The boom in AI promises to enrich our lives. AI assistants keep our schedules in order; robot "crew" members help us on cruises; and "swarm AI" even offers the chance for us to win big in the gambling world. But there's a dark side of the coin as well: AI that can cause great harm. While much thought has been devoted to the dangers of AI, and centers like the Future of Life Institute in Cambridge, Ma., and the Future of Humanity Institute at Oxford University are focusing resources on how to support the creation of'safe' AI, few have attempted to intentionally create malevolent AI. Go with TechRepublic's Steve Ranger on an inside look at the gold-plated gadget market that's received a big boost from Apple.

PDF: We made a fancy PDF of this post for printing and offline viewing. Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what's happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1--Part 2 is here. We are on the edge of change comparable to the rise of human life on Earth. It seems like a pretty intense place to be standing--but then you have to remember something about what it's like to stand on a time graph: you can't see what's to your right. So here's how it actually feels to stand there: Imagine taking a time machine back to 1750--a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything. It's impossible for us to understand what it would be like for him to see shiny capsules racing by on a highway, talk to people who had been on the other side of the ocean earlier in the day, watch sports that were being played 1,000 miles away, hear a musical performance that happened 50 years ago, and play with my magical wizard rectangle that he could use to capture a real-life image or record a living moment, generate a map with a paranormal moving blue dot that shows him where he is, look at someone's face and chat with them even though they're on the other side of the country, and worlds of other inconceivable sorcery.

Green Running was founded with a mission to use high speed energy data sampling technology and advanced Machine Learning techniques to break energy bills down to appliance level information making unknown energy bills a thing of the past. At these high speeds each appliance has its own unique'energy signature' and we were able to develop a system that recognises these signatures through self-learning Artificial Intelligent algorithms. Installing the eGenius arms you with your own consumption and usage behaviour, which can show you which appliances are consuming the majority of your bill, what a return on investment would be if you were to replace any of those appliances with the latest eco appliances and how your appliances like your fridge of freezer have worn over time. Your usage information is a great tangible asset which companies are willing to buy off you, further offsetting your energy bills. It is all calculated in real time and sent to your smartphone or tablet using our cool app!

The safety of critical maritime operations and the power output of wave energy converters can both be improved by measuring and predicting the shape and motion of sea waves. The aim of this project is extract information from monoscopic video footage of the sea's surface that enable its shape and motion to be modelled. The models will then be used to predict its future motion up to two minutes ahead. Making observations of the shapes of sea waves is difficult. We have been working with wave profiling radar, which is relatively expensive and difficult to install and run.

We consider partially observable Markov decision processes (POMDPs) with a set of target states and positive integer costs associated with every transition. The traditional optimization objective (stochastic shortest path) asks to minimize the expected total cost until the target set is reached. We extend the traditional framework of POMDPs to model energy consumption, which represents a hard constraint. The energy levels may increase and decrease with transitions, and the hard constraint requires that the energy level must remain positive in all steps till the target is reached. First, we present a novel algorithm for solving POMDPs with energy levels, developing on existing POMDP solvers and using RTDP as its main method. Our second contribution is related to policy representation. For larger POMDP instances the policies computed by existing solvers are too large to be understandable. We present an automated procedure based on machine learning techniques that automatically extracts important decisions of the policy allowing us to compute succinct human readable policies. Finally, we show experimentally that our algorithm performs well and computes succinct policies on a number of POMDP instances from the literature that were naturally enhanced with energy levels.

OpenAI and Shocking AI Salaries This week, Elon Musk and Sam Altman, head of the famed incubator Y Combinator, announced the launch of the OpenAI project, which is a company that's building AI products and services and giving away their tech for free. Just after the announcement, OpenAI's team of researchers were reportedly mobbed with huge employment offers from some of the tech world's biggest firms. Microsoft's Vice President of Research said that the cost of a top artificial intelligence researcher has eclipsed the cost of a top quarterback prospect in the National Football League. CLICK TO TWEET THIS: The cost of a top artificial intelligence researcher has eclipsed the cost of a top quarterback prospect in the National Football League. Bill Gates' 2b Clean Energy Fund Recently, the MIT Technology Review sat down for a Q&A session with Bill Gates and they discussed the Breakthrough Energy Coalition.

Co-author Paul Leiby, Distinguished Research Scientist at Oak Ridge National Laboratory, said: "Because automation has the potential to provide convenient, lower cost mobility, we see it could have large implications for transportation demand, energy use and resulting CO2 emissions, by both passengers and freight. For example, low cost automated trucking could shift more freight away from efficient railways to trucks. To make continued progress in reducing carbon emissions from light-duty vehicles and large trucks in the face of expanded mobility, it will be essential to couple vehicle automation with the extensive use of advanced low-carbon vehicles, like electric or hydrogen vehicles."

LOS ALAMOS, N.M., May 9, 2016--Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process.

Results of the application of pattern recognition techniques to the problem of identifying Giant Radio Sources (GRS) from the data in the NVSS catalog are presented and issues affecting the process are explored. Decision-tree pattern recognition software was applied to training set source pairs developed from known NVSS large angular size radio galaxies. The full training set consisted of 51,195 source pairs, 48 of which were known GRS for which each lobe was primarily represented by a single catalog component. The source pairs had a maximum separation of 20 arc minutes and a minimum component area of 1.87 square arc minutes at the 1.4 mJy level. The importance of comparing resulting probability distributions of the training and application sets for cases of unknown class ratio is demonstrated. The probability of correctly ranking a randomly selected (GRS, non-GRS) pair from the best of the tested classifiers was determined to be 97.8 +/- 1.5%. The best classifiers were applied to the over 870,000 candidate pairs from the entire catalog. Images of higher ranked sources were visually screened and a table of over sixteen hundred candidates, including morphological annotation, is presented. These systems include doubles and triples, Wide-Angle Tail (WAT) and Narrow-Angle Tail (NAT), S- or Z-shaped systems, and core-jets and resolved cores. While some resolved lobe systems are recovered with this technique, generally it is expected that such systems would require a different approach.

The SQL Server R Services available in SQL Server 2016 offer customers new opportunities to perform in-database advanced analytics. With SQL Server R Services, both open source R scripts and the high performance analytics algorithms in Microsoft R Server can be executed within SQL Server. Furthermore, you can continue to develop using familiar R Integrated Development Environments (IDEs) such as R Tools for Visual Studio, RStudio etc., and benefit from interactive development and debugging. The development process can be done on your local computer, while performing in-database computation without moving data in or out of the database. The produced R scripts are easy to operationalize on SQL Server.