Chapter 1: What is the history of artificial intelligence?
Welcome to the podcast.
Chapter 2: How did early AI and symbolic AI shape the field?
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 3: What caused the AI winters and how did expert systems emerge?
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.
Chapter 4: How did machine learning revolutionize artificial intelligence?
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.
Chapter 5: What factors contributed to the rise of modern 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: How is deep learning impacting the capabilities of AI today?
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.
Chapter 7: What are the current applications of AI in various industries?
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.
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 are relying on like intuition in our experience and also like on context. So there's a lot of things that aren't just rules.
There's not just this math that you can kind of have a computer solve. And so no matter how many rules you write, you never can actually capture everything that happens in reality. And so 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 collapsed.
And this kind of was known 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.
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Chapter 8: What does the future hold for artificial intelligence?
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, 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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