WEBVTT 00:00:00.000 --> 00:00:03.160 Hey, you, ready to dive into the crazy world 00:00:03.160 --> 00:00:06.120 of AI? I know I am. We're going to go deep on 00:00:06.120 --> 00:00:08.820 the history of artificial intelligence. OK. But 00:00:08.820 --> 00:00:11.099 not just like the latest buzz. Right. You know, 00:00:11.140 --> 00:00:14.240 we want to go way back to the twists and turns 00:00:14.240 --> 00:00:16.300 and aha moments. The stuff most people don't 00:00:16.300 --> 00:00:18.719 know about. Exactly. That led us to where we 00:00:18.719 --> 00:00:20.859 are now. Cool. Turns out there's a lot more to 00:00:20.859 --> 00:00:23.280 the story than just Chad GPT. Oh, yeah, for sure. 00:00:23.379 --> 00:00:25.899 We have an expert speaker here with us to unearth 00:00:25.899 --> 00:00:29.589 all these, like, hidden gems of AI knowledge. 00:00:29.890 --> 00:00:31.949 Yeah, I'm excited. It's like opening a treasure 00:00:31.949 --> 00:00:34.689 chest of forgotten AI. I love that. Yeah. So 00:00:34.689 --> 00:00:37.469 get this. Did you know that back in the 1950s, 00:00:37.570 --> 00:00:39.850 people thought we'd have human -level AI within 00:00:39.850 --> 00:00:43.170 decades? Oh, yeah. I've heard that it's a pattern 00:00:43.170 --> 00:00:46.090 we see a lot in AI's history, this like... Like 00:00:46.090 --> 00:00:48.750 crazy optimism. Yeah. But even those so -called 00:00:48.750 --> 00:00:51.590 failures often have these brilliant ideas that 00:00:51.590 --> 00:00:54.109 are worth revisiting. That's so true. It's like 00:00:54.109 --> 00:00:56.850 we can learn from the past. Absolutely. And apply 00:00:56.850 --> 00:01:00.189 those lessons to the future of AI. 100%. So let's 00:01:00.189 --> 00:01:02.789 rewind all the way back to like the OG genius 00:01:02.789 --> 00:01:04.909 Alan Turing. And the interesting thing is he 00:01:04.909 --> 00:01:06.950 wasn't even trying to build a computer. Right. 00:01:06.950 --> 00:01:10.549 He was tackling this super complex math problem. 00:01:10.650 --> 00:01:12.530 What was it? What was the problem? It was called 00:01:12.530 --> 00:01:15.310 the Enscheidungsproblem. OK. And he wanted to 00:01:15.310 --> 00:01:17.930 know if there was a guaranteed way to solve any 00:01:17.930 --> 00:01:20.090 mathematical problem just by following a step 00:01:20.090 --> 00:01:23.260 -by -step process. And to try to figure this 00:01:23.260 --> 00:01:26.239 out, he came up with this idea of a theoretical 00:01:26.239 --> 00:01:29.299 machine, what we now call the Turing machine. 00:01:29.299 --> 00:01:31.659 I see. That could follow a set of instructions. 00:01:31.859 --> 00:01:35.019 So like the ancient ancestor of a computer program. 00:01:35.219 --> 00:01:38.000 Exactly. Wow. What's mind blowing is that this 00:01:38.000 --> 00:01:40.659 very simple idea, a machine following instructions, 00:01:40.879 --> 00:01:43.700 is the foundation of every computer we use today. 00:01:43.950 --> 00:01:46.790 That is mind -blowing. From your phone to those 00:01:46.790 --> 00:01:49.870 massive supercomputers. Crazy. Running AI models. 00:01:50.150 --> 00:01:52.150 It's amazing to see how those basic building 00:01:52.150 --> 00:01:54.689 blocks paved the way for the AI we have now. 00:01:54.930 --> 00:01:57.709 It is. It's really remarkable. So Turing's brilliance, 00:01:57.709 --> 00:01:59.989 it didn't stop there. No, not at all. He also 00:01:59.989 --> 00:02:01.890 gave us the famous Turing test. Right. Which 00:02:01.890 --> 00:02:05.269 is the idea that if a machine can convince you 00:02:05.269 --> 00:02:08.669 it's human in a conversation, it's basically 00:02:08.669 --> 00:02:10.849 intelligent. Yeah, like a classic imitation game. 00:02:11.030 --> 00:02:13.889 Exactly, but... Does that really hold up? As 00:02:13.889 --> 00:02:15.969 a measure of true intelligence? Yeah. Well, that's 00:02:15.969 --> 00:02:18.229 a great question. I mean, a chat bot might be 00:02:18.229 --> 00:02:20.330 able to trick you, but does that mean it understands 00:02:20.330 --> 00:02:22.469 the world the way we do? I think that's a really 00:02:22.469 --> 00:02:25.469 important point. Yeah. The Turing test is definitely 00:02:25.469 --> 00:02:27.810 a thought -provoking idea. For sure. But it really 00:02:27.810 --> 00:02:31.550 focuses more on behavior than genuine understanding. 00:02:31.710 --> 00:02:34.009 Right. So like a chat bot might pass the test. 00:02:34.319 --> 00:02:36.960 Right. But does it really grasp what it's saying? 00:02:37.159 --> 00:02:40.400 Does it have emotions or self awareness or ponder 00:02:40.400 --> 00:02:42.919 the meaning of life like we do? Yeah, it's more 00:02:42.919 --> 00:02:46.240 about like mimicking human conversation. Yeah. 00:02:46.319 --> 00:02:48.759 Than actually replicating the complexity of human 00:02:48.759 --> 00:02:51.300 thought. Exactly. Okay, so this realization. 00:02:51.300 --> 00:02:53.840 Yeah. This led researchers to explore all these 00:02:53.840 --> 00:02:55.580 different approaches to creating machines that 00:02:55.580 --> 00:02:58.639 could truly think like humans. Right. This period 00:02:58.639 --> 00:03:01.939 became known as the Golden Age of AI. Yeah. the 00:03:01.939 --> 00:03:03.939 golden age. Where researchers were really trying 00:03:03.939 --> 00:03:06.139 to decode the secrets of human intelligence. 00:03:06.219 --> 00:03:09.199 That's right. So like, crack the code of how 00:03:09.199 --> 00:03:12.259 our brains work, and then translate that into 00:03:12.259 --> 00:03:15.599 algorithms. Yeah. Which sounds incredibly ambitious. 00:03:15.719 --> 00:03:18.060 It was incredibly ambitious. So they tackled 00:03:18.060 --> 00:03:21.039 all these classic problems, like the traveling 00:03:21.039 --> 00:03:24.000 salesman problem. The traveling salesman is trying 00:03:24.000 --> 00:03:27.060 to find the most efficient route. Yes. Through 00:03:27.060 --> 00:03:30.770 multiple cities. The idea was, If we can figure 00:03:30.770 --> 00:03:34.169 out how we solve these problems, maybe we can 00:03:34.169 --> 00:03:37.009 teach computers to do the same. Exactly. Which 00:03:37.009 --> 00:03:39.569 seems logical, but did they actually succeed? 00:03:40.669 --> 00:03:43.550 Well, not always. They ran into some roadblocks 00:03:43.550 --> 00:03:46.259 along the way. Like what? Well, they discovered 00:03:46.259 --> 00:03:48.560 that some problems are just incredibly complex. 00:03:48.560 --> 00:03:51.659 Okay, even for really powerful computers No, 00:03:52.000 --> 00:03:53.960 like take the traveling salesman problem. For 00:03:53.960 --> 00:03:56.219 example as you add more cities to the problem 00:03:56.219 --> 00:03:59.819 Yeah, the number of possible routes grows exponentially 00:03:59.819 --> 00:04:02.020 to the point where it quickly exceeds the number 00:04:02.020 --> 00:04:04.620 of atoms in the universe It's mind -boggling. 00:04:04.719 --> 00:04:07.560 That's wild. So how do they deal with this like? 00:04:07.800 --> 00:04:10.759 explosion of possibilities Well, they realized 00:04:10.759 --> 00:04:13.699 that brute force computation wasn't always the 00:04:13.699 --> 00:04:17.480 answer. They needed a new strategy, which led 00:04:17.480 --> 00:04:20.600 to the development of expert systems. Instead 00:04:20.600 --> 00:04:23.839 of just relying on raw computing power, they 00:04:23.839 --> 00:04:27.079 tried to equip machines with knowledge, similar 00:04:27.079 --> 00:04:29.720 to the rules a doctor might use to diagnose a 00:04:29.720 --> 00:04:32.560 patient. So instead of mimicking the process 00:04:32.560 --> 00:04:35.519 of human thought, They were trying to capture 00:04:35.519 --> 00:04:38.639 the knowledge that drives those processes. Exactly. 00:04:38.800 --> 00:04:40.779 That's interesting. And this actually led to 00:04:40.779 --> 00:04:44.060 some fascinating projects. Oh. Like Psychus. 00:04:44.060 --> 00:04:47.819 Which it did. Aimed to encode all of human knowledge. 00:04:47.920 --> 00:04:50.620 Wait, hold on. All human knowledge. all of it. 00:04:50.740 --> 00:04:53.300 That sounds like a colossal undertaking. It was 00:04:53.300 --> 00:04:56.480 incredibly ambitious. Wow. And while it didn't 00:04:56.480 --> 00:04:59.740 achieve its ultimate goal, it showed just how 00:04:59.740 --> 00:05:02.379 far AI researchers were willing to push the boundaries. 00:05:02.579 --> 00:05:04.899 It's like trying to build a digital brain that 00:05:04.899 --> 00:05:07.500 contains everything humanity knows. That was 00:05:07.500 --> 00:05:09.920 the idea. So speaking of different ways of thinking, 00:05:10.139 --> 00:05:12.699 I came across this thing called logic programming. 00:05:13.129 --> 00:05:16.029 While researching for our deep dive. Oh logic 00:05:16.029 --> 00:05:18.370 programming interesting. It sounded fascinating 00:05:18.370 --> 00:05:20.589 It is fascinating a little over my head if I'm 00:05:20.589 --> 00:05:22.829 being honest. Well, it's a beautiful idea Okay, 00:05:22.870 --> 00:05:25.730 it's rooted in formal logic. Okay, think of that 00:05:25.730 --> 00:05:29.170 classic example. All men are mortal Okay, Socrates 00:05:29.170 --> 00:05:32.689 is a man. Yes, therefore Socrates is mortal right 00:05:32.689 --> 00:05:35.730 logic programming tries to translate that kind 00:05:35.730 --> 00:05:39.069 of logical deduction into code So instead of 00:05:39.069 --> 00:05:42.050 giving the computer specific instructions for 00:05:42.050 --> 00:05:45.629 every situation, you could teach it these logical 00:05:45.629 --> 00:05:47.870 rules and let it figure things out on its own. 00:05:48.110 --> 00:05:51.149 Precisely. Wow. It was a very elegant approach 00:05:51.149 --> 00:05:54.589 in theory, but it faced challenges when applied 00:05:54.589 --> 00:05:57.910 to messy real -world problems. I see. It's interesting 00:05:57.910 --> 00:05:59.709 how the rise and fall of all these different 00:05:59.709 --> 00:06:03.310 AI approaches reflect our evolving understanding 00:06:03.310 --> 00:06:06.319 of intelligence itself. It is, isn't it? It really 00:06:06.319 --> 00:06:08.819 is. So what happened after logic programming? 00:06:09.259 --> 00:06:11.560 Well, a researcher named Rodney Brooks came along 00:06:11.560 --> 00:06:15.850 and kind of shook things up. Really? He challenged 00:06:15.850 --> 00:06:19.069 this prevailing focus on abstract reasoning. 00:06:19.350 --> 00:06:22.829 Right. And argued that robots need to experience 00:06:22.829 --> 00:06:25.930 the world directly to develop true intelligence. 00:06:26.170 --> 00:06:28.189 So instead of just thinking they needed to start 00:06:28.189 --> 00:06:31.490 doing it. Exactly. He was a champion of behavioral 00:06:31.490 --> 00:06:35.209 AI. OK. He believed that intelligence arises 00:06:35.209 --> 00:06:37.769 from interaction with the environment. I see. 00:06:37.990 --> 00:06:40.860 Not just pure thought. His approach was to build 00:06:40.860 --> 00:06:43.600 up intelligence through layers of simple reactive 00:06:43.600 --> 00:06:45.800 behaviors. So less about planning everything 00:06:45.800 --> 00:06:48.199 out in advance. Right. And more about responding 00:06:48.199 --> 00:06:51.120 to the environment in real time. Precisely. Huh. 00:06:51.560 --> 00:06:53.819 So that's like how a Roomba navigates a room. 00:06:53.920 --> 00:06:56.120 It is very much like that. Wow. And speaking 00:06:56.120 --> 00:06:58.399 of robots interacting with the world. Yeah. That 00:06:58.399 --> 00:07:00.879 brings us to your area of expertise. Multi -agent 00:07:00.879 --> 00:07:03.240 systems. Tell me about that. Imagine a world 00:07:03.240 --> 00:07:06.199 where you have your own personal AI agent that 00:07:06.199 --> 00:07:09.279 can schedule your meetings, negotiate deals, 00:07:09.680 --> 00:07:12.319 even collaborate with other AI agents on your 00:07:12.319 --> 00:07:14.500 behalf. Wow, so it's like having a whole team 00:07:14.500 --> 00:07:17.939 of AI assistants working together to make your 00:07:17.939 --> 00:07:20.120 life easier. That's the idea. That's incredible. 00:07:20.339 --> 00:07:22.819 It's a glimpse into a future where AI isn't just 00:07:22.819 --> 00:07:26.399 this one big monolithic system. but a network 00:07:26.399 --> 00:07:29.459 of intelligent helpers. Working together to achieve 00:07:29.459 --> 00:07:31.439 complex goals. You already see this happening 00:07:31.439 --> 00:07:33.579 in some ways, like self -driving cars communicating 00:07:33.579 --> 00:07:36.180 with each other. To optimize traffic flow. Exactly. 00:07:36.560 --> 00:07:39.579 Wow, or smart grids. Managing energy distribution. 00:07:39.620 --> 00:07:43.040 That paints an incredible picture of a future 00:07:43.040 --> 00:07:46.160 powered by AI collaboration. It does, doesn't 00:07:46.160 --> 00:07:48.740 it? It does. But how does all of this connect 00:07:48.740 --> 00:07:51.899 to the deep learning revolution that's happening 00:07:51.899 --> 00:07:54.579 today with neural networks? That's a great question. 00:07:54.639 --> 00:07:57.000 Yeah. And it takes us to a really fascinating 00:07:57.000 --> 00:07:59.740 turning point in AI history. OK, let's talk about 00:07:59.740 --> 00:08:01.819 that. Right. So we were talking about a turning 00:08:01.819 --> 00:08:03.939 point in AI history. Yeah, a pretty major one. 00:08:04.459 --> 00:08:06.699 Did deep learning just suddenly appear out of 00:08:06.699 --> 00:08:10.480 nowhere? Not exactly. While symbolic AI was having 00:08:10.480 --> 00:08:13.600 its moment, another approach was kind of bubbling 00:08:13.600 --> 00:08:16.480 away in the background. OK. Artificial neural 00:08:16.480 --> 00:08:18.959 networks. Neural networks. These networks took 00:08:18.959 --> 00:08:21.459 inspiration from the structure of the human brain. 00:08:21.480 --> 00:08:23.879 I see. And they were all about learning from 00:08:23.879 --> 00:08:26.839 data rather than being explicitly programmed. 00:08:27.000 --> 00:08:29.439 So instead of feeding computers a set of rules, 00:08:29.639 --> 00:08:32.080 researchers were basically saying... Yeah. Here's 00:08:32.080 --> 00:08:34.740 a ton of data, figure it out. You got it. Wow. 00:08:34.899 --> 00:08:38.220 It was a radical shift from the symbolic AI approach. 00:08:38.279 --> 00:08:40.980 OK. Now, early neural networks were pretty limited. 00:08:40.980 --> 00:08:44.539 OK. But there were a few crucial breakthroughs, 00:08:44.539 --> 00:08:47.019 like the development of backpropagation in the 00:08:47.019 --> 00:08:49.879 1980s. Backpropagation? That sounds like something 00:08:49.879 --> 00:08:52.039 out of a sci -fi movie. It does, doesn't it? 00:08:52.379 --> 00:08:54.379 What's the significance of that? Well, think 00:08:54.379 --> 00:08:57.519 of it as like a super efficient learning algorithm 00:08:57.519 --> 00:09:01.029 for neural networks. OK. Backpropagation allowed 00:09:01.029 --> 00:09:04.129 researchers to train much larger and more complex 00:09:04.129 --> 00:09:06.710 networks. So bigger networks, better learning. 00:09:06.990 --> 00:09:09.230 Right. And more data. Is that what led to the 00:09:09.230 --> 00:09:11.250 deep learning revolution? You nailed it. Wow. 00:09:11.590 --> 00:09:14.970 By the 2000s, two things happened. OK. That supercharged 00:09:14.970 --> 00:09:18.309 deep learning. OK. First, we had access to massive 00:09:18.309 --> 00:09:20.649 amounts of data thanks to the internet. Right. 00:09:20.929 --> 00:09:23.450 And the digitization of, well, everything. Everything. 00:09:23.649 --> 00:09:27.409 And second, we had powerful computers, especially 00:09:27.409 --> 00:09:30.990 GPUs, which were initially designed for graphics. 00:09:31.049 --> 00:09:32.990 Oh, interesting. It turned out to be perfect 00:09:32.990 --> 00:09:35.929 for training these massive neural networks. Those 00:09:35.929 --> 00:09:38.330 things that make video games look so realistic. 00:09:38.409 --> 00:09:41.309 The very same. Wow, who would have thought? Yeah. 00:09:41.570 --> 00:09:43.710 That the technology that could render such stunning 00:09:43.710 --> 00:09:48.120 visuals could also power the AI revolution. It's 00:09:48.120 --> 00:09:51.480 an interesting twist. It really is. So this approach 00:09:51.480 --> 00:09:55.399 that was once dismissed is now the driving force 00:09:55.399 --> 00:09:58.360 behind some of the most impressive AI achievements. 00:09:58.620 --> 00:10:01.360 Yeah, it really is quite remarkable. It's a testament 00:10:01.360 --> 00:10:04.919 to the power of mimicking the brain's structure. 00:10:05.179 --> 00:10:08.399 Absolutely. Deep learning has led to these incredible 00:10:08.399 --> 00:10:11.600 advances in image recognition, natural language 00:10:11.600 --> 00:10:14.159 processing, and even things like game playing. 00:10:14.320 --> 00:10:16.789 That's right. And, of course, the poster child 00:10:16.789 --> 00:10:20.789 for deep learning has to be ChatGPT. ChatGPT. 00:10:20.929 --> 00:10:23.289 A language model that can hold conversations. 00:10:23.769 --> 00:10:25.950 Yes. Right. Different kinds of creative content. 00:10:26.330 --> 00:10:28.210 Yeah. Answer your questions in an incredibly 00:10:28.210 --> 00:10:30.870 comprehensive way. It can do a lot. Seems like 00:10:30.870 --> 00:10:33.669 it can do it all. ChatGPT and other large language 00:10:33.669 --> 00:10:36.500 models. Right. are based on a specific type of 00:10:36.500 --> 00:10:39.500 neural network architecture called a transformer, 00:10:39.759 --> 00:10:43.299 which was introduced in 2017. Didn't you mention 00:10:43.299 --> 00:10:45.279 the transformers were originally designed for 00:10:45.279 --> 00:10:47.480 predicting the next word in a sentence? That's 00:10:47.480 --> 00:10:50.139 right. The transformer architecture was a major 00:10:50.139 --> 00:10:52.679 breakthrough for natural language processing. 00:10:52.720 --> 00:10:55.539 I see. It allowed models to process text in a 00:10:55.539 --> 00:10:58.539 much more sophisticated and efficient way. So 00:10:58.539 --> 00:11:01.799 this seemingly simple task of predicting the 00:11:01.799 --> 00:11:05.789 next word. Right. unlock this whole new level 00:11:05.789 --> 00:11:09.210 of language understanding for AI. Yeah, it was 00:11:09.210 --> 00:11:11.750 a game changer. That's incredible. But even with 00:11:11.750 --> 00:11:14.409 models like GPT -4, there are still things they 00:11:14.409 --> 00:11:16.379 struggle with, right? Absolutely. Like what? 00:11:16.559 --> 00:11:19.139 While these models are amazing at mimicking human 00:11:19.139 --> 00:11:22.840 language, they still haven't quite mastered true 00:11:22.840 --> 00:11:25.460 logical reasoning and problem solving. So it's 00:11:25.460 --> 00:11:27.659 like they can talk the talk, but they haven't 00:11:27.659 --> 00:11:31.200 quite grasped the deeper meaning behind the words. 00:11:31.340 --> 00:11:33.879 So they're still missing that common sense element 00:11:33.879 --> 00:11:36.200 that we humans take for granted. Exactly. So 00:11:36.200 --> 00:11:38.779 what now? And that's why understanding the history 00:11:38.779 --> 00:11:42.559 of AI is so valuable. It reminds us that progress 00:11:42.559 --> 00:11:45.600 isn't always a straight line. We might need to 00:11:45.600 --> 00:11:48.299 revisit some of those forgotten approaches to 00:11:48.299 --> 00:11:51.080 unlock the next level of AI. So we've journeyed 00:11:51.080 --> 00:11:53.919 from rule -based systems to the intricate world 00:11:53.919 --> 00:11:56.039 of neural networks and deep learning. It's been 00:11:56.039 --> 00:11:58.659 quite a journey. But are those the only paths 00:11:58.659 --> 00:12:01.100 to AI? Well, that's a good question. Are there 00:12:01.100 --> 00:12:03.379 other approaches out there that might hold the 00:12:03.379 --> 00:12:06.000 key to those missing ingredients? There's another 00:12:06.000 --> 00:12:09.039 approach gaining traction called neuro -symbolic 00:12:09.039 --> 00:12:12.240 AI. Neuro -symbolic AI. It's all about combining 00:12:12.240 --> 00:12:15.360 the strengths of both worlds. The logical reasoning 00:12:15.360 --> 00:12:19.580 of symbolic AI and the learning capabilities 00:12:19.580 --> 00:12:22.720 of neural networks. So a hybrid approach. Exactly, 00:12:22.860 --> 00:12:25.950 a hybrid approach. That sounds intriguing. How 00:12:25.950 --> 00:12:28.669 does that work in practice? Well, imagine this 00:12:28.669 --> 00:12:31.409 symbolic AI is great at representing knowledge 00:12:31.409 --> 00:12:33.929 and rules. Like those logical statements we talked 00:12:33.929 --> 00:12:36.110 about earlier. Neural networks, on the other 00:12:36.110 --> 00:12:40.200 hand, excel at finding patterns in data. Neurosymbolic 00:12:40.200 --> 00:12:43.740 AI aims to merge these two strengths, allowing 00:12:43.740 --> 00:12:46.539 AI systems to learn from data and reason logically 00:12:46.539 --> 00:12:48.539 about what they've learned. So it's like giving 00:12:48.539 --> 00:12:50.899 neural networks a boost of common sense and logical 00:12:50.899 --> 00:12:53.059 reasoning? You got it. That's cool. We're already 00:12:53.059 --> 00:12:55.679 seeing some really promising results in areas 00:12:55.679 --> 00:12:59.460 like robotics, where neurosymbolic AI is helping 00:12:59.460 --> 00:13:01.820 robots understand and interact with the world 00:13:01.820 --> 00:13:03.919 more intelligently. It definitely sounds like 00:13:03.919 --> 00:13:07.120 a promising avenue for AI research. It does. 00:13:07.980 --> 00:13:11.279 One other question. How do we even define what 00:13:11.279 --> 00:13:13.240 intelligence is? Oh, that's the big question. 00:13:13.419 --> 00:13:16.879 Is there a universally accepted definition? That's 00:13:16.879 --> 00:13:19.139 the million dollar question. Maybe the billion 00:13:19.139 --> 00:13:22.620 dollar question at this point. And one that philosophers 00:13:22.620 --> 00:13:25.220 and AI researchers have been grappling with for 00:13:25.220 --> 00:13:28.440 decades. There's no single universally agreed 00:13:28.440 --> 00:13:32.559 upon definition. Some define it as the ability 00:13:32.559 --> 00:13:36.299 to learn and solve problems, while others emphasize 00:13:36.299 --> 00:13:39.710 things like adaptability. creativity, or even 00:13:39.710 --> 00:13:42.250 consciousness. That's right. It seems like defining 00:13:42.250 --> 00:13:45.309 intelligence is just as complex as building it. 00:13:45.529 --> 00:13:47.850 It really is. It really is. And that complexity 00:13:47.850 --> 00:13:50.049 is reflected in all the diverse approaches we've 00:13:50.049 --> 00:13:52.370 seen in AI research. I think that's a good point. 00:13:52.529 --> 00:13:55.029 Each approach is essentially a different hypothesis 00:13:55.029 --> 00:13:57.970 about what intelligence is. Right. And how to 00:13:57.970 --> 00:13:59.870 create it. Yeah, that's a good way to put it. 00:14:00.210 --> 00:14:04.110 So we've explored symbolic AI. neural networks, 00:14:04.350 --> 00:14:08.570 deep learning, and even this fascinating hybrid 00:14:08.570 --> 00:14:10.990 approach called neuro -symbolic AI. Each has 00:14:10.990 --> 00:14:12.830 its own strengths and limitations. For sure. 00:14:12.929 --> 00:14:16.129 With all that in mind, where do you see AI heading 00:14:16.129 --> 00:14:18.889 next? That is the big question, isn't it? It 00:14:18.889 --> 00:14:22.070 is. If you had a crystal ball, I think you'd 00:14:22.070 --> 00:14:24.610 be a very rich person. That's right. But in all 00:14:24.610 --> 00:14:27.649 seriousness, predicting the future of AI with 00:14:27.649 --> 00:14:31.629 any certainty is impossible. It is, but one thing 00:14:31.629 --> 00:14:34.809 seems clear. The future will likely be shaped 00:14:34.809 --> 00:14:37.169 by a combination of these different approaches, 00:14:37.649 --> 00:14:40.330 each contributing its own unique strengths. It's 00:14:40.330 --> 00:14:44.129 like a melting pot of AI ideas, all blending 00:14:44.129 --> 00:14:47.029 together to create something new and potentially 00:14:47.029 --> 00:14:49.450 revolutionary. Yeah, I think that's a good analogy. 00:14:49.649 --> 00:14:51.669 And it's not just about the technology itself. 00:14:51.809 --> 00:14:54.629 We also need to think about the ethical implications 00:14:54.629 --> 00:14:58.879 of AI. its impact on society and how we can ensure 00:14:58.879 --> 00:15:01.700 that its development benefits all of humanity. 00:15:01.799 --> 00:15:03.799 I think those are all critical considerations. 00:15:04.279 --> 00:15:06.120 They really are. So we've got to learn from the 00:15:06.120 --> 00:15:10.700 past to help shape a future where AI is a force 00:15:10.700 --> 00:15:13.419 for good in the world. Absolutely. Who knows 00:15:13.419 --> 00:15:15.600 what amazing breakthroughs are waiting for us. 00:15:15.679 --> 00:15:18.179 It's an exciting time to be working in AI. It 00:15:18.179 --> 00:15:20.899 really is. Maybe we'll finally unlock the secrets 00:15:20.899 --> 00:15:23.240 of consciousness. That would be amazing. Develop 00:15:23.240 --> 00:15:25.879 AI that can truly understand and empathize with 00:15:25.879 --> 00:15:29.039 humans. Or even create AI that helps us solve 00:15:29.039 --> 00:15:31.299 some of the world's most pressing problems. Yeah, 00:15:31.299 --> 00:15:33.080 that's a future I'm excited to be a part of. 00:15:33.159 --> 00:15:36.700 Me too. But one thing that keeps popping up in 00:15:36.700 --> 00:15:41.779 these discussions about AI is this idea of the 00:15:41.779 --> 00:15:44.379 singularity. The singularity. This point where 00:15:44.379 --> 00:15:48.820 AI surpasses human intelligence and potentially 00:15:48.820 --> 00:15:51.899 takes over. Right, the classic sci -fi scenario. 00:15:52.159 --> 00:15:53.500 Is that something we should actually be worried 00:15:53.500 --> 00:15:56.179 about? Well, the singularity is a captivating 00:15:56.179 --> 00:15:58.820 concept. It is. But it's important to remember 00:15:58.820 --> 00:16:01.299 that it's still a hypothetical scenario. OK. 00:16:01.379 --> 00:16:04.000 And as we've discussed, AI development isn't 00:16:04.000 --> 00:16:06.539 a linear process. Right. There are still many 00:16:06.539 --> 00:16:09.100 fundamental challenges to overcome. OK. Before 00:16:09.100 --> 00:16:11.960 we reach anything close to human level AI. Right. 00:16:12.200 --> 00:16:14.639 Let alone super intelligence. So maybe we should 00:16:14.639 --> 00:16:18.549 focus less on those sci -fi scenarios and more 00:16:18.549 --> 00:16:22.649 on building AI that improves our lives, makes 00:16:22.649 --> 00:16:25.330 us healthier, helps us live more fulfilling lives. 00:16:26.070 --> 00:16:30.190 And that's where things like explainable AI become 00:16:30.190 --> 00:16:33.009 crucial. Explainable AI. We need to understand 00:16:33.009 --> 00:16:36.570 how AI systems arrive at their decisions, especially 00:16:36.570 --> 00:16:38.610 when those decisions have a direct impact on 00:16:38.610 --> 00:16:41.309 our lives. It's about trust and transparency. 00:16:41.429 --> 00:16:45.470 If we can't trust AI, we'll never fully embrace 00:16:45.470 --> 00:16:48.470 its potential. I agree. Transparency and trust, 00:16:48.690 --> 00:16:51.330 those are key ingredients for building a future 00:16:51.330 --> 00:16:55.710 where AI works for everyone. 100%. And that brings 00:16:55.710 --> 00:16:58.269 us to another fascinating chapter in AI history. 00:16:58.429 --> 00:17:01.809 Okay. the rise of machine learning. Machine learning. 00:17:02.009 --> 00:17:04.190 Which was once considered a fringe approach. 00:17:04.269 --> 00:17:06.410 Right. But has now become a dominant force in 00:17:06.410 --> 00:17:08.609 the field. It's had quite a journey. It's fascinating 00:17:08.609 --> 00:17:11.710 how AI research has gone through these cycles 00:17:11.710 --> 00:17:15.869 of intense excitement, followed by periods of 00:17:15.869 --> 00:17:17.849 disillusionment. Right. Like a roller coaster 00:17:17.849 --> 00:17:20.230 ride. It has been. Of breakthroughs and setbacks. 00:17:20.369 --> 00:17:22.269 Yeah, and these cycles have really profoundly 00:17:22.269 --> 00:17:24.609 shaped the field. In those early days, there 00:17:24.609 --> 00:17:27.430 was this unshakable belief that we were on the 00:17:27.430 --> 00:17:30.859 verge of creating Human level AI. There was so 00:17:30.859 --> 00:17:33.559 much optimism. Right, but then we hit those limitations. 00:17:33.559 --> 00:17:37.759 Right. Especially with symbolic AI. And it forced 00:17:37.759 --> 00:17:40.779 a shift in perspective. It did. So when that 00:17:40.779 --> 00:17:44.380 initial hype faded, did people just lose faith 00:17:44.380 --> 00:17:47.380 in AI altogether? No, not at all. It's more like 00:17:47.380 --> 00:17:50.720 they started exploring different paths. OK, so 00:17:50.720 --> 00:17:52.579 machine learning was one of those paths. Right. 00:17:52.859 --> 00:17:54.980 But even that had its own share of skeptics. 00:17:55.039 --> 00:17:57.619 Oh, yeah, for sure. For a long time, neural networks 00:17:57.619 --> 00:18:00.079 were seen as a dead end. Yeah, they were. Too 00:18:00.079 --> 00:18:02.589 computationally demanding. Quite. and difficult 00:18:02.589 --> 00:18:05.609 to train. Exactly. It's funny how what's considered 00:18:05.609 --> 00:18:08.950 impractical one day can become the hottest trend 00:18:08.950 --> 00:18:11.549 just a few years later. That's true. So what 00:18:11.549 --> 00:18:14.069 changed the game for machine learning? Well, 00:18:14.089 --> 00:18:17.730 a few key factors converged. First, we saw this 00:18:17.730 --> 00:18:21.150 huge explosion in computing power, particularly 00:18:21.150 --> 00:18:23.849 with the development of GPUs, which turned out 00:18:23.849 --> 00:18:26.390 to be ideal for training these massive neural 00:18:26.390 --> 00:18:28.930 networks. So those things that make video games 00:18:28.930 --> 00:18:32.829 look so incredible. Yeah. were a key ingredient 00:18:32.829 --> 00:18:36.170 in the AI revolution. They were. That's amazing. 00:18:36.309 --> 00:18:38.589 Yeah, it's pretty remarkable. So more computing 00:18:38.589 --> 00:18:42.210 power was one factor. What else contributed to 00:18:42.210 --> 00:18:45.279 deep learning's rise? Well, another crucial element 00:18:45.279 --> 00:18:48.259 was the availability of massive amounts of data. 00:18:49.079 --> 00:18:51.599 The internet and the digitization of information 00:18:51.599 --> 00:18:54.740 created this incredible treasure trove of data 00:18:54.740 --> 00:18:58.339 that could be used to train these data -hungry 00:18:58.339 --> 00:19:00.660 neural networks. So it's like the internet provided 00:19:00.660 --> 00:19:03.519 the fuel that deep learning needed to take off. 00:19:03.599 --> 00:19:05.529 Yeah, that's a good way to put it. But were there 00:19:05.529 --> 00:19:08.009 any other factors that helped push deep learning 00:19:08.009 --> 00:19:10.930 to the forefront? Yes. There were some really 00:19:10.930 --> 00:19:14.009 important algorithmic advancements, specifically 00:19:14.009 --> 00:19:16.450 the development of deep learning architectures 00:19:16.450 --> 00:19:18.970 that could effectively learn from this abundance 00:19:18.970 --> 00:19:21.950 of data. One of the most significant breakthroughs 00:19:21.950 --> 00:19:24.109 was the introduction of the transformer architecture 00:19:24.109 --> 00:19:28.150 in 2017. Ah, yes. Transformers, the powerhouse 00:19:28.150 --> 00:19:31.029 behind chat GPT. So this one architecture really 00:19:31.029 --> 00:19:33.390 changed the landscape for natural language processing. 00:19:33.680 --> 00:19:38.019 It did. It enabled this major leap forward, allowing 00:19:38.019 --> 00:19:41.559 models to process text in a much more sophisticated 00:19:41.559 --> 00:19:44.779 and efficient way. So when you combine that with 00:19:44.779 --> 00:19:46.980 massive amounts of data and powerful computing, 00:19:47.539 --> 00:19:50.180 you get something truly remarkable. You do. Like 00:19:50.180 --> 00:19:52.660 chat GPT, which can do things that would have 00:19:52.660 --> 00:19:54.279 seemed like science fiction just a few years 00:19:54.279 --> 00:19:57.119 ago. It's true. It's astonishing how far we've 00:19:57.119 --> 00:20:00.700 come. It is it really is a testament to human 00:20:00.700 --> 00:20:04.559 ingenuity. Yeah, and our relentless pursuit of 00:20:04.559 --> 00:20:08.140 understanding intelligence But even with these 00:20:08.140 --> 00:20:10.839 incredible advancements, uh -huh. It's important 00:20:10.839 --> 00:20:13.920 to remember that these models are still Tools, 00:20:13.920 --> 00:20:16.359 right? They're powerful. Yeah, but they don't 00:20:16.359 --> 00:20:19.579 possess true understanding or consciousness not 00:20:19.579 --> 00:20:22.059 yet. Anyway, they're not sentient beings, right? 00:20:22.240 --> 00:20:24.980 They're complex algorithms that have become incredibly 00:20:24.980 --> 00:20:27.859 good at mimicking human language. That's right. 00:20:28.460 --> 00:20:30.539 But that still raises some intriguing questions 00:20:30.539 --> 00:20:33.779 about creativity. Yeah. If a computer can write 00:20:33.779 --> 00:20:37.740 a poem or compose a piece of music. Yeah. That 00:20:37.740 --> 00:20:41.240 moves us emotionally. Yeah. Does that qualify 00:20:41.240 --> 00:20:44.660 as creativity? That's a deep philosophical question. 00:20:44.779 --> 00:20:46.819 It is. It touches on the essence of what it means 00:20:46.819 --> 00:20:49.140 to be human. Really does. Creativity has always 00:20:49.140 --> 00:20:52.539 been considered a uniquely human trait, but as 00:20:52.539 --> 00:20:56.599 AI systems become more sophisticated, those lines 00:20:56.599 --> 00:20:58.900 are becoming blurred. It makes you really condor 00:20:58.900 --> 00:21:01.019 the nature of creativity and intelligence. It 00:21:01.019 --> 00:21:03.480 does. So where do we go from here? What does 00:21:03.480 --> 00:21:06.559 the future hold for AI? Well, as we mentioned 00:21:06.559 --> 00:21:08.680 earlier, there's a lot of excitement around neuro 00:21:08.680 --> 00:21:12.490 -symbolic. which aims to bridge the gap between 00:21:12.490 --> 00:21:16.269 symbolic AI and neural networks. It's a promising 00:21:16.269 --> 00:21:19.190 avenue for creating AI systems that are both 00:21:19.190 --> 00:21:22.109 intelligent and explainable. Explainable AI, 00:21:22.109 --> 00:21:24.730 that's crucial. It is. Especially if we're going 00:21:24.730 --> 00:21:26.630 to trust these systems with important decisions. 00:21:26.630 --> 00:21:29.150 Right. We need to understand how they work. Absolutely. 00:21:29.230 --> 00:21:31.430 And there are so many other exciting areas of 00:21:31.430 --> 00:21:35.349 research, like AI safety, which focuses on ensuring 00:21:35.349 --> 00:21:38.109 that AI systems are aligned with human values. 00:21:38.250 --> 00:21:40.900 Right. And don't pose any existential threats. 00:21:41.619 --> 00:21:45.039 AI safety. That sounds like a whole other deep 00:21:45.039 --> 00:21:46.920 dive waiting to happen. It does, doesn't it? 00:21:47.259 --> 00:21:49.500 But for now, I think we've covered a lot of ground. 00:21:49.680 --> 00:21:52.200 I think we have. Wouldn't you say? Yeah, we've 00:21:52.200 --> 00:21:55.140 explored this fascinating history of AI. We have? 00:21:55.380 --> 00:21:58.619 From Turing's early vision to the deep learning 00:21:58.619 --> 00:22:01.299 revolution and beyond. It's been an incredible 00:22:01.299 --> 00:22:04.220 journey through the world of AI. It has. Who 00:22:04.220 --> 00:22:06.700 knew there were so many twists and turns. Right. 00:22:06.839 --> 00:22:09.420 So many different approaches. Yeah. And so many 00:22:09.420 --> 00:22:11.400 thought -provoking questions along the way. It's 00:22:11.400 --> 00:22:14.039 a fascinating field. It really is. So here's 00:22:14.039 --> 00:22:15.759 something to ponder as you go about your day. 00:22:15.819 --> 00:22:19.220 Okay. If a computer can write a poem that moves 00:22:19.220 --> 00:22:22.319 you. Yeah. Or compose a piece of music that stirs 00:22:22.319 --> 00:22:24.759 your soul. Uh -huh. Does that mean it possesses 00:22:24.759 --> 00:22:28.240 some form of creativity? What do you... Until 00:22:28.240 --> 00:22:30.740 next time, keep exploring, keep questioning, 00:22:31.160 --> 00:22:31.779 and keep learning.