BEIJING, CHINA - Media OutReach - 2 February 2018 - Fung Retailing Limited, with a network of over 3,000 stores, and JD.com (Nasdaq: JD, JD.com or JD.COM), China's largest retailer, today announced a partnership to develop AI-driven retail solutions. According to the agreement signed today, the two companies will cooperate in using AI to transform the retail landscape, and to collaborate in areas including AI platform development and the application of AI to smart retail. The agreement calls for the establishment of an AI Boundaryless Retail Center that will oversee and manage cooperative research and development projects, and facilitate the sharing of information and expertise relating to AI technology. Leveraging AI, and combining JD.com's extensive online expertise and Fung Retailing's offline expertise, the two companies aim to develop a new retail format for China and Asia. This includes creating an AI-driven retail system that seamlessly integrates online and offline retail platforms; developing an end-to-end system that enables the management of products, pricing, storage, order and payment; and enhancing consumer experience through solutions such as AI-driven virtual fitting, unmanned stores and smart shopping assistants.

I understand where the article is coming from, but it sounds to me like a classic case of projecting their biases (i.e. the author has very strong feelings about a topic which is pretty marginal to the research papers themselves, and interprets the claims made in the papers through the lens of a worldview in which that topic is very important) For example, AlphaGo Zero makes claims about not using any human (Go) knowledge, which is, by human standards, pretty close to true. It mostly only uses general assumptions which would apply to most turn-based, perfect information board games. While that is certainly "knowledge about Go" in the strictest sense, such a distinction is pretty irrelevant in practice. The paper never claimed it had spawned an AGI that could solve any general problem without human intervention -- the context makes it pretty clear that the research applies to a narrow domain, and I don't believe any claims are made about not making any assumptions which rely on the properties of that narrow domain (indeed, such assumptions existing is pretty much a given -- whether they were implemented on purpose or by pure chance, the fact that it works in a domain and not in another is proof that this is the case)

Self-driving cars and robots that can zoom on their own around warehouses are just some of what's possible because of artificial intelligence. But expect unforeseen consequences if researchers ignore the inherent ethical dilemmas in the emerging technology. That's one of the takeaways from a panel about AI ethics and education in San Francisco that was hosted by the Future of Life Institute, a research group focused on preventing societal problems created by the technology. Although humans typically program AI-powered robots to accomplish a particular goal, these robots will typically make decisions on their own to reach the goal, explained Benjamin Kuipers, a computer science professor and AI researcher at the University of Michigan. Get Data Sheet, Fortune's technology newsletter.

First, the futurist Amy Webb told the audience of journalists, librarians and foundation managers that they could easily be duped by the ever-growing purveyors of artificial intelligence. Images of their faces could be affixed to others' bodies, their voices to impostors. Media people have acknowledged to pollsters that they are so focused on the present that they don't pay close attention to what might be in store for them in five, 10 or 20 years. Later in her talk Wednesday before the Knight Media Forum in Miami, Webb told people of color that they weren't thought about when the creators of self-driving cars, GPS navigators, robotics and other such technologies were being developed. "My question is, what does all this mean for communities of color?" (video) asked Sara Lomax-Reese, president and CEO of black talk-formatted WURD radio in Philadelphia. She was one of about 500 at the sold-out conference sponsored by the John S. and James L. Knight Foundation. "It's not good," replied Webb. "Any person of color who's ever felt invisible, you're totally invisible to the networks. Right?" said the author of the 2016 book "The Signals Are Talking: Why Today's Fringe Is Tomorrow's Mainstream: Forecast and Take Action on Tomorrow's Trends, Today."

This week we'll be looking at some of the papers from WSDM'18. To kick things off I've chosen a paper tackling the problem of detecting fake news on social media. One of the challenges here is that fake news messages (the better ones anyway), are crafted to look just like real news. So classifying messages based on their content can be difficult. The big idea in'Tracking fake news footprints' is that the way a message spreads through a network gives a strong indication of the kind of information it contains.

Music genre classification is one example of content-based analysis of music signals. Traditionally, human-engineered features were used to automatize this task and 61% accuracy has been achieved in the 10-genre classification. However, it's still below the 70% accuracy that humans could achieve in the same task. Here, we propose a new method that combines knowledge of human perception study in music genre classification and the neurophysiology of the auditory system. The method works by training a simple convolutional neural network (CNN) to classify a short segment of the music signal. Then, the genre of a music is determined by splitting it into short segments and then combining CNN's predictions from all short segments. After training, this method achieves human-level (70%) accuracy and the filters learned in the CNN resemble the spectrotemporal receptive field (STRF) in the auditory system.

Netflix's sci-fi adaptations are about to expand beyond the likes of Altered Carbon and Lost in Space. Deadline has learned that Netflix has secured the rights to Life Sentence, a movie take on a Matthew Baker short story about a future where authorities wipe the memories of criminals instead of sending them to prison. Matt Reeves, the director of the two most recent Planet of the Apes movies, is producing the title. It's not known when the movie would premiere, but apparently that's not the big story -- there was reportedly a fierce bidding war. Reeves apparently had to contend with offers from conventional studios like Fox, Universal and Warner as well as Amazon and newcomer Apple.

Have a look at fixed point functional analysis, which in physics is encountered in statistical physics and chaos theory. So far, fixed point analysis within machine learning is used for theoretical analysis of gradient methods (e.g. Non linear analysis and fixed point methods might lead to better descriptions of spatio-temporal invariance that seems to be important for better network models.

There are plenty of papers on the use of machine learning for finance. Most of them end up saying that the results look somehow promising but more work is needed. When I read these papers my thinking is that if it would really work, it will not be published. It will be part of an hedge fund or somebody will be secretly trading and taking the profits. Have you found any solid paper on the use of machine learning for investing? Do you mind sharing it?

We might be tempted to think that artificial intelligence is a 21st-century invention, but to be exact, the technology claims its origin from mythical stories. Although not clearly defined then, humans always thought of creating mechanical beings that would do superhuman tasks, with the idea being prominent with Greek's Pygmalion and Hephaestus myths. In the 1940s, the discovery of a programmable digital computer brought great light to the mythical AI, and the technology got into books. Come 1956, scientists from Dartmouth College embraced artificial intelligence as their major tool for conducting scientific research. Hopes were high with the tech, which resulted in millions of dollars being driven to research.