"Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten." These words, articulated by Neil Armstrong at a speech to a joint session of Congress in 1969, fit squarely into most every decade since the turn of the century, and it seems to safe to posit that the rate of change in technology has accelerated to an exponential degree in the last two decades, especially in the areas of artificial intelligence and machine learning. Artificial intelligence is making an extreme entrance into almost every facet of society in predicted and unforeseen ways, causing both excitement and trepidation. This reaction alone is predictable, but can we really predict the associated risks involved?

Is there anyone working on either a decentralized deep learning algorithm, or a consumer facing app that uses AI to help people diagnose themselves? My wife was just diagnosed with CVID a couple of weeks ago, it's like AIDS except it's not Aquired, it's part genetic and part environmental - but it's a rare primary immunodeficiency disease. She's had this her entire life. She was misdiagnosed 3 or 4 times, most recently she was eating gluten free for the last 8 years because she was diagnosed as celiac disease. She's lost most of her hair over the last 6 months and has been in the hospital 3-4 times this year.

Tamr's platform, which combines machineMapping Startup Leverages Machine Vision. Read more ... » scientists, can prepare and unify these large, disparate …

A big data analytics platform is being deployed by the Ministry of Defence as it looks to take the weight of military analysts and improve threat identification. The analytics platform, which incorporates machine learning technology, will be used for operational support and comes from a recently signed contract by the Defence Science and Technology Laboratory's Centre for Defence Enterprise (CDE) and Massive Analytic, a London based company. The idea behind the platform from Massive Analytic is that it will help make it possible to examine outcomes of decisions before they are put into effect, effectively acting as a trusted advisor. Under the deployment the MoD will use the system to bring together surveillance data from static and dynamic sources which will feed an interactive dashboard. The dashboard will be capable of providing insights that will help to identify threats in advance and can be used for planning different scenarios. Military analysts are currently the main source for sifting through large volumes of data, it is hoped that this platform will help to remove some of that burden.

Being an analyst working on statistical analysis, more often than none, there is almost always a need to "explain" how did you come up with the conclusion with the techniques applied? The most demanding part of all this is: " you must explain this like I am 5 years old." Oki, so let me try to explain one machine learning algorithm called "randomforest" for a classification problem that is recognised as a black-box algorithm! Let's get started with a metaphor that hopefully is as close to everyday life as possible... In a congress, say that there are 100 members in total, they need to vote to decide whether they are going to pass a new law or not.

The video does not say that if Atlas comes after you, it will find you and kill you. Your own paranoid mind made that bit up for itself. But Courtney Hohne, a PR person at the corporate parent of Boston Dynamics, which made Atlas, wrote an internal email that described it as "terrifying, ready to take humans' jobs": We're not going to comment on this video because there's really not a lot we can add, and we don't want to answer most of the Qs it triggers. Here's a snatch of the video she was talking about: Google is now planning to sell Boston Dynamics, the company under Google-parent Alphabet that makes military robots, because they aren't commercial enough. "We don't want to answer most of the Qs it triggers" could mean a lot of different things -- most likely that it is difficult to get across the benefit of a product that can do a bunch of things humans can do when the media atmosphere is filled with humans worrying about machines taking their jobs (robots don't steal jobs, by the way).

South Korea, well known for its IT infrastructure, is promising 3.5 trillion won ( 3 billion) in funding from the public and private sectors to develop artificial intelligence for corporate and university AI projects. South Korea's President Park Geun-hye assembled leaders across the country's tech industry and senior government officials in Seoul last week to announce plans to invest the amount over the next five years. It appears to be largely a reaction to the phenomenal performance of Google's algorithm AlphaGo in an historic AI-versus-human game in Seoul earlier this month, which captured the South Korean media's imagination. "Above all, Korean society is ironically lucky, that thanks to the'AlphaGo shock' we have learned the importance of AI before it is too late," the president told local reporters assembled for the meeting, describing the game as a watershed moment of an imminent "fourth industrial revolution". It also calls for the private sector to match the public sector's commitment with 2.5 trillion won ( 2.14 billion).

Neuromorphic computing – brain inspired computing – has long been a tantalizing goal. The human brain does with around 20 watts what supercomputers do with megawatts. While neuromorphic computing progress has been intriguing, it has still not proven very practical. This week neuromorphic computing takes another step forward with a workshop being offered to users from academia, industry and education interested in using two European neuromorphic systems that have been years in development and are coming online for broader use – the BrainScaleS system launching at the Kirchhoff Institute for Physics of Heidelberg University and SpiNNaker, a complementary approach and similarly sized system at the University of Manchester. Ramping up BrainScaleS and SpiNNaker is an important milestone, strengthening Europe's position in hardware development for alternative computing. Both projects are part of the European Human Brain Project, originally funded by the European Commission's Future Emerging Technologies program (2005-2015).

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Apple has revealed a 29 armed robot that can rip apart an iPhone in 11 seconds for recycling. It is hoped the machine will help recycle silver, tungsten and other metals from the handsets. The system started to operate at full capacity last month and can take apart one iPhone 6 every 11 seconds to recover aluminum, copper, tin, tungsten, cobalt, gold and silver parts, according to Apple. The system started to operate at full capacity last month and can take apart one iPhone 6 every 11 seconds to recover aluminum, copper, tin, tungsten, cobalt, gold and silver parts, according to Apple. It has already been installed near Apple's HQ in Cupertino, and it plans to build a second in Europe.