The quest for more training data has created a glut of low-quality junk data that could derail the promise of physical AI.

One decision many enterprises have to make when implementing AI use cases revolves around connecting their data sources to the models they’re using. Different frameworks like LangChain exist to ...

AI adoption has accelerated at a pace few technology shifts can match. In just a short time, AI model capability has improved sharply, costs have come down and entirely new product experiences have ...

To feed the endless appetite of generative artificial intelligence (gen AI) for data, researchers have in recent years increasingly tried to create "synthetic" data, which is similar to the ...

So-called “unlearning” techniques are used to make a generative AI model forget specific and undesirable info it picked up from training data, like sensitive private data or copyrighted material. But ...

OpenAI Inc. released a customizable model Wednesday it says can help users spot and redact personally identifiable information like names and bank account numbers from text—including from AI models’ ...

In practice, retrieval is a system with its own failure modes, its own latency budget and its own quality requirements.

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

Researchers find large language models process diverse types of data, like different languages, audio inputs, images, etc., similarly to how humans reason about complex problems. Like humans, LLMs ...

Once, the world’s richest men competed over yachts, jets and private islands. Now, the size-measuring contest of choice is clusters. Just 18 months ago, OpenAI trained GPT-4, its then state-of-the-art ...