In the foothills of the Appalachian Mountains in a corner of South Carolina sits a town that should be economically dead. For decades, Greenville was the heart of the state's textile industry--and its economic engine. First attracted by the area's fast-moving rivers as a way to power looms, textile manufacturers employed tens of thousands of people here. Beginning in the 1970s, however, facing competition from lower-cost manufacturing regions like Mexico and Southeast Asia, these companies began to struggle. Over the next decades, many factories closed.

"We used Machine Learning as a Service (MLaaS) software for a year, and loved it. I thought it was exactly what we needed until I realized that the models I was writing on the side were better than the ones we had in production," explains Johnny Hilgers, the VP of Business Analytics at Spring Venture Group, an inside sales and marketing company. "We used Machine Learning as a Service (MLaaS) software for a year, and loved it. I thought it was exactly what we needed until I realized that the models I was writing on the side were better than the ones we had in production." What Johnny reffered to as Machine Learning as a Service software professes to make machine learning so simple that even a ML novice can click and point their way to a predictive algorithm that will help their company make smarter, data-driven decisions after only a few weeks of training.

If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Here is a reading roadmap of Deep Learning papers! You will find many papers that are quite new but really worth reading. After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues. The following papers will take you in-depth understanding of the Deep Learning method, Deep Learning in different areas of application and the frontiers.

Japanese team plans AI medical supercomputer to rival Watson Dr. Watson, your Japanese brother should be arriving soon. An artificial intelligence system that can accurately diagnose a patient and suggest the best treatment is being developed by Kyoto University and Fujitsu Ltd. The hope is that it will emulate IBM's Watson supercomputer, which is famed for AI use in medicine using a big data system, and is named after Thomas J. Watson, the founder of IBM, rather than Sherlock Holmes' sidekick. The new system will analyze the genetic codes of patients to make its assessments. To run different simulations on relationships between diseases and a number of genes with integrated various data, it will be fed databases of worldwide medical records as well as gene information.

Could this be any more disturbing? A dozen years after they went off the air, the cast of Friends has been virtually reunited for a project that turned one of them into a video chatbot. As spotted by Prosthetic Knowledge, researchers at the University of Leeds used machine learning to create automated video avatars that speak in the voices of their characters. The result is a system that uses the original performances recorded by the actors to generate brief new scenes, starting with Matt LeBlanc's immortal Joey Tribbiani. The system was demonstrated this weekend at a European Conference on Computer Vision workshop.

In May, the White House Office of Science and Technology Policy (OSTP) announced "a new series of workshops and an interagency working group to learn more about the benefits and risks of artificial intelligence." They hosted a June Workshop on Safety and Control for AI (videos), along with three other workshops, and issued a general request for information on AI (see MIRI's primary submission here). The OSTP has now released a report summarizing its conclusions, "Preparing for the Future of Artificial Intelligence," and the result is very promising. The OSTP acknowledges the ongoing discussion about AI risk, and recommends "investing in research on longer-term capabilities and how their challenges might be managed": General AI (sometimes called Artificial General Intelligence, or AGI) refers to a notional future AI system that exhibits apparently intelligent behavior at least as advanced as a person across the full range of cognitive tasks. A broad chasm seems to separate today's Narrow AI from the much more difficult challenge of General AI. Attempts to reach General AI by expanding Narrow AI solutions have made little headway over many decades of research.

Nate, Malo, Jessica, Tsvi, and I will be answering questions tomorrow at the Effective Altruism Forum. If you've been curious about anything related to our research, plans, or general thoughts, you're invited to submit your own questions in the comments below or at Ask MIRI Anything. We've also posted a more detailed version of our fundraiser overview and case for MIRI at the EA Forum. In other news, we have a new talk out with an overview of "Logical Induction," our recent paper presenting (as Critch puts it) "a financial solution to the computer science problem of metamathematics": This version of the talk goes into more technical detail than our previous talk on logical induction. For some recent discussions of the new framework, see Shtetl-Optimized, n-Category Café, and Hacker News.

Salesforce has a history of staying close to the cutting edge of technology, so it shouldn't be surprising that it announced an artificial intelligence initiative recently, it dubbed Einstein. We caught up with some key members of the Einstein team at Dreamforce earlier this month and asked them to explain the new technology for us. Einstein isn't a product so much as a set of intelligence functionality that underlies the entire Salesforce platform, and while the types of functionality that it's enabling now are somewhat limited, the idea is to provide a base on top of which the company can continue to add new capabilities into the future. Today, Einstein can provide information like predictive lead scoring and opportunity insights, which alert a rep how a deal is trending -- the kinds of information many CRM applications have been offering for some time -- but as the technology develops, the company sees a much bigger role for it. As John Ball, GM of Einstein at Salesforce explains, it's really aimed at making life easier for users.

Like many in Silicon Valley, technology entrepreneur Bryan Johnson sees a future in which intelligent machines can do things like drive cars on their own and anticipate our needs before we ask. What's uncommon is how Johnson wants to respond: find a way to supercharge the human brain so that we can keep up with the machines. From an unassuming office in Venice Beach, his science-fiction-meets-science start-up, Kernel, is building a tiny chip that can be implanted in the brain to help people suffering from neurological damage caused by strokes, Alzheimer's or concussions. Top neuroscientists who are building the chip -- they call it a neuroprosthetic -- hope that in the longer term, it will be able to boost intelligence, memory and other cognitive tasks. The medical device is years in the making, Johnson acknowledges, but he can afford the time.

The smart energy startup Verdigris announced today that it has raised 6.7 million to scale production of its Einstein smart sensor and frequency detectors. The sensors are used to predict the failure of machines and improve energy efficiency. Factories, manufacturing facilities, and other large buildings using Verdigris technology reduce energy use 8 to 22 percent, CEO Mark Chung told VentureBeat in a phone interview. The Einstein frequency detector from Verdigris made its debut in August. "Rather than take a big data approach where we study thousands of motors and this is the failure pattern, we instead take a physics based model which is looking at a signal through our sensors," Chung said.