Inside the Battle for Chips That Will Power Artificial Intelligence

Neural Networks

Neural networks form the backbone of AI advancements, functioning through interconnected neurons that mimic the human brain. Stacy Rasgon explains that these networks consist of layers of neurons, each with inputs and outputs weighted differently, allowing them to process complex data 1. The neurons fire based on a threshold, known as the bias, which can be adjusted to make the network more or less sensitive to inputs 2.

Each neuron can have more than one input coming in from more than one neuron in the previous. These are called layers, by the way.

--- Stacy Rasgon

This intricate structure enables neural networks to perform tasks like image recognition and language processing, forming the foundation for AI models like ChatGPT.

   

Model Training

Training large language models like ChatGPT involves immense computational power and cost. Stacy Rasgon highlights that training requires processing vast amounts of data through a method called backpropagation, which adjusts the network's parameters to improve accuracy 3. This process is computationally intensive, with ChatGPT's training estimated to involve 300 sextillion operations and costing around $80 million 4.

The network itself is accurate to wherever you want it to be, to be accurate at.

--- Stacy Rasgon

Despite the high costs, the potential for scaling inference operations presents a significant opportunity for companies investing in AI technology.

   

Machine Learning

Machine learning, a key component of AI, relies on neural networks to process data in parallel, unlike traditional sequential computing. Stacy Rasgon uses the analogy of a black box with knobs to explain how models are trained to perform specific tasks, such as recognizing images 5. The training process involves adjusting numerous parameters to optimize the model's accuracy, which is computationally demanding.

It's a bunch of different matrices, a bunch of different arrays of numbers that contain all of the different parameters and things.

--- Stacy Rasgon

Nvidia's GPUs, with their efficient architecture and robust software ecosystem, have become essential in handling these complex computations, giving them a competitive edge in the AI market 6.