George Hotz has landed 3.1 million from Andreessen Horowitz and others to help fulfill… more George Hotz has been bragging for months about his plan to beat Tesla and Google with his self-driving car technology. Now his San Francisco startup, Comma.ai, has raised 3.1 million from Andreessen Horowitz (A16Z) and others who are betting he might be able to do it. George Hotz has landed 3.1 million from Andreessen Horowitz and others to help fulfill… more Chris Dixon, a partner at A16Z, disclosed in a blog on Sunday that his firm led the funding which had been rumored since early last month. Dixon said he was initially skeptical of Hotz' claims that he could mass produce technology that would convert existing cars into semi-autonomous vehicles. Click here to get the free daily TechFlash Silicon Valley newsletter.

More than 20 private companies working to advance artificial intelligence technologies have been acquired in the last 3 years by corporate giants competing in the space, including Google, Amazon, Apple, IBM, Yahoo, Facebook, Intel, and, more recently, Salesforce. There have been 4 major acquisitions already in 2016. Google has been the most prominent global player, with 5 key acquisitions under its belt (follow all of Google's M&A activity here, through our real-time Google acquisitions tracker). In 2013, the corporate giant picked up deep learning and neural network startup DNNresearch from the computer science department at the University of Toronto. This acquisition reportedly helped Google make major upgrades to its image search feature.

Over 25 companies raised equity funding rounds in the last quarter. Deal activity was up 7x from Q1'11. Deal activity in artificial intelligence has now hit record highs for two quarters straight. Deal count had already leapt to 24 in Q4'15, ten more deals than the previous quarter. The trend continued in Q1'16, with deals reaching a 5-year quarterly high, and passing the 25-deal threshold.

Technology advances not so much when it exhibits innovation, but when it becomes truly practical for everyday people. In 2016, we'll see an acceleration of that shift of technologies from the drawing board and geek-only curiosities to consumer devices that change our lives in ways small and big. Here are a handful of technologies that are on the cusp of major action in the coming year. For decades, artificial intelligence was a thing best understood by sci-fi fanatics and screenwriters. That started to change n 2011 with Apple's Siri voice assistant, but 2015 turned out to be a watershed year for computer algorithms that could ape human thought and interaction.

The next level of A.I. goes more towards the abstract idea in what this unlimited amount of intelligence can do with our society if it flows through networks and becomes available to everyone. You can also compare this towards the idea of the current storage of space in the cloud, but then with a steady stream of intelligence that you can "plug in to". Soon after this is launched it will become a commodity, and a layer that can be applied on top of any service or product. In combination with the broadening possibilities of physical robots this becomes way more fun. Imagine it as "IQ as a service" or "on demand IQ".

Machine learning (ML) is the unsung hero that powers many applications, systems, sensors, devices, and products. Machine learning is so pervasive that we can often assume its presence in most of the applications and systems without having to specifically call it out. In simple terms, machine learning is a computer's ability to learn from data, and it is one of the most useful tools we have to develop intelligent systems and applications. Machine learning is used widely today for all kinds of tasks, from churn prediction in large companies, to web search, to medical diagnostics, to robotics. It's hard to find a field that cannot benefit from machine learning in one way or another.

Originally posted here by Dr Granville. Check out the original article to read numerous comments, not re-published here. What are the differences between data science, data mining, machine learning, statistics, operations research, and so on? Here I compare several analytic disciplines that overlap, to explain the differences and common denominators. Sometimes differences exist for nothing else other than historical reasons.

We caught up with Matt Taylor from Numenta -- an organization whose mission is to lead a new era of machine intelligence and build computer systems around the principles of the brain. Matt shared his thoughts and insights on the open source community around their exciting projects. Find out what he says, and check out the Numenta community channel on Gitter. Tell us about a little bit about yourself and the Numenta community. How did it all begin?

There already is a market for solving prediction challenges, and more and more companies are advertising their data science problems publicly in a competitive environment,

You Might Have Heard: The Microsoft AI experiment with Tay, their machine learning Twitter bot, ended after a mere 24-hours. The company pulled the plug when she almost immediately turned into a sexist, racist Nazi. Tay was suppose to learn how to communicate like a human by engaging in conversations with Twitter users. "This gets to the underlying problem," Vice argues. "Microsoft's AI developers sent Tay to the internet to learn how to be human, but the internet is a terrible place to figure that out."