Last December, Tesla CEO Elon Musk teamed up with Y Combinator president Sam Altman and former Google Brain Team scientist Ilya Sutskever to launch OpenAI, a 1 billion non-profit organization dedicated to furthering our understanding of artificial intelligence with a promise to share its research openly with the world. Today, it's taken its first step in that direction by launching a free toolkit for developers to build and train their own AI bots with games and algorithmic challenges. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May. The OpenAI Gym, currently in beta, includes environments to simulate situations for your AI to learn from, as well as a site to compare and reproduce results. The tools are designed for use with Reinforcement Learning (RL), one of the technologies used to develop Google's AlphaGo AI that defeated Go world champion Lee Se-Dol recently.

That's because artificial intelligence programs, like the tech industry itself, suffer from a diversity problem. OpenAI says there are essentially two problems with programming A.I. right now: There's not enough variety, and the language to communicate with other developers is not the same.

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In any scientific arena, good research is able to be replicated. If others can mimic your experiment and get the same results, that bodes well for the validity of the finding. And if others can tweak your study to get better results, that's of even more benefit to the community. These ideas are the driving force behind OpenAI Gym, a new platform for artificial intelligence research. OpenAI, announced earlier this year, is the brainchild of Elon Musk, Y Combinator's Sam Altman, and former Googler Ilya Sutskever.

Creativity is alive and well at Samsung, which is developing several cool devices in its labs, including a home companion robot called Otto. The multi-talented robot can answer questions and double as a part-time security system when needed.

In January, the Information Technology and Innovation Foundation in Washington DC gave its annual Luddite Award to "a loose coalition of scientists and luminaries who stirred fear and hysteria in 2015 by raising alarms that artificial intelligence (AI) could spell doom for humanity". The winners -- if that is the correct word -- included pioneering inventor Elon Musk and physicist Stephen Hawking.

Google's self-driving car just got a boost from the National Highway Traffic Safety Administration. SAN FRANCISCO - Federal auto safety and standards regulators should set rules governing self-driving cars and not state agencies that may not have the technological know-how to assess the rapidly evolving technology. That was the message delivered to federal administrators Wednesday by Chris Urmson, the chief architect of Google's seven-year-old autonomous car program. Urmson was one of a variety of auto experts speaking at a Stanford University forum organized by the National Highway Traffic Safety Administration, which is soliciting comments as it aims to establish a set of guidelines later this summer for companies developing autonomous cars. The event took place the day after Google announced it was part of the Self-Driving Coalition for Safer Streets, a lobbying group of autonomous-car focused companies that also includes Ford, Lyft, Uber and Volvo.

Creating mood sensing technology has become very popular in recent years. There is a wide range of companies trying to detect your emotions from what you write, the tone of your voice, or from the expressions on your face. All of these companies offer their technology online through cloud-based programming interfaces (APIs). As part of my offline emotion sensing hardware (Project Jammin), I have already built early prototypes of facial expression and speech content recognition for emotion detection. In this short article I describe the missing part, a voice tone analyzer.

In current analytics, there is a bias towards using statistical correlations to understand causes. An effective AI system, however, will understand the reasons behind the correlations and can handle uncertainty.

We live in the era of data. Its almost inevitable now that we need to delegate our knowledge and understanding of the world to computers who can model this behaviour on a large scale. So, this is the age of machine learning. With the advent of BIG data, enterprises are sitting at lots of data that is not being utilized effectively. By iteratively exploring data, computers can be made to find hidden patterns in the data, without explicitly programming where to look for it.