Academic Torrents is a platform for researchers to share data. It consists of two pieces: a site where users can search for datasets, and a BitTorrent backbone which makes sharing data scalable and fast. The goal is to facilitate the sharing of datasets amongst researchers. It was created by the Institute for Reproducible Research (a U.S. 501(c)3 non-profit). The site provides access to over 15TB of data including popular machine learning datasets such as all of UCI, Imagenet, and Wikipedia.

Data science, advanced machine learning and artificial intelligence will be centre stage technologies shaping business in 2017 and beyond. Data science, advanced machine learning and artificial intelligence will be centre stage technologies shaping business in 2017 and beyond. That's according to analyst firm, Gartner, which predicts that enterprise will see "intelligence everywhere" as new software-based systems, which are programmed to learn and adapt, permeate businesses within the next three to five years. As outlined by Gartner vice president and fellow, David Cearley, these intelligent trends will intertwine to form a'digital mesh', blurring physical and digital workspaces. Artificial intelligence (AI) and advanced machine learning (ML) are intelligent machines that can understand, learn and operate autonomously.

So what benefits could machine learning bring to the world of work? Machine learning presents an opportunity to obtain insights from data that human analysts may not see on their own. Artificial intelligent systems using machine learning have steadily improved in accuracy, for example in identifying images, which they can now do with greater accuracy than humans. If machines are used to'double check' human analysis and help with processing large amounts of data, this may result in less likelihood of mistakes, and reduction of risks and errors in some decision making. In fact, an algorithm (mathematical recipe) can be designed to ruthlessly deliver the range of tasks it is given, thus eliminating any human bias from the output.

This paper provides an analysis of the impact of using machine learning to conduct profiling of individuals in the context of the EU General Data Protection Regulation. We look at what profiling means and at the right that data subjects have not to be subject to decisions based solely on automated processing, including profiling, which produce legal effects concerning them or significantly affect them. We also look at data subjects' right to be informed about the existence of automated decision-making, including profiling, and their right to receive meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing. The purpose of this paper is to explore the application of relevant data protection rights and obligations to machine learning, including implications for the development and deployment of machine learning systems and the ways in which personal data are collected and used. In particular, we consider what compliance with the first data protection principle of lawful, fair, and transparent processing means in the context of using machine learning for profiling purposes.

One of the latest trends in the world of technology and engineering is "machine learning" -- in fact, all of the big technology companies today have invested in artificial intelligence and machine learning projects. The term "machine learning" was first defined by Arthur Samuel, way back in 1959. He defined it as "the ability to learn without being explicitly programmed," which basically means that a machine could learn from its own mistakes and reprogram itself to improve its performance over time. The idea gained popularity in the 90s when the concept of data mining came into existence. Data mining uses algorithms to look for patterns in a given set of information, which led to data-driven predictions and decision making.

Artificial Intelligence is not about statistics, is about experience. In this webinar Stuart Waplington, Co-Founder and CEO, will walk you through what deep learning really means and the possibilities that have been unlocked to the marketing world. The global market for AI is set to be worth $5.05 Billion by 2020 (Markets & Markets). Happy Finish is already ahead of the curve; we've just presented Shoegazer, a unique Proof of Concept that uses AI and Transfer Learning to identify the exact brand and style of trainers in real-time – with 95% accuracy and Buzzteam, our on-demand workforce resource platform which allows individual companies building teams by employing global network of resources that can be discovered by skill-set, experience, cost or rating. Join this webinar to understand AI and how its rapid adoption is set to transform a range of markets, from advertising and media to finance and retail, offering benefits such as improved productivity and increased customer satisfaction.

Machine Learning is cognitive computing process that integrates artificial intelligence with our every day machines. As the term suggests, the concept of Machine Learning creates a continuous learning procedure for the machine itself. With Machine Learning, the most familiar of machines, our everyday computers can now learn on their own and make decisions. This leads to lesser human interaction and interference along with decreased amount of programming. The idea is to program the computer in a manner to allow it to program itself in the future with smart and implicit decisions.

They say great data is 95% of the problem in machine learning. We saw first hand at Udacity that this is the case, with the amazing reception from the machine learning community when we open sourced over 250GB of driving data. But, finding interesting data is really hard, and actively holds the industry back from progress. In trying to learn more about this problem I searched far and wide, and cataloged just a sliver of the datasets I found. I've also been fascinated with the militarized interstates disputes dataset, which includes 200 years of international threats and conflicts.

As the election results rolled in last night, it became increasingly clear that America -- and the world -- would never be the same. The American people overlooked all of Republican nominee Donald Trump's faults and elected him to office in the belief that he will fix the nation's deep-seated problems of inequity and injustice. And they rebelled against the business interests and corruption that they believed Hillary Clinton represented. Trump's victory was enabled by technology -- everything from his use of social media to Clinton's email scandals to Russian hacking. But advancements in technology and how they reshape our economy may also keep him from delivering on some of the major promises that made him so popular during the campaign season.

The online shopping industry is about to get a facelift, or at least, a lift in intelligence technology. According to Entrepreneur, nearly 68 percent of online shoppers abandon their shopping carts, which has many in the ecommerce industry wondering how to hold consumer attention for longer. For the CEO of Sentient Technologies, Antoine Blondeau, the solution just might be Artificial intelligence. Here are 5 ways Artificial Intelligence could forever alter the ecommerce playing field. Hard to please shoppers just might have met their match in software designed to actually analyze images uploaded by customers and find similar, or matching, products for sale.