The quest for answers to anything imaginable has fueled technological progress for centuries. As the complexity of underlying physical systems grew, so did the need for intelligent automation to scale and advance.
We are building a generalist control agent for diverse experimental tasks — scalable AI systems capable of learning meaningful representations from raw sensory data and interacting with real-world scenarios, to test the acquired knowledge — harnessing machine intelligence to accelerate discovery.
Expanding the horizons of scientific understanding, while building the foundation for self-driving experimental labs of the future.
Imagine, plan, and adapt
Learn from raw sensory data
Quantized reinforcement learning
Pre-trainable feature learning
Optimal out-of-the-box performance
Real-time control across domains
Autonomous experimental control
Generalising across hardware
Discovery automation