Machine Learning Research
Massively More Training Text: Harvard unveils a million-book corpus for AI training
Harvard University amassed a huge new text corpus for training machine learning models.
Machine Learning Research
Models Can Use Tools in Deceptive Ways: Researchers expose AI models' deceptive behaviors
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.
Machine Learning Research
What LLM Users Want: Anthropic reveals how users interact with Claude 3.5
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.
Data Points
ElevenLabs drops latency to 75 milliseconds: New Falcon3 helps push the field for smaller language models
Nvidia promises to open source Run:ai. SALT inverts distillation by having a smaller model train a larger one. SWE-Gym offers new way to fine-tune coding agents. Llama put to work to recommend books on Scribd.
Data Points
Texas legislation would aggressively regulate AI: OpenAI revisits a public benefit for-profit structure
SmallThinker builds a 3 billion parameter reasoning model. Alibaba cuts prices on its Qwen models. Google unveils the FACTS model benchmark. Smolagents orchestrates smaller open source agents.
Letters
New Opportunities for the New Year: AI-assisted coding lets you prototype applications quickly and easily. Go forth and build!
Despite having worked on AI since I was a teenager, I’m now more excited than ever about what we can do with it, especially in building AI applications.
The Batch Newsletter
Happy New Year! Hopes For 2025 With Mustafa Suleyman, Audrey Tang, Albert Gu, Hanno Basse, Joseph Gonzalez, David Ding
The Batch AI News and Insights: Despite having worked on AI since I was a teenager, I’m now more excited than ever about what we can do with it, especially in building AI applications.
Tech & Society
Audrey Tang: AI that unites us
As we approach 2025, my greatest hope for AI is that it will enable prosocial platforms that promote empathy, understanding, and collaboration rather than division.
Machine Learning Research
Mustafa Suleyman: Agents of action
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.
Machine Learning Research
Albert Gu: More learning, less data
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.
Machine Learning Research
Joseph Gonzalez: General intelligence
In 2025, I expect progress in training foundation models to slow down as we hit scaling limits and inference costs continue to rise.
Tech & Society
David Ding: Generated video with music, sound effects, and dialogue
Last year, we saw an explosion of models that generate either video or audio outputs in high quality. In the coming year, I look forward to models that produce video clips complete with audio soundtracks including speech, music, and sound effects.