As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb? I would take the fame, I suppose, but perhaps the critics are right. Maybe I shouldn't avoid asking: As an AI expert, what do I fear about artificial intelligence?

It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb? I would take the fame, I suppose, but perhaps the critics are right. Maybe I shouldn't avoid asking: As an AI expert, what do I fear about artificial intelligence?

Researchers have tapped into artificial intelligence to help overcome some of fusion energy's greatest challenges. By feeding a machine-learning program data from past experiments, it can reveal links between processes that cause complications in the plasma's behaviour This could help to avoid such disruptions, which lead to rapid loss of stored thermal and magnetic energy, and can even threaten the machine itself. According to the researchers, this approach could be used to analyze the behaviour of plasma inside a tokamak. Fusion involves placing hydrogen atoms under high heat and pressure until they fuse into helium atoms. When deuterium and tritium nuclei - which can be found in hydrogen - fuse, they form a helium nucleus, a neutron and a lot of energy.

Data is the foundation of success, from fueling scientific research to delivering a personalized shopping experience. Today's organizations are utilizing machine learning to harness the full power of their data. Data is the foundation of success, from fueling scientific research and creating new medical treatments, to delivering a personalized shopping experience and optimizing business operations. Among these advancements, machine learning is a powerful tool that allows organizations to ingest continuous streams of information and glean actionable intelligence.

Data is the foundation of success, from fueling scientific research to delivering a personalized shopping experience. Today's organizations are utilizing machine learning to harness the full power of their data. Data is the foundation of success, from fueling scientific research and creating new medical treatments, to delivering a personalized shopping experience and optimizing business operations. Today's organizations are utilizing cutting-edge technologies to harness the full power of their data. However, legacy IT lacks the management and analytics capabilities required to handle growing datasets.

A team of researchers is working to build trust between humans and artificial intelligence (AI) by creating an "interpreter" that can explain how an AI arrived at the answer to a specific... AI has reputation as a threat to our working lives, but it might not be as bad as we think. Research shows that 230,000 jobs in the business sector could disappear by 2025, filled by "artificial intelligence agents". It's widely accepted that Artificial Intelligence (AI) will have a huge impact on our lives in the coming decades - but what's its value to the global economy? Whether it's renewable energy investment, supercomputers, building megacities or mobile technology, China is spreading its wings and taking flight.

An academic who is a vocal critic of the price of renewable power is the government's preferred choice to head a review of the financial cost of energy in the UK. Dieter Helm, an economist at the University of Oxford, has been chosen by the Department for Business, Energy and Industrial Strategy (BEIS) to carry out the review, the Guardian has learned. The Conservative manifesto promised that the resulting report would be the first step towards "competitive and affordable energy costs". Theresa May is among those in the government taking an interest in the cost-of-energy review, which will examine how power prices can be kept down while meeting the UK's carbon targets and keeping the lights on. But the choice of Helm, author of a new book on the slow demise of oil companies in the face of energy trends, will be controversial in some quarters because of his criticism of wind and solar power.

We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables. By using a series of code transformations, the evidence of any probabilistic program, and therefore of any graphical model, can be optimized with respect to an arbitrary subset of its sampled variables. To carry out this optimization, we develop the first Bayesian optimization package to directly exploit the source code of its target, leading to innovations in problem-independent hyperpriors, unbounded optimization, and implicit constraint satisfaction; delivering significant performance improvements over prominent existing packages.

Artificial intelligence (AI) might soon become one of the biggest competitive differentiators for these businesses. Within the oil and gas industry, AI and machine learning are already being used for processing high volume data and to achieve operational efficiency, said Arunkumar Ranganathan, associate vice president and head of the domain and process consulting groups for energy, utilities, and services at technology consulting firm Infosys. "AI techniques are yet to be applied [for] interpreting geophysical and geological functions and in other core business functions," Ranganathan said. For example, AI is being used to optimize the drilling process and improve operational efficiency, leading to a reduction in drilling costs.

Solar energy products and services developers and vendors continue to leverage the latest in distributed information and communications technology (ICT) in bids to drive further declines in the cost and boost the productivity of solar energy systems. Development and use of an expanding range of machine-to-machine (M2M) communications and "Internet of Things" devices – wireless network sensors and "smart," network-connected inverters, meters and other devices – along with high-reliability wireless/mobile networking and cloud software- and infrastructure-as-a-service (SaaS and IaaS) platforms are enabling vendors and their customers to collect, analyze and act upon continuous streams of digital data and approach ideal maximum electrical power and energy production while coincidentally minimizing installation, operations and maintenance costs. With more than nine gigawatts (GWs) worth of its products installed on five continents, in 1991 Fremont, California-based NEXTracker published a groundbreaking white paper describing a new algorithm that improved solar tracking and resulted in gains of around three percent in solar PV facility production. While that methodology continues to be applied in nearly all solar energy tracking systems today, NEXTracker is pushing the technological envelope out further. On July 11, the company introduced its latest innovation to the market, a "first-of-its-kind intelligent, self-adjusting tracker control system for solar power plants."