Microsoft's latest breakthrough could make DNA-based data centers possible

In this issue of Communications, as evidenced by the cover and lead article, we celebrate the latest recipients of the ACM A.M. Turing Award. Yoshua Bengio, Yann LeCun, and Geoffrey Hinton carried out pioneering work in deep learning that has touched all our lives. As Turing Laureates, they now join the eminent group of technology visionaries recognized with the world's highest distinction in computing. The Turing Award is one of a suite of professional honors ACM bestows annually to recognize technical achievements that have made significant contributions to our field. This month, I will have the pleasure of joining the awardees, ACM Fellows, and other luminaries in San Francisco for the ACM Awards Banquet.

Human decision-making underlies all economic behavior. For the past four decades, human decision-making under uncertainty has continued to be explained by theoretical models based on prospect theory, a framework that was awarded the Nobel Prize in Economic Sciences. However, theoretical models of this kind have developed slowly, and robust, high-precision predictive models of human decisions remain a challenge. While machine learning is a natural candidate for solving these problems, it is currently unclear to what extent it can improve predictions obtained by current theories. We argue that this is mainly due to data scarcity, since noisy human behavior requires massive sample sizes to be accurately captured by off-the-shelf machine learning methods. To solve this problem, what is needed are machine learning models with appropriate inductive biases for capturing human behavior, and larger datasets. We offer two contributions towards this end: first, we construct "cognitive model priors" by pretraining neural networks with synthetic data generated by cognitive models (i.e., theoretical models developed by cognitive psychologists). We find that fine-tuning these networks on small datasets of real human decisions results in unprecedented state-of-the-art improvements on two benchmark datasets. Second, we present the first large-scale dataset for human decision-making, containing over 240,000 human judgments across over 13,000 decision problems. This dataset reveals the circumstances where cognitive model priors are useful, and provides a new standard for benchmarking prediction of human decisions under uncertainty.

In this Oct. 31, 2018, file photo, a man, who declined to be identified, has his face painted to represent efforts to defeat facial recognition during a protest at Amazon headquarters over the company's facial recognition system, "Rekognition," in Seattle. San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies. These days, with facial recognition technology, you've got a face that can launch a thousand applications, so to speak. Sure, you may love the ease of opening your phone just by facing it instead of tapping in a code. But how do you feel about having your mug scanned, identifying you as you drive across a bridge, when you board an airplane or to confirm you're not a stalker on your way into a Taylor Swift concert?

Last week in the USA alone, 18.4 million people watched the penultimate "Game of Thrones" episode which was a record audience for the show. Although, I'm sure that the last episode yesterday even beat those numbers (I have not seen it yet, so no spoilers ahead!). One thing that made the series especially intriguing for viewers was the element of surprise or uncertainty, which made the unfolding of the story very unique compared to today's usual tv experience. Whether it's the sudden death of beloved characters, a completely unexpected change of heart or the failure of a supposed secret weapon at the decisive moment – Westeros is the land of uncertainty. If there were any internal logistics managers watching the series, I'm sure they felt a sense of familiarity, since their job is characterized by unpredictability that it is Game of Thrones twice over, day after day.

This week, Nvidia reported earnings and revenues that were down compared to a year ago. But they did signal a return to growth after a couple of week quarters as the company worked off inventory pile-ups related to the collapse of cryptocurrency mining. People aren't buying graphics cards to mine for cryptocurrency anymore, but they are beefing up their gaming PCs to play high-end games, and developers are now embracing Nvidia's Turing architecture in its RTX graphics cards, said Jensen Huang, CEO of Nvidia, in an analyst call this week. But the artificial intelligence chip market had a pause with a slowdown in hyperscale deployments in data centers. We caught up with Huang for a few minutes on Thursday to talk about the state of the gaming market.

Robert Hoyle Brown is a member of the Center for the Future of Work, a global think tank with a charter from Cognizant Technology Solutions to examine how work is changing, and will change, in response to the emergence of the Age of Algorithms, Automation and AI. His research emphasis has been on the topics of robotics, automation, privacy and augmented reality and their impact on business processes. Since joining Cognizant in 2014, he has worked extensively with its Digital Operations practice as head of strategy, as well as Cognizant's Business Accelerator leadership to drive the development of its intelligent automation strategy, messaging and go-to-market outreach. He was the lead author on the Center for the Future of Work whitepapers "The Robot and I: How New Digital Technologies Are Making Smart People and Businesses Smarter by Automating Rote Work" (2015), "Every Move You Make: The Future of Privacy in the Age of the Algorithm" (2018), "Augmenting the Reality of Everything" (2017), and "The 2nd Half of the Chessboard: The Work Ahead in Media & Entertainment" (2018). He was also a co-author of "21 Jobs of the Future and 21 More Jobs of the Future: A Guide to Getting – and Staying – Employed Over the Next 10 Years", as well as Cognizant's Jobs of the Future Index (2018). He is also a frequent blogger at www.futureofwork.com.

Robert Hoyle Brown is a member of the Center for the Future of Work, a global think tank with a charter from Cognizant Technology Solutions to examine how work is changing, and will change, in response to the emergence of the Age of Algorithms, Automation and AI. His research emphasis has been on the topics of robotics, automation, privacy and augmented reality and their impact on business processes. Since joining Cognizant in 2014, he has worked extensively with its Digital Operations practice as head of strategy, as well as Cognizant's Business Accelerator leadership to drive the development of its intelligent automation strategy, messaging and go-to-market outreach. He was the lead author on the Center for the Future of Work whitepapers "The Robot and I: How New Digital Technologies Are Making Smart People and Businesses Smarter by Automating Rote Work" (2015), "Every Move You Make: The Future of Privacy in the Age of the Algorithm" (2018), "Augmenting the Reality of Everything" (2017), and "The 2nd Half of the Chessboard: The Work Ahead in Media & Entertainment" (2018). He was also a co-author of "21 Jobs of the Future and 21 More Jobs of the Future: A Guide to Getting – and Staying – Employed Over the Next 10 Years", as well as Cognizant's Jobs of the Future Index (2018). He is also a frequent blogger at www.futureofwork.com.

I have been a programmer since before I can remember. I enjoy writing codes from scratch – this helps me understand that topic (or technique) clearly. This approach is especially helpful when we're learning data science initially. Try to implement a neural network from scratch and you'll understand a lot of interest things. But do you think this is a good idea when building deep learning models on a real-world dataset? It's definitely possible if you have days or weeks to spare waiting for the model to build.

Match is becoming the first major dating app to provide its premium users with personally-tailored advice through a free human coach. The company announced today that it is beginning to roll out a new service called AskMatch which allows its paid users to chat on the phone with one of the company's dating hired'experts.' According to a report from TechCrunch, Match members can pick their coach's brains on a variety of topics that include how to set up a good dating profile, getting over a break up, or more general advice on dating. In multiple phone interviews, Match CEO, Hesam Hosseini said that the service will help to push the online dating platform, which has been in existence since 1995, into the future. 'Match's mission has always been around relationships and bringing people together.