Elon Musk wasn't wrong about automating the Model 3 assembly line -- he was just ahead of his time

The net effect of this was twofold: (1) solutions became much more robust (e.g. a face could still be identified as a face, even if it were oriented slightly differently, or in shadow), and (2) the creation of good solutions became reliant upon large amounts of high-quality training data (models learn features based on the training data, so it is critical that the training data is accurate, sufficient in quantity and represents the full diversity of situations the algorithm may later see).Next, new approaches like GANs (Generative Adversarial Networks), unsupervised learning and synthetic ground truth offer the potential to substantially reduce both the amount of training data required to develop high-quality computer vision models, as well as the time and effort required to collect the data. Now, unsupervised learning and the ability to create synthetic data (led by the likes of Nvidia) are starting to be used to train the system on corner cases that even billions of recorded driving miles cannot uncover.Agriculture: Companies like Blue River Technology (acquired by John Deere) are now reliably able to differentiate between weeds and crops, and selectively spray herbicide automatically, enabling a dramatic reduction in the quantity of toxic chemicals in use by commercial agriculture.Real Estate and Property Information: Using computer vision on top of geospatial imagery could allow companies to automatically identify when floods, wildfires or hurricane-force winds may pose a danger to specific properties — allowing homeowners to take action faster, before disaster strikes.When looking at these advances, one thing quickly becomes clear: Elon Musk wasn’t wrong.