In 2014, Apple introduced a programming language called Swift that made waves in the developer community -- not just for its power and flexibility, but for how easy it is to learn. So easy, in fact, that Apple believes it could be anyone's first programming language. That's why it went ahead and created Swift Playgrounds, a free iPad app designed to teach kids how to code. Now, a year after its release, Apple is ready to step up its educational repertoire further. With the June 5th release of Swift Playgrounds 1.5, Apple's app will now teach kids to program robots and drones as well.

The EU must adopt policies for the development, deployment and use of artificial intelligence (AI) in Europe in such a way as to ensure it works for, rather than against, society and social well-being, the European Economic and Social Committee (EESC) said in an own-initiative opinion on the societal impact of AI, identifying 11 areas that need to be addressed. "We need a human-in-command approach to AI, where machines remain machines and people retain control over these machines at all times," said rapporteur Catelijne Muller (NL – Workers' Group). She did not refer to technical control alone. "Humans can and should also be in command of if, when and how AI is used in our daily lives – what tasks we transfer to AI, how transparent it is, if it is to be an ethical player. After all, it is up to us to decide if we want certain jobs to be performed, care to be given or medical decisions to be made by AI, and if we want to accept AI that may jeopardise our safety, privacy or autonomy," Muller argued.

Researchers at UC Berkeley have developed a robot that can pick up awkward and unusually shaped objects. The robot learned how to grasp different objects by studying a virtual library of 10,000 3D objects and suitable grasps. When a new object is placed in front of the bot, its deep-learning system quickly figures out what grasp the arm should use. When the robot was unsure of how to grasp an object, it poked it to figure out how to better grasp it. Deep-learning software tries to mimic the activity in layers of neurons in the neocortex, which makes up 80 percent of the brain and is where thinking occurs.

First of all, let me state that I was provided an advance electronic copy to review. I have no link or affiliation whatsoever with the author, so please consider my review to be written as objectively as possible. Also, my perspective is that of the lead strategic planner for technology for a large government entity, plus I have a degree in Industrial Engineering and about 20 years of IT and manufacturing experience combined. Finally, as a hobby I study and collect books regarding what futurists of the past predicted, and I find it fascinating what they got wrong and got right. So I suppose I am pretty much the exact target demographic for the book!

Editor's note: This is an overview of a multi-part tutorial on data science for newbies. The author has given the series a different -- tongue-in-cheek -- title; take it in stride and recognize that the series' approach and content is a fresh look at getting started with various aspects of data science from a software engineering perspective. To do some serious statistics with Python one should use a proper distribution like the one provided by Continuum Analytics. Of course, a manual installation of all the needed packages (Pandas, NumPy, Matplotlib etc.) is possible but beware the complexities and convoluted package dependencies. The installation under Windows is straightforward but avoid the usage of multiple Python installations (for example, Python3 and Python2 in parallel).

A global study finds several new categories of human jobs emerging, requiring skills and training that will take many companies by surprise. The threat that automation will eliminate a broad swath of jobs across the world economy is now well established. As artificial intelligence (AI) systems become ever more sophisticated, another wave of job displacement will almost certainly occur. It can be a distressing picture. But here's what we've been overlooking: Many new jobs will also be created -- jobs that look nothing like those that exist today.

Chatbots are about to change the world in more ways than we can imagine. Already, bots around the globe can complete a diverse set of varying tasks. From ordering pizza online to mashing faces together in Project Murphy, chatbots are about to become a normal element in everyday life. As the scope of chatbots becomes broader every day, there are new applications popping up constantly. Education has traditionally been known as a sector where innovation moves slowly.

I describe here the projects that I worked on, as well as career progress, starting 25 years ago as a PhD student in statistics, until today, and the transformation from statistician to data scientist that occurred slowly and started more than 20 years ago. This also illustrates many applications of data science, most are still active. My interest in mathematics started when I was 7 or 8, I remember being fascinated by the powers of 2 in primary school, and later purchasing cheap russian math books (Mir publisher) translated in French, for my entertainement. In high school, I participated in the mathematical olympiads, and did my own math research during math classes, rather than listening to the very boring lessons. When I attended college, I stopped showing up in the classroom altogether - afterall, you could just read the syllabus, memorize the material before the exam and regurgitate it at the exam.

We consider the online one-class collaborative filtering (CF) problem that consists of recommending items to users over time in an online fashion based on positive ratings only. This problem arises when users respond only occasionally to a recommendation with a positive rating, and never with a negative one. We study the impact of the probability of a user responding to a recommendation, p_f, on the sample complexity, i.e., the number of ratings required to make `good' recommendations, and ask whether receiving positive and negative ratings, instead of positive ratings only, improves the sample complexity. Both questions arise in the design of recommender systems. We introduce a simple probabilistic user model, and analyze the performance of an online user-based CF algorithm. We prove that after an initial cold start phase, where recommendations are invested in exploring the user's preferences, this algorithm makes---up to a fraction of the recommendations required for updating the user's preferences---perfect recommendations. The number of ratings required for the cold start phase is nearly proportional to 1/p_f, and that for updating the user's preferences is essentially independent of p_f. As a consequence we find that, receiving positive and negative ratings instead of only positive ones improves the number of ratings required for initial exploration by a factor of 1/p_f, which can be significant.

Fairness is a desirable property of decision rules applied to a population of individuals. For example, college admissions should be decided on variables describing merit, but may also need to take into account the fact that certain communities are inherently disadvantaged. At the same time, individuals should not feel that another individual in a similar situation obtained an unfair advantage. All this must be taken into account while still caring about optimizing for a decision maker's utility function. In particular, for a given distribution over a population, we wish to derive a decision rule that takes into account a merit variable, but also ensures fairness for members of disadvantaged groups. The problem becomes even more challenging when we take into account potential uncertainties in decision making models, which can even make strict notions of fairness impossible to satisfy. As an example, consider the problem of fair prediction with disparate impact as defined by Chouldechova [2016]. Informally, their formulation defines a statistic a such that true category y (also called outcome or true label) is conditionally independent of a sensitive variable z given the statistic and the model parameters θ, i.e. y