Wardrobes could sharpen up for online shoppers everywhere, while retailers cut costs, thanks to AI that puts clothes on fashion models in a virtual photo shoot. A fully staffed, photo fashion shoot can run up to $500 per outfit. Given the thousands of potential looks, many online retailers display their garments without models.
Source: nvidia.com
There are a lot of neat things going on in deep reinforcement learning. One of the coolest things from last year was OpenAI and DeepMind’s work on training an agent using feedback from a human rather than a classical reward signal. There’s a great blog post about it at Learning from Human Preferences, and the original paper is at Deep Reinforcement Learning from Human Preferences.
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Neural networks, which underlie many of Uber’s machine learning systems, have proven highly successful in solving complex problems, including image recognition, language understanding, and game-playing. However, these networks are usually trained to a stopping point through gradient descent, which incrementally adjusts the connections of the network based on its performance over many trials. Once the training is complete, the network is fixed and the connections can no longer change; as a result, barring any later re-training (again requiring many examples), the network in effect stops learning at the moment training ends.
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The potential harm of A.I.s deliberately designed to kill in warfare is much more pressing. The U.S. and other countries are working hard to develop military A.I., in the form of automated weapons, that enhance battlefield capabilities while exposing fewer soldiers to injury or death. For the U.S., this would be a natural extension of the existing imperfect drone warfare program—failures in military intelligence have led to the mistaken killing of non-combatants in Iraq.
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