Articles recently posted on DSC. But taking into account that we want data scientists to have a balance of common sense, business skills, creativity, a geeky love of statistics, and data skills management skills, I was curious what sorts of traits would be the opposite? What would add up to make a bad data scientist? Click here to read the article. Code samples is another great tool to start learning R, especially if you already use a different programming language.

Even today, a hacker with a command of artificial intelligence may be able to force a self-driving car to miss a stop sign, or a facial recognition system to believe it's seeing a completely different person in a security setting. Researchers have shown that virtual personal assistants like Siri or Google Now can be tricked into visiting potentially malicious websites by audio that sounds like white noise to humans. To thwart such hackers, Elon Musk's OpenAI and Pennsylvania State University released a new tool this week called "cleverhans," that lets artificial intelligence researchers test how vulnerable their AI is to adversarial examples, or purposefully malicious data meant to confuse the algorithms. Once the vulnerability has been found, a defense to the attack can automatically be applied. The tool is meant to be a "collection of attacks and defenses, along with tutorials on how to use them," according to Nicolas Papernot, co-creator and security researcher at Pennsylvania State University, in an email to Quartz.

Speaker: Richard E. Fairley, PhD, Principal Associate of Software and Systems Engineering Associates (S2EA) View bio (PDF, 228 KB) This full-day presentation will cover practical applications of systems engineering processes and methods as documented in SEBoK and 15288. SEBOK is the guide to the systems engineering body of knowledge; 15288 is the ISO/IEC/IEEE standard 15288:2015 for system engineering processes. Examples and case studies will be used to illustrate processes, methods, and techniques for developing purposefully engineered systems. In addition, the problems that arise when systems engineers and software engineers work together will be covered, as will approaches that can be used to mitigate those problems. This presentation is intended for those who are, or will be, involved in development or modification of a multidisciplinary engineered system and others who wish to learn more about systems engineering when software is a system element.

I am a data mining trainer and consultant. This book not only has good content, but it offers a 90 day license of software with which to rehearse the case study examples. My comments on the book will be accompanied by comments on the software. The book is the perfect fit for its intended audience. With the caution that certain readers will do better elsewhere, I think it is a great book.

The current crisis of refugees has divided European countries and societies into those who welcome refugees and those who oppose taking them. In this Viewpoint, we reflect on the role of universities and of computer science in such situations. As a case study, we describe an activity taken at the TU Wien (Vienna University of Technology): when the crisis of refugees culminated in summer 2015, a group of professors and students of the Faculty of Informatics initiated computer courses for unaccompanied young refugees. This project allowed the refugees to gain computer-related knowledge, and, equally important, to make contacts with local students. Another major goal of the project was to give a clear message that refugees are welcome.

LinkedIn wants to become more useful to workers by adding personalized news feeds, helpful messaging "bots" and recommendations for online training courses, as the professional networking service strives to be more than just a tool for job-hunting. The new services will arrive just as LinkedIn itself gains a new boss -- Microsoft -- which is paying 26 billion to acquire the Silicon Valley company later this year. LinkedIn said the new features, which it showed off to reporters Thursday, were in the works before the Microsoft takeover was announced in June. But LinkedIn CEO Jeff Weiner said his company hopes to incorporate some of Microsoft's technology as it builds more things like conversational "chat bots," or software that can carry on limited conversations, answer questions and perform tasks like making reservations. Chat bots are a hot new feature in the consumer tech world, where companies like Facebook, Apple and Google are already racing to offer useful services based on artificial intelligence. As a first step, LinkedIn says it will soon introduce a bot that could help someone schedule a meeting with another LinkedIn user, by comparing calendars and suggesting a convenient time and meeting place.

Google's smart messaging service Allo is finally available. The app differentiates itself from the plethora of messaging clients by integrating Google Assistant, which calls upon the company's vast machine learning and artificial intelligence capabilities to provide contextual information as and when you need it. Here's how you can get started with Allo. Allo is mobile-first, which means you'll have to set it up using your phone number. As Allo is a mobile-first messaging service, it relies on your phone contacts.

This is a follow up to an article I wrote last year, Machine Learning in a Week, on how I kickstarted my way into machine learning (ml) by devoting five days to the subject. After this highly effective introduction, I continued learning on my spare time and almost exactly one year later I did my first ml project at work, which involved using various ml and natural language processing (nlp) techniques to qualify sales leads at Xeneta. This felt like a blessing: getting paid to do something I normally did for fun! It also ripped me out of the delusion that only people with masters degrees or Ph.D's work with ml professionally. The truth is you don't need much maths to get started with machine learning, and you don't need a degree to use it professionally.

Much ink has been spilled on the subject of how the jobs market is being impacted by artificial intelligence (AI) and robotics. The well-known study by economists Frey and Osborne published in 2013, which predicts that 47% of all currently existing jobs in the United States will come under threat over the next twenty years, is regularly brought out of the cupboard as a terrifying spectre. Other far more optimistic studies, based on longer time-frames, have delivered a riposte to this – largely unfounded – scaremongering, which has in fact been repeated many times over throughout our history. However, the potential impact of AI on general education and vocational skills training – two means of preparing people for the labour market – is still being largely disregarded. The model whereby you learn during the first half of your life and spend the remaining years applying what you have learned in the world of work has held up pretty well.

Artificial Intelligence, or AI, is all around us and we might not even realize it! As a technology that imitates human decision making, it is present in a lot of what we use every day. AI can be found in Amazon.com Facebook tells you whom you could know and want to be friends with. Cars can automatically park for you.