These pictures are unlikely to make it into the glossy tourist brochures that sell the Caribbean as a paradise destination. For they show the much grimmer reality of clear blue seas increasingly choked by a tide of discarded plastic. In one photograph taken near Roatan, an island off the coast of Honduras, a diver grimaces as he prepares to enter the water almost completely covered by waste. Another, taken from below the waterline, shows plastic bottles, bags and other rubbish on the surface blocking out sunlight. Waste: Disposable cutlery and other rubbish trapped in seaweed.

Machines won't become intelligent unless they incorporate certain features of the human brain. Europe's massive €1 billion project has shifted focus from simulation to informatics By Megan Scudellari Large-scale brainlike systems are possible with existing technology--if we're willing to spend the money By Jennifer Hasler Researchers in this specialized field have hitched their wagon to deep learning's star By Lee Gomes Running algorithms that mimic a rat's navigation neurons, heavy machines will soon plumb Australia's underground mines By Jean Kumagai Artificial intelligence might endow some computers with self-awareness.

We stand on the brink of fruition of two technical revolutions that are, in reality, one and the same. And the two halves of this revolution will feed one another: The more information IoT can provide, the more quickly AI will develop and the greater its potential impact. The more AI advances, the more value it provides in the capacity to process information. This will drive a desire to gather more and more information about more of the world and therefore open the opportunity for IoT to become even more pervasive and valuable in its role as data collector. For organizations, governments and societies that can harness this virtuous spiral, there is almost unlimited opportunity -- the possibility to gain greater insight into everything from global weather patterns to treatment of infectious diseases to building hyper-efficient cities. Information is the lifeline of businesses and the currency of the future, and the capacity to handle that information (to gather it and process it) will be what creates the new mega-enterprises of the next two decades.

Simply put, machine learning and AI, in general, will become commonplace in our lives because we need them to be. Alongside development of the capability to process massive amounts of data in innovative ways, there exists another technical revolution whose time has also most definitely come -- the internet of things (IoT). If AI offers the promise of processing immense quantities of data in ways that we can't, then IoT provides the very tangible mechanism for generating that raw data in ways we might not expect. Perhaps more telling, there is already an emerging trend of AI development "following the data" in order to accelerate the capability to deliver human-machine interactions and insight based on the availability of more of those very same interactions.

In the near future, you may be able to eat a robot that will heal you or provide nutrients. It may sound like science fiction, but researchers are closing in on the creation of an ingestible robot that can perform a variety of functions from within the human body. At the International Conference on Intelligent Robots and Systems in Vancouver last week, researchers from Switzerland's École Polytechnique Fédérale de Lausanne (EPFL) presented a prototype of a gelatin-based actuator, according to the Institute of Electrical and Electronics Engineers' magazine, Spectrum. Actuators are the components that allow a mechanism to physically move. So, while doctors can already insert machines like pacemakers into your body, those are stationary and also require invasive surgery.

Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive attributes of data does not solve this problem because a \textit{disparate impact} can arise when non-sensitive attributes and sensitive attributes are correlated. Here, we study a fair machine learning algorithm that avoids such a disparate impact when making a decision. Inspired by the two-stage least squares method that is widely used in the field of economics, we propose a two-stage algorithm that removes bias in the training data. The proposed algorithm is conceptually simple. Unlike most of existing fair algorithms that are designed for classification tasks, the proposed method is able to (i) deal with regression tasks, (ii) combine explanatory attributes to remove reverse discrimination, and (iii) deal with numerical sensitive attributes. The performance and fairness of the proposed algorithm are evaluated in simulations with synthetic and real-world datasets.

Ahead of China's own autonomous train reveal, mining corporation Rio Tinto have given the world its first fully-autonomous train, and it's currently in operation in Western Australia. Mining corporation Rio Tinto, which also developed the train, announced earlier this week the train had successfully completed its first unmanned mission, traveling nearly 100 kilometers (62 miles) without a person on board. "Rio Tinto is proud to be a leader in innovation and autonomous technology in the global mining industry which is delivering long-term competitive advantages as we build the mines of the future," said Rio Tinto Iron Ore Chief Executive Chris Salisbury, in a statement. "New roles are being created to manage our future operations and we are preparing our current workforce for new ways of working to ensure they remain part of our industry." The mission, located at Rio Tinto's iron ore operations in the Pilbara region of Western Australia, is the first big step in the company's plans to have a fully autonomous train network.

Direct observation of chemical reactions is notoriously difficult. Reaction rates tend to be too fast for chemists to be able to see how molecules move as they combine and change, and individual electrons -- the species that are directly involved with reactions-- are subject to the laws of quantum mechanics that make direct observation of their position impossible. It'd especially difficult to observe reactions between organic molecules involving catalysts, because the reactions can take place at extreme temperatures and pressure, often proceed via very short-lived and unstable intermediates formed by combinations of the reactants with the catalyst. This makes it difficult to determine the mechanism of the reaction, which in turn complicates the design of improved catalysts. An interdisciplinary team of chemists, physicists and computer scientists at the US Department of Energy's Brookhaven National Laboratory in New York State and nearby Stony Brook University have devised a method to analyse data from X-ray crystallography to decipher the three-dimensional nanostructures that form during catalysed reactions.

The field of machine learning and artificial intelligence (ML/AI) is rapidly evolving today and slowly beginning to reshape the mining sector. With the mining machinery becoming larger and equipment more sophisticated, the sector can gain immensely from these advanced technologies in terms of operational efficiency and ramping down costs. ML/AI is a field of computer study that deals with the creation of intelligent machines that work and react like humans. It covers a wide spectrum from speech recognition and visual perception up to language translations and decision-making, which normally require human intelligence. ML algorithms and AI is considered the next step for digital mine transformation.

Development of black-box modeling techniques, like support vector machine (SVM), neural networks, etc., has shown rather rapid in the past decades (Yuan et al., 2016; Zhao et al., 2015; Wu et al., 2013). This sort of techniques, compared to white-box modeling methods (also called mechanism-based modeling or first-principles modeling), works without any need of knowing the internal structure or details on variables interaction in systems considered, so they are suited to describe extremely complex objectives, such as human brain (Khosrowabadi et al., 2014), black hole (Grumiller et al., 2012), integrated industrial processes (Gao et al., 2012) and so on. Essentially, blackbox modeling is an input-output data-based approach, and the model precision mainly depends on data quality, model structure and parameters identification algorithm. In order to develop high-precision black-box models, it always needs reliable and representative data, smart mathematical treatment and efficient identification algorithms. All of these are challenging the development of the black-box modeling techniques.