Their machine-learning platform predicted a trio of reagents that was able to generate a never-before-seen cancer-like phenotype in tadpoles. The research, reported in Scientific Reports on January 27, shows how artificial intelligence (AI) can help human researchers in fields such as oncology and regenerative medicine control complex biological systems to reach new and previously unachievable outcomes. The researchers had previously shown that pigment cells (melanocytes) in developing frogs could be converted to a cancer-like, metastatic form by disrupting their normal bioelectric and serotonergic signaling and had used AI to reverse-engineer a model that explained this complex process. However, during these extensive experiments, the biologists observed something remarkable: All the melanocytes in a single frog larva either converted to the cancer-like form or remained completely normal. Conversion of only some of the pigment cells in a single tadpole was never seen; how, the researchers asked, could such an all-or-none coordination of cells across the tadpole body be explained and controlled?

GovTech Business Watch is a weekly roundup of news in the government technology market. Following the explosive popularity of body cameras at police departments across the country, a video analytics company is training up programs that could help automate some of the work it takes to get value out of the huge volume of visual data those cameras produce. Dextro, a New York-based company founded in 2013, has traditionally sold its services to the private sector. But now, according to an article from the Knight Foundation-backed news site Undark, the company is building up its artificial intelligence capacities to handle video from police departments. With companies such as Taser selling thousands upon thousands of cameras, many of them now producing high-definition video, police departments are beginning to compile terabyte-sized archives of data.

For the last day of this week, I've chosen to play poker with artificial intelligence (AI). Past this metaphorically first sentence, i would like to share with The Information Age a recent post from the MIT Technology Review, another irresistible one, and this time about a most recent AI conquest of a typically human capacity – the card game of Poker. This is a game that involves several human psychological traits that we might think an artificial device would never master. For example, the human ability to read other people's minds would have seemed intractable form an AI perspective; or the ability to induce contradictory beliefs in others, like when the poker players uses the strategy of bluffing. Strategic thinking under uncertainty and imperfect information appears to being conquered by the AI community of computer scientists and software geeks. Or so that is what is claimed in the article below, where a few links to some of the pioneers is well recommended a follow through, as well as the full readership of the paper DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker, that is also provided below in this post, with its abstract description.

This article was written in collaboration with Gowling WLG. Gowling WLG is one of world's largest law firms and advises clients from offices in many of the world's most dynamic markets. It was recently ranked as the second most innovative firm in Europe in the prestigious FT Innovative Lawyer Awards 2016. "Gowling WLG is one of world's largest law firms and advises clients from offices in many of the world's most dynamic markets. It was recently ranked as the second most innovative firm in Europe in the prestigious FT Innovative Lawyer Awards 2016."

A recent Conversation piece pointed out that the British electricity mix in 2016 was the cleanest in 60 years, with record capacity from renewable energy, mainly from wind and solar power. But one problem with this great expansion in renewables is they are intermittent, meaning they depend on weather conditions such as the wind blowing or sun shining. Unlike conventional power, this means they can't necessarily meet surges in demand. National Grid, the UK grid operator, has several ways of ensuring supply can always meet demand. For shorter gaps in generation, it asks electricity suppliers to run their conventional power stations at below maximum potential output and ramp up as needed.

Ordering your lunch or coffee using a self-serve computer screen instead of speaking to a human is one of the most obvious examples of how the workplace and retail experience is becoming more automated. Such automation has become so common that Starbucks is taking steps to make sure the process doesn't feel so, well, robotic. Starbucks announced this month that it was installing two-way video screens at its drive-thus to personalize the transaction, "allowing customers and baristas to see each other and truly interact when the order is being placed." Starbucks also said its mobile order and pay program, which allows customers to order through a phone app and swoop in to pick up an order without waiting in line, also provides "personalized customer experiences." Other fast food outlets, including A&W and McDonald's, also give customers the option to bypass or minimize human interaction with self-serve screen kiosks, and experts predict the automation of work tasks is likely going to speed up in coming years.

When you buy a book from Amazon, you know you'll get several book recommendations based on that purchase and other past purchases. The suggestions won't be about what other people in your age group have bought or what people in your neighborhood liked. And they won't be based on months-old data. Learn how large financial service and health care companies are tackling the issue – to enhance customer experience, to stake out positions in their business ecosystems, and to manage risk – on our Feb. When you make a bank transaction, you're unlikely to receive any recommendation or advice, even though the bank may have more relevant data about your money than Amazon does.

Magna International Inc. chief executive officer Don Walker has a colourful description of what artificial intelligence (AI) is going to mean to the auto industry. A car with a human at the wheel swerves to avoid a ball that rolls across the road, and an experienced driver knows to watch for a child chasing the ball, Mr. Walker told a conference Wednesday. Upgrade that car with AI, and it will automatically avoid the ball, and know to check for a child, by using its own sensors and by networking with AI systems in nearby cars that may have a better view. To continue reading this article, you must be a Globe Unlimited subscriber. Click here to get full access to Globe Unlimited.

A week after President Donald Trump's inauguration, George Orwell's '1984' is the best-selling book on Amazon.com. The hearts of a thousand English teachers must be warmed as people flock to a novel published in 1949 for ways to think about their present moment. Orwell set his story in Oceania, one of three blocs or mega-states fighting over the globe in 1984. A week after President Donald Trump's inauguration, George Orwell's '1984' is the best-selling book on Amazon.com, as many are comparing it to today's America. Orwell could not have imagined the internet and its role in distributing alternative facts.

We study the problem of identifying the causal relationship between two discrete random variables from observational data. We recently proposed a novel framework called entropic causality that works in a very general functional model but makes the assumption that the unobserved exogenous variable has small entropy in the true causal direction. This framework requires the solution of a minimum entropy coupling problem: Given marginal distributions of m discrete random variables, each on n states, find the joint distribution with minimum entropy, that respects the given marginals. This corresponds to minimizing a concave function of nm variables over a convex polytope defined by nm linear constraints, called a transportation polytope. Unfortunately, it was recently shown that this minimum entropy coupling problem is NP-hard, even for 2 variables with n states. Even representing points (joint distributions) over this space can require exponential complexity (in n, m) if done naively. In our recent work we introduced an efficient greedy algorithm to find an approximate solution for this problem. In this paper we analyze this algorithm and establish two results: that our algorithm always finds a local minimum and also is within an additive approximation error from the unknown global optimum.