2048 is an engaging single-player, nondeterministic video puzzle game, which, thanks to the simple rules and hard-to-master gameplay, has gained massive popularity in recent years. As 2048 can be conveniently embedded into the discrete-state Markov decision processes framework, we treat it as a testbed for evaluating existing and new methods in reinforcement learning. With the aim to develop a strong 2048 playing program, we employ temporal difference learning with systematic n-tuple networks. We show that this basic method can be significantly improved with temporal coherence learning, multi-stage function approximator with weight promotion, carousel shaping, and redundant encoding. In addition, we demonstrate how to take advantage of the characteristics of the n-tuple network, to improve the algorithmic effectiveness of the learning process by i) delaying the (decayed) update and applying lock-free optimistic parallelism to effortlessly make advantage of multiple CPU cores. This way, we were able to develop the best known 2048 playing program to date, which confirms the effectiveness of the introduced methods for discrete-state Markov decision problems.

Across the globe, energy systems are changing and creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, National Grid is facing shrinking margins, looming capacity shortages and unpredictable peaks and troughs in energy supply caused by increasing levels of renewable penetration. At the Reinventing Energy Summit, Michael Bironneau, Head of Technology Development at Open Energi, will explore how the same machine learning techniques that have let machines defeat chess and Go masters, can also be leveraged to orchestrate massive amounts of flexible demand-side capacity – from industrial equipment, co-generation and battery storage systems – towards the one goal of creating a smarter grid; one that is cleaner, cheaper, more secure and more efficient. For World Cities Day 2016, I asked Michael a few questions to learn more about utilising data science in energy, creating a smarter grid, political challenges, and more. A smarter grid is one where we can integrate renewable energy efficiently without having to keep polluting power stations online to manage intermittency.

Last week, completed its acquisition of for $26.2 billion. After winning a purported bidding war with, Microsoft again battled the Customer Relationship Management (CRM) leader in front of EU regulators. Salesforce argued that the acquisition would make Microsoft too powerful in the CRM market, an argument the regulators rejected. The stakes were much higher than that. In fact, the CRM card was a red herring that Microsoft played masterfully.

Beware shiny new technology and make sure you proceed with planning and a clear understanding of your goals: that was the conclusion from the recent Insurance Analytics Europe Summit held in London. Often when talking about machine learning in insurance, or the deeper neural networks that are shaping up to transform the industry in a more profound way, there's a tendency to be dazzled by the technology and assume that machines will simply replace humans. In fact the reality is more interesting and more complex than that. Deep learning in the last few years has begun to revolutionize computer vision, speech recognition and natural language process and it has already started to disrupt insurance. Deep learning allows computational models composed of multiple processing layers of abstraction.

Since the 1970s, when automated teller machines arrived, the number of bank tellers in America has more than doubled. James Bessen, an economist who teaches at Boston University School of Law, points to that seeming paradox amid new concerns that automation is "stealing" human jobs. To the contrary, he says, jobs and automation often grow hand in hand. Sometimes, of course, machines really do replace humans, as in agriculture and manufacturing, says Massachusetts Institute of Technology labor economist David Autor in a succinct and illuminating TED talk, which could have served as the headline for this column. Across an entire economy, however, Dr. Autor says that's never happened.

Artificial intelligence and machine learning are predicted to be part of the next industrial revolution and could help business and industry save billions of dollars by the next decade. The tech giants Google, Facebook, Apple, IBM and others are applying artificial intelligence to all sorts of data. Machine learning methods are being used in areas such as translating language almost in real time, and even to identify images of cats on the internet. So why haven't we seen artificial intelligence used to the same extent in healthcare? Radiologists still rely on visual inspection of magnetic resonance imaging (MRI) or X-ray scans – although IBM and others are working on this issue – and doctors have no access to AI for guiding and supporting their diagnoses.

An artificial intelligence program has written a Christmas carol. Researchers at the University of Toronto used their "" program to write a Christmas carol based off of a "Christmassy photograph," The Guardian reports. The scientists simply uploaded the photograph into a computer hosting the program and then "let it do its thing." The artificial intelligence program then wrote several lines of lyrics and sang them to music it Read More ... Tags: Computer systems Computer Software Artificial intelligence Google's DeepMind'Lab' opens up source code, joins race to develop artificial general intelligence Following right on its heels came Google's that, its machine learning platform, has created a "lab." The DeepMind Lab offers a 3D training environment, and its entire code library is now available on the open source hosting service GitHub. With Universe, OpenAI aimed to address a primary challenge for AI research, which is a lack of "a large variety of realistic environments [for AI agents to] learn progressively more Read More ... Tags: Computer systems Artificial intelligence Artificial general intelligence Places: Americas North America United States Microsoft's digital assistant, Cortana, could be getting an upgrade--and Intel may be helping.

Would you like to translate terabytes of data into unforgettable holidays for millions of people around the globe? Booking.com, the world's largest accommodation booking website, is looking for rock star Data Scientists to add to join our highly successful Personalization Team within the Front End department. This product development team crunches endless amounts of data to provide our customers with the best possible experience. They focus on anything from understanding and predicting market data, to ranking all properties on our website, and providing our customers with the most relevant personalized recommendations. As a Data Scientist you'll work side by side with Developers, Designers and Product Owners, and take full ownership of your work – from the initial idea-generation phase to the implementation of the final product on our website. Our ideal candidate is result-focused, innovative and has solid quantitative background and a good business understanding.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Paul Kemp: Welcome to another episode of The App Guy Podcast. I am your host, it's Paul Kemp. This is a very, very special episode. We actually made it to #500, can you believe it? Now, before I just start celebrating, I have a terrific episode lined up for you. Skype is the reasons why I can get so many guests on the show.