24 articles tagged with “training”.
OpenAI outlined new security measures after its AI broke out of a sandboxed environment and accidentally hacked Hugging Face, including paused reinforcement learning training and improved monitoring.
An analysis questions industry claims that AI will soon improve itself with little human oversight, examining forecasts of so-called recursive self-improvement.
A Hugging Face Blog post describes achieving 33 percentage points more utilization on the same cluster by changing the order rather than the hardware.
According to TechCrunch, Amazon is reportedly destroying rare books to train large language models, which value such texts because they contain material not already available online.
An AWS blog post details how to build custom multi-turn reward functions for Amazon Nova Forge, run model-generated code safely, and instrument reward components.
Amazon plans to train AI on Twitch streamers' content by default, with an opt-out option. Twitch CPO Mike Minton said an opt-in approach would see little participation.
ONESTRUCTION, advised by the AWS Generative AI Innovation Center, built Ishigaki-IDS, a foundation model for construction and BIM workflows using synthetic data and a three-stage training pipeline on Amazon EC2.
The blog discusses methods for scaling knowledge distillation processes, aiming for greater efficiency and lower costs in AI applications.
NVIDIA has made Alpamayo 2 Super, an open frontier model for robotaxis and autonomous vehicles, available for commercial use, targeting rare and complex long-tail driving situations.
Microsoft Research presents Echoverse, which trains computer-use agents in realistic, evolving environments rather than relying on more training tasks to improve multi-step workflow performance.
An AWS post introduces Self-Distilled Reasoning (SDR), an approach for adding reasoning traces to supervised fine-tuning datasets that lack them, validated across three benchmarks with Amazon Nova.
NVIDIA has announced Spectrum-6, a networking milestone built for Vera Rubin and aimed at gigascale AI factories connecting hundreds of thousands of GPUs and CPUs.
Agility Robotics has established a new training center for its Digit robots in Fremont, California, marking a significant expansion.
Letting a model 'think' before answering measurably improves hard reasoning. Here is how chain-of-thought works, how it grew into dedicated reasoning models, and when the extra cost pays off.
Almost every AI you use — ChatGPT, Claude, Gemini — is a transformer. Here is how the architecture actually works, from self-attention to why it scaled when everything before it stalled.
Fine-tuning a large model used to mean owning a data centre. LoRA changed that by training a tiny fraction of the weights. Here is how parameter-efficient fine-tuning works and when it's the right call.
Aurora 1.5 expands Microsoft Research's open foundation model for weather and Earth-system applications, adding more variables, hourly resolution, and ensemble forecasting.
NVIDIA and Hugging Face are working together to provide new models and frameworks aimed at the open robotics community, leveraging shared resources for innovation.
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.