They solved AI’s memory problem!

The Kimi team's recent breakthrough fixes the amnesia problem of current AI models.

The Kimi team's breakthrough created a new AI architecture design that can reconfigure itself and learn on the fly.

Human working memory has strict physical limits, leading to overwhelm if thinking too long or deep.

For years, AI models could not be built very deep because they would be hard to train.

If AI models are built too deeply, the learning signal flowing backwards through the model would vanish before reaching the beginning, a problem called the vanishing gradient problem.

Residual connections allowed AI models to scale from only a few dozen layers to hundreds or even thousands of layers deep.

In models with residual connections, the final result is a massive cumulative pile of data, where the importance of any single layer's contribution shrinks, burying early information.

Layers in deep AI models with residual connections have to produce stronger and stronger signals just to have any noticeable influence on the final result.

Transformers fixed the amnesia issue by introducing an attention mechanism, allowing the model to look back at any previous word directly and selectively get exactly the information it needed.

The Kimi researchers realized that the amnesia problem in deep AI models is analogous to the amnesia problem in older recurrent neural networks, but in the depth dimension instead of the text dimension.

The Kimi team designed attention residuals where every layer can reach back and look at any previous layer's output, preventing the AI from getting confused or forgetting its earlier thoughts.

The attention residuals design allows the AI to stay perfectly focused on the most important details by selectively choosing which layers' information to use.

Applying attention residuals to top AI models with hundreds of billions or even over a trillion parameters runs into physics limitations due to infrastructure.

Models with attention residuals kept improving with increased depth, demonstrating that depth is an advantage, not a limitation.