Hello there, now some of the catchphrases that you hear a lot of today are artificial intelligence machine learning and neural networks and they are often used interchangeably but actually, they're very separate different things so if you want to know what the differences are between artificial intelligence and machine learning then please let me explain.
Basically, it works like this when a company's trying to sell a product they call everything artificial intelligence so my smartphone has artificial intelligence, your house has artificial intelligence, when they're talking to maybe to the engineers or talking about job descriptions then they may put in the word machine learning and then when it comes to actually write some actual specifications actually developing the technology then they're probably going to be using neural networks so let's have a look at what these three things are and see how really they're quite different but of course very interconnected we'll start with a simpler one which is Machine Learning.
Simpler in that it's simpler to define, now machine learning is the process by which a machine can learn from its experience, learning in our context like learning to recognize voice learning to recognize images learning to recognize text is basically you take a data set you feed it into a program that is capable of learning from that data set and then of the output it knows how to use and recognize things that fit inside that data set even if they've never actually seen that specific example before so you can teach the computer the word you know like hello and you can say in lots of different accents like British accents and maybe in an American accent and a sort of South deep in the south part of America maybe from Scotland, you know maybe from Australia and you can say hello in all these different ways and because it's actually learnt these different formats of understanding the word hello then you can understand hello when anybody says it all around the world that's basically the idea now the thing about machine learning is very specific so if you teach a computer to recognize the word hello from America and with the British accent from Scotland from Australia that's great but if you then say goodbye it doesn't understand doesn't recognize what you're trying to say so let me give an example, we've got this alpha go system that can play go the Chinese ancient game of go much better than any human player now the point is that go is played on a 19 by 19 board now they trained up this program they just taught it how to play go it played millions of games and then it worked out how to play go but if they actually present it now this with a twenty by twenty board the machine learning program would just fail you would not know how to play go, there's no creativity, there's no abstract thinking, there's no strategy there's no like oh well I know how to play this I've seen it similar before In go board a 19 by 19 board but a human player particularly one who's an expert at go would have a very good attempt at playing that game at a fairly high level so machine learning is impressive when it's in a very particular thing and it says well this computer knows how to play goal this could be to knows how to recognize voices but if you give it something outside of what it's used to that it just completely fails because a very specific application.
Now when it comes to Neural Network, they are one of the key ways of doing deep learning today so the idea is and it's modeled on the way our brain is thought to function with the idea of neurons and when the neurons are combining to get together you can have a set of inputs and then the signals when hose inputs are propagated further on down the network if a certain threshold is met and then that threshold can be tweaked and trained using a feedback system so as you present it's kind of distal zeros at the beginning as empty and then as you present it data and as you tell it what the desired result is, it can kind of have this feedback and it creates this ability to recognize things in a general pattern and it's pattern recognition, you know that's a dog that's the word hello and so on that gives it its power but neural net was not the only method of machine learning and there are lots of different algorithms that can be used for machine learning however neural networks are the ones that are being used maybe for the greatest benefit at the moment.
Now Artificial Intelligence is different, artificial intelligence actually talks about the idea of being able to be creative to have abstract thinking, to be able to analyze things inside a context, but basically the thing we have to remember is that machine learning is very about pattern recognition about seeing a particular situation whereas artificial intelligence is being able to be intelligent about that and of course we could have a whole discussion about what intelligence is but basically you have to remember that a computer can exhibit the ideas of intelligence without being intelligent itself the example that is often used is if you have a man inside of a room that has an infinite number of dictionaries about Chinese mandarin and it has an infinite number of responses that it can give to any number of questions that if you stick some Chinese Mandarin under the door of his room and he can pick it up, he can look it up in the book and he can give you the reply and you would be convinced that the man inside the room actually is fluent in speaking Mandarin in fact he's not he's just looking up an input and giving you the appropriate output so it exhibits the idea of intelligence without actually being intelligent and then further on we've got the idea that of course an AI is generalistic so machine learning is very specific but an AI would be generalistic, so if it was presented with a 20 by 20 go board it would have a good crack at saying well I can play this game I've never seen it before but I can play this game because I'm going to use my sort of abstract thinking my strategy my context here to work out what's going on and then AI, you've also got the idea of a weak AI and strong AI, weak AI basically being a system that exhibits the ideas of intelligence the characteristics of intelligence without actually being self-aware without actually being sentient whereas strong AI is the idea that some people believe that you can create a computer that actually forms its consciousness it becomes self-aware and that cause brings in the whole issues of morality and ethics.
So to recap machine learning is an algorithm, a technique for recognizing patterns and then doing something according to those patterns whether it be words images you know strategy games it's just basically pattern recognition if I was in this position before what did I do that would lead me to a win that's the strategy one or if I've seen this picture before what does it look like when it renders remembers another picture I've seen of a cat or a dog or a car or whatever and neural networks are a part of machine learning in how you actually build the software to do that process of learning.
Artificial intelligence, on the other hand is the idea of exhibiting intelligence which includes abstract thinking creativity strategy context which is much broader and much more generalistic so some people even talk about it as general AI general artificial intelligence so although the marketing people will kind of talk about AI and machine learning and neural networks all in the same phrase and they will just mix them up they are actually very specifically different things, of course, neural networks are part of machine learning machine learning has to be part of AI but they're not all exactly the same thing so if you have a smartphone that claims it has AI because actually it's got machine learning well now you know different.
Alright, guys, that's the end of the blog, thanks for reading the whole way through if you enjoyed this blog please share it with someone who would be interested and leave a comment stating, the first AI-powered mobile phone that you ever used, Thanks for reading guys.
