DeepSeek’s success learning from bigger AI models raises questions about the billions being spent on the most advanced technology.

The Medium post goes over various flavors of distillation, including response-based distillation, feature-based distillation and relation-based distillation. It also covers two fundamentally different modes of distillation – off-line and online distillation.

Since Chinese artificial intelligence (AI) start-up DeepSeek rattled Silicon Valley and Wall Street with its cost-effective models, the company has been accused of data theft through a practice that is common across the industry.

David Sacks says OpenAI has evidence that Chinese company DeepSeek used a technique called "distillation" to build a rival model.

OpenAI accuses Chinese AI firm DeepSeek of stealing its content through "knowledge distillation," sparking concerns over security, ethics, and national interests.

Whether it's ChatGPT since the past couple of years or DeepSeek more recently, the field of artificial intelligence (AI) has seen rapid advancements, with models becoming increasingly large and complex.

One possible answer being floated in tech circles is distillation, an AI training method that uses bigger "teacher" models to train smaller but faster-operating "student" models.

AI-driven knowledge distillation is gaining attention. LLMs are teaching SLMs. Expect this trend to increase. Here's the insider scoop.

Microsoft and OpenAI are investigating whether DeepSeek, a Chinese artificial intelligence startup, illegally copying proprietary American technology, sources told Bloomberg

Top White House advisers this week expressed alarm that China's DeepSeek may have benefited from a method that allegedly piggybacks off the advances of U.S. rivals called "distillation."

After DeepSeek AI shocked the world and tanked the market, OpenAI says it has evidence that ChatGPT distillation was used to train the model.

OpenAI believes DeepSeek used a process called “distillation,” which helps make smaller AI models perform better by learning from larger ones.