Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, ...

Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio ...

Thinking Machines Lab Inc., the artificial intelligence startup led by former OpenAI executive Mira Murati, today introduced its first commercial offering. Tinker is a cloud-based service that ...

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 ...

Why AI agents stall in production: fine-tuning forgets, RAG leaks context. Hypernetworks generate a task-specific model from ...

Fine-tuning an AI model is like teaching a student who already knows a lot to become an expert in a specific subject. Instead of starting from scratch, we take a model that has learned from a vast ...

Microsoft has announced significant enhancements to model fine-tuning within Azure AI Foundry, including upcoming support for Reinforcement Fine-Tuning (RFT). Microsoft Azure AI Foundry already ...

Using calculated infrared spectroscopy as input, the proposed machine learning framework, consisting of multiple blocks and a fully connected layer could accurately predict target structural and ...

The hype and awe around generative AI have waned to some extent. “Generalist” large language models (LLMs) like GPT-4, Gemini (formerly Bard), and Llama whip up smart-sounding sentences, but their ...

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