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Speaker 3: I want to start off by making and giving y'all
Speaker 3: my framework for how to think about what artificial intelligence is.
Speaker 3: So first, AI is not just one thing, right, just
Speaker 3: like when you think of the word car, car is
Speaker 3: not one thing. You can have a two door car,
Speaker 3: you could have a four door car, you could have
Speaker 3: a sports car, you could have a truck. And even
Speaker 3: though there are all these you know, different types of cars,
Speaker 3: like whether they're electric or whether they're gas, even though
Speaker 3: they literally end up different in the way that they look,
Speaker 3: all cars pretty much do the same thing. Which is
Speaker 3: that they get you from point A to point B
Speaker 3: faster than you would get there if you were walking right,
Speaker 3: unless you're in LA traffic for it card. Similarly, artificial
Speaker 3: intelligence covers a whole bunch of different tools and technology
Speaker 3: across different disciplines. It touches tools that are being built
Speaker 3: in computer science and mathematics and electrical engineering and mechanical engineering.
Speaker 3: But all AI aims to do pretty much the same thing.
Speaker 3: Artificial intelligence is trying to teach machines how to think
Speaker 3: and act like humans. So all those different technologies and
Speaker 3: all those different disciplines are just trying to teach machines
Speaker 3: how to think and act like humans. Well what does
Speaker 3: it mean to think and act like a human? Well,
Speaker 3: humans have five senses. We have hearing, we have sight,
Speaker 3: we have touched, we have smell, and we have taste,
Speaker 3: and artificial intelligence kind of has five domains. So teaching
Speaker 3: an AI, teaching a machine how to hear and how
Speaker 3: to speak is called natural language processing. That's what's behind
Speaker 3: tools like Siri inside of your phone. When you talk
Speaker 3: to her and she writes the text down, that's called
Speaker 3: text to speech. Then when she understands what you just said,
Speaker 3: to her. That's called natural language understanding. Then when she
Speaker 3: comes up with a response to you, that's called natural
Speaker 3: language generation. And then when she reads that response back
Speaker 3: out loud to you, that's called text to speech right
Speaker 3: where she reads it back out loud to you. So NLP,
Speaker 3: or natural language processing, is teaching machines how to hear
Speaker 3: and how to speak. Teaching machines how to see is
Speaker 3: called computer vision. So if you have a ring camera
Speaker 3: in your house and it's like, oh, person detected her,
Speaker 3: and well detect it, that's using computer vision. That's also
Speaker 3: behind stuff like face ID inside of your phone, when
Speaker 3: you go to the airport and TSA asks you to
Speaker 3: stand there and it checks the picture of your face.
Speaker 3: All of that stuff is computer vision. Teaching a machine
Speaker 3: how to touch is called haptics. Now, this is a
Speaker 3: field that you mostly see in robotics, where they're doing
Speaker 3: things like creating artificial skin that's as sensitive as human skin.
Speaker 3: Now why would they need that? Well, think about it.
Speaker 3: If you have like a robot and an Amazon factory,
Speaker 3: you want it to be able to understand things like
Speaker 3: pressure and weight so that when it picks up whatever
Speaker 3: you ordered off an Amazon that might be fragile. It
Speaker 3: doesn't pick it up and shake it around and mishandle it, right,
Speaker 3: So they need that artificial skin to be able to
Speaker 3: teach robots how to understand what it is that they're
Speaker 3: physically touching and grabbing. Then teaching machines how to smell
Speaker 3: is a pretty new field. It's called machine oh factory. Now,
Speaker 3: I'm not gonna lie, I don't really like this field
Speaker 3: because in order to teach the algorithm and what a
Speaker 3: smell is, you got to use like somebody's nose as
Speaker 3: an example. And I don't know about y'all, but some
Speaker 3: people out of the colognes they be picking the perfumings.
Speaker 3: They be picking like I don't trust everybody's nose, right,
Speaker 3: And so MACHINEO factory is a newer field. We've seen
Speaker 3: like maybe ten twenty research papers in this space altogether,
Speaker 3: where they're trying to do stuff like come up with
Speaker 3: new perfumed smells or like detect smoke from a smell,
Speaker 3: et cetera. And then teaching machines how to taste doesn't
Speaker 3: actually have a fancy name, but it has the same
Speaker 3: problem of kind of like teaching a machine how to smell.
Speaker 3: Like I'm not eating at everybody's house, So whose taste
Speaker 3: buds are you using as the example to like teach
Speaker 3: these machines how to taste you?
Speaker 1: Feel me definitely got you there, So that the five
Speaker 1: senses in the five domains makes a lot of sense
Speaker 1: when people start hearing these things like computer vision, Definitely
Speaker 1: that the smell and the taste, it is subjective. It
Speaker 1: starts to lead to uncertainty. And one of the things
Speaker 1: with human nature is that we don't like uncertainty, which
Speaker 1: then leads to fear. So how can people become less
Speaker 1: fearful when they hear about AI and very broken down?
Speaker 1: How you just did it?
Speaker 3: So I feel like a lot of the fear is
Speaker 3: based on the unknown. Like the human brain generally is
Speaker 3: wired to protect you from danger, right, Like the amygdala,
Speaker 3: which we call the fight or flight part of the brain,
Speaker 3: is literally the oldest part of the brain, and it's
Speaker 3: the only part of the brain that can hijack the rest.
Speaker 3: So if you ever had like a panic attack, that's
Speaker 3: literally your amygdala hijacking in your brain and shutting down
Speaker 3: all the other different functions in your brain because it
Speaker 3: literally thinks that you're dying. And so a huge part
Speaker 3: of overcoming fear to AI is learning about it, doing
Speaker 3: things like sitting in this class, taking notes, understanding what
Speaker 3: the difference is between NLP and computer vision, so that
Speaker 3: you're able to not feel like because you don't understand it,
Speaker 3: that it instantly means that it's going to be negative
Speaker 3: against you. Now, there are real challenges with AI, with
Speaker 3: which will probably get into later, but even then, the
Speaker 3: problems inside of these AI systems, with things like bias
Speaker 3: or things like them coming for you know, jobs that
Speaker 3: particularly affect our communities. A lot of that stuff can
Speaker 3: be solved if we step up in other areas, like
Speaker 3: in policy, or if we step up in terms of
Speaker 3: like regulation, or if we step up in terms of
Speaker 3: building and designing our own systems, and so even the
Speaker 3: stuff out there that is actually proven to be scary
Speaker 3: or dangerous to our communities, there are solutions out there
Speaker 3: that require all of us coming together and putting into play.
Speaker 4: Okay, all right, so I know we have the next step,
Speaker 4: which we're going to talk about how to utilize it
Speaker 4: in business. Anything you need you want to talk about
Speaker 4: before that before we go to the business lide.
Speaker 3: Yeah, So before we jump into the business side, I
Speaker 3: want to give a little bit more context about you know,
Speaker 3: how you teach a machine to think at like a human.
Speaker 3: So we know that AI is this group of technologies
Speaker 3: that are trying to do that using this five Census example. Now,
Speaker 3: generative AI specifically is attempting to teach AI teach machines
Speaker 3: how to think and act like the human imagination. Like,
Speaker 3: if I ask everybody on a chat right now to
Speaker 3: imagine a car, you would come up with an image
Speaker 3: in your head of a car. Some of you guys
Speaker 3: would imagine a two door car, a four door car,
Speaker 3: or a sports car. Some of y'all might even imagine
Speaker 3: a semi truck. Now, what I did is I just
Speaker 3: prompted you to create something from the prompt that I
Speaker 3: gave you. You're using your human imagination to create something
Speaker 3: from an idea of it. And so generative AI is
Speaker 3: teaching machines how to think and act more along the
Speaker 3: lines of a human imagination. And I'm gonna get a
Speaker 3: little bit technical just so you guys understand some of
Speaker 3: the differences between the techniques used to build AI and
Speaker 3: AI itself. So, how do you teach a machine how
Speaker 3: to think and act like a human? What else where
Speaker 3: the term machine learning comes in. You've probably heard this
Speaker 3: a lot. Machine learning is the most popular way that
Speaker 3: people teach machines how to think and act like humans,
Speaker 3: but it's not the only way. Are the only method,
Speaker 3: and machine learning is when you teach a machine, which
Speaker 3: they call training, how to complete a task by giving
Speaker 3: it examples which they call training data of that task
Speaker 3: so that it can learn and improve itself on how
Speaker 3: to do that task right. So, for example, if I
Speaker 3: wanted to teach a car how to drive on the road,
Speaker 3: if it was old school coding without machine learning, I'd
Speaker 3: have to code every single step that the car needed
Speaker 3: to do well. A lot of those rules are you know,
Speaker 3: there's like millions of rules of driving, like to have
Speaker 3: the code like if your right tire gets within this
Speaker 3: many inches of the line, then move this steering will
Speaker 3: you know point two centimeters. That's a lot to learn
Speaker 3: and a lot to code. We probably wouldn't even be
Speaker 3: able to make an app that would be able to
Speaker 3: let the car drive like that. It was just take
Speaker 3: too much code. So instead, with machine learning, we can
Speaker 3: take a bunch of examples of cars driving on the road.
Speaker 3: That's why like Tesla's have eight cameras. They're constantly capturing
Speaker 3: that data and then let it learn from those examples
Speaker 3: of things that it should do and things that it
Speaker 3: shouldn't do. So there are two types of machine learning.
Speaker 3: The first is called supervised machine learning, and supervised machine
Speaker 3: learning is when you teach a machine how to complete
Speaker 3: a task by giving it examples of the right answer
Speaker 3: up front. So an example of this is like, let's
Speaker 3: say I wanted to teach a computer vision algorithm to
Speaker 3: be able to tell the difference between a strawberry and avocado. Right,
Speaker 3: I would give it examples of images that I knew
Speaker 3: upfront were a strawberry. I would tell it upfront that
Speaker 3: it's a strawberry. We call that data labeling, and then
Speaker 3: I would check it as it's learning how good it
Speaker 3: gets at telling me that's a strawberry versus telling me
Speaker 3: that's an avocado. Right. So, again, supervised machine learning is
Speaker 3: I already know what the right answer is up front,
Speaker 3: so I'm gonna give you these examples, and then I'm
Speaker 3: gonna test you against how well you're learning how to
Speaker 3: do the task because I already know the right answer.
Speaker 3: The second type of AI machine learning is called unsupervised
Speaker 3: machine learning, And this is when you're training a machine
Speaker 3: how to complete a task by getting given it examples,
Speaker 3: but you're letting the algorithmic self discover what the right
Speaker 3: answer is. Right, So you're giving it a whole bunch
Speaker 3: of data, and then it goes in and finds patterns
Speaker 3: across that data that it uses to give you the
Speaker 3: right answer. So, for example, a lot of AI is
Speaker 3: being used in things like drug discovery, So how can
Speaker 3: we find a new medicine to treat AIDS or to
Speaker 3: treat diabetes, or even when COVID came out, there was
Speaker 3: a whole effort between a bunch of big tech companies
Speaker 3: to figure out medicines that could be used as a vaccine. Right,
Speaker 3: So in that case, they didn't know the right answer
Speaker 3: up front, but they had a whole bunch of data
Speaker 3: and then the machine uncovered patterns that help them understand
Speaker 3: what the right answer should be. Right, all right. So
Speaker 3: then you have one more thing, which is called deep learning.
Speaker 3: So deep learning is basically, without going into all the
Speaker 3: math behind it, it's a more complex way of doing
Speaker 3: machine learning when you have things that are more complicated
Speaker 3: in their patterns, and so it teaches the algorithms in
Speaker 3: a way that mimics how neurons fire in the human brain. Right,
Speaker 3: So machine learning, like if I want to tell the
Speaker 3: difference between the strawberry and avocado, that's not a very
Speaker 3: complex task. But like teaching a plane how to fly itself,
Speaker 3: like the US Air Force just did, is gonna require
Speaker 3: a lot more complex relationships and a lot more complex
Speaker 3: understanding and nuance inside of that data and the patterns
Speaker 3: and how it's supposed to do that task. So they
Speaker 3: would use something like a deep learning algorithm instead to
Speaker 3: be able to capture that. Now, I know that was
Speaker 3: a lot of information. I don't expect y'all to have
Speaker 3: this memorized. So while yeah, before we move on to
Speaker 3: the next part, I made a little TOLDR slide for y'all.
Speaker 3: You know, take a moment, take a screenshot, Make sure
Speaker 3: to share this with your friends. It just recaps what
Speaker 3: I just covered.
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