Microsoft has announced the formation of its AI and Research Group, which it says will help the company democratize artificial intelligence use across individuals and organizations. The group unites Microsoft Research, which has been its own unit since 1991, with more than 5,000 computer scientists and engineers working on the company's artificial intelligence products. News of the Microsoft AI and Research Group came a day after the company announced the Partnership on AI with Amazon, Facebook, Google and IBM in an effort to "study and formulate best practices on AI technologies, to advance the public's understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society." AI and machine learning action at Microsoft has been going nonstop in recent years, in terms of internal development, acquisitions and industry efforts. In fact, Microsoft just bought an intelligent scheduling company called Genee.

In recent weeks, Uber launched autonomous cars in Pittsburgh, Tesla updated its autopilot software and researchers in Amsterdam announced an initiative to explore driverless boats. And now, Nissan offers the world self-driving chairs. The Japanese automaker unveiled a high-tech, battery-powered autonomous chair it says is designed to make waiting in line "easy and fun." The ProPilot chair transports passengers along fixed paths without any instruction from the user. The ProPilot chair "drives" on a predetermined path.

But oh, this is just the beginning, according the management gurus at McKinsey. They issued a report outlining the strange new world of automotive data, and how lucrative it will be. That torrent of info streaming out of your ride as cars are increasingly connected to smartphones, smart infrastructure, and other smart cars? McKinsey says it will be worth as much as 750 billion by 2030. A hunk of metal and rubber may not look like a fertile ecosystem teeming with data, but it is.

Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals recorded at multiple electrodes. We recently introduced the MERLiN (Mixture Effect Recovery in Linear Networks) algorithm that is able to recover, from an observed linear mixture, a causal variable that is a linear effect of another given variable. Here we relax the assumption of this cause-effect relationship being linear and present an extended algorithm that can pick up non-linear cause-effect relationships. Thus, the main contribution is an algorithm (and ready to use code) that has broader applicability and allows for a richer model class. Furthermore, a comparative analysis indicates that the assumption of linear cause-effect relationships is not restrictive in analysing electroencephalographic data.

The Partnership on AI consolidates control for a handful of corporations and their stakeholders, pitting them against Elon Musk's OpenAI open source, non-profit that is available to everyone. Both the Partnership on AI and Elon Musk's OpenAI were established to advance humanity through Artificial Intelligence. While Musk's OpenAI was devised to be open and available to the public, the Partnership on AI is more of a consolidation of power that benefits stakeholders whose findings will later be used to enact public policies. Google/DeepMind, Microsoft, Facebook, IBM, and Amazon have collaborated to form the Partnership on AI to "formulate best practices on the challenges and opportunities within the field." While third-party groups such as academics, non-profits, and policy specialists have been invited to be on the new Board of Directors, the Partnership on AI ultimately serves the private stakeholders, and whatever the stakeholders dictate will be relayed to the public.

I think it's very relevant that we contribute to the ideas of SingularityU and to the summits and all the workshops that are being organized. It is relevant to explain why we do what we do, via communication media like this YouTube video. My role in SingularityU is to help set up the summits and give presentations at these summits. My field is robotics, I really like to talk about the work I do with my group. I also think that robotics will be a very important development in many aspects of our society.

The world's top tech firms, including Google's DeepMind, Facebook and Amazon, have formed the tech equivalent of a musical supergroup, joining forces to ensure the ethical development of artificial intelligence and "advance public understanding" of the technology which is increasingly working its way into our lives. The group, Partnership on AI, will connect academics, policy experts, and non-profits alongside businesses, which also include Microsoft and IBM, to hot house best practice in the burgeoning technology being used to help solve world health problems and reduce energy consumption - an industry expected to be worth 9.2bn by 2019. "The objective of the Partnership on AI is to address opportunities and challenges with AI technologies to benefit people and society," the group said, outlining its goals. "Together, the organisation's members will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology. It does not intend to lobby government or other policymaking bodies." The five tech groups will fund the project as well as provide resources for research and expects more experts and groups working on AI technology to join the partnership in future.

A new partnership of some of the biggest names in the technology world will seek to explore the benefits of artificial intelligence (AI). Amazon, Google, DeepMind (owned by Google), Facebook, IBM and Microsoft have formed a non-profit organisation that will seek to unite AI experts and knowledge called the Partnership on Artificial Intelligence to Benefit People and Society. The group says that it will support research and recommend best practices in areas including "ethics, fairness, and inclusivity; transparency and interoperability; privacy; collaboration between people and AI systems; and of the trustworthiness, reliability, and robustness of the technology." This will include creating an open platform for engagement, where researchers and stakeholders can discuss AI-related issues. It will also aim to engage experts across multiple disciplines in academia, including psychology, philosophy, economics, finance, sociology, public policy, and law, aiming to produce informational materials on the topic. It will also support third-party studies and engage AI users and developers, as well as industries that may be impacted by AI.

Amazon, Google, Facebook, IBM, and Microsoft have announced they are forming a non-for-profit organisation to educate the public about artificial intelligence (AI) technologies, as well as alleviate anxieties around its application. The collective, which includes Google's AI subsidiary DeepMind, also plans to develop best practices on the challenges and opportunities within the field of AI. The organisation, called Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI), will address legal and ethical challenges that AI presents, encourage public discourse, and identify opportunities to use AI to bring improvements to society. The organisation does not intend to be a regulatory body, with a statement saying it does "not intend to lobby government or other policymaking bodies." Members of the Partnership on AI will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology.

Researchers at the University of Basel in Switzerland have used machine learning to predict the thermodynamic characteristics of 90 new mineral compounds with potential commercial use. The machine learning models were able to predict the chemical stability of all possible iterations of a particular type of class of crystals several orders of magnitude faster than if the researchers had relied on quantum mechanical calculations. The class of compounds in this case was elpasolite, a crystalline material that in nature is made up of four chemical elements: sodium, potassium, aluminum, and fluorine. Some elpasolites emit light when exposed to ionic radiation, which makes them candidates for scintillators, a material that can be used in devices like radiation detectors, CT scanners, and various types of medical diagnostic equipment. Elpasolites could also have applications in electronic equipment, since it can serve as a conductor, semiconductor or insulator, depending upon its specific composition.