Global warming and pollution are causing severe stress to coral reefs across the world. Researchers from the University of California Berkeley and University of Queensland developed a deep learning process that automatically analyzes reef photos that will help measure reef health and changes over time. Reefs provide food and shelter for more than a quarter of all marine species, and support fish stocks that feed more than a billion people and provide jobs to millions of people in coastal areas. The new technology "will allow the world's scientists to more quickly assess the health of coral reefs at scales never dreamed of before," said Ove Hoegh-Guldberg, chief scientist of the global reef record and a professor at the University of Queensland. With that information, they can more effectively take steps to protect and save them.

Artificial intelligence (AI) will make society smarter, leaner and more efficient. But first, startups and businesses must enable the workforce of the future and pivot business models to incorporate AI. Mundane tasks such as driving, scheduling and logistics will all be handled by an AI assistant with multiple input points, such as microphones around your house and smartphones. The AI assistant will be the central nervous system of your life and connected smart home. In the future, when you summon a shared autonomous car from your phone to go out to dinner, your AI assistant will automatically notify the restaurant of your ETA and dietary restrictions.

KPMG plans to use IBM's Watson cognitive computing technology for its professional services in Australia. The artificial intelligence deal with IBM includes a focus on audit and assurance services. IBM's Watson has been doing everything from diagnosing cancer and recommending treatment to analysing the Harry Potter books and running online university courses. "Already, data and analytics techniques are transforming audit by allowing analysis of much bigger populations of data than traditional sampling from which to draw conclusions," says Duncan McLennan, KPMG's national managing partner of audit. "Cognitive technology will allow us to build on these data and analytics advances. They will be a game changer in how the value of audit is perceived by the marketplace."

A spacecraft launched into orbit by the Chinese space agency this week will be the first piece of technology aimed at tackling the growing problem of space debris, according to the Chinese government. But experts have warned the trash-clearing robot could may have been deployed for more nefarious ends, saying it could be poised to take out communications satellites. While China's space agency (CNSA), a branch of the military, has said the craft is aimed at collecting potentially hazardous debris, analysts have said it could remain dormant until needed in wartime situation. China's space agency (CNSA) has said a prototype craft launched aboard the Long March 7 rocket last week (pictured) aimed at collecting potentially hazardous debris, analysts have said it could remain dormant until needed in wartime situation The Chinese space agency (CNSA) has launched a robotic prototype into orbit which it said is aimed at tackling space debris. Analysts have warned that the if successful, more prototype could easily be produced and delivered into orbit.

Two essential tasks in managing description logic knowledge bases are eliminating problematic axioms and incorporating newly formed ones. Such elimination and incorporation are formalised as the operations of contraction and revision in belief change. In this paper, we deal with contraction and revision for the DL-Lite family through a model-theoretic approach. Standard description logic semantics yields an infinite number of models for DL-Lite knowledge bases, thus it is difficult to develop algorithms for contraction and revision that involve DL models. The key to our approach is the introduction of an alternative semantics called type semantics which can replace the standard semantics in characterising the standard inference tasks of DL-Lite. Type semantics has several advantages over the standard one. It is more succinct and importantly, with a finite signature, the semantics always yields a finite number of models. We then define model-based contraction and revision functions for DL-Lite knowledge bases under type semantics and provide representation theorems for them. Finally, the finiteness and succinctness of type semantics allow us to develop tractable algorithms for instantiating the functions.

Jean-Philip Poulin was feeling "joyful" and "excited" when I interviewed him recently in Montreal. I know this because he showed me his real-time emotion metrics during our conversation, which were being parsed by a machine-learning algorithm that uses heart-rate data transmitted from his Microsoft Band 2 fitness tracker. Poulin is the COO of Sensaura, a Montreal-based software startup that proposes to bridge the gap between consumer wearables and affective computing. If its founders are as successful as they believe they will be, their product will hasten the inevitable future of emotionally intelligent machines: video games will know when you're bored, advertisers will know when you're swayed, and mental health professionals will know when you need a check-in. So far, progress in affective computing has depended on facial recognition software, which reads people's emotions pretty much the same way that people do: by looking at their faces for cues.

With Australia's rates of skin cancers one of the highest in the world, IBM Research and MoleMap -- one of the world's largest melanoma screening programs -- are partnering with the Melanoma Institute Australia to help further advances in the identification of melanoma. The partnership and the planned research builds on IBM's existing research agreement with MoleMap, which uses advanced visual analytics to analyse more than 40,000 datasets including images and text. Under the new partnership, IBM Research plans to analyse dermatological images of skin lesions to help identify specific clinical patterns in the early stages of melanoma. The Melanoma Institute cites current statistics indicating that two in three Australians will be diagnosed with skin cancer before the age of 70, yet 95 to 99% of all skin cancers are preventable – with early diagnosis of skin cancer critical for survival rates, notably for melanoma which is considered among the most life-threatening. And, according to national cancer statistics, someone in Australia dies from melanoma every six hours.

Google uses a machine-learning artificial intelligence system called "RankBrain" to help sort through its search results. Wondering how that works and fits in with Google's overall ranking system? Here's what we know about RankBrain. The information covered below comes from three original sources and has been updated over time, with notes where updates have happened. First is the Bloomberg story that broke the news about RankBrain (See also our write-up of it).

Mumbai: There is tremendous buzz around machine learning, broadly described as a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. However, despite an exponential increase in power, computers have typically proved incompetent at things that are really simple to human beings--like recognizing the dog in a picture containing a dog, or understanding speech. The trend, however, is changing. Consider'Deep Learning', which describes a collection of techniques that allow computational tasks that were previously thought impossible. Facebook Inc, for instance, uses it to identify faces, and when Google Inc recently announced that their algorithms could not only'see' a dog but also identify it as a Pomeranian, they heralded the maturity of Deep Learning techniques.

A ROBOT with an algorithm-based persona is being used to help companies make data-driven decisions in real time. South Australian company Complexica has developed Larry, the Digital Analyst, which is basically a set of algorithms tuned to complex problems to quickly generate answers that would otherwise take people a very long time to work out. Big Data software algorithms are taking decision-making to a new level, delivering solutions and efficiencies like never before. The global Artificial Intelligence market is forecast to exceed USD 5 billion by 2020. Father and son team Matthew Michalewicz and Dr Zbigniew "Mike" Michalewicz, a former professor at the University of Adelaide's School of Computer Science and Artificial Intelligence pioneer, started the company in 2014 with software architect Constantin Chiriac.