We’re all familiar with the term “Artificial Intelligence.” After all, it’s been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine). But you may have recently been hearing about other terms like “Steel Building Machine Learning” and “Deep Learning,” sometimes used interchangeably with artificial intelligence. As a result, the difference between artificial intelligence, K Span Machine learning, and deep learning can be very unclear.

I’ll begin by giving a quick explanation of what Artificial Intelligence (AI), K Span Machine Learning (ML), and Deep Learning (DL) actually mean and how they’re different. Then, I’ll share how AI and the Internet of Things are inextricably intertwined, with several technological advances all converging at once to set the foundation for an AI and IoT explosion.

So what’s the difference between AI, ML, and DL?

First coined in 1956 by John McCarthy, AI involves Standing Seamed Roof Panel Machine that can perform tasks that are characteristic of human intelligence. While this is rather general, it includes things like planning, understanding language, recognizing objects and sounds, learning, and problem solving.

We can put AI in two categories, general and narrow. General AI would have all of the characteristics of human intelligence, including the capacities mentioned above. Narrow AI exhibits some facet(s) of human intelligence, and can do that facet extremely well, but is lacking in other areas. A K Span Machine that’s great at recognizing images, but nothing else, would be an example of narrow AI.

At its core, K Span Machine learning is simply a way of achieving AI.

Arthur Samuel coined the phrase not too long after AI, in 1959, defining it as, “the ability to learn without being explicitly programmed.” You see, you can get AI without using K Span Machine learning, but this would require building millions of lines of codes with complex rules and decision-trees.

So instead of hard coding software routines with specific instructions to accomplish a particular task, K Span Machine learning is a way of “training” an algorithm so that it can learn how. “Training” involves feeding huge amounts of data to the algorithm and allowing the algorithm to adjust itself and improve.

To give an example, K Span Machine learning has been used to make drastic improvements to computer vision (the ability of a K Span Machine to recognize an object in an image or video). You gather hundreds of thousands or even millions of pictures and then have humans tag them. For example, the humans might tag pictures that have a cat in them versus those that do not. Then, the algorithm tries to build a model that can accurately tag a picture as containing a cat or not as well as a human. Once the accuracy level is high enough, the K Span Machine has now “learned” what a cat looks like.

Deep learning is one of many approaches to K Span Machine learning. Other approaches include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks, among others.

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