"AI will fundamentally change the way we live and work. Therefore, we need to get it right and develop this technology in a way, which ensures the trust and security of our citizens while benefitting our economy," a commission spokesperson told EUobserver. In the EU, the largest and most well-established companies are likely to become first adopters of AI technologies, such as automotive companies in Germany or finance firms in the UK. However, the LinkedIn findings suggest that the current market ecosystem for AI in Europe is uneven across both gender and demographic lines. The EU Commission president-elect Ursula von der Leyen has promised that during the first three months in office, the college of commissioners will put forward legislation for a "coordinated approach on the human and ethical implication of AI".

Bill Gates stopped working full-time at Microsoft in 2008 and stepped down as chairman in 2014. Over the course of nearly two decades, Gates has shifted his focus from personal computers to philanthropy. Yet despite easing his workload at Microsoft and his dedication to giving away money, Bill Gates is once again the world's richest person. As of Nov. 16, Gates has a net worth of $110 billion, according to Bloomberg. Gates narrowly unseated Amazon head Jeff Bezos, who's worth approx.

AI brings mixed emotions and opinions when referenced in the context of jobs. If you ask the question "Do you think Artificial Intelligence will be a net job killer or net job creator?" to colleagues, friends, or strangers you're bound to get some very strong opinions on this subject. For sure you will hear an interesting and conflicting set of opinions that range from "AI will destroy all jobs as we know it" to "AI will enable us to work better and do new things we've never been able to do". If you look at various economic and analyst predictions, their assessments are all over the place, ranging from dramatic job losses across most economic sectors to large increases in employment due to dramatic increases in job productivity. Of course, as with everything, the true answer will be somewhere in the middle.

DUBAI: Dubai Government Workshop (DGW) recently organized an interactive seminar entitled "How to Measure Productivity" as part of its ongoing partnership with Dubai Customs. The key features of the Productivity Engine, which aims to utilize Artificial Intelligence (AI) technologies, were reviewed in order to help improve productivity and increase efficiency. The move aligns with DGW s efforts to speed up the digital transformation process and integrate AI to be part and parcel of any tools intended to enhance the efficiency and effectiveness of the government sector. Furthermore, it complements the vision of the wise leadership in steering Dubai and the UAE towards developing an integrated system that employs AI in all vital areas. The workshop was held in the presence of Khalid Ahmed Al Doubi, Director-Corporate Support, DGW and Ahmed Abdul Salam Kazim, Director of Strategy and Corporate Excellence, Dubai Customs, during which time DGW employees were acquainted with the importance of the Productivity Engine.

In this episode, I'll explain 2 common types of attacks and 2 common types of defenses using various code demos from across the Web. There's some really dope mathematics involved with adversarial attacks, and it was a lot of fun reading about the'cat and mouse' game between new attack techniques, followed by new defense techniques. I encourage anyone new to the field who finds this stuff interesting to learn more about it. Let's look into some math, code, and examples. Are you a total beginner to machine learning?

OK, let's start from the beginning: I just read that in 2018 the US government announced a new security clearance program - including for individuals in civilian roles - which would run "continuous evaluations" of all applicants, thanks to machine learning technology. The article itself highlights the obvious risks of such a system "going off the rails", but the really interesting questions here are: At least in certain cases, we may never know the answer to the first question because, as the article says, certain systems "offer little to no insight as to how their highly accurate predictions are actually made". But hey, we are only talking of national security, no big deal right? So let's focus on the second question. The article does correctly acknowledge my first thought when I read its title: "if the system works, it might actually generate deeper problems still".

Machine learning is automating the more manually-based, routine incident analysis, and escalation tasks that are overwhelming security analysts today. Capitalizing on supervised machine learnings' innate ability to fine-tune algorythms in milliseconds based on the analysis of incidence data, endpoint security providers are prioritizing this area in product developnent. Demand from potential customers remains strong, as nearly everyone is facing a cybersecurity skills shortage while facing an onslaught of breach attempts. "The cybersecurity skills shortage has been growing for some time, and so have the number and complexity of attacks; using machine learning to augment the few available skilled people can help ease this. What's exciting about the state of the industry right now is that recent advances in Machine Learning methods are poised to make their way into deployable products," Absolute's CTO Nicko van Someren added.

With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First we detect rumors as a type of misinformation propagation and next we go beyond detection to perform the task of rumor classification. WE explore the problem using a standard data set. We devise novel features and study their impact on the task. We experiment with various levels of preprocessing as a precursor of the classification as well as grouping of features. We achieve and f-measure of over 0.82 in RDC task in mixed rumors data set and 84 percent in a single rumor data set using a two-step classification approach.

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.

This publication draws on a wide range of expertise to illuminate the year ahead. Even so, all our contributors have one thing in common: they are human. But advances in technology mean it is now possible to ask an artificial intelligence (AI) for its views on the coming year. We asked an AI called GPT-2, created by Openai, a research outfit. GPT-2 is an "unsupervised language model" trained using 40 gigabytes of text from the internet.