The much-heralded technological singularity will happen in 2029, according to Google's director of engineering. Ray Kurzweil, a futurist who has made a name for himself through his predictions, shared his thoughts about what's in store for humans and machines in an interview with SXSW in Texas. He believes that the so-called singularity – the moment when artificial intelligence exceeds man's intellectual capacity and creates a runaway effect, which many believe will lead to the demise of the human race – is little over a decade away. "By 2029, computers will have human-level intelligence," said Mr Kurzweil. "That leads to computers having human intelligence, our putting them inside our brains, connecting them to the cloud, expanding who we are. It's here, in part, and it's going to accelerate."

Hangovers are a fixture of South by Southwest. Free branded booze abounds, turning late nights into too-early mornings filled with product demos and repetitive panels. But determined marketers and wide-eyed founders pitch on through the pain, in the unbridled belief they might just be SXSW's next breakout star. But this year, the conference itself feels a lot like a hangover. It's as if the coastal elites who attend each year finally woke up with a serious case of the Sunday scaries, realizing that the many apps, platforms, and doodads SXSW has launched and glorified over the years haven't really made the world a better place.

Keras, a popular deep learning library, has been updated with a new API to make it easier for developers to use machine learning in Python. Artificial intelligence is all the rage right now and techies are keen to explore ways they can use machine learning. Built in 2015 by Google software engineer and AI researcher François Chollet, Keras was designed to be used on top of TensorFlow and Theano – open-source software libraries developed by machine learning researchers at Google and the University of Montreal, Canada. The update, dubbed Keras 2, has been changed to adapt to TensorFlow API better, allowing developers to mix and match TensorFlow and Keras components together. Since the software runs on TensorFlow and Theano, there is no performance cost to using Keras compared to the other more complex frameworks. Keras is more specialized for deep learning than TensorFlow or Theano.

Wi-Fi is crucial to the way we work today. Fast, reliable, and consistent wireless coverage in an enterprise is business-critical. Many day-to-day operations in the enterprise depend on it. And yet, most of the time, IT teams are flying blind when it comes to individual experience. This springs from two main challenges. The first challenge is data collection.

AI has achieved milestones in mastering games like chess, Go, and, recently, poker--illustrating how successful machines have become at completing specific, narrow tasks. But can AI move beyond the narrow, toward achieving more general, human-like skills? On Thursday, Microsoft launched a competition to address this question. Microsoft's Project Malmo, which the company calls a "sophisticated AI experimentation platform," brings researchers together to use Minecraft as a testing tool for developing AI--smart, collaborative AI that can compete in a virtual world. The Malmo Collaborative AI Challenge asks PhD students to enter this world and create AI that can team up with randomly assigned players to compete for a high score in Minecraft.

New powerful InsurTech solutions are coming to market and they are driving insurance companies to rethink core systems strategies. Google's "Alpha Go" artificial intelligence (AI) program provides insight into the impact and change these latest solutions are bringing. Last year Alpha Go triumphed over the Go World Champion Lee Sodol in a 5-game match. For those imagining this as an example of computing brute force calculating all possible game moves to overtake a human--think again. Alpha Go actually taught itself how to play the game of Go using real matches and then playing matches itself; just like a human does but much, much faster.

And, it was first published on Dataconomy on Dec 21, 2016; I'm reposting here for ease of access.] In recent years, AI (artificial intelligence) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB. We can think of blockchains as "blue ocean" databases: they escape the "bloody red ocean" of sharks competing in an existing market, opting instead to be in a blue ocean of uncontested market space.

If you're a company entrenched in an arms race for artificial intelligence (AI) technologies, you could do worse than tapping into a pool of thousands of data scientists to augment your digital products and services. That's the pole position Google holds after acquiring crowdsourcing platform Kaggle last week for an undisclosed sum. Some 600,000 professional data crunchers use Kaggle to build prediction models for such heady challenges as cancer detection and heart disease diagnoses. And experts say Kaggle could help Google facilitate broader adoption of AI technologies. "Data science and machine learning is now global and this is a validation of the idea that Google recognizes that most of the smartest people in the world work for somebody else," Neil Jacobstein, who chairs the artificial intelligence and robotics track at Singularity University, told CIO.com.

Cloud computing was the first major attack on IT teams. It shattered our role as "procurement gatekeepers" for every new technology purchase, forcing us to find new ways to add value to the business. Now, an even greater threat is coming. Forrester predicts that by 2025, technologies like robots, artificial intelligence (AI), machine learning, and automation will replace 7% (or 22.7 million) jobs in the US alone. It won't just be blue-collar jobs operating factory machinery, either.

National Grid is to examine how artificial intelligence can be used to make the UK's power distribution infrastructure more efficient. The company admitted over the weekend that it is in talks with Google's DeepMind artificial intelligence unit, which it acquired for $400m in January 2014, as well as a number of other AI specialists. "We are in the very early stages of looking at the potential of working with DeepMind and exploring what opportunities they could offer for us," a spokesperson for National Grid told City AM. "There's huge potential for predictive machine learning technology to help energy systems reduce their environmental impact," they added. The news was broken on Saturday when DeepMind co-founder and CEO Demis Hassabis claimed in an interview with the Financial Times. "We're [in] early stages talking to National Grid and other big providers about how we could look at the sorts of problems they have.