Today, YouTube is updating its mobile app to provide a better'Home' experience. You'll now see larger thumbnails for recommended videos, which YouTube says is curated based on a deep neural network technology that looks at your search and watch patterns. YouTube says the update will display fewer recommendations, but show ones that should be more interesting to you based on your viewing history. The deep neural network is designed to detect patterns automatically, so it should keep up with your interests should they change week over week. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

Mike Stute recently wrote in Datanami, "Fraud detection. These use cases–and so many more–all owe a debt to machine learning. By automatically discovering patterns that lead to insights and creating predictive models that drive actions, the technology has proven its value many times, and to many industries. More recently, machine learning has begun to make a name for itself in the field of cybersecurity. As part of a larger cybersecurity solution, machine learning can help human security analysts when it comes to detecting real threats more quickly, so that an enterprise can act on them more swiftly. The technology can plumb the depths of historical security data to learn what attacks look like based on hidden variables and their relationships to each other, all in preparation for'seeing' the next attack when it hits."

In January, the Information Technology and Innovation Foundation in Washington DC gave its annual Luddite Award to "a loose coalition of scientists and luminaries who stirred fear and hysteria in 2015 by raising alarms that artificial intelligence (AI) could spell doom for humanity". The winners -- if that is the correct word -- included pioneering inventor Elon Musk and physicist Stephen Hawking. In January last year, both signed an open letter that argued for research and regulatory and ethical frameworks to ensure that AI benefits humanity and to guarantee that "our AI systems must do what we want them to do". As AI converges with progress in robotics, cloud computing and precision manufacturing, tipping points will arise at which significant technological changes are likely to occur very quickly. Crucially, advances in robot vision and hearing, combined with AI, are allowing robots to better perceive their environments.

There is no getting around it, humanoid robots are going to be our servers, coworkers and well, possibly our killers. Okay, maybe we are getting a little bit ahead of ourselves on that last part, but you should definitely get familiar with the idea of being around them, because it is very likely they will be walking among us within the next 20 years. As we have reported before, artificial intelligence is progressing at a rapid rate and robots are starting to look more and more like us. Take for example the newest robot by Hanson Robotics, who goes by the name Sophia. She made her debut at SXSW in Austin last month, showcasing her 65 different facial expressions and expressing her rather positive attitude about destroying all humans.

Whether it's PCs, tablets, smart watches or now, even smartphones, the outlook for most major hardware device categories is not looking good, particularly here in the US. The issue is that both consumers and businesses have already bought a lot of these devices. Plus, they're hanging on to their purchases longer than they used to, and longer than many people originally thought they would. Many companies, including both Intel (NASDAQ:INTC) and Qualcomm (NASDAQ:QCOM), have been forced to make some painful employee reductions as a result of these challenges, and there are likely more from other vendors still to come. So, does this signal the end of hardware as we know it?

This Best Energy Stocks forecast is designed for investors and analysts who need predictions of the best performing stocks for the whole Energy Industry (See Industry Package). Package Name: Energy Stocks Recommended Positions: Long Forecast Length: 14 Days (04/11/16– 04/25/16) I Know First Average: 27.11% All 10 top stocks for this forecast from the Energy Stocks package increased as predicted by the algorithm. LGCY was the highest-earning stock, more than doubling in share price, with a return of 105.65% for the 14-day time period. DNR also had a strong week with its return of 58.00% and ERF and PES also performed well with returns of 23.33% and 19.83% respectively.

Medical imaging is expected to be one of the early useful applications of artificial intelligence and machine learning in healthcare. And a slew of deals have been built around that premise in the last year or so--IBM Watson Health bought cloud-based imaging company Merge for 1 billion; Philips partnered with Hitachi to incorporate AI into its image management; and GE added deep learning software from startup Arterys to its cardiac imaging. Now, another major cloud-based imaging startup is working to incorporate machine learning, first into X-ray analysis and eventually into other imaging modalities including CT and MRI. The Goldman Sachs-backed startup Imaging Advantage, which reportedly tapped into up to 250 million in debt in January 2015, has partnered with the Massachusetts Institute of Technology as well as Harvard Medical School and Massachusetts General Hospital to develop an artificial intelligence engine known as Singularity Healthcare. The result is expected to launch this quarter.

TensorFlow is a great new deep learning framework provided by the team at Google Brain. It supports the symbolic construction of functions (similar to Theano) to perform some computation, generally a neural network based model. Unlike Theano, TensorFlow supports a number of ways to feed data into your machine learning model. The processes of getting data into a model can be rather annoying, with a lot of glue code. TensorFlow tries to fix this by providing a few ways to feed in data.

About a year ago I wrote a blog post about recent research in Deep Learning for Natural Language Processing covering several subareas. One of the areas I didn't cover was Deep Learning for Named Entity Recognition – so here are some interesting recent (2015-2016) papers related to that:

SEOUL--Humanity has fallen to artificial intelligence in checkers, chess, and, last month, Go, the complex ancient Chinese board game. But some of the world's biggest nerds are confident that machines will meet their Waterloo on the pixelated battlefields of the computer strategy game StarCraft. A key reason: Unlike machines, humans are good at lying. StarCraft, created in 1998, is one of the world's most popular computer game franchises. It pits three races against one another: the humanlike Terrans, the slimy insectoid Zerg and a mystical race with psionic powers called the Protoss.