Artificial-intelligence systems can do increasingly complex tasks but they can't yet figure much out on their own without help from humans. In a paper published Wednesday in the journal Nature, researchers at Alphabet Inc.'s Google DeepMind describe experimental software that they say gets closer to that goal and could be more accurate and less costly than current systems. "There's a lot of things it could be used for," he said. One obvious future application is "chatbots," software that answers questions autonomously, he said. The new DeepMind prototype couples so-called artificial neural networks -- which are widely used for image and speech recognition -- with an external memory.

Baidu, a China-based search engine business, took the wraps off a digital health tool to field medical queries and conversations between physicians and their patients called Melody medical assistant. The company claimed in a news release that the app uses deep learning to help doctors gather information from patients about their medical conditions and help physicians arrive at a diagnosis. To give an idea how the bot is designed to work, a spokeswoman provided an overview, in response to emailed questions. When a patient opens the app to pose a question, Melody asks the patient relevant follow-up questions to clarify information such as the duration, severity, and frequency of symptoms. The questions can also touch on additional symptoms related to the condition, even though the patient may not have mentioned them. The point is to give the doctor a more detailed sense of the patient's condition to decide whether to recommend the patient for an appointment sooner rather than later.

The White House National Science and Technology Council released a report on the future of artificial intelligence Wednesday that predicted regulatory challenges, future job losses, more capable U.S. cyber-defenses and little chance of a Terminator-esque super-intelligent computer apocalypse. "If computers could exert control over many critical systems, the result could be havoc, with humans no longer in control of their destiny at its best and extinct at its worst," the report, titled "Preparing for the Future of Artificial Intelligence" said. "This scenario has long been the subject of science fiction series, and recent pronouncements from some influential industry leaders have highlighted these fears." The report was likely referencing comments from Microsoft founder Bill Gates, theoretical physicist Stephen Hawking and Tesla CEO Elon Musk--and, for what it's worth, several Arnold Schwarzenegger-led dystopian films--warning of the dangers of AI. But the NSTC, a Cabinet-level group that coordinates science and tech policy, held "a more positive view" of AI's future, with the technology's systems serving as "helpers, assistants, trainers and teammates of humans."

IBM (NYSE:IBM) has a lot of moving parts, and a vociferous crowd of critics. In the process of analysis, it's easy to be overwhelmed by complexity, or sidetracked into refuting mindless attacks by the ill-informed. In the interest of simplicity, this article focuses on hard evidence of the company's progress in exploiting the developing market for Artificial Intelligence, Machine Learning, or Cognitive Computing. The quarterly and annual financial results provide segment information, to include year over year revenue growth and pre-tax margins. Looking at 2Q 2016, Cognitive Solutions at 4.4% is the only segment showing growth, and at 27.5% the second highest (after Global Financing) in pre-tax margins.

You must know how to load data before you can use it to train a machine learning model. When starting out, it is a good idea to stick with small in-memory datasets using standard file formats like comma separated value (.csv). How to Load Machine Learning Data From Scratch In Python Photo by Amanda B, some rights reserved. The standard file format for small datasets is Comma Separated Values or CSV. In it's simplest form, CSV files are comprised of rows of data.

Brown University researchers have developed a new image analysis technique to distinguish two key cancer cell types associated with tumor progression. The approach could help in pre-clinical screening of cancer drugs and shed light on a cellular metamorphosis that is associated with more malignant and drug-resistant cancers. The epithelial-mesenchymal transition, or EMT, is a process by which more docile epithelial cells transform into more aggressive mesenchymal cells. Tumors with higher numbers of mesenchymal cells are often more malignant and more resistant to drug therapies. The new technique combines microscopic imaging with a machine learning algorithm to better identify and distinguish between the two cell types in laboratory samples.

Google scientists have created a computer program that uses basic reasoning to learn to navigate the London Underground system by itself. The same Artificial Intelligence (AI) agent could also answer questions about the content of snippets of stories and work out family relationships by looking at a family tree. Scientists predict that in future a similar approach could pave the way for virtual assistants that would be able to instantaneously scour the internet to answer questions and carry out instructions with precision. Herbert Jaegar, a computer scientist at the University of Bremen, said: "I think this can be described as rational reasoning. They [the tasks] involve planning and structuring information into chunks and re-combining them."

This article was written By Henri Waelbroeck, Head of Research at Portware. Execution management has matured from laying the foundation for electronification by automating repetitive workflows to extracting progressively more value from the infrastructure as it evolves. Each generation in execution management technology has pushed automation one level higher in the decision hierarchy. The FM London Summit is almost here. Today, we are seeing the dawning of the next generation of execution management systems: one where AI works with the trader to combine the best of quantitative optimization (at speed and at scale) and the trader's domain knowledge.

In an interview in Vox, Marc Andreessen asserted that Vinod Khosla "has written all these stories about how doctors are going to go away…And I think he is completely wrong." Mr. Khosla was quick to respond via Twitter: "Maybe @pmarca [Mr. Andreessen] should read what I think before assuming what I said about doctors going away." He included a link to his detailed "speculations and musings" on the topic. It turns out that Mr. Khosla believes that AI will take away 80 percent of physicians' work, but not necessarily 80 percent of their jobs, leaving them more time to focus on the "human aspects of medical practice such as empathy and ethical choices." That is not necessarily much different than Mr. Andreessen's prediction that "the job of a doctor shifts and becomes a higher-level, more important job that pays better as the doctor becomes augmented by smarter computers."

There's a lot of movement in the tech space today, as developments in AI, machine learning and now deep learning are coming at a pace best described as rapid-fire. There's a substantial amount of buzz around that last term, though--the newest to the group of powerhouses with the potential to change everything. Let's examine what exactly makes deep learning so promising and explore what it means for the enterprise. Deep learning falls under the umbrella of artificial neural networks (ANNs), which, essentially, are clusters of virtual neurons created to learn from data sans human supervision. If this sounds a whole lot like what you know of machine learning, that's because it is--both techniques extract statistics and classify results after looking through large amounts of data.