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.

Recently, there has been a sibilation about Chatbots. Chatbots are the applications that, instead of a graphical interface, make use of conversational UI. With any buzz-commendable innovation, there is the typical perplexity about what precisely a Chatbot is – and relying upon what number of articles you have perused, it is anywhere betwixt an improved IVR or the innovation which will bring about world peace. Conversational UI & Chatbots - neither of the two are new. Because of the underneath mentionedreasons the prevailing sibilation is ascribed. All organizations must think whether they need a system to have a "Conversational Application" – similarly they are needed for mobile applications.

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.

Read the posts here, then write your own. The idea that we're shifting to the "next economy," to borrow the title of an O'Reilly Media conference I recently co-hosted with Tim O'Reilly in San Francisco, presupposes that our current one is ending. And that of course can be an unsettling prospect. People are wondering how they'll pay the bills. What life will be like in a world where AI, augmented reality, and the Internet of Things proliferate so rapidly that even the most diehard technophiles begin to wonder how long they can keep up with the treadmill of progress.

What goes into making a computer understand the world through senses, learning and experience, as IBM says Watson does? To build a body of knowledge for Watson to work on Jeopardy, researchers put together 200 million pages of content, both structured and unstructured, including dictionaries and encyclopedias. When asked a question, Watson initially analyzes it using more than 100 algorithms, identifying any names, dates, geographic locations or other entities. It also examines the phrase structure and the grammar of the question to better gauge what's being asked. In all, it uses millions of logic rules to determine the best answers. Today Watson is frequently being applied to new areas, which means learning new material.