AI that works for everyone

AI that works for everyone

Our work emphasizes human-AI collaboration. Rather than focusing exclusively on fully autonomous artificial intelligence systems, we are committed to developing multimodal systems that operate collaboratively with people.

We are building more flexible, adaptable, and personalized artificial intelligence systems. We recognize substantial potential for artificial intelligence to contribute across every field of work. While contemporary systems demonstrate excellence in programming and mathematics, we are developing AI systems capable of adapting to the full spectrum of human expertise and enabling a broader range of applications.

Our approach

We maintain that the most effective artificial intelligence systems are those that augment human capabilities rather than replace them. Our focus centers on creating tools that comprehend context, adapt to individual requirements, and operate seamlessly alongside people in their professional and personal endeavors.

Learning by doing

Research and product co-design

Products facilitate iterative learning through deployment, while exceptional products and research mutually reinforce one another. Products maintain our connection to practical reality and direct us toward solving the most impactful problems.

Measure what truly matters

We will focus on understanding how our systems generate genuine value in real-world contexts. The most significant breakthroughs frequently emerge from rethinking our objectives, rather than merely optimizing existing metrics.

Empirical and iterative approach to AI safety

The most effective safety measures emerge from a combination of proactive research and meticulous real-world testing. We intend to contribute to artificial intelligence safety through the following approaches:

• Maintaining a high safety standard – preventing misuse of our released models while maximizing user freedom
• Sharing best practices – methodologies for building safe artificial intelligence systems with the industry
• Accelerating external research – on alignment through sharing code, datasets, and model specifications

We maintain that methods developed for contemporary systems, including effective red-teaming and post-deployment monitoring, provide valuable insights that will extend to future, more capable systems.

Our philosophy

By integrating rigorous research with real-world deployment, we establish a feedback mechanism that continuously enhances both our comprehension of artificial intelligence capabilities and our capacity to develop safer, more effective systems.