Chapter 1: What are the origins of artificial intelligence?
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.
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. 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. If you want to check it out, there's a link in the description to AIbox.ai. OK, 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 basically just glorified calculators. I mean, we've all seen the pictures of these computers.
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 early AI. And so in 1956, this officially got a name.
There was a group of researchers 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.
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Chapter 2: What is symbolic AI and how did it evolve?
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, you know, 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 these tools worked for like a moment. And as things change in the world, they stop 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. So instead of telling a computer exactly what to do, you let it learn from data.
And the idea was inspired by human brain neurons, the connections, and then kind of learning from experience. So Early kind of versions of neural networks existed like all the way as far back as the 1950s, but they were super, super limited. Computers were very slow. Data, of course, there's not a lot of data on this and the math was very hard.
So for many decades, these kind of neural networks were basically ignored. But that all stopped after three main things happened. So first, of course, data exploded. You have the internet, you have smartphones, you have social media, so so much data is being created. And suddenly we have all of this data specifically about languages and images and behavior and everything. So all of this data.
And then second... compute got super, super cheap and also powerful. So the GPUs that were, you know, originally built for gaming, they turned out to be really perfect for training neural networks. And I mean, I would even say go so far as to say, like a lot of the hardware that was built for crypto mining. And then when the crypto winter came, that just kind of perfectly pivoted into AI.
And we had like all of this infrastructure built out that had we not been through that, we wouldn't have been able to kind of uptick training AI models as fast as we did. So Those that all helped. And I think the last thing that really helped was that researchers figured out some better techniques for training deep neural networks.
And this is like this is kind of where this deep learning comes in. It's basically the idea that you stack a whole bunch of layers of neural networks to learn harder and more complex patterns. And basically by kind of adding all of that, the data, the compute and that new strategy, everything changed. So in the early 2010s, deep learning started to crush a lot of benchmarks.
It's also hilarious to talk about crushing benchmarks in 2010 because it's definitely different than what we have today. But you had like image recognition that all of a sudden it actually worked. You had speak recognition that got really good. Translations went from being super terrible to usable. I mean, I even remember early days of Google Translate. You know, everyone would make fun of it.
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