Machine Learning Research

Contrastive loss functions make it possible to produce good embeddings without labeled data. A twist on this idea makes even more useful embeddings.

Nvidia’s new desktop computer is built specifically to run large AI models.

A new model from Hangzhou upstart DeepSeek delivers outstanding performance and may change the equation for training costs.

Merging multiple fine-tuned models is a less expensive alternative to hosting multiple specialized models. But, while model merging can deliver higher average performance across several tasks, it often results in lower performance on specific tasks. New work addresses this issue.

Harvard University amassed a huge new text corpus for training machine learning models.

Large language models have been shown to be capable of lying when users unintentionally give them an incentive to do so. Further research shows that LLMs with access to tools can be incentivized to use them in deceptive ways.

Anthropic analyzed 1 million anonymized conversations between users and Claude 3.5 Sonnet. The study found that most people used the model for software development and also revealed malfunctions and jailbreaks.

In 2025, AI will have learned to see, it will be way smarter and more accurate, and it will start to do things on your behalf.

Building a foundation model takes tremendous amounts of data. In the coming year, I hope we’ll enable models to learn more from less data.

In 2025, I expect progress in training foundation models to slow down as we hit scaling limits and inference costs continue to rise.

For years, the best AI models got bigger and bigger. But in 2024, some popular large language models were small enough to run on a smartphone.

Video generation exploded in an abundance of powerful models.

Fierce competition among model makers and cloud providers drove down the price of access to state-of-the-art models.

The AI community laid the foundation for systems that can act by prompting large language models iteratively, leading to much higher performance across a range of applications.

How do agents based on large language models compare to human experts when it comes to proposing machine learning research? Pretty well, according to one study.