Today at the Frankfurt motor show, one of the biggest and most prestigious motor shows in the world, Sheryl Sandberg, COO of Facebook, spoke before German Chancellor Angela Merkel. Now what is Facebook and most importantly, Sheryl Sandberg doing at an automotive industry event? The obvious answer that comes to mind when one relates Facebook and the car industry is the billions of advertising dollars the industry spends on marketing and advertising. However, that does not seem to be Facebook's game plan, as highlighted by Sheryl and shown at their pavilion. Facebook seems to have a strategy of leveraging its capabilities in social marketing, AR & VR and interestingly, who would have thought of it, leveraging its advanced AI and deep learning capabilities to support the development of autonomous vehicles.
Although the social medium Twitter grants users freedom of speech, its instantaneous nature and retweeting features also amplify hate speech. Because Twitter has a sizeable black constituency, racist tweets against blacks are especially detrimental in the Twitter community, though this effect may not be obvious against a backdrop of half a billion tweets a day.1 We apply a supervised machine learning approach, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels “racist” and “nonracist.” The classifier has a 76% average accuracy on individual tweets, suggesting that with further improvements, our work can contribute data on the sources of anti-black hate speech.
In the race to continue building more sophisticated AI deep learning models, Facebook has a secret weapon: billions of images on Instagram. In research the company is presenting today at F8, Facebook details how it took what amounted to billions of public Instagram photos that had been annotated by users with hashtags and used that data to train their own image recognition models. They relied on hundreds of GPUs running around the clock to parse the data, but were ultimately left with deep learning models that beat industry benchmarks, the best of which achieved 85.4 percent accuracy on ImageNet. If you've ever put a few hashtags onto an Instagram photo, you'll know doing so isn't exactly a research-grade process. There is generally some sort of method to why users tag an image with a specific hashtag; the challenge for Facebook was sorting what was relevant across billions of images.
Artificial intelligence has begun seeping its way into every tech product and service. Now, companies are changing the underlying hardware to accommodate this shift. Apple is the latest company creating a dedicated AI processing chip to speed up the AI algorithms and save battery life on its devices, according to Bloomberg. The Bloomberg report said the chip is internally known as the Apple Neural Engine and will be used to assist devices for facial and speech recognition tasks. The latest iPhone 7 runs some of its AI tasks (mostly related to photographer) using the image signal processor and the graphics processing unit integrated on its A10 Fusion chip.
Have you noticed that Facebook has developed an uncanny ability to recognize your friends in your photographs? In the old days, Facebook used to make you to tag your friends in photos by clicking on them and typing in their name. This technology is called face recognition. Facebook's algorithms are able to recognize your friends' faces after they have been tagged only a few times. It's pretty amazing technology -- Facebook can recognize faces with 98% accuracy which is pretty much as good as humans can do!