Amid surging investment in artificial intelligence over the past few years and continuing concern about the implications of the technology, the White House announced on Tuesday that it intends to hold a series of workshops and form an inter-agency working group to examine the benefits and risks of AI. In a blog post, Ed Felten, Deputy US Chief Technology Officer, framed the issue in a way that excludes speculative scenarios presenting AI as a threat to humanity, a concern raised by the likes of Stephen Hawking and Elon Musk. While worries about runaway malevolent AI are often raised in public discussions of the technology, real AI research is more mundane, as in Google's effort to improve the conversational capabilities of its software by feeding it romance novels. "Today's AI is confined to narrow, specific tasks, and isn't anything like the general, adaptable intelligence that humans exhibit," said Felten. "Despite this, AI's influence on the world is growing. The rate of progress we have seen will have broad implications for fields ranging from healthcare to image- and voice-recognition."

Unexpected convergent consequences…this is what happens when eight different exponential technologies all explode onto the scene at once. This post (the third of seven) is a look at virtual and augmented reality. Future posts will look at other tech areas. And be sure to read the first two posts if you haven't already: When the World Is Wired: The Magic of the Internet of Everything Where Artificial Intelligence Is Now and What's Just Around the Corner An expert might be reasonably good at predicting the growth of a single exponential technology (e.g., the Internet of Things), but try to predict the future when AI, robotics, VR, synthetic biology, and computation are all doubling, morphing and recombining… You have a very exciting (read: unpredictable) future. This year at my Abundance 360 Summit I decided to explore this concept in sessions I called Convergence Catalyzers.

Machines have already started their march towards artificial intelligence. Deep Learning and Neural Networks are probably the hottest topics in machine learning research today. Companies like Google, Facebook and Baidu are heavily investing into this field of research. Researchers believe that machine learning will highly influence human life in near future. Human tasks will be automated using robots with negligible margin of error. I'm sure many of us would never have imagined such gigantic power of machine learning.

Sentiment analysis is a powerful example of how machine learning can help developers build better products with unique features. In short, sentiment analysis is the automated process of understanding if text written in a natural language (English, Spanish, etc.) is positive, neutral, or negative about a given subject. Nowadays, we have many instances where people express opinions and sentiment: tweets, comments, reviews, articles, chats, emails and more. One popular example is Twitter, where real-time opinions from millions of users are expressed constantly. Companies use sentiment analysis on Twitter to discover insights about their products and services.

Despite gripes about the usefulness of Facebook chatbots, "tens of thousands" of developers are building them, Messenger's head of product Stan Chudnovsky revealed onstage at TechCrunch Disrupt NY. And, 5,000 Shopify merchants are now distributing order confirmations and shipping alerts via Messenger, showing e-commerce companies are eager to reach the app's nearly one billion users. These bots are getting pretty chatty. One of the more recent high-profile chatbots introduced on the Messenger platform, Activision's Call of Duty bot, saw engagement soar to nearly 6 million messaged exchanged between users in its first week of existence. The problem is that if bots get spammy, they could drown out people's friends in Messenger.

This five-fingered robot hand can learn how to perform dexterous manipulation -- like spinning a tube full of coffee beans -- on its own, rather than having humans program its actions.University of Washington Robots today can perform space missions, solve a Rubik's cube, sort hospital medication and even make pancakes. But most can't manage the simple act of grasping a pencil and spinning it around to get a solid grip. Intricate tasks that require dexterous in-hand manipulation -- rolling, pivoting, bending, sensing friction and other things humans do effortlessly with our hands -- have proved notoriously difficult for robots. Now, a University of Washington team of computer scientists and engineers has built a robot hand that can not only perform dexterous manipulation but also learn from its own experience without needing humans to direct it. Their latest results are detailed in a paper to be presented May 17 at the IEEE International Conference on Robotics and Automation.

Neural networks provide the possibility to solve complicated non linear problems. They can be used in various areas such as signal classification, forecasting timeseries and pattern recognition. A neural network is a model inspired by the human brain and consists of multiple connected neurons. For getting a deeper understanding, I recommend checking out Neural Networks and Deep Learning. Within the last years, multiple Javascript frameworks were developed that can help you to create, train and use Neural Networks for different purposes.

However, in this case Bob is short for robot. He/she/it comes from one of the companies that are pushing back the boundaries of what robots are capable of. Smart, flexible, easy to instruct/program, capable of working side-by-side with humans -- not to mention cheap. The list describes just some of the areas where robotics is making progress by leaps and bounds. The tempo is such that while Bob might today be a slightly surprising choice, he and his kin could be the natural first choice for your new colleague in factories and enterprises within a few years.

Google claims its new quantum computer--the D-Wave 2X system--will leave its competitors in the dust. But I'm wondering if there'll be any competitors for a while if Google's "Ace of Spades" newbie performs as they predict. "We found that for problem instances involving nearly 1,000 binary variables, quantum annealing significantly outperforms its classical counterpart, simulated annealing. It is more than 10 to the power of 8 times faster than simulated annealing running on a single core." In layperson's lingo: this sucker will run 100 million times faster than the clunker on your desk.

To make this a reality, chip maker Movidius has introduced the Fathom Neural Compute Stick. Dubbed as the "the world's first embedded neural network accelerator", this USB stick contains a deep learning processing accelerator. This stick could be connected to an existing Linux device to increase the efficiency of neural networking tasks 20-30 times. Movidius Fathom performs its task at more than 150GFLOPS, consuming less than 1.2W. "Featuring a full-fledged Myriad 2 VPU, the Fathom Neural Compute Stick not only enables rapid prototyping, but also delivers high levels of neural network compute to existing devices via a USB port," tells the explainer on Movidius website.