Decoding AI: Past, Present, and Future
There’s a moment in human problem-solving that’s easy to overlook: the pause before the answer. Not silence, exactly — something is happening in there, some rearrangement of partial ideas that never surfaces as words. For decades, language models had no equivalent. They generated token after token in a single forward pass, their “thinking” invisible even…
Give a frontier reasoning model a hard mathematical olympiad problem and something unusual happens: it argues with itself. It proposes an approach, notices a flaw three steps in, backtracks, tries a different decomposition, and eventually converges on a proof that checks out. The final answer looks clean. The path to it looked almost human. This…
When GPT-4 was released, one of the first things researchers did was run it on MMLU — the Massive Multitask Language Understanding benchmark, a sprawling set of multiple-choice questions covering medicine, law, history, and dozens of other domains. The model scored impressively. Within months, that score had become almost meaningless. Not because the model got…
Somewhere in the lifecycle of every major AI benchmark, there is a quiet inflection point where the test stops measuring capability and starts measuring exposure to itself. We may be past that point for most of the benchmarks the industry currently cites with confidence. The pattern is familiar enough to have a name — benchmark…
In the race to build ever-larger AI models, a quiet revolution is underway—not in scale, but in efficiency. Enter the era of sparse expert models, where systems like Mixture of Experts (MoE) and Switch Transformers are redefining what it means to train and deploy AI. Unlike traditional dense models, which activate all their parameters for…