Artificial Intelligence promises to benefit humankind in unprecedented ways. But small states are especially vulnerable to the technology's downside short of strengthening social cohesion and resilience. Artificial intelligence or AI, broadly defined as human-like intelligence and qualities exhibited by machines, has made a huge technological leap since 1956 when the term was first coined. Tech giants like Google and IBM believe that AI will benefit mankind in unprecedented ways. For example, autonomous vehicles are expected to enhance both traffic safety and flow whereas care-bots will aid in areas such as elderly and patient care.

MRI images like this one might help researchers learn about organs before disease sets in, which could help them discover new treatments and prevention tactics. Doctors have found lots of ways to see right through you. Now a team of researchers throughout the United Kingdom will be doing a lot of that--they are kicking off the world's largest imaging study. The scientists, who are affiliated with the UK nonprofit Biobank, intend to capture images of the brains, hearts, bones, and arteries of 100,000 patients, with the help of MRIs, X-rays, and ultrasounds. By combining those images with other types of lifestyle and health data that the researchers have spent the past decade collecting, the researchers hope to better understand how to prevent and treat disease.

On the evening of March 31st, Elon Musk unveiled Tesla's sinuous Model 3, the company's first "affordable" electric-car model. After touting the sedan's punchy acceleration, two-hundred-and-fifteen-mile battery range, and sweeping, seamless glass roof, he mentioned its base price of thirty-five thousand dollars and told the audience that prospective buyers had already reserved more than a hundred and fifteen thousand of the vehicles, to rapturous applause and shouts of "You did it!" Not one to miss a marketing trick, Musk capped the night on Twitter, with a cryptic thank-you message that promised more: "Thanks for tuning in to the Model 3 unveil Part 1! Part 2 is super next level, but that's for later . . . Within hours, the tech community was awash in speculation about what more Tesla could have in store for the Model 3. Some wondered, specifically, whether it would be the world's first mass-market, fully autonomous self-driving car. Spurred forward by Google and other Silicon Valley companies, the auto industry has been tinkering with autonomous vehicles for years.

"I never thought that I would be accepted into a human school," the robot said upon hearing of his successful enrollment at Hisashi High School in Waseda, Fukushima Prefecture. He also promised to "try my best," TASS reported. Pepper comes to the school with an impressive array of language skills, speaking both Japanese and English. He will mostly take part in English classes, though the school has told Pepper than he can also visit other classes and activities. Teachers believe learning alongside Pepper will be a positive experience for students, encouraging their desire to learn new information.

Outcome expected on October 21, 2045.

When viewed on a cosmic scale, humanity lives on a tiny grain of sand floating in an unimaginably-deep ocean. Huge expanses of space separate even the closest stars, ensuring that, should any sufficiently intelligent life form want to spread across the galaxy, it would take a momentous effort to launch across the interstellar seas. As we look toward the stars, hoping that we may visit them some day, many would argue that interstellar travel is impossible. After all, the nearest-known star system is over 4 light-years away. Let's think about that for a moment: It takes light 8 minutes and 20 seconds to travel from the sun's surface to our planet's atmosphere.

Somewhere in the middle of the night in a Central African rainforest, a chimpanzee gives birth. Soon after, as the sun rises, mother and newborn sit there, dazed, amid a coffee klatch of friends and relatives. Inevitably, at some point, virtually every member of the group will come over, pull the kid's legs apart and sniff: Boy or girl? It's the most binary question in biology, producing an answer that is set in stone. Biologists have long known about exceptions to the boring, staid notion that organisms are, and remain, either female or male. Now our culture is inching toward recognizing that the permanent, cleanly binary nature of gender is incorrect.

Let ${\mathcal S}_m$ be the set of all $m\times m$ density matrices (Hermitian positively semi-definite matrices of unit trace). Consider a problem of estimation of an unknown density matrix $\rho\in {\mathcal S}_m$ based on outcomes of $n$ measurements of observables $X_1,\dots, X_n\in {\mathbb H}_m$ (${\mathbb H}_m$ being the space of $m\times m$ Hermitian matrices) for a quantum system identically prepared $n$ times in state $\rho.$ Outcomes $Y_1,\dots, Y_n$ of such measurements could be described by a trace regression model in which ${\mathbb E}_{\rho}(Y_j|X_j)={\rm tr}(\rho X_j), j=1,\dots, n.$ The design variables $X_1,\dots, X_n$ are often sampled at random from the uniform distribution in an orthonormal basis $\{E_1,\dots, E_{m^2}\}$ of ${\mathbb H}_m$ (such as Pauli basis). The goal is to estimate the unknown density matrix $\rho$ based on the data $(X_1,Y_1), \dots, (X_n,Y_n).$ Let $$ \hat Z:=\frac{m^2}{n}\sum_{j=1}^n Y_j X_j $$ and let $\check \rho$ be the projection of $\hat Z$ onto the convex set ${\mathcal S}_m$ of density matrices. It is shown that for estimator $\check \rho$ the minimax lower bounds in classes of low rank density matrices (established earlier) are attained up logarithmic factors for all Schatten $p$-norm distances, $p\in [1,\infty]$ and for Bures version of quantum Hellinger distance. Moreover, for a slightly modified version of estimator $\check \rho$ the same property holds also for quantum relative entropy (Kullback-Leibler) distance between density matrices.

In the biclustering problem, we seek to simultaneously group observations and features. While biclustering has applications in a wide array of domains, ranging from text mining to collaborative filtering, the problem of identifying structure in high dimensional genomic data motivates this work. In this context, biclustering enables us to identify subsets of genes that are co-expressed only within a subset of experimental conditions. We present a convex formulation of the biclustering problem that possesses a unique global minimizer and an iterative algorithm, COBRA, that is guaranteed to identify it. Our approach generates an entire solution path of possible biclusters as a single tuning parameter is varied. We also show how to reduce the problem of selecting this tuning parameter to solving a trivial modification of the convex biclustering problem. The key contributions of our work are its simplicity, interpretability, and algorithmic guarantees - features that arguably are lacking in the current alternative algorithms. We demonstrate the advantages of our approach, which includes stably and reproducibly identifying biclusterings, on simulated and real microarray data.

Statistical machine learning plays an important role in modern statistics and computer science. One main goal of statistical machine learning is to provide universally consistent algorithms, i.e., the estimator converges in probability or in some stronger sense to the Bayes risk or to the Bayes decision function. Kernel methods based on minimizing the regularized risk over a reproducing kernel Hilbert space (RKHS) belong to these statistical machine learning methods. It is in general unknown which kernel yields optimal results for a particular data set or for the unknown probability measure. Hence various kernel learning methods were proposed to choose the kernel and therefore also its RKHS in a data adaptive manner. Nevertheless, many practitioners often use the classical Gaussian RBF kernel or certain Sobolev kernels with good success. The goal of this short note is to offer one possible theoretical explanation for this empirical fact.