have you ever wondered, like really wondered, how close we are to AI that can actually think? I mean seriously, we're just surrounded by AI that just seems so amazingly smart. You know, it can write these poems, it can have conversations, it can generate art, but what's really going on, like under the hood? Yeah, that's the real question, isn't it? A lot of what we see as, you know, quote unquote smart AI today, um, it's really just good at picking up on these patterns from massive, massive amounts of data. Yeah, it's mimicry, but like on this grand scale. It's not true reasoning. Okay, so I get that. So it's like, um, like my phone can like autocomplete my sentences, but it doesn't actually know what I'm trying to say. Exactly. And that's really, that's really where the challenge lies, right? Yeah, humans, we reason by breaking down these complex problems into smaller logical steps, right? Like a chain of thought. Okay, and um, you know, until recently, getting AI to do that has been like a major hurdle. So it's like the

difference between um like a parrot repeating phrases and a human understanding the meaning behind the phrases. A Perfect Analogy, and that's what makes this research paper that we're um we're diving into so fascinating today. Okay, it's titled Chain of Thought prompting elicits reasoning in large language models. Okay, and it might just hold the key to unlocking this new level of AI reasoning. All right, you've definitely got my attention. So, what is this Chain of Thought prompting, and how does it actually work? Okay, so imagine um imagine you're teaching a child to solve a word problem. You wouldn't just give them the problem and expect them to magically know the answer, right? You would walk them through it, show them the steps, explain your thought process. You'd say something like, Okay, well first we need to figure out how many apples are in each basket, and so on and so forth. Precisely, right? Chain of Thought prompting is really similar, right? Instead of just giving an AI a problem, we show it these examples of how to Think Through the problem, right, with

the steps laid out in this clear natural language, so it's like giving the AI a behind-the-scenes look at how our brains are solving a problem. Yes, W, and the exciting part is that the AI, it doesn't just memorize these examples; it starts to internalize the process. It starts generating its own Chain of Thought for new problems. That sounds incredible. Um, do you have an example from the paper that really shows this in action? Absolutely, let's look at the um, the tennis ball example. Okay, imagine you ask the AI this question: Roger has five tennis balls. He buys two more cans of tennis balls. Each can has three tennis balls. How many tennis balls does he have now? Okay, pretty straightforward word problem. Um, I'm guessing the AI nailed it. Oh, it did. It did more than just get the right answer. Okay, with standard prompting, you might just get the number 11. Okay, but with Chain of Thought prompting, it's a whole different ball game. Okay, now you've got me on the edge of my seat. Yeah, what did the AI do?

okay, get this. It actually wrote out its reasoning process just like a human would. Wow. It said Roger started with five balls, two cans of three tennis balls each is six tennis balls, 5 + 6 = 11. The answer is 11. Wow. It didn't just spit out the answer; it actually showed its work. Exactly. That's it. It is pretty remarkable. And this wasn't just some fluke with like a simple math problem. The researchers tested this technique on a whole range of reasoning challenges. So, sounds like we're not just talking about AI acing like Elementary School math problems here. Like, what kind of complex reasoning are we talking about? We're talking about AI tackling problems that have like long stumped even the most um Advanced systems. Things like, you know, understanding implied meanings in text, figuring out like cause and effect in these complex scenarios, right? Even symbolic reasoning, which you know, that involves manipulating these abstract Concepts. Wow. Okay, so this is some seriously high-level thinking. But did Chain of Thought prompting actually make a difference? That's the

real kicker. Okay, the researchers found that even adding just a few of these Chain of Thought examples to the prompts, okay, led to significant improvements in the AI's performance. Wow, especially with those massive language models, the ones with, you know, hundreds of billions of parameters. So, the bigger the AI brain, the better it can kind of grasp these thought chains. It seems that way, okay. It's as if those larger models have this higher cognitive capacity, right, allowing them to kind of process and utilize these complex thought processes more effectively. That's fascinating. Um, yeah, but I guess even if we're training AI to reason better, I mean, does it mean it actually understands what it's doing? That's a good question. Like, it's not like we've suddenly created conscious machines, right? Right, to be cautious. Yeah, while this research is, you know, groundbreaking, right, it's important to remember that we're still observing the output, the AI's ability to mimic human-like reasoning, okay. We don't fully understand the inner workings of these models, right.

the um, the how behind the what. That makes sense, yeah. It's like I can teach my dog to fetch a specific toy, right? But I don't really know what's going on in his head when he does it exactly. And just like with your dog, you know, even with the best training, there's always a chance of mistakes. The paper acknowledges that these models can still make, you know, logical errors, get fooled by um misleading information. So Chain of Thought prompting is a powerful tool, yes, but it's not a magic wand that suddenly makes AI infallible, precisely, yeah. And this is where it gets really interesting, great. One of the um, one of the key takeaways from the paper is that the way we craft these prompts, the way we present those example thought processes to the AI, right, can significantly influence its ability to learn and apply the technique. So it's not just about giving the AI more data, it's true, it's about giving it the right kind of data, presented in the right way, yeah, yeah. It's almost like we're

learning a new language, like the language of prompting, right, to communicate effectively with these AI systems, that's a great way to put it. Yeah, and as we get better at this language, you know, as we develop these more sophisticated prompting techniques, who knows what incredible feats of reasoning we might be able to unlock in these AI models. Yes, it's both exciting and a little daunting, to be honest. Yeah, it really is. It feels like this research is opening up this whole new world of possibilities. It's true, while also presenting us with some pretty profound questions about the nature of intelligence, like both human and artificial. Absolutely. It makes you wonder if we can train AI to reason this well, you know, what other aspects of human cognition might we be able to replicate or even enhance in the future. Yeah, yeah, yeah, and it's mind-boggling to think we could be on the verge of AI that can not only process information but actually reason about it, right, in a way that was once thought

to be like uniquely human, yeah, and that's what makes this whole field so captivating. You know, we're not just building tools here; we're exploring the very nature of intelligence itself, right? But, um, as you hinted at, it also raises some pretty big questions. Exactly, like if we can guide AI's reasoning this effectively, what are the implications? Like, where do we draw the line between, you know, helpful tools, right, and potentially unpredictable entities? That is the million-dollar question, isn't it? This research, you know, it doesn't give us all the answers, but it certainly illuminates the path, you know, the path ahead. Okay, and that path leads us to a fascinating question for you, our listener. Okay, I like it, yeah. Think of this as your own personal deep-dive prompt. You ready for this? I'm ready. Hit me right here. It is: If we can already elicit this level of reasoning from AI, right, what happens as these models become even more vast, even

more complex. What happens as we humans become more adept at crafting those guiding prompts, at speaking the language of AI thought, right? The possibilities, much like the potential of these large language models themselves, are vast and, dare I say, a bit awe-inspiring. Yeah, it's a future full of potential and, let's be honest, a healthy dose of the unknown a bit, but that's what makes exploring these topics so exciting. It's true, right? Yeah, it's a journey of discovery really for all of us, and as with any good deep dive, we hope we've given you plenty to ponder. Absolutely, next time, yeah, keep asking those big questions. Yes, and keep exploring the world with that same curiosity.

What is Chain of Thought? 💥 Understand Chain of Thought Reasoning in 10 mins💥

What is Chain of Thought? 💥 Understand Chain of Thought Reasoning in 10 mins💥