Machine Learning<\/strong><\/a>, Artificial Intelligence would simply run long lists of “if X is true do Y or else do Z”. However, this innovation gives computers the power to solve things without having them explicitly programmed. As an example of machine learning, let’s say you want a program to be able to identify cats in images:<\/p>\nGive your AI a set of characteristics of what a cat is like so that it knows how to recognize it. Colors, shapes, etc. \nShow him pictures (if any are labeled ‘cat’, the AI will be able to identify them more easily). \nOnce the program has seen enough cats, it should be able to identify them in other images: “If the image contains X, Y, and Z features, then there is 95% of it being a cat.” \nMachine Learning<\/p>\n
Although it sounds complicated, it can be summarized as follows: “We humans tell the computer what to look for, and the computer refines the criteria until it has a specific model of what we have asked of it.” It is quite simple, very useful and it is what filters SPAM emails, makes recommendations to you on Netflix, and modifies your activity on Facebook.<\/p>\n
DL (Deep Learning)<\/h2>\n Since 2018, this is the forefront of Artificial Intelligence. Think of it as machine learning with deep “neural networks” that process data similar to the human brain. The key difference from its predecessor is that humans don’t have to teach the show what cats are like; just give him enough pictures of cats and he will be able to figure it out himself:<\/p>\n
Give her lots of pictures of cats. \nThe algorithm will inspect the photos to see what they have in common (hint: they are cats). \nEach photo will be deconstructed at multiple levels of detail, from large, general shapes to small lines. If a shape repeats a lot, the algorithm will label it as an important feature. \nAfter analyzing enough photos, the algorithm will already know how to recognize the patterns that define what a cat is and will be able to identify it in any other scenario. \nDeep Learning<\/p>\n
In summary: Deep Learning is machine learning, in which the computer is capable of learning by itself (although it goes much further than cats, of course, since currently, machines are already capable of capturing many more parameters within the photos, such as the landscape for example).<\/p>\n
Deep Learning requires much more initial data and computing power than Machine Learning, yes, but companies like Facebook or Amazon are already beginning to implement it.<\/p>\n","protected":false},"excerpt":{"rendered":"
Building an AI (Artificial Intelligence) is complicated, but understanding what it consists of does not have to be. To do this, in this article we are going to explain what AI, Machine Learning, and Deep Learning are, as well as the differences between them. Most existing AIs are actually computers designed to solve problems, or […]<\/p>\n","protected":false},"author":2,"featured_media":102,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[138,6],"tags":[365,364,366],"yoast_head":"\n
AI, Machine Learning and Deep Learning, what is the difference? - FollowMyStep<\/title>\n \n \n \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n \n \n \n\t \n\t \n\t \n