The Economist Originally published January 7, 2017 Here is an excerpt: Another important biological hurdle that AI can help people surmount is complexity. Experimental science progresses by holding steady one variable at a time, an approach that is not always easy when dealing with networks of genes, proteins or other molecules. AI can handle this more easily than human beings. At BERG Health, the firm's AI system starts by analysing tissue samples, genomics and other clinical data relevant to a particular disease. It then tries to model from this information the network of protein interactions that underlie that disease.

The University of Southern California has recently created the Center on Artificial Intelligence for Social Solutions or CAISS, specifically to develop uses of artificial intelligence--AI--for use cases of interest to social entrepreneurs. The Center is a collaboration between the USC Viterbi School of Engineering and the USC School of Social Work. Eric Rice, an Associate Professor from the School of Social Work, has been tapped to lead CAISS. The potential for AI to be an effective tool for entrepreneurs with no background in technology is confirmed by Rice himself. When asked about his LinkedIn profile URL, he acknowledged not only that he doesn't have one, but also said, "I'm a bit of a Luddite."

DeepMind is a British AI startup which was relatively unknown until it was bought by Google for around $600 million in 2014. Since then DeepMind has continued to refine its neural-network driven technology which has broken new frontiers with machine learning, particularly deep learning. Perhaps DeepMind's most famous accomplishment so far is being the brains behind AlphaGo, the first computer program to beat a professional human player of the board game Go. AlphaGo was developed by feeding DeepMind's machine learning algorithms with 30 million moves from historical tournament data, and then having it play against itself and learn from each defeat or victory. DeepMind's work is based on a solid grounding in neuroscience.

Facebook is celebrating "Friends Day" around the world, showing a video to more than a billion people. The company has celebrated the made up holiday – ostensibly to celebrate "the importance of connecting" – the last two years. But in 2015 and 2016 it was celebrated on 4 February, which Facebook said at the time was also its birthday, and comes two days after it is being celebrated this year. It isn't clear how the day came about, how it is intended to help its users or what it's actually for. Facebook is celebrating by showing a video to every one of the more than a billion people who log into it each day, in which a person made up of tiny pictures of a users' friends is shown dancing around.

Retail Real Estate is experiencing greater change than ever before. The impact of changes in real estate and the consumer retail world is comparable to tectonic plates shifting across the surface of the earth, producing an energy force that few know how to harness or predict the outcome. The way we conduct retail and real estate are rubbing, creating friction & forces, sometimes against, and sometimes in harmony with each other. Year-end 2016 came and went with little time to pause and reflect as Amazon Go made the headlines in the final few weeks and continues to do so. We asked some of the Movvo team to share their predictions for 2017, the trends & innovations that will have an impact on retail real estate as we know it. "2016 is going to be remembered as the year a retail platform came from nowhere to hit Apple & Google where it hurts. Alexa from Amazon is an intuitive AI interface already flexing its muscle and proving incredible popular. Alexa is already present in 4% of US households and dozens of Alexa-enabled devices launched at CES Las Vegas from fridges to cars. It's an early vision of the death of the keyboard as an interface & will have an impact on the way consumers shop".

When Intel unveiled its BigDL machine learning framework, it emphasized CPU rather than GPU power. With this move, Intel hopes to keep its present and future hardware competitive in the expanding world of machine learning. IBM also is interested in boosting the use of its CPUs for machine learning in PowerAI, though with a different strategy. It pairs IBM's Power8 processors with Nvidia GPUs, and IBM says it's a prime method for running common machine learning applications like Caffe, Torch, and Theano. Now IBM has added TensorFlow to the mix. Google's deep-leaning framework spans both CPUs and GPUs, and IBM is pushing PowerAI as the perfect hardware blend to make the most of TensorFlow.

Machine Learning techniques can help modern businesses run much more efficiently and in a more predictable way. In this article, I am going to scratch the surface of machine learning basics and explain what the key takeaways are for business owners. Before we get started, let's try to define what machine learning means. In general, machine learning is a technique, where behavior of an application or algorithm changes based on past iteration. So in other words, it's a way of machine self-education that mimics humans by learning from the past.

Scala is a highly scalable general purpose programming language that combines aspects of both object-oriented and functional programming. It's become increasingly important in the world of data science, rivaling more established languages like Java and Python. One of the main drivers of Scala's rise to prominence has been the explosive growth of Apache Spark (which is written in Scala), giving Scala a well-earned reputation as a powerful language for data processing, machine learning, and streaming analytics. What is Scala, and what makes it so well suited to handling big data? In this article we're going to look at what sets Scala apart as a programming language, why it's becoming increasingly important to data scientists, and what skills you should look for in a Scala developer.

Artificial intelligence (AI) developed by a University of Cincinnati doctoral graduate was recently assessed by subject-matter expert and retired United States Air Force Colonel Gene Lee - who holds extensive aerial combat experience as an instructor and Air Battle Manager with considerable fighter aircraft expertise - in a high-fidelity air combat simulator. The artificial intelligence, dubbed ALPHA, was the victor in that simulated scenario, and according to Lee, is "the most aggressive, responsive, dynamic and credible AI I've seen to date." Details on ALPHA - a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

No one doubts that artificial intelligence has unimaginable potential. Within the next couple of years, it will revolutionize every area of our life, including medicine. Although many have their fears and doubts about AI taking over the world, Stephen Hawking even said that the development of full artificial intelligence could spell the end of the human race. However, I am fully convinced if humanity prepares appropriately for the AI-age, artificial intelligence will prove to be the next successful area of cooperation between humans and machines. Concerning healthcare, artificial intelligence will redesign it completely – and for the better.