Explosions from a decades-old conflict have left a pockmarked and unstable territory, though many more improvised bombs lie concealed in its vast reaches. Sunlight splays off the beaten edges of Optimus, the smaller robot. If Optimus were programmed to hope, it would hope the object was just a rock and not another bomb. It couldn't afford to take many more hits, and its algorithms have grown wary of the risk. A hulking shape shimmers in the heat as it approaches Optimus, lolling like a huge, headless cowboy.

When the machine learning renaissance started in 2013 – thanks to the development of new graphic processing units accelerating data processing by over 100 times – Frans Cronje and Daniel Schwartzkopff started positioning themselves to fill the gap in the South African market. "Daniel and I met while doing our undergrad studies at the University of Cape Town and collectively came up with the idea to start an artificial intelligence (AI) company," says Frans Cronje, the managing director of DataProphet. "At the time there was hardly any competition in the field and skills were scarce, as you could not even study machine learning here." They initially offered machine learning consultancy services to retail, manufacturing and finance companies and from there catapulted into the products market when they started DataProphet in 2015. "We were relatively new to the game. Working as consultants helped us gain valuable experience and insights into the common challenges faced by these industries, which in turn allowed us to create service solutions for these challenges," says Cronje.

Agriculture has come a long way in the past century. We produce more food than ever before -- but our current model is unsustainable, and as the world's population rapidly approaches the 8 billion mark, modern food production methods will need a radical transformation if they're going to keep up. But luckily, there's a range of new technologies that might make it possible. In this series, we'll explore some of the innovative new solutions that farmers, scientists, and entrepreneurs are working on to make sure that nobody goes hungry in our increasingly crowded world. Ever since American citizens' industrial age migration from the country to the city, urban areas have tended to be associated with cutting-edge technologies.

"Microsoft Monday" is a weekly column that focuses on all things Microsoft. In the process of rebranding MSN News, Microsoft has launched a news app for Android and iOS called Microsoft News. The MSN brand will remain on the website, but Microsoft will not be using that brand for the Android and iOS news apps going forward. Some of the features built into the Microsoft News app includes a dark mode, integration with Android and iOS widgets, continuous reading and news alerts. And the Microsoft News service will also be used for powering the news on Microsoft Edge, the News app in Windows 10, Skype, Xbox and Outlook.com.

We specialize the decoupled extended Kalman filter (DEKF) for online parameter learning in factorization models, including factorization machines, matrix and tensor factorization, and illustrate the effectiveness of the approach through simulations. Learning model parameters through the DEKF makes factorization models more broadly useful by allowing for more flexible observations through the entire exponential family, modeling parameter drift, and producing parameter uncertainty estimates that can enable explore/exploit and other applications. We use a more general dynamics of the parameters than the standard DEKF, allowing parameter drift while encouraging reasonable values. We also present an alternate derivation of the regular extended Kalman filter and DEKF that connects these methods to natural gradient methods, and suggests a similarly decoupled version of the iterated extended Kalman filter.

We consider the following conditional linear regression problem: the task is to identify both (i) a $k$-DNF condition $c$ and (ii) a linear rule $f$ such that the probability of $c$ is (approximately) at least some given bound $\mu$, and $f$ minimizes the $\ell_p$ loss of predicting the target $z$ in the distribution of examples conditioned on $c$. Thus, the task is to identify a portion of the distribution on which a linear rule can provide a good fit. Algorithms for this task are useful in cases where simple, learnable rules only accurately model portions of the distribution. The prior state-of-the-art for such algorithms could only guarantee finding a condition of probability $\Omega(\mu/n^k)$ when a condition of probability $\mu$ exists, and achieved an $O(n^k)$-approximation to the target loss, where $n$ is the number of Boolean attributes. Here, we give efficient algorithms for solving this task with a condition $c$ that nearly matches the probability of the ideal condition, while also improving the approximation to the target loss. We also give an algorithm for finding a $k$-DNF reference class for prediction at a given query point, that obtains a sparse regression fit that has loss within $O(n^k)$ of optimal among all sparse regression parameters and sufficiently large $k$-DNF reference classes containing the query point.

The nearest neighbor problem is defined as follows: Given a set $P$ of $n$ points in some metric space $(X,D)$, build a data structure that, given any point $q$, returns a point in $P$ that is closest to $q$ (its "nearest neighbor" in $P$). The data structure stores additional information about the set $P$, which is then used to find the nearest neighbor without computing all distances between $q$ and $P$. The problem has a wide range of applications in machine learning, computer vision, databases and other fields. To reduce the time needed to find nearest neighbors and the amount of memory used by the data structure, one can formulate the {\em approximate} nearest neighbor problem, where the the goal is to return any point $p' \in P$ such that the distance from $q$ to $p'$ is at most $c \cdot \min_{p \in P} D(q,p)$, for some $c \geq 1$. Over the last two decades, many efficient solutions to this problem were developed. In this article we survey these developments, as well as their connections to questions in geometric functional analysis and combinatorial geometry.

James Pamment has written that for most of the 20th century the term public diplomacy was associated with the term propaganda. According to the Oxford Dictionary propaganda relates to information, especially of a biased or misleading nature, used to promote a political cause or point of view. During the 21st century, the field of public diplomacy faced a conceptual shift known as the "new" public diplomacy. This shift saw the goals of public diplomacy change from influence and opinion formation to creating long lasting relationships with foreign populations. These relationships, built on dialogue and two way interactions, could be used to create a receptive environment for a country's foreign policy.

When it comes to sports betting, most people lose. But during the 2014 World Cup, a team of statisticians beat the bookmakers. They correctly predicted Germany -- their home country and 6-to-1 underdogs -- as the final champions and raked in a 30 percent return with bets placed on regular matches. This year, the team, led by Andreas Groll of Germany's Technical University of Dortmund, is back with an artificial intelligence program with even better odds. As of Friday morning, it had correctly predicted 15 of 24 games -- the winners, the losers and those who tied. As for the ultimate victor, their new model has picked Spain, but only if Germany falters before the final.

WHEN TAY MADE HER DEBUT in March 2016, Microsoft had high hopes for the artificial intelligence–powered "social chatbot." Like the automated, text-based chat programs that many people had already encountered on e-commerce sites and in customer service conversations, Tay could answer written questions; by doing so on Twitter and other social media, she could engage with the masses. But rather than simply doling out facts, Tay was engineered to converse in a more sophisticated way--one that had an emotional dimension. She would be able to show a sense of humor, to banter with people like a friend. Her creators had even engineered her to talk like a wisecracking teenage girl. When Twitter users asked Tay who her parents were, she might respond, "Oh a team of scientists in a Microsoft lab. They're what u would call my parents." If someone asked her how her day had been, she could quip, "omg totes exhausted."