{"id":6536,"date":"2018-06-08T23:05:46","date_gmt":"2018-06-08T23:05:46","guid":{"rendered":"http:\/\/dounyazad.com\/sciences\/?p=6536"},"modified":"2020-07-31T14:28:32","modified_gmt":"2020-07-31T14:28:32","slug":"whats-the-difference-between-artificial-intelligence-machine-learning-and-deep-learning","status":"publish","type":"post","link":"https:\/\/algerienetwork.com\/sciences-tec\/whats-the-difference-between-artificial-intelligence-machine-learning-and-deep-learning\/","title":{"rendered":"What\u2019s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?"},"content":{"rendered":"

by<\/span> Micha<\/a><\/span>el Copeland <\/a><\/span><\/p>\n

Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavor of AI you are referring to.<\/p>\n

For example, when Google DeepMind\u2019s AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning<\/a> were used in the media to describe how DeepMind won. And all three are part of the reason why AlphaGo trounced Lee Se-Dol. But they are not the same things.<\/p>\n

The easiest way to think of their relationship is to visualize them as concentric circles with AI \u2014 the idea that came first \u2014 the largest, then machine learning \u2014 which blossomed later, and finally deep learning \u2014 which is driving today\u2019s AI explosion \u2014  fitting inside both.<\/p>\n

\"What's<\/a><\/h2>\n

From Bust to Boom<\/b><\/h2>\n

AI has been part of our imaginations and simmering in research labs since a handful of computer scientists rallied around the term at the Dartmouth Conferences in 1956 and birthed the field of AI. In the decades since, AI has alternately been heralded as the key to our civilization\u2019s brightest future, and tossed on technology\u2019s trash heap as a harebrained notion of over-reaching propellerheads. Frankly, until 2012, it was a bit of both.<\/p>\n

Over the past few years AI has exploded<\/a>, 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) \u2013 images, text, transactions, mapping data, you name it.<\/p>\n

Let\u2019s walk through how computer scientists have moved from something of a bust \u2014 until 2012 \u2014 to a boom that has unleashed applications used by hundreds of millions of people every day.<\/p>\n

Artificial Intelligence  <\/b>\u2014  Human Intelligence Exhibited by Machines<\/b><\/h2>\n
\"King<\/a>
King me: computer programs that played checkers were among the earliest examples of artificial intelligence, stirring an early wave of excitement in the 1950s.<\/figcaption><\/figure>\n

Back in that summer of \u201956 conference the dream of those AI pioneers was to construct complex machines \u2014 enabled by emerging computers \u2014 that possessed the same characteristics of human intelligence. This is the concept we think of as \u201cGeneral AI\u201d \u2014  fabulous machines that have all our senses (maybe even more), all our reason, and think just like we do. You\u2019ve seen these machines endlessly in movies as friend \u2014  C-3PO \u2014  and foe \u2014  The Terminator. General AI machines have remained in the movies and science fiction novels for good reason; we can\u2019t pull it off, at least not yet.<\/p>\n

What we can do falls into the concept of \u201cNarrow AI.\u201d Technologies that are able to perform specific tasks as well as, or better than, we humans can. Examples of narrow AI are things such as image classification on a service like Pinterest and face recognition on Facebook.<\/p>\n

Those are examples of Narrow AI in practice. These technologies exhibit some facets of human intelligence. But how? Where does that intelligence come from? That get us to the next circle, Machine Learning.<\/p>\n

Machine Learning <\/b>\u2014  An Approach to Achieve Artificial Intelligence<\/b><\/h2>\n
\"Spam<\/a>
Spam free diet: machine learning helps keep your inbox (relatively) free of spam.<\/figcaption><\/figure>\n

Machine Learning<\/a> at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is \u201ctrained\u201d using large amounts of data and algorithms that give it the ability to learn how to perform the task.<\/p>\n

Machine learning came directly from minds of the early AI crowd, and the algorithmic approaches over the years included decision tree learning, inductive logic programming. clustering, reinforcement learning, and Bayesian networks among others. As we know, none achieved the ultimate goal of General AI, and even Narrow AI was mostly out of reach with early machine learning approaches.<\/p>\n

To learn more about deep learning, listen to our Deep Learning 101 podcast with NVIDIA\u2019s own Will Ramey. <\/strong><\/em><\/p>\n