What’s the difference between Artificial Intelligence and Machine Learning? (AI vs ML ) when I used to work with clients they quite often ask me about AI vs ML. Learning lots of people seem to use these terms interchangeably and considered Artificial Intelligence and Machine Learning to be the same but actually, they’re not quite the same.
Artificial Intelligence ( AI )
Artificial intelligence in simple words is basically the umbrella term and within it, we have machine learning which is a subset of AI. Artificial intelligence it’s basically the leading edge of artificial intelligence (AI) both of those concepts are now quite old they were developed in it and were probably ahead of their time.
And if explaining Artificial Intelligence (AI) with examples, what we had is we had traditional artificial intelligence tools which basically meant we had expert systems where we had to tell computers exactly the rules of how to analyze data what data to use and what results to spit out, so we had expert systems that worked sometimes quite well but we have this typical problem of computer says no it.
Just didn’t work it didn’t know the perfect answer so for me a great example is a natural language or language
translations so if we try to design a rule-based artificial intelligence program to translate from English into
This doesn’t work and we’ve seen this in the past that these things didn’t really work that well because
there are so many exceptions to our human language and to program all these exceptions into this algorithm is almost impossible going back to the 1950s.
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Machine Learning (ML)
The simpler explanation of Machine Learning with ML or Machine Learning Examples.
Imagine someone then thought actually instead of telling the computer all the rules why don’t we give the computer lots of data so the computer can make up the rules by itself this is what we are referring to as machine learning. Where the machine learns from data and this is a bit like how we learn by ourselves our this basically simulates the brain copies the process that we as humans use to learn.
And be intelligent so in our head we have a brain and this has trillions of neurons and these neurons
are all connected and when we learn something so you might learn how to grab something in a Sur or how to speak a language and as a child it takes quite a long time to do this and we go through.
Lots of trial and error so we learn by experience and how do I grab this toy for example and then as a baby lots of things don’t quite work and then suddenly things on this worked and then your neurons make connections you say sending these signals to these muscles really worked in the same way when you pick up a language your parents and the teachers will correct you and overtime.
You will learn how to speak the language you can’t really learn this by rules you learn this by experiencing the challenge is that learning a language or grabbing a toy or cycling on a bike are things we can’t actually explain they are what we call it tacit knowledge we have explicit knowledge that we can write down on a
piece of paper.
I can write down this is how you operate a camera give this to you can read this and then operate the camera I can’t do this with this is how you swim this is how you cycle this is how you speak English because this is
something we have learned through experience and this is exactly what machines are able now to do we give them data and they learn from this data so initially, we had tools that could recognize characters.
And we all right handwritten characters differently so there’s very hard to write Louisville is but we can give a machine a million or billion versions of how someone writes an O and an A and a T or whatever and then the machine learning algorithm will say okay I know to build my own system and identify how probable that this is an O on A so this is again not brand-new since the 1960s in 1965.
Machine Learning Examples IRL
Since now you must have knowledge about Artificial Intelligence, Machine Learning, and the difference between Artificial Intelligence and Machine Learning (AI vs ML). You will even clear your concepts with the following real-life example.
The US Postal Service implemented its first handwriting scanner in their Detroit post office that is able to read someone’s address on a letter handwritten letters and this helped them to improve things what we now have is have machine learning capabilities why do we have them today two things have changed we now have more data because we’re now living in the big data world where we have lots of sensors everything is digital so we have huge volumes of data and we have the processing power so our chips are getting better.
And we have things like cloud computing that gives every device has access to huge computing the ability to store huge volumes of data and analyze them and this is now made machine learning possible so
instead of saying this is how you translate from English into Chinese you simply give the machines billions of
words and text translated from Chinese into English and then the machines will write their own algorithms.
Artificial Intelligence and Machine Learning Interconnection
To do this and this is basically the leading edge of artificial intelligence it now enables machines to learn to walk to learn to write we now have tools like natural language and voice recognition tools like Alexa that can pick up our language whether we are speaking with a Scottish accent or an American accent.
And this is all made possible by machines learning and improving by the hopefully this has given you a better understanding of the difference between artificial intelligence and Machine Learning (AI vs ML) which is the rule-based overarching concept of artificial intelligence and the more specific leading-edge application of machine learning the abilities of machines to learn from data a bit as we learn from experience.
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