Over the past few years, LLMs have developed rapidly, significantly expanding our cognitive capabilities. Many people probably now turn to AI first when they encounter a problem. It may be that LLMs already know more than my brain does, and perhaps possess higher reasoning abilities than my brain. However, in this article, I will discuss […]
Why Your Brain Matters in the Era of AI: You Can’t Outsource Intuition Read Post »
In this article, I will argue that transformers are RNNs. I will not merely point out formal containment; I will also explain how similar transformers and RNNs are, and what practical implications follow from that viewpoint. Transformers are RNNs First, I will show that transformers are RNNs. To put it simply, a transformer is an
Transformers are RNNs: A Kernel Perspective Read Post »
Sorting is a classic task in computer science, but it has recently intersected with state-of-the-art LLMs and sparked a new research trend. Sorting can be executed as long as you define a comparison function. Traditional comparison functions assumed measurable numeric quantities such as height, price, or distance. However, if we call an LLM inside the
“Generating…”We are all familiar with the cursor blinking, slowly revealing text word by word. Whether it’s ChatGPT or a local LLM, the generation speed of autoregressive models based on Transformers is fundamentally bound by their sequential nature. It computes, emits one token, computes again, emits the next token, and repeats. This strict dependence chain introduces
Speculative Decoding Explained Read Post »
