Chapter 1: What are the early visions and the birth of AI?
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Chapter 2: What caused the AI winters and the rise of expert systems?
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Welcome to the podcast.
Chapter 3: How did machine learning emerge in the AI landscape?
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 4: What breakthroughs led to modern AI advancements?
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 background that's been, you know, that basically laid the foundation for what we have in AI today.
So before we get into all of that, you probably pay for multiple subscriptions to get access to all of the best AI tools. I know it can definitely add up fast.
Chapter 5: How did deep learning change the AI field in the 2010s?
I had the same problem and so I actually built AIbox.ai and so you can spend $20 a month and you get over 40, actually I believe now we're up to 50 of the top AI models on one platform. So you get text, image, audio, everything you need in one place. You don't have to juggle through tabs. You don't have to waste money on a whole bunch of overlapping subscriptions.
Chapter 6: What are the limitations of early AI systems?
If you want to check it out, there's a link in the description to AIbox.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 um 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
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 there's a lot of funny twists and in all of 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 early AI.
Chapter 7: How is AI transforming industries today?
And so in 1956, this officially got a name. There was a group of researchers who, 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 uh 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.
Chapter 8: What does the future hold for AI technology?
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. these hand-coded rules. So if this happens, then that, right? It's kind of the if-then.
You see this pattern, and if the computer sees a pattern, it's going to respond in a specific way. And in that really narrow domain, this actually worked. You could build programs that played chess or that solved logic puzzles or 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. 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 are relying on intuition in our experience and also on context.
There's a lot of things that aren't just rules. There's not just this math that you can have a computer solve. No matter how many rules you write, you never can actually capture everything that happens in reality.
this led to one of the first big ai disappointments you could say and because of this a lot of funding to the program dried up a lot of expectations people just you know like kind of basically they collapsed and this kind of was uh known as by a lot of researchers as ai winter so governments universities basically all just like yeah well this isn't really ai it's not really working we'll continue developing computers but we're not really focusing on that specific direction so
That kind of froze for a while. But then in the 1980s, AI came back in a bit of a new form, and that was these expert systems. So they were essentially programs designed to replicate decision making of human experts.
So doctors, engineers, chemists, people with very specialized knowledge and companies poured a ton of money into those systems because, again, you know, in really narrow domains, they actually worked quite well. So you could encode expert knowledge. You could get really interesting outputs. The problem was that they were very brittle systems. They're also incredibly expensive to build.
And I don't think enough people talk about that. They're really expensive to maintain. And then of course they don't scale, right? Every time the world changed, you had to update the rules manually. And then once that happens and if it breaks, then of course the hype is kind of ahead of the reality. And so everyone gets disappointed and then you get another AI winter, right?
Because these tools worked for like a moment and as things changed in the world, they stopped working. So this is where I think it kind of gets a little bit interesting for AI. The whole field took an interesting turn. So symbolic AI was definitely struggling. It was definitely a totally different approach than what we needed to do because what we needed to do was machine learning.
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