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).

With ever increasing data, Machine Learning is becoming the only way to get analytics done, making it possible to glean insights from vast amounts of data. But when starting a project, it is easy to ignore a critical fact: the value of data is also time sensitive – it expires! In order to get the highest value from data, Machine Learning needs to be applied in a rapid and repeatable way, so you can go from data to insight quickly. A Machine Learning API makes this possible. In this workshop, Poul Petersen CIO of BigML will give an overview of BigML's Machine Learning API and then show real-world examples of predictive applications that can be built using Python and node.js. Several tools that have been built on top of BigML's API will be demonstrated including a loan risk assessment, real estate arbitrage, and the world's first voice controlled predictive assistant.

Japanese roboticist Hiroshi Ishiguro displayed one of his androids on Sunday at the SXSW Interactive Festival. The android, which is modeled after Ishiguro, held an autonomous conversation in Japanese on stage with an Ishiguro associate. Pepper the robot looks on as panelists discuss advances in robot technology at the SXSW Interactive Festival. Robots played a key role at this year's gathering. AUSTIN – Androids, geminoids and old-fashioned robots roamed the halls of the South by Southwest Interactive Festival this weekend in a plethora of sessions and demonstrations.

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. Thereby we will see that a neuron and a convolutional net are very similar information processing machines. While performing this comparison, I will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. I will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century. This blog post is complex as it arcs over multiple topics in order to unify them into a coherent framework of thought. I have tried to make this article as readable as possible, but I might have not succeeded in all places.

China's ambitions to build its own version of AlphaGo or Sophia the Robot could be a boon for voice technology company iFlytek and surveillance equipment maker Hangzhou Hikvision Digital Technology. While China lags the U.S. in hardware like advanced circuit boards that are key to becoming an artificial intelligence powerhouse, the two Chinese companies showcase the nation's emerging strength in other key technologies that are vital to helping advanced computers learn: algorithms and big data.

IBM announced that its Watson AI is getting image recognition capabilities earlier in the year, but this site that lets you feed in your own photos to see what it thinks is in them is both impressive and scary. The visual recognition demo lets you give Watson an image URL or upload a photo and it'll come back in a few seconds with what it thinks it sees. This year's edition of TNW Conference in Amsterdam includes some of the biggest names in tech. In my tests I fed Watson a few random photos I had on hand and the accuracy was quite surprising. It could figure out what was in landscape shots, animals (down to the breed) and even what's in the background.

There are lots of conversations about the lack of diversity in science and tech these days. But along with them, people constantly ask, "So what? There are lots of ways to answer that question, but perhaps the easiest way is this: because a homogenous team produces homogenous products for a very heterogeneous world. This column will explore the products, research programs, and conclusions that are made not because any designer or scientist or engineer sets out to discriminate, but because the "normal" user always looks exactly the same. The result is products and research that are biased by design. Facial recognition systems are all over the place: Facebook, airports, shopping malls. And they're poised to become nearly ubiquitous as everything from a security measure to a way to recognize frequent shoppers. For some people that will make certain interactions even more seamless. But because many facial recognition systems struggle with non-white faces, for others, facial recognition is ...

DeepMind's stunning victories over Go legend Lee Se-dol have stoked excitement over artificial intelligence's potential more than any event in recent memory. But the Google subsidiary's AlphaGo program is far from its only project -- it's not even the main one. As co-founder Demis Hassabis said earlier in the week, DeepMind wants to "solve intelligence," and he has more than a few ideas about how to get there. Hassabis himself has had an unusual path to this point, but one that makes perfect sense in retrospect. A child chess prodigy who won the Pentamind championship at the Mind Sports Olympiad five times, he rose to fame at a young age with UK computer games developers Bullfrog and Lionhead, working on AI-heavy games like Theme Park and Black & White, and later forming his own studio, Elixir. Hassabis then left the games industry in the mid-2000s to complete a PhD in neuroscience before co-founding DeepMind in 2010.

Awesome post, Ray! It's always encouraging to learn that one is not alone in thinking there's a Tsumani of change ahead. And that we need to try and tame the beast, or the beast will tame us. I was yesterday at a (fascinating) breakfast seminar with a very small group of business leaders here in Argentina. One of the founders of the Singularity University was leading the debate. And one of the topics we discussed at large was the consequence of AI automating jobs… everywhere, anywhere.

A new Federal Government report has outlined the future of Australia's workforce and revealed growing demand for professionals in STEM industries. The report by CSIRO and the Australian Computer Society, titled Tomorrow's Digitally Enabled Workforce, identified six megatrends. The trends include continued advances in automation and artificial intelligence; jobs will be more flexible and agile due to digital technology; a requirement for entrepreneurial skills; and an increase in skills and education requirements for many professions. While the report found that 44% of Australian jobs would be impacted by these changes, Andrew Johnson, CEO of the Australian Computer Society and one of the report's authors, said there are numerous opportunities for engineers. 'The intent of this report is to look at a 15- to 20-year timeframe.