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Why AI is ‘artificial’ intelligence without machine learning

Why AI is ‘artificial’ intelligence without machine learning

SwissCognitivePeople often use the term “ ” without really understanding what it’s supposed to mean. But sometimes even experts don’t really recognize as a technology but more as a marketing buzzword used to sell

People often use the term “” without really understanding what it’s supposed to mean. We can’t blame them. can mean what you want it to. It’s an abstract term, to a good extent. And if you talk to experts in the science of , you might even learn that they don’t really recognize as a technology but more as a marketing buzzword used to sell . So, for the sake of simplicity, understand that is one of the most effective and mature approaches to realizing algorithms that make programs and machines seem “intelligent.”

Well, we’re going to go a lot deeper than that to help you understand how is the key force shaping the world of .

Machine learning is about data — mostly

Machine learning is mostly based on using lots and lots of training data and good algorithms. Though there’s a lot of excitement in technology circles about sophisticated algorithms, particularly , it must be understood that most applications of are a result of good data. Machine learning could exist without good algorithms, but it can’t exist without good data.

This is a major fact for everyone involved in the industry. The path to the future is paved on the foundation of good data more than anything else. That’s why you’d observe that the most remarkable examples of are in industries where data scientists have access to massive data.

“Garbage in, garbage out” — this is the rule of thumb in software development, as old as the idea of software itself. It applies particularly well to , and it’s very important that developers and data scientists understand it. This is a key limitation of . It can only identify patterns that exist in the training data, and that’s the sole basis for its learning.

If a model generates good results based on its training data, expect it to generate results of a similar quality in a production environment. However, that’s only when the production data follows the same distribution as that of the training data. Skews between training and production data are guaranteed to result in the errant behavior of your model.

If a model generates good results based on its training data, expect it to generate results of a similar quality in a production environment. However, that’s only when the production data follows the same distribution as that of the training data. Skews between training and production data are guaranteed to result in the errant behavior of your model.

This is why continual improvement is a key to successful . The best models performing consistently in real life scenarios are the ones that are being constantly reviewed and improved. That’s the guiding light for anybody looking to be successful in the industry.

read more – copyright by techgenix.com

 

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