Today’s neural networks mimic memory through optimization. They compress vast datasets into billions of parameters, encoding patterns indirectly through iterative adjustments during training. This process is powerful but also time-consuming, requiring substantial computational resources to fine-tune these parameters for each new task. But what if there were a way to store memories explicitly, bypassing this […]

The pursuit of artificial intelligence systems capable of processing sequences with effectively infinite context has been a longstanding goal in the field. Early models, such as recurrent neural networks (RNNs), were theoretically designed to handle sequences of arbitrary length. However, in practice, their ability to utilize information across more than approximately 50 timesteps was limited […]

Londeree Technologies Unlocking In-Context Learning at Scale Artificial intelligence has taken remarkable strides, with models like generative pre-trained transformers (GPTs) demonstrating emergent behaviors that were once unimaginable. Among these is in-context learning—the ability to absorb new skills or facts within the scope of an interaction, simply by being shown directions or examples in a prompt. […]