Sharon Zhou et al. at Stanford have proposed HYPE, a new metric for evaluating GANs.
Generative Adversarial Networks have made some incredible progress in generating realistic-looking images in the last few years (see for example StyleGAN in DT #4
). One problem, however, is that “there is currently no standardized, validated evaluation” to objectively compare the quality of images generated by different models. Zhou proposes Human eYe Perceptual Eveluation, or HYPE (an amazing name for a metric in this space), which is a measure of how quickly human evaluators can tell that a generated image is fake (HYPE-Time), and whether they can tell if it’s fake at all (HYPE-Infinity). More:
Wayve demonstrated their self-driving car in an urban environment.
A few months ago the company showed off an autonomous vehicle that used end-to-end deep learning to translate video inputs directly to driving commands (see DT #6
), driving on a simple rural road. They’ve refined their models and added satellite navigation as additional input, and they’re now able to drive on public urban roads in Cambridge, UK. Their car initially learns policies by imitating human drivers, and then uses reinforcement learning to improve these policies by using safety driver interventions as rewards. With only 20 hours of non-simulated training, their results are very impressive. More here:
Li Yuan at the New York Times did a feature on Chinese data labeling companies. A lot of modern machine learning is done in a supervised setting, requiring labeling enormous datasets. In China, where labor is relatively cheap compared to Europe and the United States, “data labeling factories” like Ruijin Technology Company do exactly that:
“We’re the construction workers in the digital world. Our job is to lay one brick after another,” said Yi Yake, co-founder of a data labeling factory in Jiaxian, a city in central Henan province. “But we play an important role in A.I. Without us, they can’t build the skyscrapers.”
The companies are popping up in areas outside of large cities where labor and office spaces are cheap, enabling them to pay relatively high wages for the area while still providing labeling services at extremely low prices. Yuan interviewed some workers at the factories: some find the work too boring, while others prefer it over “the same movement, day after day” that they’d otherwise do at an assembly line. Read the full piece here: How Cheap Labor Drives China’s A.I. Ambitions
Google has disbanded its AI ethics board less than two weeks after launch.
Just a few weeks ago, Google announced that it had formed ATEAC, a council in charge of enforcing the company’s AI ethics guidelines (see DT #10
). One member of the board, Kay Cole James, is involved with a foundation known for its anti-LGBT views; another, Dyan Gibbens, is CEO of a company that built drones for the US military. Thousands of Googlers signed a petition against their positions on the board, and two other board members also voiced their protest (one even publicly resigned
). In response, Google decided to disband ATEAC and to go “back to the drawing board.” James wrote a Washington Post op-ed about her disappointment at the decision
, which I think is worth reading, but take it with a grain of salt: people like Kara Swisher think that ATEAC was always always “a rubber stamp PR move”
and that James was never qualified
to serve on it. More by Jilian D'Onfro for Forbes: Google Scraps Its AI Ethics Board Less Than Two Weeks After Launch In The Wake Of Employee Protest
Quick ML resource links ⚡️
- tf-encrypted is an experimental layer on top of TensorFlow to do machine learning on encrypted data. Links: code (GitHub); paper (arXiv)
- Dragonfly is a library for Bayesian optimization, or as they call it, “tuning hyperparameters without grad students.” Links: code (GitHub); paper (arXiv)
- Dent Reality is a company that does indoor augmented reality positioning and wayfinding without the need for additional hardware; they are now ready to deploy their tech. Links: Dent Reality; demo (YouTube)