"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
One of the curious features of the deep neural networks behind machine learning is that they are surprisingly different from the neural networks in biological systems. While there are similarities, some critical machine-learning mechanisms have no analogue in the natural world, where learning seems ...
Google just made it a lot easier to build your very own custom AI system. A new service, called Cloud AutoML, uses several machine-learning tricks to automatically build and train a deep-learning algorithm that can recognize things in images. The technology is limited for now, but it could be the start of something big. Building and optimizing a deep neural network algorithm normally requires a detailed understanding of the underlying math and code, as well as extensive practice tweaking the parameters of algorithms to get things just right. The difficulty of developing AI systems has created a race to recruit talent, and it means that only big companies with deep pockets can usually afford to build their own bespoke AI algorithms.
American authorities have decided that Alibaba's digital payment firm, Ant Financial, won't be allowed to acquire the cash transfer company Moneygram. Ant Financial, which was one of our 50 Smartest Companies in 2017, is a Chinese tech company that handles mountains of data generated by its mobile payment business and other banking services. It was created in 2014 by e-commerce giant Alibaba to operate Alipay, a dominant mobile payment platform in China with 520 million users, and uses tools like computer vision and natural-language processing to reimagine financial services (see "Meet the Chinese Finance Giant That's Secretly an AI Company.") In 2017, Ant Financial tried to expand its international footprint, by buying U.S. money transfer company MoneyGram in a $1.2 billion deal. But on Tuesday, the two companies said that the Committee on Foreign Investment in the United States rejected their proposals to merge.
Google on Monday released DeepVariant, an artificial intelligence tool that uses gene sequencing data to build a more accurate genomic model. Google on Monday released DeepVariant, an artificial intelligence (AI) tool that uses gene sequencing data to build a more accurate genomic model by automatically spotting small insertion and deletion mutations and single-base-pair mutations. The Google Brain team compiled millions of high-throughput reads and fully sequenced genomes from the Genome in a Bottle project, feeding the data to a deep-learning system and modifying the parameters of the model until it learned to interpret sequenced data with very high accuracy. "DeepVariant...demonstrates that in genomics, deep learning can be used to automatically train systems that perform better than complicated hand-engineered systems," says Deep Genomics CEO Brendan Frey. Frey predicts AI will ultimately transcend its ability to help sequence genomic data.
Over the last few years, the quest to build fully autonomous vehicles has shifted into high gear. Yet, despite huge advances in both the sensors and artificial intelligence (AI) required to operate these cars, one thing has so far proved elusive: developing algorithms that can accurately and consistently identify objects, movements, and road conditions. As Mathew Monfort, a postdoctoral associate and researcher at the Massachusetts Institute of Technology (MIT) puts it: "An autonomous vehicle must actually function in the real world. However, it's extremely difficult and expensive to drive actual cars around to collect all the data necessary to make the technology completely reliable and safe." All of this is leading researchers down a different path: the use of game simulations and machine learning to build better algorithms and smarter vehicles.
In September, Michal Kosinski published a study that he feared might end his career. The Economist broke the news first, giving it a self-consciously anodyne title: "Advances in A.I. Are Used to Spot Signs of Sexuality." But the headlines quickly grew more alarmed. By the next day, the Human Rights Campaign and Glaad, formerly known as the Gay and Lesbian Alliance Against Defamation, had labeled Kosinski's work "dangerous" and "junk science." In the next week, the tech-news site The Verge had run an article that, while carefully reported, was nonetheless topped with a scorching headline: "The Invention of A.I. 'Gaydar' Could Be the Start of Something Much Worse."
Yoshua Bengio is one of the foremost thinkers in a field within artificial intelligence known as artifical neural networks and deep learning. Although significant progress has been made in recent years due to (among other factors) the combination of the proliferation of data, the decreasing cost of compute, and the tremendous amount of money and talent now devoted to artificial intelligence, Bengio chose this as a field of study during the 1980s, in the throes of what some referred to as the AI winter, seeing through a period when money and enthusiasm for artificial intelligence had dried up. Bengio is the co-author (with Ian Goodfellow and Aaron Courville) of Deep Learning, a book that Elon Musk referred to as "the definitive textbook on deep learning." On top of his growing influence in this field, he has also been enormously influential in shaping Montreal to become a hotbed for artificial intelligence. Bengio co-founded Element AI in 2016, which has a stated mission to "turn the world's leading AI research into transformative business applications."
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.
Artificial intelligence software can beat the world's most widely used test of a machine's ability to act human, Google's reCAPTCHA, by copying how human vision works, a new study finds. These new findings suggest the need for more robust automated human-checking techniques, and could help improve computer perception for robotics tasks, scientists add. The founder of modern computing, Alan Turing, conceived of the Turing test, the most famous version of which asks if one could devise a machine capable of mimicking a human well enough in a conversation over text to be indistinguishable from human. In doing so, Turing helped give rise to the field of artificial intelligence. The most commonly used Turing test is the CAPTCHA, an acronym for "Completely Automated Public Turing test to tell Computers and Humans Apart."