While cognitive computing, often referred to as artificial intelligence (AI), is hardly new, the recent level of interest in it is astounding. The combination of vendor marketing, concerns about job losses, and even discussion of "robot overlords" have prompted massive interest in the media. There is also plenty of substance behind the hype. Cognitive technologies offer the possibility of increased productivity, better knowledge-based interactions with customers, and the ability to solve problems that are too complex for human brains. While there have been several "AI winters" and "AI springs" over the past 50 years, there is reason to be confident that the flowering this AI spring is changing the garden permanently.

Spending several days playing Space Invaders may seem like a waste of time. But it has just been used to prove the viability of a technique which could one day allow AI to match human intelligence - and eventually surpass it. Researchers at Google's DeepMind project have created an AI program that allows machines to learn and retain knowledge in the same way as people. Google's DeepMind AI project has created an algorithm which allows machines to learn and retain knowledge in the same way as people. Google's DeepMind AI relies on artificial neural networks, which try to simulate the way the brain works in order to learn.

Machine learning, and by extension deep learning, has without a doubt seen its influence across many different industries. Even if you have never worked with it, and know nothing about it, chances are that you have used it; either directly through a chat-bot or indirectly in detection of spam and malware. This piece will largely be based on a a talk by Andrew NG about this very topic, he talks about two trends in the deep learning community; scale and end to end deep learning. For most people even mildly interested in deep learning, scale wouldn't come as a surprise. Over the last 10 to 20 years we have acquired a lot more data.

Salesforce CEO Marc Benioff said at a recent conference: "This is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what's the next best offer, next best opportunity, how to make things a little bit more efficient." Machine Learning (ML) and AI powering "Systems that Learn at scale" are at the bleeding edge of data science, deep learning and predictive search today. Everyone is jumping on this AI enabled engagem ent ("ambient reality, experience and convenience") trend in retail, banking and even healthcare. Facebook is a case study of where AI/ML are being used to augment user engagement and experiences. I am starting to see many leading firms investing in ML Accelerators and Platforms as part of their data science strategy.

The moment of the next revolution in the history of mankind is fast approaching – the difference between this and all previous revolutions, both social and economic, is that this time we won't be in it alone. We will be accompanied by complex computer systems carrying out activities, which, due to our cognitive abilities, were previously reserved only for human beings. The era of artificial intelligence is here. Probably the most famous examples of the application of artificial intelligence, though rather simplistic, were the chess duels between grandmaster Garry Kasparov and IBM computer Deep Blue in 1996 and 1997. These duels were supposed to show that a machine can not only perform certain actions similar to a human being, but also outperform its human counterpart.

Researchers have overcome one of the major stumbling blocks in artificial intelligence with a program that can learn one task after another using skills it acquires on the way. Developed by Google's AI company, DeepMind, the program has taken on a range of different tasks and performed almost as well as a human. Crucially, and uniquely, the AI does not forget how it solved past problems, and uses the knowledge to tackle new ones.

There's no doubt about it that our future is one that involves artificial intelligence (AI) in a big way. While some companies are faster than others at adapting to the likes of AI technology, most will get there eventually as they begin to realize they form image can not continue to operate without it. One area that's becoming more and more apparent is in regards to the online shopping industry. Deep learning is an advancement of AI that's come about in the past few years and focuses on creating software that can learn and replicate many tasks that a human can. These deep learning algorithms have been used in the autonomous driving industry for quite some time, and only now is it beginning to branch out into other industries, such as online shopping.

Intelligence agents and military operatives may come to rely heavily on machine learning to parse huge quantities of data, and to control a growing arsenal of autonomous systems. But the U.S. military wants to make sure that this doesn't lead to blindly trusting in any algorithm. The Defense Advanced Research Projects Agency (DARPA), a division of the Defense Department that explores new technologies, is funding several projects that aim to make artificial intelligence explain itself. The approaches range from adding further machine-learning systems geared toward providing an explanation, to the development of new machine-learning approaches that incorporate an elucidation by design. "We now have this real explosion of AI," says David Gunning, the DARPA program manager who is funding an effort to develop AI techniques that include some explanation of their reasoning.

DeepMind, the London-based artificial intelligence company owned by Alphabet Inc., claims it overcame a key limitation affecting one of the most promising machine learning technologies: the software's inability to remember. The breakthrough, described in a paper published Tuesday in the academic journal Proceedings of the National Academy of Sciences, may open the way for artificial intelligence systems to be more easily applied to multiple tasks, instead of being narrowly trained for one purpose. It should also improve the ability of AI systems to transfer knowledge between tasks and to master a sequence of linked steps. Neural networks, software which is loosely based on the structure of synapses in the human brain, are considered the best machine learning technique for language translation, image classification and image generation. But these networks suffer from a major flaw scientists call "catastrophic forgetting." They exist in a kind of perpetual present: every time the network is given new data, it overwrites what it has previously learned.

When DeepMind burst into prominent view in 2014 it taught its machine learning systems how to play Atari games. The system could learn to defeat the games, and score higher than humans, but not remember how it had done so. For each of the Atari games, a separate neural network had to be created. The same system could not be used to play Space Invaders and Breakout without the information for both being given to the artificial intelligence at the same time. Now, a team of DeepMind and Imperial College London researchers have created an algorithm that allows its neural networks to learn, retain the information, and use it again.