"In January 2018, advocates for data privacy celebrated when the Chinese government released a new national standard on the protection of personal information, which contains more comprehensive and onerous requirements than even the European Union's General Data Protection Regulation, per analysis by some experts." In the decades ahead, the countries that dominate AI in any domain could influence how our world is shaped. Jeff Ding leads research on China's development of artificial intelligence at the Future of Humanity Institute's Governance of AI Program at Oxford University. He's been interested in studying China since his high school years. Ding says that once he realized the potential of AI, he became more interested in China's investment in this area. Ding's new study, Deciphering China's AI Dream, is a detailed analysis of the country's AI strategy moving forward.

In this special guest feature, Ashok Reddy, Group General Manager for DevOps at CA, discusses the data scientist talent gap and how in this high-demand market for data scientists, companies need to think differently about the position, and how it relates to other parts of the organization. Ashok is completing a MS degree in CS with specialization in Interactive Intelligence, Machine Learning at Georgia Tech. It doesn't take a data scientist to figure out that data scientists are in very high- demand. "America's hottest job!" screams a Bloomberg headline. "Best job in America," says Glassdoor two years in a row, citing a number of job openings, high salaries and high job satisfaction rates.

The corporate culture, the pinnacle of'profession above all' atmospheres, still has a long way to grow out of personal biases that come in with people walking into the door. It is observed even within the hiring process where personality and likeability tend to outweigh the significance of candidate's qualification and skillset that constitute the actual parameter of any job role. However, likability is often a primary requirement for any role in professional arena, taking critical decisions based on favoritism is both unethical and unprogressive. However, Artificial Intelligence promises to make hiring unbiased in all its potential. There are certainly many areas to start with.

Twenty years ago Lego introduced Mindstorms as a way to engage kids who were becoming more interested in video games and the internet than plastic building blocks. It was successful enough that the kits became a regular sight in robotics classes and competitions. Now the line is on its fourth generation, and it's been joined by other STEM-friendly Lego kits like Boost and Powered Up to bring tech skills to many different types of kids. Now Lego's educational division goes even younger with Coding Express, a set that will teach 3- and 4-year-olds the basics of programming while they construct a world of trains, picnics and wandering deer. This isn't going to teach your kids popular coding languages like Python or Swift. Coding Express is firmly for the preschool crowd, so the idea here is to just get kids acclimated to concepts like loops and subprograms.

The idea of using robots in classrooms to teach our children is unsettling to many people. Fumihide Tanaka of the University of Tsukuba's Department of Intelligent Interaction Technologies, however, uses a technique that cleverly allays fears of robot superiority. He uses robots in the role of novices in the classroom. "Our solutions do not replace humans but help humans to feel, think and act," he says. Rather than the conventional roles of the robots as the teachers or caretakers of children, in Tanaka's method, this is reversed.

Almost every other day, either one of my colleague, college friend or an online contact from LinkedIn/Twitter will ask me "I have been reading a lot of hype around artificial intelligence and machine learning, I tried to read some of the articles and watched some videos but I really don't know where to start. Can you help or share something?". It is difficult to give a structured answer. It is totally crazy to learn everything about artificial intelligence. This field is so wide that it is easy to hit a roadblock because you started learning it in the wrong way (difficult way) without assessing your readiness.

This is such an incomplete description of what Kaggle is! I believe that competitions (and their highly lucrative cash prizes) are not even the true gems of Kaggle. Take a look at their website's header-- All of these together have made Kaggle much more than simply a website that hosts competitions. It has, now, also become a complete project-based learning environment for data science. I will talk about that aspect of Kaggle in details after this section.

When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval. Previous models of agent responsiveness, termed "strategic manipulation," have analyzed the interaction between a learner and agents in a world where all agents are equally able to manipulate their features in an attempt to "trick" a published classifier. In cases of real world classification, however, an agent's ability to adapt to an algorithm, is not simply a function of her personal interest in receiving a positive classification, but is bound up in a complex web of social factors that affect her ability to pursue certain action responses. In this paper, we adapt models of strategic manipulation to better capture dynamics that may arise in a setting of social inequality wherein candidate groups face different costs to manipulation. We find that whenever one group's costs are higher than the other's, the learner's equilibrium strategy exhibits an inequality-reinforcing phenomenon wherein the learner erroneously admits some members of the advantaged group, while erroneously excluding some members of the disadvantaged group. We also consider the effects of potential interventions in which a learner can subsidize members of the disadvantaged group, lowering their costs in order to improve her own classification performance. Here we encounter a paradoxical result: there exist cases in which providing a subsidy improves only the learner's utility while actually making both candidate groups worse-off--even the group receiving the subsidy. Our results reveal the potentially adverse social ramifications of deploying tools that attempt to evaluate an individual's "quality" when agents' capacities to adaptively respond differ.

A challenge in creating a dataset for machine reading comprehension (MRC) is to collect questions that require a sophisticated understanding of language to answer beyond using superficial cues. In this work, we investigate what makes questions easier across recent 12 MRC datasets with three question styles (answer extraction, description, and multiple choice). We propose to employ simple heuristics to split each dataset into easy and hard subsets and examine the performance of two baseline models for each of the subsets. We then manually annotate questions sampled from each subset with both validity and requisite reasoning skills to investigate which skills explain the difference between easy and hard questions. From this study, we observed that (i) the baseline performances for the hard subsets remarkably degrade compared to those of entire datasets, (ii) hard questions require knowledge inference and multiple-sentence reasoning in comparison with easy questions, and (iii) multiple-choice questions tend to require a broader range of reasoning skills than answer extraction and description questions. These results suggest that one might overestimate recent advances in MRC.

Two new $1 million awards from the National Science Foundation aim to help researchers take advantage of the latest advances in data science, networking and computation while protecting the integrity of their scientific work. The Renaissance Computing Institute (RENCI) of the University of North Carolina at Chapel Hill will serve as lead institution on both projects. Many scientists today use sophisticated data-intensive approaches to combine and analyze large data sets from scientific instruments and data stores all over the country. While these techniques hold great value for discovery and innovation, integrating the necessary data and tools into a scientist's workflow is often a complex undertaking. In addition, errors can be introduced when data is moved or analyzed; if those errors go undetected, it can compromise the science.