AI 101: A Beginner's Guide to Understanding Artificial Intelligence

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a sports car, you could have a truck. And even though there are all these you know, different types of cars, like whether they're electric or whether they're gas, even though they literally end up different in the way that they look, all cars pretty much do the same thing. Which is that they get you from point A to point B faster than you would get there if you were walking right,

unless you're in LA traffic for it card. Similarly, artificial intelligence covers a whole bunch of different tools and technology across different disciplines. It touches tools that are being built in computer science and mathematics and electrical engineering and mechanical engineering. But all AI aims to do pretty much the same thing. Artificial intelligence is trying to teach machines how to think

and act like humans. So all those different technologies and all those different disciplines are just trying to teach machines how to think and act like humans. Well what does it mean to think and act like a human? Well, humans have five senses. We have hearing, we have sight, we have touched, we have smell, and we have taste, and artificial intelligence kind of has five domains. So teaching an AI, teaching a machine how to hear and how

to speak is called natural language processing. That's what's behind tools like Siri inside of your phone. When you talk to her and she writes the text down, that's called text to speech. Then when she understands what you just said, to her. That's called natural language understanding. Then when she comes up with a response to you, that's called natural language generation. And then when she reads that response back out loud to you, that's called text to speech right

where she reads it back out loud to you. So NLP, or natural language processing, is teaching machines how to hear and how to speak. Teaching machines how to see is called computer vision. So if you have a ring camera in your house and it's like, oh, person detected her, and well detect it, that's using computer vision. That's also behind stuff like face ID inside of your phone, when you go to the airport and TSA asks you to stand there and it checks the picture of your face.

All of that stuff is computer vision. Teaching a machine how to touch is called haptics. Now, this is a field that you mostly see in robotics, where they're doing things like creating artificial skin that's as sensitive as human skin. Now why would they need that? Well, think about it. If you have like a robot and an Amazon factory, you want it to be able to understand things like pressure and weight so that when it picks up whatever

you ordered off an Amazon that might be fragile. It doesn't pick it up and shake it around and mishandle it, right, So they need that artificial skin to be able to teach robots how to understand what it is that they're physically touching and grabbing. Then teaching machines how to smell is a pretty new field. It's called machine oh factory. Now, I'm not gonna lie, I don't really like this field because in order to teach the algorithm and what a smell is, you got to use like somebody's nose as

an example. And I don't know about y'all, but some people out of the colognes they be picking the perfumings. They be picking like I don't trust everybody's nose, right, And so MACHINEO factory is a newer field. We've seen like maybe ten twenty research papers in this space altogether, where they're trying to do stuff like come up with new perfumed smells or like detect smoke from a smell,

et cetera. And then teaching machines how to taste doesn't actually have a fancy name, but it has the same problem of kind of like teaching a machine how to smell. Like I'm not eating at everybody's house, So whose taste buds are you using as the example to like teach these machines how to taste you?

literally thinks that you're dying. And so a huge part of overcoming fear to AI is learning about it, doing things like sitting in this class, taking notes, understanding what the difference is between NLP and computer vision, so that you're able to not feel like because you don't understand it, that it instantly means that it's going to be negative

against you. Now, there are real challenges with AI, with which will probably get into later, but even then, the problems inside of these AI systems, with things like bias or things like them coming for you know, jobs that

particularly affect our communities. A lot of that stuff can be solved if we step up in other areas, like in policy, or if we step up in terms of like regulation, or if we step up in terms of building and designing our own systems, and so even the stuff out there that is actually proven to be scary or dangerous to our communities, there are solutions out there that require all of us coming together and putting into play.

in your head of a car. Some of you guys would imagine a two door car, a four door car, or a sports car. Some of y'all might even imagine a semi truck. Now, what I did is I just prompted you to create something from the prompt that I gave you. You're using your human imagination to create something from an idea of it. And so generative AI is teaching machines how to think and act more along the

lines of a human imagination. And I'm gonna get a little bit technical just so you guys understand some of the differences between the techniques used to build AI and AI itself. So, how do you teach a machine how to think and act like a human? What else where the term machine learning comes in. You've probably heard this a lot. Machine learning is the most popular way that people teach machines how to think and act like humans,

but it's not the only way. Are the only method, and machine learning is when you teach a machine, which they call training, how to complete a task by giving it examples which they call training data of that task so that it can learn and improve itself on how to do that task right. So, for example, if I wanted to teach a car how to drive on the road, if it was old school coding without machine learning, I'd have to code every single step that the car needed

to do well. A lot of those rules are you know, there's like millions of rules of driving, like to have the code like if your right tire gets within this many inches of the line, then move this steering will you know point two centimeters. That's a lot to learn and a lot to code. We probably wouldn't even be able to make an app that would be able to let the car drive like that. It was just take

too much code. So instead, with machine learning, we can take a bunch of examples of cars driving on the road. That's why like Tesla's have eight cameras. They're constantly capturing that data and then let it learn from those examples of things that it should do and things that it shouldn't do. So there are two types of machine learning.

The first is called supervised machine learning, and supervised machine learning is when you teach a machine how to complete a task by giving it examples of the right answer up front. So an example of this is like, let's say I wanted to teach a computer vision algorithm to be able to tell the difference between a strawberry and avocado. Right, I would give it examples of images that I knew upfront were a strawberry. I would tell it upfront that

it's a strawberry. We call that data labeling, and then I would check it as it's learning how good it gets at telling me that's a strawberry versus telling me that's an avocado. Right. So, again, supervised machine learning is I already know what the right answer is up front, so I'm gonna give you these examples, and then I'm gonna test you against how well you're learning how to do the task because I already know the right answer.

The second type of AI machine learning is called unsupervised machine learning, And this is when you're training a machine how to complete a task by getting given it examples, but you're letting the algorithmic self discover what the right answer is. Right, So you're giving it a whole bunch of data, and then it goes in and finds patterns across that data that it uses to give you the

right answer. So, for example, a lot of AI is being used in things like drug discovery, So how can we find a new medicine to treat AIDS or to treat diabetes, or even when COVID came out, there was a whole effort between a bunch of big tech companies to figure out medicines that could be used as a vaccine. Right, So in that case, they didn't know the right answer up front, but they had a whole bunch of data and then the machine uncovered patterns that help them understand

what the right answer should be. Right, all right. So then you have one more thing, which is called deep learning. So deep learning is basically, without going into all the math behind it, it's a more complex way of doing machine learning when you have things that are more complicated in their patterns, and so it teaches the algorithms in

a way that mimics how neurons fire in the human brain. Right, So machine learning, like if I want to tell the difference between the strawberry and avocado, that's not a very complex task. But like teaching a plane how to fly itself, like the US Air Force just did, is gonna require a lot more complex relationships and a lot more complex understanding and nuance inside of that data and the patterns

and how it's supposed to do that task. So they would use something like a deep learning algorithm instead to be able to capture that. Now, I know that was a lot of information. I don't expect y'all to have this memorized. So while yeah, before we move on to the next part, I made a little TOLDR slide for y'all. You know, take a moment, take a screenshot, Make sure to share this with your friends. It just recaps what I just covered.