You might not know it, but deep learning already plays a part in our everyday life. When you speak to your phone via Cortana, Siri or Google Now and it fetches information, or you type in the Google search box and it predicts what you are looking for before you finish, you are doing something that has only been made possible by deep learning. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. It also is known as deep structured learning or hierarchical learning. The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to Artificial Neural Networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.
Again, remember that this is not a comprehensive list, but that it is notable in that there have been so very many new additions to the base of literature from many disciplines added in just the last few weeks. Just one year ago, we pulled the hype hat over our eyes to some extent–after all, this was most useful in tagging images on social sites and getting machines to paint pictures. The potential for higher purposes was there (the supercomputing world is seeing it too) but just beyond reach. We are, it is safe to say, at the real beginning of mainstream applications for deep learning.
There is no doubt that Big Data has been one of the most popular topics among marketers and tech enthusiasts for several years. Within the big data domain one of the most promising fields is deep learning which has evolved into one of tech's most exciting and promising disciplines in the field of AI (Artificial Intelligence). The popularity of deep learning peaked in March 2016, when Google's DeepMind AI program called AlphaGo bested Lee Sedol, the celebrated player of the board game "Go", by winning four out of five games. After the match it was revealed that a relatively new AI technique called "deep learning" was responsible for the victory. According to scientists, deep learning technology has the potential to transform the entire AI area.
You might have detected a "bit" of intentional tongue-in-cheek in my recent coverage of the most "intriguing" products released at the 2017 Consumer Electronics Show, but rest assured that the news wasn't all bizarre. Some truly significant technologies (and products based on them) were both introduced for the first time and notably advanced from prior versions. I thought I'd devote this particular post to showcasing three that particularly stuck with me. Learning goes deep While the world may not need an electric toothbrush that claims to have "artificial intelligence", both that term and the comparable "deep learning" were everywhere at CES, often for good reason. Traditionally, computer vision, audio analysis, and other similar applications have relied on special-purpose algorithms custom-designed to recognize particular patterns.
Artificial intelligence (AI) is suddenly everywhere. From phones and TVs to air conditioners and even a toilet, the flashy new products at the year's CES (Consumer Electronics Show) in Las Vegas - the world's largest tech show - are showing the 180,000 attendees that if a device doesn't have AI inside, it's not worth having.