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.

Fifty years ago this month, the Outer Space Treaty came into force. To date, 105 nations are party to it, including the dozen or so countries with space-launch capabilities. But the agreement could soon be strained by forces that were only science fiction at the time of its drafting. These include a burgeoning commercial space industry that is preparing to send robots to asteroids and the moon to mine for minerals and fuel--a legally ambiguous area that the treaty is mostly silent on. Critics say the companies cannot claim ownership of the moon or an asteroid or any part them. But the companies argue that they can extract materials without claiming ownership--just as with maritime law, where anyone can fish and own what they catch without owning the sea.

Perching is turning out to be a very desirable skill for aerial robots. The ability to land on walls or ceilings, rather than having to go to the ground, gives a drone the advantage of being high up in the air (probably why you're using a drone in the first place) without the disadvantage of having to spend a lot of energy not falling. We've seen lots of different perching techniques, most of them bio-inspired, including many different flavors of claws, spines, grippers, and adhesives. One of the best perchers in the animal kingdom (although it rarely gets credited as such) is the spider. And spiders don't just perch: They build infrastructure.

One of the biggest advances in transportation has been self-driving technology, which has facilitated breakthroughs in not just self-driven cars, but even driverless trains, self-flying planes and self-navigating ships. Rio Tinto, an Australian mining corporation, has unveiled the first operational driverless train in Western Australia, even before China, which has its own similar automated train in the works. The train completed its first run of 100 kilometers (62 miles) in the Pilbara region of Western Australia, without anyone manning the train. "This successful pilot run puts us firmly on track to meet our goal of operating the world's first fully-autonomous heavy haul, long distance rail network, which will unlock significant safety and productivity benefits for the business," Rio Tinto Iron Ore chief executive Chris Salisbury stated in the press release issued Monday. "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."

AI will impact every facet of business–changing how we work, and the pace of innovation. It has the power to deliver immense business value, and help brands create the personalized experiences that customers demand. However, to realize its full potential requires a shift in how organizations invest their resources, develop their talent, and foster an AI mindset focused on unlocking creativity and collaboration. Join Bluewolf CEO Eric Berridge to learn key strategies for designing your workforce around a mindset and culture needed to compete and win with AI.

A drone that can switch between flying and rolling could soon be exploring underground mines without the aid of a human pilot. In open air, drones can navigate autonomously using GPS, but these satellite signals don't penetrate deep underground, meaning robot spelunkers require human pilots. Ahmed AlNomany and his colleagues at Swedish company Inkonova are working on an alternative. "It's complicated because we are trying to invent another way of positioning using bits and pieces of technologies," says AlNomany. Having a view of its surroundings is the first step.

In a development that wouldn't be out of place in a Black Mirror episode, we could soon be worshipping an artificially intelligent god figure. And this isn't a satirical take on our existing roles as disciples of social media, or the transcendental joy we feel when an Uber Eats delivery arrives. According to state filings uncovered by Wired, in September 2015 disgraced engineer Anthony Levandowski established a non-profit religious corporation called Way of the Future. Its mission: "To develop and promote the realization of a Godhead based on artificial intelligence and through understanding and worship of the Godhead contribute to the betterment of society." In the last two months alone we've seen robots perform funeral ceremonies and AI chips mimic the human brain -- could a Deus ex Machina really be that far behind? The organization hasn't responded to requests for further information about its activity, but a quick look at Levandowski's resume shows that he'd be a strong contender for divine leadership within an AI-driven faith.

This supervised machine learning algorithm has strong regularization and can be leveraged both for classification or regression challenges. They are characterized by usage of kernels, the sparseness of the solution and the capacity control gained by acting on the margin, or on number of support vectors, etc. The capacity of the system is controlled by parameters that do not depend on the dimensionality of feature space. Since the SVM algorithm operates natively on numeric attributes, it uses a z-score normalization on numeric attributes. In regression, Support Vector Machines algorithms use epsilon-insensitivity (margin of tolerance) loss function to solve regression problems.