The challenge sounds daunting: Use artificial intelligence "to address humanity's Grand Challenges." But if that sounds like the kind of thing that stimulates your creative juices, and you have the engineering chops to back it up, you could win a few million bucks and a lot of attention. IBM Watson and XPrize announced earlier this year that they'd be linking up to offer a top award of 3 million and additional prizes totaling 2 million to winning entrants in the competition. Roughly 3,500 parties responded to the announcement of the prize in February to say they were interested in competing. The five-month registration window ends Dec. 1, after which applicants will go through four selection rounds over the course of four years.

Keep calm and carry on; artificial intelligence will not take all our jobs and achieve world domination, according to a report released by Forrester. Prominent figures including Elon Musk, co-chairman of OpenAI, and Professor Stephen Hawking have publicly warned people about how the advent of AI will cause an existential threat to humankind. The frenzy around AI has led to groups rallying for future safety measures. Google's DeepMind has even collaborated with Oxford University's Future of Humanity Institute to develop [PDF] an AI "panic button." Forrester, a technological research and advisory firm, believes the panic is exaggerated, however, and said: "Don't believe the hype – Google AlphaGo's gaming successes and IBM Watson will not usher in a dystopian triumph of machines over humans."

In 1942, Russian science fiction writer Isaac Asimov drew up his'Three Laws of Robotics': 1) A robot may not injure a human being or, through inaction, allow a human being to come to harm; 2) A robot must obey orders given it by human beings except where such orders would conflict with the First Law; 3) A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. For 75 years, these clauses have inspired research and thinking on robot rights. They were even taken up in the first'Ethics Charter for Robots' drawn up by South Korea in 2007. However, today Asimov's laws seem rather simplistic and obsolete as they are centred on humans rather than robots. Nowadays ethics and the rights of machines are starting to go further.

Artificial Intelligence (AI) is gathering pace as one of the biggest ICT trends of our time – and the topic of "robot journalism" provides important insight on how AI is being applied to industry. How is natural language generation (NLG) being used to explain big data in narrative form that broader audiences can easily understand? What insights are being found by machines producing 2,000 articles per second? How are some of the world's biggest media agencies currently using vendors that provide these solutions to build their reach, engagement, and businesses? Will "robots" (or automated content) soon render journalists obsolete?

As we've mentioned, corporate giants in tech are competing to acquire private artificial intelligence startups, with Google emerging as the most active acquirer over the last 5 years. So far in 2016, there have already been over 20 acquisitions of AI startups, the most recent being Twitter's acquisition of visual-processing startup Magic Pony Technology. We analyzed M&A and IPO activity in this space from 2011. Our artificial intelligence category includes companies developing core AI algorithms as well as those applying AI solutions to specific industries like healthcare and cybersecurity. We used our database to analyze at what stage the AI companies exited.

IMAGE: This plot shows the proportion of articles about machine learning in four natural science disciplines from 1994 to 2015, illustrating the slow penetration of the method in three of four... view more Increasingly becoming a necessity in Ecology and Earth Science research, handling complex data can be a tough nut when traditional statistical methods are applied. As one of its first publications, the new technologically-advanced Open Access journal One Ecosystem features a review paper describing the benefits of using machine learning technologies when working with highly-dimensional and non-linear data. Natural sciences, such as Ecology and Earth science, focus on the complex interactions between biotic and abiotic systems in order to infer understand these systems and make predictions. Traditional statistical methods can impose unrealistic assumptions that result in unsound conclusions as the era of'big data' meets ecology and earth science. Machine-learning-based methods, capable of inferring missing data and handling complex interactions, are more apt for handling complex scientific data.

Fahad Aziz is the Co-founder of Caremerge, an "award-winning technology company revolutionizing communication and coordination of care for seniors." As an industry leader in the tech space, Aziz has his finger on the pulse of disruptive healthcare tech. He says a few technologies are poised to seriously upend'business as usual' in healthcare in the near future, and they are not necessarily what you'd expect. In 2012, Vinod Khosla predicted that in time, "technology will replace 80% of what doctors do." He is spot on, according to Aziz.

Google is now using RankBrain, its machine-learning system, to process the more than 2 trillion queries sent each year through its search engine, according to Search Engine Land. This is a considerable increase from when RankBrain was first introduced in Q3 2015, when it was used for roughly 15% of search queries. The full time deployment of RankBrain will make Google Search far more intuitive for users. RankBrain "interprets" users' search terms using machine learning -- a type of artificial intelligence -- to find pages that may not have contained the exact keywords that were used in the search query. The system is able to "guess" what a user means when they type in an ambiguous search query and then pass along its assumptions to the search platform. Furthermore, it learns over time, better refining search results.

Joachim Buhmann, Professor for Computer Science and Head of the Institute for Machine Learning at ETH Zurich, offers a more sober assessment of the situation: "The Go player's algorithm has, of course, set a milestone in machine learning, but it's a milestone in a very limited, artificial field," he says. Since the early days of computer science as a scientific discipline, one of the challenges against which it has been relatively easy to measure progress has been strategy games. It started with simple games such as Nine Men's Morris and Draughts. In 1997, IBM's computer Deep Blue beat the reigning chess world champion Garry Kasparov. Soon thereafter, programmers set their sights on the considerably more complex game Go as the next potential milestone.

A future in which humanity is subservient to computers and giant corporations was one of the predictions that came out of last week's Code Conference in LA. This forecast came from one of today's most forward-thinking entrepreneurs: Elon Musk. Musk, who is well known for touting the dangers of AI, gave some of the boldest predictions on AI at this year's conference. Left unchecked, he warned that AI could have a devastating effect on humanity, which is why he supports OpenAI to bring artificial intelligence to the masses. Jeff Bezos also spoke at the conference, giving a more positive prediction of how AI can help consumers.