The AI era is real, but it has ushered in the fear that livelihoods will be lost. However, businesses must ensure that this doesn't happen.

If the history of human advancement has taught us one thing it is this: genuine step-change progress does not occur because of a single technology breakthrough, but a combination of multiple complementary factors coming together at the same time. The Industrial Revolution, which began in the UK around 1760, was driven by an amalgamation of steam power, improvements in iron production and the development of the first machine tools. Similarly, the PC revolution of the early 1970's was the outcome of simultaneous advancements in micro-processing, memory storage, software programming and other factors. Now, as we enter 2018, we are at the cusp of a new revolution, one that will ultimately transform every organisation, every industry and every public service across the world. This is referring to Artificial Intelligence – or AI – and this 2018 is the year that this will start to become mainstream, to begin to impact many aspects of our lives in a truly ubiquitous and meaningful way.

I originally wrote and published a version of this article in September 2016. Since then, quite a bit has happened, further cementing my view that these changes are coming and that the implications will be even more substantial. I decided it was time to update this article with some additional ideas and a few changes.

Linear least-squares regression with a "design" matrix A approximates a given matrix B via minimization of the spectral- or Frobenius-norm discrepancy ||AX-B|| over every conformingly sized matrix X. Another popular approximation is low-rank approximation via principal component analysis (PCA) -- which is essentially singular value decomposition (SVD) -- or interpolative decomposition (ID). Classically, PCA/SVD and ID operate solely with the matrix B being approximated, not supervised by any auxiliary matrix A. However, linear least-squares regression models can inform the ID, yielding regression-aware ID. As a bonus, this provides an interpretation as regression-aware PCA for a kind of canonical correlation analysis between A and B. The regression-aware decompositions effectively enable supervision to inform classical dimensionality reduction, which classically has been totally unsupervised. The regression-aware decompositions reveal the structure inherent in B that is relevant to regression against A.

Structured prediction requires searching over a combinatorial number of structures. To tackle it, we introduce SparseMAP, a new method for sparse structured inference, together with corresponding loss functions. SparseMAP inference is able to automatically select only a few global structures: it is situated between MAP inference, which picks a single structure, and marginal inference, which assigns probability mass to all structures, including implausible ones. Importantly, SparseMAP can be computed using only calls to a MAP oracle, hence it is applicable even to problems where marginal inference is intractable, such as linear assignment. Moreover, thanks to the solution sparsity, gradient backpropagation is efficient regardless of the structure. SparseMAP thus enables us to augment deep neural networks with generic and sparse structured hidden layers. Experiments in dependency parsing and natural language inference reveal competitive accuracy, improved interpretability, and the ability to capture natural language ambiguities, which is attractive for pipeline systems.

Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of having access to weather information for solar power generation prediction and find weather information that can help best predict photovoltaic power.

Artificial Intelligence is a phrase that gets thrown around quite a bit. Part of the problem is we don't understand what AI actually means. In the energy industry, Artificial Intelligence represents a technology that can dramatically decrease our reliance on fossil fuels. In this guide, we'll dive deep into the many ways that Artificial Intelligence can, and in some cases already is, radically transforming the power industry by helping to reduce power grid demands. There are two primary places where AI can revolutionize the way we conserve energy.

The Agung volcano erupts, spewing magma and ash thousands of feet into the air on the island of Bali in Indonesia in November 2017.Josh Edelson/ZUMA This story was originally published by Grist and appears here as part of the Climate Desk collaboration. A trope of sci-fi movies these days, from Snowpiercer to Geostorm, is that our failure to tackle climate change will eventually force us to deploy an arsenal of unproven technologies to save the planet. Think sun-deflecting space mirrors or chemically altered clouds. And because these are sci-fi movies, it's assumed that these grand experiments in geoengineering will go horribly wrong. The fiction, new evidence suggests, may be much closer to reality than we thought.

This year's iPhones will feature the iPhone X's Face ID, according to two recent supply chain reports from Korea spotted by 9to5Mac. KGI Securities said last fall that Apple will release three iPhones with the Face ID this year. Apple released the iPhone X with the Face ID in November. The system in the $1,000 smartphone includes 3D face recognition modules for unlocking the screens, making payments and other iPhone tasks. This week's reports by ETNews and The Korea Herald say LG Innotek will continue to be Apple's main supplier for the Face ID modules.

The world's most patient mom may be a deep-sea octopus that tends her eggs for nearly 4.5 years. But now, there may be a new contender for her throne. Scientists have caught a rare glimpse of another deep-sea dweller that may also spend four or more years nursing its eggs, and it does it in an even more unusual place: on hydrothermal vents, where hot water spews from the ocean floor. It's called the Pacific white skate (Bathyraja spinosissima), a bone-white, bug-eyed relative of sharks that can live almost two miles (2,900 meters) underwater. Deep-sea skates, which are shark relatives that resemble rays, lay large eggs that can take years to hatch in cold water.