Tesla Motors and SpaceX founder and CEO Elon Musk (pictured) has been particularly vocal of his concerns, describing AI as potentially the biggest threat to humanity, even once describing the potential threat as more dangerous than nuclear bombs. Such are his concerns, Musk recently donated $10 million to the Future of Life Institute (FoLI) as part of a global research programme to ensure AI remains "beneficial to humanity," and not run the risk of getting out of control. "This partnership will provide consumer and industrial users of cognitive systems a vital voice in the advancement of the defining technology of this century –one that will foster collaboration between people and machines to solve some of the world's most enduring problems –in a way that is both trustworthy and beneficial." "Exciting opportunities" Amazon Director of Machine Learning Science and Core Machine Learning Ralf Herbrich, said he was "excited" about the opportunities the partnership will provide by bringing together the industry's leading personnel for the first time in such an environment.

Exercising good judgement in difficult situations is a much tougher standard. Above all, ethics must be realistic, and in our real world, bad things happen. Abe is very specific: he means "biased" in a technical, statistical sense. Cathy O'Neil has frequently argued that secret algorithms and secret data models are the real danger.

Companies in the vanguard of developing and deploying machine learning and AI are now starting to talk openly about ethical challenges raised by their increasingly smart creations. "We're here at an inflection point for AI," said Eric Horvitz, managing director of Microsoft Research, at MIT Technology Review's EmTech conference this week. Maya Gupta, a researcher at Google, called for the industry to work harder on developing processes to ensure data used to train algorithms isn't skewed. In the past year, many efforts to research the ethical challenges of machine learning and AI have sprung up in academia and industry.

After decades of dystopian science fiction novels and movies where sentient machines end up turning on humanity, we can't help but worry as real world AI continues to improve at such a rapid rate. That's why Amazon, Facebook, Google's DeepMind division, IBM, and Microsoft have founded a new organization called the Partnership on Artificial Intelligence to Benefit People and Society. Williams is encouraged that tech giants like Facebook and Google are even asking questions about ethics and bias in AI. Ideally, the group will help establish new standards for thinking about artificial intelligence, big data, and algorithms that can weed out harmful assumptions and biases.

Three key guiding philosophies were presented across the reports: AI needs to augment humanity instead of replacing it, AI needs to be ethical, and there must be an equal opportunity for everyone to develop these systems. Human-machine collaboration in its many forms is major theme in the reports, titled "Preparing for the Future of Artificial Intelligence" and "National Artificial Intelligence Research and Development Strategic Plan." "The walls between humans and AI systems are slowly beginning to erode, with AI systems augmenting and enhancing human capabilities," the Strategic Plan report says. The White House imagines virtual personal assistants housed in smart glasses, automated factories that assist humans in complex building tasks, and systems that provide better data for farmers, all in the context that these could be job creators and not job stealers.

Called the Partnership on Artificial Intelligence to Benefit People and Society, the group consists of Amazon, Facebook, Google, Microsoft and IBM. Executives from four of the five founding members of the Partnership on AI (from left): Eric Horvitz of Microsoft, Francesca Rossi of IBM, Yann LeCun of Facebook and Mustafa Suleyman of Google's DeepMind. Executives from four of the five founding members of the Partnership on AI (from left): Eric Horvitz of Microsoft, Francesca Rossi of IBM, Yann LeCun of Facebook and Mustafa Suleyman of Google's DeepMind. But Banavar hopes the group's work will make its way into educational curricula around the world that will inspire the new generations of AI researchers.

Some of the world's largest tech companies are coming together to form a partnership aimed at educating the public about the advancements of artificial intelligence and ensure they meet ethical standards. "We believe that artificial intelligence technologies hold great promise for raising the quality of people's lives and can be leveraged to help humanity address important global challenges such as climate change, food, inequality, health, and education," the group stated in a series of "tenets." Another nexus of interest will be around ethics, with the group inviting academic experts to work with companies on AI for the best of humanity. But it's not clear whether this means opposing working with government surveillance authorities, or opposing forms of online censorship.

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When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. A computer algorithm issues orders to buy a stock and floods the market with hundreds or thousands of apparently separate orders to buy the same stock. Other algorithms take note of this sudden demand and start raising their buy and sell offers, confident that the market is demanding a higher price. The first algorithm registers this response and sells its shares of stock for the newly higher price, making a tidy profit.

When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. A computer algorithm issues orders to buy a stock and floods the market with hundreds or thousands of apparently separate orders to buy the same stock. Other algorithms take note of this sudden demand and start raising their buy and sell offers, confident that the market is demanding a higher price. The first algorithm registers this response and sells its shares of stock for the newly higher price, making a tidy profit.