The Misconception that Almost Stopped AI [How Models Learn Part 1]

Jeff Hinton, who won the Nobel Prize in 2024 for his AI work, initially dismissed training neural networks with gradient descent because he believed models would inevitably get stuck in local minima. This skepticism was shared by many early AI pioneers who abandoned this approach entirely.

Models like Llama and ChatGPT are trained exclusively to predict the next token (word or word fragment) that follows a sequence of text. This simple objective drives all their capabilities.

Large language models like Llama and ChatGPT use cross-entropy loss as their primary learning metric. Shannon’s information theory provides the mathematical foundation for why this approach outperforms simpler error measures.

The curse of dimensionality typically describes how problems become exponentially harder as dimensions increase. However, neural network optimization reveals a counterintuitive reversal: more parameters can actually help rather than hinder training.

Anyone attempting to optimize neural network parameters encounters this fundamental challenge. Even simple models demonstrate how parameter interactions prevent naive optimization approaches from succeeding.

All modern AI models, from GPT to Llama, learn using gradient descent. The algorithm operates like a lost hiker in a forest trying to reach the valley below without a map, relying only on local slope information.

Researchers exploring loss landscapes use random direction probing to visualize the 1.2-billion-dimensional parameter space of models like Llama. This technique reveals structures with hills, valleys, cliffs, and plateaus that cannot be directly visualized.

When researchers visualize gradient descent training in two-dimensional loss landscape projections, they observe a startling phenomenon: instead of watching parameters slowly descend a hill, a wormhole appears to open in the landscape, instantly transporting parameters to low-loss valleys.