Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford ...

Choosing flexible hardware for DNN architectures for apps like ATM camera systems. How "embeddings" can be effective in the image-recognition reidentification process. How Arcturus Networks developed ...

Google recently published research on a technique to train a model to be able to solve natural language processing problems in a way that can be applied to multiple tasks. Rather than train a model to ...

Making high-performance proteins for medicines or consumer products can take trial after trial of tweaks, experiments and fine-tuning. A new machine learning framework squeezes all that into a single ...

Back in the ancient days of machine learning, before you could use large language models (LLMs) as foundations for tuned models, you essentially had to train every possible machine learning model on ...

Microsoft CEO Nadella argues learning loops beat picking the best AI model. Here's what a learning loop is, why it builds a moat, and where OpenAI and others disagree.

From movie suggestions to self-driving vehicles, machine learning has revolutionised modern life. Experts are now using it to help solve one of humanity’s biggest problems: climate change. With ...

A Microsoft and Amazon joint effort makes neural networks easier to program and use with the MXNet and Microsoft Cognitive Toolkit frameworks Deep learning systems have long been tough to work with, ...

When people hear “artificial intelligence,” many envision “big data.” There’s a reason for that: some of the most prominent AI breakthroughs in the past decade have relied on enormous data sets. Image ...