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.

To date, no computer has ever beaten the world's best poker players when it comes to Texas Hold'Em. However, that could all be about to change during a twenty-day contest in Pittsburgh where even some of the best specialists are saying they don't have a chance this time and it's all down to artificial intelligence. The new AI machine in question was designed by two computer scientists at Carnegie Mellon and is called Libratus which is Latin for balanced. Dong Kim is a professional poker player who specializes in Texas Hold'Em and is among the best in the world at this game. Although he typically competes against others on high-stakes internet sites or Las Vegas casinos, this time Kim's traveled to Pittsburgh to try and defeat the mighty Libratus even though he feels doubtful.

They cover more than 70% of the earth's surface and the sheer size of the oceans makes tracking and measuring life under water an enormous task. New advances in satellite observation, open data and machine learning now allow us to process the massive amounts of data being produced. And they could not have come at a better time for protecting life under water, which is United Nations' Sustainable Development Goal No. 14 (SDG14). Last year was particularly challenging for most life underneath our world's oceans. Despite the dearth of bad news for the world's oceans, there is hope that 2017 can turn the tide for life under the sea thanks, in part, to machine learning.

Through techniques like machine learning, our devices are finally beginning to understand us on a much more fundamental level than ever before. Though true artificial intelligence is still not here quite yet, contextual data storage combined with the simplicity of almost perfected speech recognition has changed the way we interact with our devices, and will only be iterated upon until technology is so seamless that we will forget we are even using it. It seems quite clear that AI will be the future, but what about AR? Will augmented reality integrate with artificial intelligence to make our lives as simplistic as possible? Let's take a look a few possible scenarios, along with technologies on the market today that seem to be headed towards this transition. Artificial intelligence is used to describe a technology that can make decisions based on varying efficiency algorithms.

Parag Mital is director of machine intelligence at Kadenze, as well as an artist and interdisciplinary researcher obsessed with the nature of information, representation and attention. Artificial intelligence allows machines to reason and interact with the world, and it's evolving at a breakneck pace. Many advances in AI can be attributed to machine learning, which works by tapping massive computing power to crunch through enormous amounts of digitized data. Now consider that most of our data, the best minds in the business and more computing power than you could ever imagine sit with just a handful of companies. For these reasons, only a few companies in the world are best situated to understand the true potential -- and the current limits -- of AI.

How can we compare education and artificial intelligence? Well, some of you may say that we should include more courses related to artificial intelligence (AI). Yes, we should include artificial intelligence in the course, but I'm not talking about this type of connection here. I'm trying to emphasize on the advancement of AI here, and why education should be like AI in this article. AI is everywhere nowadays from our cars to our pockets.

A Northwestern University team developed a new computational model that performs at human levels on a standard intelligence test. This work is an important step toward making artificial intelligence systems that see and understand the world as humans do. "The model performs in the 75th percentile for American adults, making it better than average," said Northwestern Engineering's Ken Forbus. "The problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition."The The platform has the ability to solve visual problems and understand sketches in order to give immediate, interactive feedback.

Mel Silverman walked over to a whiteboard and picked up a marker, listing all the academic disciplines that the band of renegade scientists asking him for money represented. Assembled there 12 years ago at Vancouver's Metropolitan Hotel was a group of about 15 people, ranging from computer scientists to biologists to experimental engineers. What united them was their interest in a concept that was, at the time, generally perceived as the domain of the lunatic fringe. They believed it was possible to teach a machine to learn the same way a child does, through artificial neural networks that mimic the function of the human brain. In the process of teaching a machine to learn like a human, they figured there was likely a lot to discover about how humans learn as well.

Summary: In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In our second article, "5 Types of Recommenders" we attempted to detail the most dominant styles of Recommenders. Our third article, "Recommenders: Packaged Solutions or Home Grown" focused on how to acquire different types of recommenders and how those sources differ. In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance performance.

This week US scientists announced they have developed an algorithm, or a computerised tool, to identify skin cancers through analysis of photographs. Rather than relying on human eyes, the new method scans a photo of a patch of skin to look for common and dangerous forms of skin cancer. The authors report their approach performs on par with board-certified dermatologists to distinguish two forms of cancer, keratinocyte carcinoma and malignant melanoma, from benign skin lesions. The skin cancer diagnostic tool is based on a powerful type of machine learning that extracts information from images. The critical factor in achieving the accuracy and reliability required for a medical diagnostic tool is the large volume of training data the authors have used. This data consists of 129,450 skin images, and a label for each which indicates whether it contains a cancerous region.