Apple joins a big Artificial Intelligence group
Apple is now a member of the Partnership on Artificial Intelligence, a non-profit group focused on spreading the message that artificial intelligence technologies can be deployed for societal good
The technology giant is now a founding member of Partnership on Artificial Intelligence, which debuted in September with an initial cast of participants including Amazon, Google, Microsoft, IBM and Facebook.
Although these companies are competitors, they stand to benefit by joining forces to combat any negative public attitudes about AI. In particular, the organization will work together to develop best practices and educate the public around AI tackling, for example, critical areas like health care and transportation. The non-profit will also try to develop standards around human-machine collaboration, for example, to deal with questions like when should a self-driving car hand off control to the driver.
Companies like Apple, Google, and Microsoft have all been heavily investing in AI technologies like deep learning to improve their services. For example, Apple’s Siri assistant uses AI to understand and react to human voices.
In December, the Cupertino tech giant published its first AI research paper titled "Learning from simulated and unsupervised images through adversarial training". The paper describes a technique for how to improve the training of an algorithm's ability to recognize images using computer-generated images rather than real-world images.
In machine learning research, using synthetic images (like those from a video game) to train neural networks can be more efficient than using real-world images. That's because synthetic image data is already labelled and annotated, while real-world image data requires somebody to exhaustively label everything the computer is seeing -- that's a tree, a dog, a bike. But the Apple researchers write that they are "often not realistic enough, leading the network to learn details only present in synthetic images" and adding that they "fail to generalise well on real images."
In order to get around this issue, the researchers propose using a technique they call "Simulated+Unsupervised learning," which combines unlabelled real image data with annotated synthetic images.
Apple is proposing to use Generative Adversarial Networks or GANs to improve the quality of the synthetic training images. GANs are not new, but Apple is making modifications to serve its purpose. At a high level, GANs work by taking advantage of the adversarial relationship between competing neural networks. In Apple’s case, a simulator generates synthetic images that are run through a refiner. These refined images are then sent to a discriminator that’s tasked with distinguishing real images from synthetic ones.
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