Artificial intelligence simplified

The easiest way to think of relationship is to visualize them – idea that came first, largest one Artificial intelligence, then Machine learning – which blossomed later, and finally Deep Learning – which is driving today's explosion – fitting inside both.

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“Artificial Intelligence(AI) is the science of how to get machines to do the things they do in the movie.” – Astro Teller

Now, what exactly is AI..?
The man who put the main characteristics of artificial intelligence in order was Alan Turing, creator of the famous test (1950) that defines AI as a machine that can convince a human that it is a human.

Every day, we interact with forms of AI(artificial intelligence): the monotonous voice of Siri from our iPhone, self-driving cars, and facial recognition of friends in Facebook pictures.

AI exhibits some facets of human intelligence. But how? Where does that intelligence come from? That get us to the next circle, Machine Learning.

Machine learning is a term that is taken from the real world of a person, and applied on something that can’t actually learn – a machine

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Machine learning will not (usually) change the code, but it might change its execution path and decision based on previous data or new gathered data and hence the "learning" effect.

There are many ways to "teach" a machine - you give weights to many parameter of an algorithm, and then have the machine solve it for many cases, each time you give her a feedback about the answer and the machine adjusts the weights according to how close the machine answer was to your answer or according to the score you gave it's answer, or according to some results test algorithm.

Good, but not mind-bendingly great.. hmm..

Let’s have a look at Deep Learning – A technique of implementing Machine Learning..

This technique involves feeding our model large volumes of data, but it requires less feature engineering than a linear regression model would. But How does this translate to real life..?

If we’re looking at classifying images for a cat, we’ll have to feed our data set with a bunch of images of a cat. But we don’t necessarily have to say that a cat is something with cute ears or whiskers and then.. Taaaaaadaaaaaa…..

cat.jpg

Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely Driverless cars, better preventive healthcare or even better movie recommendation, are all here today or on the horizon. AI has the present and the future with the help of Deep Learning. AI even get to that science fiction movies we’ve so long imagined. You have a C-3PO, I’ll take it.. You can keep your Terminator.

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simplified tutorial , I am working in the same domain , this is just awesome , even , I can relate this to what einstein said "You should explain in such a way so that your grandma can understand"

Thank you so much.. it mean a lot :) :)

Very nice and smooth introduction :)

One thing that I would like to point out is the strong connection machine learning has to statistics. In the early days of AI (often referred as "good old-fashioned artificial intelligence" - GOFAI), the brain was seen as a symbol processor, so that's also what the AI researchers were trying to do. But it turned out, that symbol processing approaches have difficulties dealing with complex settings (such as the "real world"), while statistical approaches are doing much better there.

Thanks, will definitely explore and read more about GOFAI :)

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I'm sure that many people still have various concerns about AI, although personally I don't see anything wrong with using it, except for the advantages. By the way, you can read more about this in article https://anyforsoft.com/blog/ai-in-media-and-entertainment/. AI opens up new opportunities for media and entertainment.