Other
Data for the Real World
- Current AI foundation models have achieved superhuman performance in code, language, and image processing but remain constrained by sparse physical world data derived from human-centric remote sensors.
- Declining sensor costs combined with improved foundation models are making dense physical world data collection feasible and are currently driving new market entrants.
- Gecko Robotics is utilizing robots to gather data in inaccessible environments to build predictive models.
- Sorcer (referred to as Sorcerer in the call to action) employs autonomous weather balloons to collect atmospheric data for the US government to improve weather forecast accuracy.
- Significant market opportunities exist within the energy, agriculture, logistics, and construction industries, which currently rely on limited data and intuition-based modeling.
- The strategic advantage of dense real-world data is the ability to transition from modeling systems to controlling them.
- Speculative forward-looking applications of this control capability include steering hurricanes, reversing certification processes, and cooling the planet.
- The organization is actively seeking collaboration with other companies developing physical world data collection methods for the identified industrial sectors.