Earlier this month, Apple made a splash when it told the artificial intelligence research community that the secretive company would start publishing AI papers of its own. Not even a month later, it's already starting to make good on that promise. Apple has published its very first AI paper on December 22. (The paper was submitted for publication on November 15.) 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.

Earlier this month, Apple made a splash when it told the artificial intelligence research community that the secretive company would start publishing AI papers of its own. Not even a month later, it's already starting to make good on that promise. Apple has published its very first AI paper on December 22. (The paper was submitted for publication on November 15.) 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.

Earlier this month, Apple announced that it would allow its artificial intelligence researchers to publish research papers -- a major shift in the notoriously secretive company's policy. Now, just a few weeks later, the first of these papers has been made public on the preprint server arXiv. The paper -- titled "Learning from Simulated and Unsupervised Images through Adversarial Training"-- deals with intelligent image recognition technology. Specifically, it describes a technique that would enable a program to recognize and decipher computer-generated images. So far, this has not been possible because, as the researchers from Apple note, "synthetic data is often not realistic enough," and increasing the realism is "computationally expensive."

Apple's first public research paper on AI was penned by vision expert Ashish Shrivastava and a team of engineers including Tomas Pfister, Oncel Tuzel, Wenda Wang, Russ Webb and Apple Director of Artificial Intelligence Research Josh Susskind, appleinsider.com Shrivastava holds a PhD in computer vision from the University of Maryland. Titled'Learning from Simulated and Unsupervised Images through Adversarial Training', the paper describes techniques of training computer vision algorithms to recognise objects using synthetic, or computer generated, images. However, learning from synthetic images may not achieve the desired performance owing to a gap between synthetic and real image distributions. To reduce this gap, Apple has proposed Simulated plus Unsupervised (S U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabelled real data while preserving the annotation information from the simulator.

Earlier this month, Apple made a splash when it told the artificial intelligence research community that the secretive company would start publishing AI papers of its own. Not even a month later, it's already starting to make good on that promise. Apple has published its very first AI paper on December 22. (The paper was submitted for publication on November 15.) 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.

Earlier this month, Apple made a splash when it told the artificial intelligence research community that the secretive company would start publishing AI papers of its own. Not even a month later, it's already starting to make good on that promise. Apple has published its very first AI paper on December 22. (The paper was submitted for publication on November 15.) 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.

The paper tackles the problem of teaching AI to recognize objects using simulated images, which are easier to use than photos (since you don't need a human to tag items) but poor for adapting to real-world situations. The trick, Apple says, is to use the increasingly popular technique of pitting neural networks against each other: one network trains itself to improve the realism of simulated images (in this case, using photo examples) until they're good enough to fool a rival "discriminator" network. Ideally, this pre-training would save massive amounts of time and account for hard-to-predict situations that don't always turn up in photos. This doesn't mean that Apple is suddenly an open book. It could take years before it's clear how transparent Apple has become with its scientific findings.

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 labeled 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 synthetic image approach can be problematic as what the algorithm learns doesn't always carry over neatly to real world scenes. The synthetic image data "is often not realistic enough, leading the network to learn details only present in synthetic images and fail to generalize well on real images," the paper from Apple says.

Earlier this month, Apple made a splash when it told the artificial intelligence research community that the secretive company would start publishing AI papers of its own. Not even a month later, it's already starting to make good on that promise. Apple has published its very first AI paper on December 22. (The paper was submitted for publication on November 15.) 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.

When Apple said it would publish its artificial intelligence research, it raised at least a couple of big questions. When would we see the first paper? And would the public data be important, or would the company keep potential trade secrets close to the vest? At last, we have answers. Apple researchers have published their first AI paper, and the findings could clearly be useful for computer vision technology.