Zero-shot Forecast
Assembly Line
Can AI Learn Physics from Sensor Data?
The challenge of understanding the physical world, whether for humans or AI, is that we cannot experience or learn about it directly — we can only observe it indirectly through sensors. These sensors might be our natural biological ones, like our eyes and ears, or the countless artificial sensors humans have invented to measure everything from acceleration to gas concentration. However, sensors always affect the outcome, distorting or obscuring the “true” physical behavior, making it harder to uncover the underlying laws that govern the physical world.
In both cases, the Newton model received real-time data from sensors and was able to accurately predict the behavior of the physical systems simply by observing the concurrent sensor data. What’s remarkable is that Newton had not been specifically trained to understand these experiments – it was encountering them for the first time and was still able to predict outcomes even for chaotic and complex behaviors. This ability of an AI model to accurately predict data it has not encountered previously is often referred to as zero-shot forecasting.
While these classic experiments were exciting, real world systems are far more complex and harder to describe. We then tested Newton on predicting the behavior of such systems as city electrical demand, daily temperature, and temperature in electrical transformers, to name a few. Figure 2 demonstrates that Newton was able to accurately zero-shot forecast the behavior of these complex systems with no additional training data for systems that are challenging even for humans to model.