6 articles tagged with “training”.
Modern AI runs on a scarce resource: specialized compute. This feature unpacks why GPUs became the bottleneck, why everyone is building custom chips, and what it means for the balance of power in AI.
Fine-tuning takes a general-purpose AI model and specializes it for your task, tone, or format by training it further on your examples. Here is when it helps — and when prompting or RAG is the better tool.
Before GPT dominated headlines, BERT showed that pretraining a Transformer to read text in both directions could transform language understanding. Here is what the landmark 2018 paper introduced.
The 2020 GPT-3 paper made a startling claim: make a language model big enough and it learns new tasks from a few examples in the prompt, with no retraining. Here is what it showed.
Every modern language model — GPT, Claude, Gemini, Llama — descends from one 2017 paper. Here is what 'Attention Is All You Need' actually proposed, in plain English, and why it changed everything.
Fine-tuning a giant model used to require giant resources. LoRA showed you can adapt one by training a tiny number of extra parameters — and it became the default method for customizing open models.