If conventional psychology isn't up to the task, perhaps we should step back and consider a tantalizing sci-fi alternative -- that Trump doesn't operate within conventional human cognitive constraints, but rather is a new life form, a rudimentary artificial intelligence-based learning machine. When we strip away all moral, ethical and ideological considerations from his decisions and see them strictly in the light of machine learning, his behavior makes perfect sense. Consider how deep learning occurs in neural networks such as Google's Deep Mind or IBM's Deep Blue and Watson. The goal of DNA is self-reproduction; the sole intent of Deep Mind or Watson is to win.
In its public debut last year at a tournament in Seoul, AlphaGo thrashed Lee Sedol, the best player of last decade. Tomorrow morning, AlphaGo is set to play 19-year-old Ke Jie in Wuzhen, a town crisscrossed by canals 80 miles west of Shanghai. Though he is now ranked higher than Lee Sedol, Ke Jie doesn't stand much of a chance against AlphaGo. In January, via the internet, DeepMind secretly matched AlphaGo against several of the world's top players, including Ke Jie.
The pair joined forces to deliver an in-depth webinar on Machine Learning and business intelligence, which you can view in full here. Or, put another way: when does it make sense to invest in Machine Learning projects for my business? One of the most exciting applications, says Boaz, is Natural Language Processing (NLP). For example, Sisense Everywhere uses bots and NLP to deliver data insights outside of the usual dashboard environment.
The company said it has added picture-in-picture video, so that that users can do things like watch video while looking at the calendar. Android gets Notification Dots, which places a small dot on an app icon if there is a notification coming from the app. Machine learning is helping Android recognize and select bits of text like phone numbers and addresses in emails. Google's machine learning library for developers is now available a mobile version called Tensor Flow Lite.
The data that tracks our behavior feeds into machine-learning algorithms that make judgments about us. Latanya Sweeney, the director of the Data Privacy Lab at Harvard University, found that Google searches for black-sounding names more often resulted in ads for arrest records compared to searches for white-sounding names, likely a result of the algorithm learning to predict what users are likely to click on. And when algorithms learn what we like and feed us more of it, they amplify the notorious filter bubble and deepen political polarization. This means that even if you clear your cookies or log out of a website, your device fingerprint can still give away who you are.
Big Data and machine learning would seem to be a perfect match, coming together at just the right time. But having vast amounts of data and computing power isn't enough. For machine learning tools to work, they need to be fed high-quality data, and they must also be guided by highly skilled humans. What is clear is that the business of combining Big Data and big computing power for new insight is harder than it looks.
Five years ago, researchers made a sudden leap in the accuracy of software that can interpret images. The technology behind it, artificial neural networks, underpins the recent boom in artificial intelligence (see "10 Breakthrough Technologies 2013: Deep Learning"). Yann LeCun, director of Facebook's AI research group and a professor at New York University, helped pioneer the use of neural networks for machine vision. That's what would allow them to acquire common sense, in the end.
Together with the company's deep learning technology, it can automatically craft your photos and videos into short films. You just have to drop a 3D effect onto the soccer ball and letting Story Remix do the mapping work. While there will surely be users who clamor for even more manual control in Story Remix, Pratley isn't too worried about that. Of course, there are reasons to be skeptical of this push towards deep learning-enhanced productivity apps.
Nvidia has benefitted from a rapid explosion of investment in machine learning from tech companies. Can this rapid growth in the use cases for machine learning continue? Recent research results from applying machine learning to diagnosis are impressive (see "An AI Ophthalmologist Shows How Machine Learning May Transform Medicine"). Your chips are already driving some cars: all Tesla vehicles now use Nvidia's Drive PX 2 computer to power the Autopilot feature that automates highway driving.