China's government has announced a goal of becoming a global leader in artificial intelligence in just over a decade, putting political muscle behind growing investment by Chinese companies in developing self-driving cars and other advances. Communist leaders see AI as key to making China an "economic power," said a Cabinet statement on Thursday. It calls for developing skills and research and educational resources to achieve "major breakthroughs" by 2025 and make China a world leader by 2030.

Billionaire Mark Cuban made an appearance today in New York City's Central Park at the second annual "OZY Fest", and he didn't disappoint. Naturally, the conversation first gravitated towards President Trump, with moderator Carlos Watson leading a panel that also included Republican presidential candidate Jeb Bush and comedian Samantha Bee. Watson first asked if any of the panelists would join President Trump's cabinet. Cuban proclaimed that he wouldn't join Trump's cabinet, but he would meet with the President to converse about the state of our nation. When it was Jeb Bush's turn, the former governor simply replied: "Let's move onto something [more] fun." Watson then shifted gears to the hot-button topic of police brutality. "I think every city is different," Cuban responded when asked if our police system nationwide is broken.

Permutation testing is a non-parametric method for obtaining the max null distribution used to compute corrected $p$-values that provide strong control of false positives. In neuroimaging, however, the computational burden of running such an algorithm can be significant. We find that by viewing the permutation testing procedure as the construction of a very large permutation testing matrix, $T$, one can exploit structural properties derived from the data and the test statistics to reduce the runtime under certain conditions. In particular, we see that $T$ is low-rank plus a low-variance residual. This makes $T$ a good candidate for low-rank matrix completion, where only a very small number of entries of $T$ ($\sim0.35\%$ of all entries in our experiments) have to be computed to obtain a good estimate. Based on this observation, we present RapidPT, an algorithm that efficiently recovers the max null distribution commonly obtained through regular permutation testing in voxel-wise analysis. We present an extensive validation on a synthetic dataset and four varying sized datasets against two baselines: Statistical NonParametric Mapping (SnPM13) and a standard permutation testing implementation (referred as NaivePT). We find that RapidPT achieves its best runtime performance on medium sized datasets ($50 \leq n \leq 200$), with speedups of 1.5x - 38x (vs. SnPM13) and 20x-1000x (vs. NaivePT). For larger datasets ($n \geq 200$) RapidPT outperforms NaivePT (6x - 200x) on all datasets, and provides large speedups over SnPM13 when more than 10000 permutations (2x - 15x) are needed. The implementation is a standalone toolbox and also integrated within SnPM13, able to leverage multi-core architectures when available.

Scaling regression to large datasets is a common problem in many application areas. We propose a two step approach to scaling regression to large datasets. Using a regression tree (CART) to segment the large dataset constitutes the first step of this approach. The second step of this approach is to develop a suitable regression model for each segment. Since segment sizes are not very large, we have the ability to apply sophisticated regression techniques if required. A nice feature of this two step approach is that it can yield models that have good explanatory power as well as good predictive performance. Ensemble methods like Gradient Boosted Trees can offer excellent predictive performance but may not provide interpretable models. In the experiments reported in this study, we found that the predictive performance of the proposed approach matched the predictive performance of Gradient Boosted Trees.

In this work we present the novel ASTRID method for investigating which attribute interactions classifiers exploit when making predictions. Attribute interactions in classification tasks mean that two or more attributes together provide stronger evidence for a particular class label. Knowledge of such interactions makes models more interpretable by revealing associations between attributes. This has applications, e.g., in pharmacovigilance to identify interactions between drugs or in bioinformatics to investigate associations between single nucleotide polymorphisms. We also show how the found attribute partitioning is related to a factorisation of the data generating distribution and empirically demonstrate the utility of the proposed method.

Robot developers say they are close to a breakthrough--getting a machine to pick up a toy and put it in a box. It is a simple task for a child, but for retailers it has been a big hurdle to automating one of the most labor-intensive aspects of e-commerce: grabbing items off shelves and packing them for shipping. HBC -1.08% and Chinese online-retail giant JD.com Inc., JD 0.37% have recently begun testing robotic "pickers" in their distribution centers. Some robotics companies say their machines can move gadgets, toys and consumer products 50% faster than human workers. Retailers and logistics companies are counting on the new advances to help them keep pace with explosive growth in online sales and pressure to ship faster.

The car's underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. The resulting program, which the researchers named Deep Patient, was trained using data from about 700,000 individuals, and when tested on new records, it proved incredibly good at predicting disease. But it was not until the start of this decade, after several clever tweaks and refinements, that very large--or "deep"--neural networks demonstrated dramatic improvements in automated perception. Deep learning has transformed computer vision and dramatically improved machine translation.

When considering how to draw the line between whether an application is AI or not, I'm tempted to paraphrase U.S. Supreme Court Justice Potter Stewart: I shall not today attempt further to define the kinds of applications I understand to be embraced within that shorthand description "artificial intelligence", and perhaps I could never succeed in intelligibly doing so. But I know it when I see it. A machine developing its own concept of what a "cat" is and learning to detect it in videos feels like AI to me. Although the more I read about it and understand it the more it just feels like a clever use of deep learning, creating a mathematical construct that is fit for purpose. It's like magic: when the trick is revealed I can still be impressed, but it doesn't feel like magic anymore.

Regarding Fear and Artificial Intelligence (AI), one question often comes up:'Will we be killed by a Terminator Doppelganger?' I don't know if this will happen eventually, but I do know that we already have robots fighting our wars. This century is therefore, the first time in human history that we engage in Unmanned Warfare. What is the current status of this'Unmanned Warfare'? What do people think about drone strikes and will terminators be the next step?

Digitization and the next wave of automation will change the nature of work, eliminating some traditional jobs and impacting many more. But it will also create growth and new employment opportunities, and if we manage the transition, everyone could benefit. Political promises to "bring back" well paid jobs in manufacturing and others sectors are a cruel deception that ignore the realities of a global inter-connected economy. Instead, we should focus on the challenges and opportunities presented by new technology trends, including artificial intelligence, machine learning, and Big Data analytics. The concerns some employees and young people have about the impact of next-generation automation on jobs and pay are understandable.