AI vs Machine Learning

artificial intelligence and machine learning what's the difference are they the same well some people kind of frame the question this way it's AI versus ml is that the right way to think of this or is it AI equals ml or is it AI is somehow something different than ml so here's three equations I wonder which one is going to be right well let's talk about this first of all when we talk about AI I think it's important to come with definitions because a lot of people have different ideas of what thi

s is so I'm going to assert the simple definition that AI is basically exceeding or matching the capabilities of a human so we're trying to match the intelligence whatever that means and capabilities of a human subject now what could that involve there's a number of different things for instance one of them is the ability to discover to find out new information another is the ability to infer to read in information from other sources that maybe has not been explicitly stated and then also the ab

ility to reason the ability to figure things out I put this and this together and I come up with something else so I'm going to suggest to you this is what AI is and that's the definition we'll use for this discussion now what kinds of things then would be involved if we were talking about doing machine learning well Machine learning I'm going to put that over here is basically a capability we'll start with a Venn diagram machine learning involves predictions or decisions based on data think abo

ut this as a very sophisticated form of statistical analysis it's looking for predictions based upon information that we have so the more we feed into the system the more it's able to give us accurate predictions and decisions based upon that data it's something that learns that's the L part rather than having to be programmed when we program a system I have to come up with all the code and if I wanted to do something different I have to go change the code and then get a different outcome in the

machine learning situation what I'm doing could be adjusting some models but is different than programming and mostly it's learning the more data that I give to it so it's based on large amounts of information and there's a couple of different fields within couple of different types there is supervised machine learning and as you might guess there's an unsupervised machine learning and the main difference as the name implies is one has more human oversight looking at the training of the data us

ing labels that are superimposed on the data unsupervised is kind of able to run more uh and and find things that were not explicitly stated okay so that's machine learning it turns out that there's a subfield of machine learning that we call Deep learning and what is deep learning well this involves things like neural networks neural networks involve nodes and statistical relationships between those nodes to model the way that our minds work and it's called Deep because we're doing multiple lay

ers of those neural networks now the interesting thing about deep learning is we can end up with some very interesting insights but we might not always be able to tell how the system came up with that it doesn't always show its work fully so we could end up with some really interesting information not know in some cases how reliable that is because we don't know exactly how it was derived but it's still a very important part of all of this realm that we're dealing with so those are two areas and

you can see DL is a subset of ml but what about artificial intelligence where does that fit in the Venn diagram and I'm going to suggest to you it is the superset of mldl and a bunch of other things what could the other of things be well we can involve things like natural language processing uh it could be vision so we want a system that's able to see we might even want a system that's able to hear and be able to distinguish what it's hearing and what it's seeing because after all humans are ab

le to do that and that's part of what our brains do is distinguish those kinds of things it can involve other things like the ability to do text to speech so if we take written words Concepts and be able to speak those out so this first one involved being able to see things this is now being able to speak those things as well and then other things that humans are able to do naturally that we often take for granted is motion this is the field of Robotics which is a subset of AI the ability to jus

t do simple things like tie our shoes open and close the door lift something walk somewhere that's all something that would be part of human capabilities and involves certain sorts of perceptions calculations that we do in our brains that we don't even think about so here's what it comes down to it's a Venn diagram and we've got machine learning We've Got Deep learning and we've got AI so I'm going to suggest to you the right way to think about this is not these equations those are not the way t

o look at it in fact what we should think about this as machine learning is a subset of a high and that's how we need to think about this when I'm doing machine learning in fact I am doing AI when I'm doing these other things I'm doing AI but none of them are all of AI but they're a very important part thanks for watching please remember to like this video And subscribe to this channel so we can continue to bring you content that matters to you