Collecting audio data in the real world.
Live music, real-world RL, good vibes across the city, and better tools made for musicians 🎵🌉
micro1
@micro1_ai
data lab to train frontier models & evaluate agents
- Join us!Excited to be hosting a discussion on world models with Derek Sarshad from @odysseyml and Sam Sinha from @1x_tech. We'll get into why world models are emerging as an important complement to language models, how they're shaping robotics, embodied AI, gaming, and simulation.
- Mission AI has a data conversion problem. Defense programs already collect massive volumes of aerial EO/IR video. The bottleneck is turning that footage into trustworthy training data with the quality, consistency, and provenance required for deployment. Our latest article
- As robotics foundation models improve, the highest-value training data will become increasingly specialized to specific tasks, environments, and edge cases. Our latest paper breaks down how we’re building data pipelines based on this.The more we think about robotics, the more it seems the field is moving toward increasingly specialized datasets. Better foundation models increase the value of task/environment-specific datasets. As base models become more capable, annotation becomes more about data
- For physical AI to become truly useful, it needs to learn from people who have spent years mastering their trade.Expert-level reasoning layered with real world data directly translates to increased capability in embodied models. Real expert reasoning, synced with the work that is happening is likely most accurate route for models to understand constraints, failures, and logic. We need
