Apple has made good on its promise to publish a research paper about artificial intelligence.
Apple researcher Ashish Shrivastava, along with colleagues, last week published a paper through the Cornell University Library describing a technique for improving artificial intelligence. The paper, which was submitted for review on Nov. 15, is called “Learning from Simulated and Unsupervised Images through Adversarial Training.”
The paper focuses on improving a computer’s ability to recognize images, a difficult task for machines in the past. It proposes using what it called “Simulated+Unsupervised (S+U)” learning to improve that process. It centers on using digital images that have data within them to help computers “see” images rather than real-world objects that need to be interpreted. The paper also suggests modifying certain artificial intelligence algorithms to improve the feature.
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While the topic might not matter to anyone outside the tech community, its publication is notable. Scientists around the world have long criticized Apple for not publishing research about artificial intelligence. Apple had previously banned its researchers from publishing their findings so it could keep secret anything that may be added to future products.
Apple’s ban was in line with its overall affinity for secrecy, but it flew in the face of the scientific community’s tradition of sharing knowledge. It also made some question if Apple was on the cutting edge of artificial intelligence, and hurt the company’s ability to attract top talent.
In response, Apple, which has been a major player in the artificial intelligence for years with its Siri virtual assistant, announced earlier this month that it would allow its scientists to publish research papers on artificial intelligence.
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Still, there are competitive concerns that Apple (AAPL) will need to balance against publishing artificial intelligence research. While the scientific community is pleased with its move, Apple is still competing against Google(GOOGL), Microsoft(MSFT), Amazon(AMZN), and others for artificial intelligence supremacy. Apple hasn’t said how it decides which research can be published and which advancements it keeps secret.
Apple’s competitors generally publish their own papers on a number of topics, although they, too, keep some advancements secret.
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. However, this is a big step -- if also a necessary one. AI is an increasingly competitive field, and Apple's past reluctance to contribute to scientific knowledge may have scared away potential hires who wanted their discoveries recognized. If papers like these become relatively commonplace, Apple might have an easier time attracting the talent it needs for self-driving car platforms, Siri and other AI-based projects.