We consider finite and infinite horizon dynamic programming problems, where the control at each stage consists of several distinct decisions, each one made by one of several agents. We introduce an algorithm, whereby at every stage, each agent's decision is made by executing a local rollout algorithm that uses a base policy, together with some coordinating information from the other agents. The amount of local computation required at every stage by each agent is independent of the number of agents, while the amount of global computation (over all agents) grows linearly with the number of agents. By contrast, with the standard rollout algorithm, the amount of global computation grows exponentially with the number of agents. Despite the drastic reduction in required computation, we show that our algorithm has the fundamental cost improvement property of rollout: an improved performance relative to the base policy. We also explore related reinforcement learning and approximate policy iteration algorithms, and we discuss how this cost improvement property is affected when we attempt to improve further the method's computational efficiency through parallelization of the agents' computations.

On October 15 the Democratic presidential candidates will once again have the opportunity to debate their positions on a range of issues affecting this country. Let's hope that this go-round we hear their visions for Digital America. Yes, health care, immigration, climate change and other topics that consumed the previous debates are important. Yes, impeachment is on the front page. But what is the agenda that provides hope and opportunity for Americans in a new digital-based economy? So far, much of the campaign focus on the new economy has been reduced to a misleadingly simple "break'em up!" solution for Big Tech.

This meme has been all over social media lately, producing appreciative chuckles across the internet as the hype around deep learning begins to subside. The sentiment that machine learning is really nothing to get excited about, or that it's just a redressing of age-old statistical techniques, is growing increasingly ubiquitous; the trouble is it isn't true. I get it -- it's not fashionable to be part of the overly enthusiastic, hype-drunk crowd of deep learning evangelists. ML experts who in 2013 preached deep learning from the rooftops now use the term only with a hint of chagrin, preferring instead to downplay the power of modern neural networks lest they be associated with the scores of people that still seem to think that import keras is the leap for every hurdle, and that they, in knowing it, have some tremendous advantage over their competition. While it's true that deep learning has outlived its usefulness as a buzzword, as Yann LeCun put it, this overcorrection of attitudes has yielded an unhealthy skepticism about the progress, future, and usefulness of artificial intelligence.

Of course we've all watched – and very much enjoyed! Obviously, they achieved this through an extraordinary talented team, a surplus of technical skills and a philosophy of attractive creative play. But did you know that artificial intelligence also played a big role in this success? Curious to see what insurers can learn from this provocative combination of creative skills and AI we met with Max Reckers, performance technology expert at AFC Ajax, and before that at Bayern Munchen, Manchester United and the Dutch National Team. Max, just to set the stage; using data, algorithms, AI, performance technology … Why is Ajax doing all this?

Financial technology (Fintech) companies are finding new ways to meet consumer demands and create more financial inclusion on a global scale. While Fintechs are on the rise, these companies still have to manage the same problems traditional financial institutions face: fraud. And while fraud permeates throughout nearly all aspects of a financial transaction, one particular area of concern is onboarding. Client onboarding is when a new client begins their relationship with the fintech. Companies naturally want to make this process easy and simplified, but in the financial world, this can be complicated.

Artificial intelligence (AI) is rarely out of the headlines these days, due in large part to a steady stream of controversies from many of the major tech players. Facial recognition software is increasingly permeating society without much regard for ethics or accuracy. And crashes and near-misses blighted the recent launch of Tesla's new Smart Summon feature, which allows drivers -- in theory -- to remotely beckon their vehicle in parking lots. There is a strong case for the suggestion that AI, in its current form at least, is more artificial stupidity than anything else. Nonetheless, AI is here and here to stay.

With increased connectivity comes increased concerns - especially for parents with children that are active online. Parents obviously want to shield their children from the horrific experiences we all hear, see, and read about. However, it takes more than just telling children to not share personal information to protect them from toxic online behavior such as bullying, hate speech, and sexual predators. It's a frightening new world online, especially for kids and the stats are eye-opening. The need for a better all-encompassing solution becomes magnified when you consider the fact that oftentimes, it's the children who are so tech-savvy their parents are unaware of their actions, much less the new online behavioral norms of gameplay or slang and worse yet many of those actions are difficult to trace.

"AI will probably most likely lead to the end of the world, but in the meantime, there'll be great companies." If investors are looking for that one great AI company that will also end the world, then they should forget Alphabet or Amazon. I'd put my money on the Japanese firm Cyberdyne Inc. Why? Because it bears the same name as the company that created the Skynet AI in the Terminator films. Skynet fulfilled Altman's prophecy before he made it, albeit on the silver screen, and wiped out human civilization.

Woodie Flowers SM '68, MEng '71, PhD '73, the Pappalardo Professor Emeritus of Mechanical Engineering, passed away on Oct. 11 at the age of 75. Flowers' passion for design and his infectious kindness have impacted countless engineering students across the world. Flowers was instrumental in shaping MIT's hands-on approach to engineering design education, first developing teaching methods and learning opportunities that culminated in a design competition for class 2.70, now called 2.007 (Design and Manufacturing I). This annual MIT event, which has now been held for nearly five decades, has impacted generations of students and has been emulated at universities around the world. Flowers expanded this concept to high school and elementary school students, working to help found the world-wide FIRST Robotics Competition, which has introduced millions of children to science and engineering.

One of the biggest issues with Artificial Intelligence and Data Science is the integrity of our data. Even if we did all the right things in our models, and our testing, data might conform to some technical standard of "cleanliness," there might still be biases in our data as well as "common sense" issues. With Big Data, it is difficult to get to a certain granularity of data validity without proper real-world testing. By real-world testing, we mean that when data is being used to make decisions, as consumers, as testers, as programmers, as data scientists, we look at groups of scenarios to see if the decisions made conform to a kind of "common sense" standard. This is when we discover the most important biases in our data.