Solar panels on Earth already provide us with a clean source of power, but they can be a blot on the landscape and are practically useless when it's dark. Now, scientists in California have provided a solution – sending solar panels to space so they can harness the sun's power 24/7. In a world first, the researchers beamed solar energy to Earth from a spacecraft called MAPLE, which was launched to orbit in January. MAPLE is equipped with solar panels that can withstand'the harsh environment of space', including wild temperature swings and solar radiation. 'Space solar power' – a concept conjured by science-fiction writer Isaac Asimov in 1941 – could potentially yield eight times more power than solar panels at any location on Earth's surface.

Danelfin has released its new December ranking of the stocks most likely to beat the market. Until now, the monthly rankings were based solely on the AI Score, a score that reflects each company's probability of beating the market (the S&P 500 TR for U.S. stocks and the STOXX 600 for European stocks) in the next 30 to 90 days. But it is important to also consider the risk associated with each stock. Therefore, Danelfin has created a new ranking, which ranks companies according to the AI Risk/Reward Score, which is an average of the AI Score and the Low Risk Score. The Low Risk Score is a score based on negative price fluctuations (semi-deviation) over the last 500 market days. The higher the score (from 1 to 10), the lower the downside risk.

NYSEG, an electric and gas utility that serves areas of the Capital Region not served by National Grid, is developing a new computer-based outage prediction system that will use artificial intelligence. New York State Electric & Gas says it is developing what it is calling an "outage prediction model," essentially a software program that will use machine learning or artificial intelligence -- AI -- to predict outages during storm events. NYSEG and its parent company, Avangrid, along with its sister utility, RG&E, short for Rochester Gas & Electric, are working with researchers at the University at Albany and the University of Connecticut on developing the AI system. The system will use AI to analyze weather forecasts to predict -- or guess -- which parts of the electrical grid will be hit hardest by storms. That way the utility can prepare to deploy resources to those areas in advance.

We hear it said all the time, most recently in a national campaign for BT: "Technology will save us." The slogan was plastered on billboards across the country as part of BT's new advertising campaign, linked to a "UK-wide digital skills movement" developed partly with Google. The sentiment is so ubiquitous that it even led to a dispute with a startup of a similar name. But in an era dominated by the "big four" (Google, Amazon, Facebook and Apple) the idea that tech will save us rings hollow, an example of utopian messaging being used to conceal the simple pursuit of profit. Having proposed solutions to everything from food shortages to suicide prevention to climate breakdown, companies such as Google and Facebook – two of the leading western companies in the artificial intelligence arms race – claim there's almost nothing that cannot be tackled through tech.

For dynamical systems that can be modelled as asymptotically stable linear systems forced by Gaussian noise, this paper develops methods to infer their modes from observations in real time. The modes can be real or complex. For a real mode, we infer its damping rate, mode shape and amplitude. For a complex mode, we infer its frequency, damping rate, (complex) mode shape and (complex) amplitude. The work is motivated and illustrated by the problem of detection of oscillations in power flow in AC electrical networks. Suggestions of other applications are given.

DeepMind claims it has trained an artificial intelligence system how to predict the energy output of Google wind farms in the U.S. The variable nature of wind makes it difficult to accurately predict how much energy a wind farm could produce in any given time period. But DeepMind says that its AI system-- a neural network trained on widely available weather forecasts and historical turbine data -- can predict wind power output 36 hours ahead of actual generation with a reasonable degree of accuracy. "Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance," a team of DeepMind researchers wrote in a blog post on Tuesday. "This is important because energy sources that can be scheduled (i.e. can deliver a set amount of electricity at a set time) are often more valuable to the grid." Google claims that DeepMind's AI system has boosted the "value" of its wind energy by roughly 20 per cent.

National Grid has turned to artificial intelligence to help it maintain the wires and pylons that transmit electricity from power stations to homes and businesses across the UK. The firm has been using six drones for the past two years to help inspect its 7,200 miles of overhead lines around England and Wales. Equipped with high-res still, video and infrared cameras, the drones are deployed to assess the steelwork, wear and corrosion, and faults such as damaged conductors. Artificial Intelligence has various definitions, but in general it means a program that uses data to build a model of some aspect of the world. This model is then used to make informed decisions and predictions about future events.

This picture show the facilities of the Google data center in Changhua, central Taiwan, on December 11, 2013. US search engine giant Google announced that it has decided to double its investment in Taiwan to $600 million while opening its first data centre in Asia cashing in on the robust demands. Google is trusting an artificial intelligence (AI) system developed by DeepMind to stop its data centres around the world from overheating. The AI system -- able to reduce the amount of energy Google used to cool its data centres by 40% -- has been giving cooling recommendations to Google's data centre operators since 2016. But now Google is allowing the data centre operators to take a back seat, giving the AI an unprecedented level of autonomy in the process.

In the 21st century the world still faces many geographical challenges including climate change, disease outbreaks, natural disasters and a growing scarcity of vital resources such as water, food and land. Overcoming these problems is dependent on our ability to chart these issues and analyse them spatially. This comes at a time when we're increasingly able to produce millions of data points from connected devices – the internet of things (IoT) – such as mobiles, drones, satellites, vehicles and social media, combined with more affordable, powerful cloud computing and machine-learning. Technologists realise the potential for smart mapping has never been greater. "If you think about it, there isn't an area that isn't touched by location, from responses to hurricanes and typhoons, wars, international health scares or utility outages," explains Stuart Bonthrone, managing director of Esri UK, a world leader in mapping and spatial analytics software.

This is an article I had originally written as part of a stream of work that has now been put on hold indefinitely. I thought it a shame for it to languish in OneNote. Well that is a very good question. To be perfectly frank, not that much has changed of late in the world of Artificial Intelligence (AI) as a whole that should justify all the current excitement. That's not to say that there isn't cool stuff going on; there really is great progress being made… in the world of Machine Learning.