on a single, massive task: predicting the next word based on the previous word [04:38]. This training involves terabytes of text and trillions of tokens, encompassing books, code, textbooks, web pages, and news articles [04:41].
The process involves:
- Pre-training: The model (often using a Transformer architecture) is trained on an enormous dataset, like the entire internet, to predict the next word [04:38]. This makes it good at predicting but not yet practically useful [04:41].
- Post-training (Fine-tuning): The model is then fine-tuned to become a functional chatbot by training it to produce good responses to human inputs [04:47]. This phase involves collecting specific data for tasks like software programming, email compression, document summarization, or general conversational outputs [04:51].
The ability of a single neural network to perform tasks traditionally requiring many different programs (e.g., writing code, poems, essays, summarizing documents) is what signifies its “generality” and makes it remarkable [02:37]. This shift from narrow to more general intelligence is why the current era of AI is generating significant excitement and has economic implications [02:55].