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."

Fall Armyworm first appeared in Africa in 2016, in West Africa, and then rapidly spread across all countries in sub-Saharan Africa in 2017, infecting millions of hectares of maize, and threatening the food security of more than 300 million people. Many African farmers might have heard about Fall Armyworm but are seeing it for the first time, and are often unable to recognize it or unsure of what they are facing. With the new application, they can hold the phone next to an infested plant, and Nuru can immediately confirm if Fall Armyworm has caused the damage. Nuru is an app that uses cutting-edge technologies involving machine learning and artificial intelligence. It runs inside a standard Android phone and can work also offline.

Custodian Investment Plc – leading insurance company in Nigeria – has launched Nigeria's first ever Artificial Intelligence (AI) Chatbot in the insurance sector, leveraging technology to deploy enhanced personalized services to its customers. Max is available to interact with customers 24/7 via three platforms; Facebook messenger, Telegram and Web messenger. On Telegram, simply search for'Custodian Max', to chat with Max. Max was officially launched on Thursday 21st June, 2018. In the words of Mr. Oladele Akinsanya, Head of Service Delivery, Custodian Investment PLC, "Customer service is at the heart of what we do at Custodian. This is why we are happy to introduce Max to Nigerians. Max is a tool that enables the customer to drive insurance, based on convenience. "Max is just like a relationship officer that is readily available at your beck and call; anytime of the day and from any country in the world!

Among the various companies, non-profits and researchers using tech company Google's TensorFlow platform, one application that has caught the attention of developers at the internet giant is PlantMD. Created by high school students Shaza Mehdi and Nile Ravenell, the app can detect diseases in plants. The duo, who showcased the app at Google's I/O annual developer conference this year, built it based on the Internet company's open-source machine learning library for data programming--TensorFlow. "PlantMD's machine learning model was inspired by a dataset from PlantVillage, a research and development unit at Penn State University. PlantVillage created an app called Nuru, Swahili for'light', to assist farmers to grow better cassava, a crop in Africa that provides food for over half a billion people daily," Fred Alcober, a member of Google's TensorFlow team, wrote in a blog post. Cassava plants, wrote Alcober, though very tolerant of harsh weather conditions, is susceptible to pests and diseases.

This article focuses on the political and geopolitical consequences of the feedback relationship linking Artificial Intelligence (AI) in its Deep Learning component and computing power – hardware – or rather high performance computing power (HPC). It builds on a first part where we explained and detailed this connection. There we underlined notably three typical phases where computation is required: creation of the AI program, training, and inference or production (usage). We showed that a quest for improvement across phases, and the overwhelming and determining importance of architecture design – which takes place during the creation phase – generates a crucial need for ever more powerful computing power. Meanwhile, we identified a feedback spiral between AI-DL and computing power, where more computing power allows for advances in terms of AI and where new AI and the need to optimize it demand more computing power.