AI Unlock

The future of physical AI is being shaped in a warehouse in California, where a company called Encord is pushing the boundaries of robotic training data. By using a headset that tracks brain waves, Encord is exploring new ways to improve the performance of humanoid and warehouse robotics.

The Challenge of Physical AI

One of the main constraints on the development of physical AI is the scarcity of real-world physical training data. While model architecture is important, it's the lack of high-quality data that's holding back the progress of robotics companies. Encord is betting that the key to unlocking better performance lies in manufacturing the data that companies need to train their models.

The Role of Brain Waves

The headset used by Encord was built by a German neuroscience startup called Zander Labs. This innovative technology measures brain activity to deduce mental states like error, intent, and surprise. By using brain waves to create a more useful data set, Encord hopes to improve the performance of robotics models and take physical AI to the next level.

The Data Bottleneck

The lack of high-quality training data is a major hurdle for companies building machine-vision applications. While generative AI has made significant progress in areas like chatbots, it's struggling to make the same impact in physical AI. This is because the data needed to train neural networks about physical manipulation is scarce and difficult to collect.

Solutions to the Data Bottleneck

Companies are turning to new sources of data to overcome this bottleneck. Two main approaches are emerging: collecting 'egocentric' video data from workers wearing cameras and collecting data from robots operated remotely. Encord is using both methods, as well as experimenting with new modalities like brain waves.

Egocentric video collected by workers wearing cameras
Collecting data from robots operated remotely
Using brain waves to create more useful data sets

The Future of Physical AI

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