There are huge numbers of variants of deep architectures as it's a fast developing field and so it helps to mention other leading algorithms. The list is intended to be comprehensive but not exhaustive since so many algorithms are being developed [1] [2][1],[2]. LAMSTAR is Large memory storage and retrieval neural networks. Google DeepMIND uses this and it is based on reinforcement learning which is a major branch of psychology, aside from evolution. LAMSTAR are increasingly being used in medical and financial applications.

While these have their benefits, will this eventually begin the rise of AI overlords? Deep learning systems are becoming a growing trend in some of the world's largest tech companies. This allows AI systems to be able to "see" and learn for themselves depending on how they are programmed. These are new technologies alongside cloud computing and even speech recognition, among others. However, According to Wired, it appears a lot of companies are being re-oriented toward focusing on AI.

Artificial intelligence (AI) was once a science fiction dream, but today's technology brings it ever closer to reality. And, unlike those science fiction movies where AI resided in life-like android bodies, it may soon fit neatly in our pockets. Through advances in areas such as deep learning and low-power computer chips, AI could soon find a home in one of our most frequently used devices--our smartphones. Your smartphone is probably one of the most useful tools you own. Not only does it allow you to stay in touch with friends, family, and business associates, it permits you to access nearly any information on the internet, get directions (and arrival times), find entertainment, play games, check email, maintain your calendar … you get the idea.

The next time you enter a query into Google's search engine or consult the company's map service for directions to a movie theater, remember that a big brain is working behind the scenes to provide relevant search results and make sure you don't get lost while driving. As Fortune's Roger Parloff wrote, the Google Brain research team has created over 1,000 so-called deep learning projects that have supercharged many of Google's products over the past few years like YouTube, translation, and photos. With deep learning, researchers can feed huge amounts of data into software systems called neural nets that learn to recognize patterns within the vast information faster than humans. In an interview with Fortune, one of Google Brain's co-founders and leaders, Jeff Dean, talks about cutting-edge A.I. research, the challenges involved, and using A.I. in its products. The following has been edited for length and clarity. A lot of human learning comes from unsupervised learning where you're just sort of observing the world around you and understanding how things behave.

Researchers from Google's UK-based artificial intelligence division DeepMind have collaborated with scientists from the University of Oxford to develop the world's most advanced lip-reading software – and it probably reads lips better than you. To accomplish this, the researchers fed thousands of hours of TV footage from the BBC to a neural network, training it to annotate videos based on mouth movement analysis with an accuracy of 46.8 percent. For context, when tasked with captioning the same video, a professional human lip-reader proved to be almost four times less efficient, accurately guessing the right word only 12.4 percent of the time. The research builds upon previously published work by the University of Oxford that used similar techniques to build a lip-reading app called LipNet that could read video recordings of volunteers speaking in simple sentences with an accuracy of over 90 percent. However, unlike Oxford's program, DeepMind's software – dubbed "Watch, Listen, Attend, and Spell" – was trained and tested on much more challenging footage.

In Supercomputing Conference (SC) years past, chipmaker Intel has always come forth with a strong story, either as an enabling processor or co-processor force, or more recently, as a prime contractor for a leading-class national lab supercomputer. But outside of a few announcements at this year's SC related to beefed-up SKUs for high performance computing and Skylake plans, the real emphasis back in Portland seemed to ring far fainter for HPC and much louder for the newest server tech darlings, deep learning and machine learning. Far from the HPC crowd last week was Intel's AI Day, an event in San Francisco chock full of announcements on both the hardware and software fronts during a week that has historically emphasized Intel's revolving efforts in supercomputing. As we have noted before, there is a great deal of overlap between these two segments, so it is not fair to suggest that Intel is ditching one community for the other. In fact, it is quite the opposite--or more specifically, these areas are merging to a greater degree (and far faster) than most could have anticipated.

Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Neural networking with advanced parallel processing is beginning to take root in a number of markets ranging from predicting earthquakes and hurricanes to parsing MRI image datasets in order to identify and classify tumors. As this approach gets implemented in more places, it is being customized and parsed in ways that many experts never envisioned. And it is driving new research into how else these kinds of compute architectures can be applied. Fjodor van Veen, deep learning researcher at The Asimov Institute in the Netherlands, has identified 27 distinct neural net architecture types. The differences are largely application-specific. Neural networking is based on the concept of threshold logic algorithms, which were first proposed in 1943 by Warren McCulloch, a neurophysiologist, and Walter Pitts, a logician.

The announcement at BlizzCon 2016 that met with the most muted response was arguably the most revolutionary. While new content for the likes of Hearthstone, Heroes of the Storm, Overwatch, and Diablo III drew appreciative roars from the Blizzard faithful, the news that Google's DeepMind branch--which is dedicated to developing sophisticated Intelligence--would be teaming up with the makers of Starcraft 2 to further its research on AI elicited more of a murmur. Perhaps the lack of enthusiasm was down to taste. After all, why would the plans of AI scientists be of interest to Starcraft 2 players? As it turns out, if the collaboration between DeepMind and Blizzard is what its developers hope it could be, players will see very tangible benefits--and so will many others outside the video game space.