Chapter 1: What are the origins of artificial intelligence?
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Chapter 2: How did early AI and symbolic AI evolve?
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Chapter 3: What caused the AI winters and the rise of expert systems?
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Chapter 4: How did machine learning change the landscape of AI?
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Chapter 5: What innovations led to the rise of modern AI?
Welcome to the podcast. I'm your host, Jaden Schaefer. Today on the show, I wanted to go back in time a little bit and actually talk about the history of AI. Typically, I'm talking about news and AI or interviewing people that are working on, you know, some of the biggest AI companies. But I wanted to talk a little bit about the history because I've been researching it lately.
Chapter 6: How has AI impacted various industries and innovation?
And personally, for me, it is definitely not boring. There's just so many wild twists in this. And I think, you know, if this is an area that we all spend so much time focusing on, there's so much money in the world being poured into it.
I want to go back and talk a little bit about some of the some of the background that's been, you know, that basically laid the foundation for what we have in AI today.
Chapter 7: What distinguishes the current AI boom from previous trends?
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Chapter 8: What does the future hold for artificial intelligence?
You don't have to waste money on a whole bunch of overlapping subscriptions. If you want to check it out, there's a link in the description to AI box dot AI. Okay, let's get into the podcast today. So I think the idea of artificial intelligence actually starts way earlier than a lot of people think. So it's actually before computers were very powerful at all.
So people are already kind of asking the question, can machines think and if you go back to the 1940s and 1950s, computers were, you know, they're basically just glorified calculators. I mean, we've all seen the pictures of these calculators.
computers that are you know the size of a room when they got more advanced but before that there were size of the house and before that it was like basically the size of like a warehouse right for one single computer and so even back then there was a whole bunch of these kind of visionary thinkers that believed that these machines could eventually reason or learn or maybe even mimic human intelligence and of course there's like a lot of funny twists and in all this we'll get into but
I think one of the earliest turning points was the idea that thinking itself could just basically be reduced to kind of like math and logic. So if human reasoning followed rules, then kind of the theory was that you could encode those rules into a machine. And that was basically the foundational belief of the early AI. And so in 1956, this officially got a name.
There was a group of researchers called that were gathered for a workshop and they coined this artificial intelligence. And that's basically the moment that most people consider to be kind of like the birth of the field of AI that we have today. So this early AI obviously was, you know, what they thought it could do was extremely optimistic. I think it was wildly optimistic.
So basically, these researchers believed that non-human level intelligence was maybe 20 years away. They thought things like vision, language, reasoning were basically solved problems. And of course, I think the spoiler alert is that they were not because we're here, you know, like over 50 years later and bringing a lot of this stuff out. I mean, 75 years later for some of this stuff.
So a lot of these early AI systems were what we now call symbolic AI. Basically, the systems worked by hand. These hand coded rules. So if this happens, then that right, it's kind of the if then you see this pattern. And you know, if the computer sees a pattern, it's going to respond in a specific way.
And in that like really narrow domain, this actually worked, you could build programs, you know, that played chess, or that solved logic puzzles, or, you know, things that did basic math proofs. But the second that you took them outside of these kind of, you know, really small controlled environments, everything broke, right? This is not actual intelligence. It's, I mean, we know what these are.
It's just kind of computer systems. But at the time, they believed they truly had achieved, you know, artificial intelligence. So... Of course, we know that the real world is very messy. Language is ambiguous. Vision is very noisy. As humans, we're relying on like intuition in our experience and also like on context. So there's a lot of things that aren't just rules.
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