The Future of AI Reasoning: Can Machines Truly Think?

Kognetiks Chatbot – The Deep Dive Podcast – 2024 10 21

Welcome to an exciting edition of The Deep Dive Podcast!  Today, we’re diving deep into one of the most fascinating questions in the world of artificial intelligence: Can AI truly reason like a human?  Join us as we explore groundbreaking experiments, from AI’s ability to solve classic riddles to its struggles with abstract concepts, and dive into the ongoing debate about what it means for machines to understand the world as humans do.  Get ready for an enlightening conversation that challenges the very foundation of AI research.

Host-2: OK, so get this right.  We’re diving deep into AI today.  But not just like how it can spit out facts or whatever.  We’re talking about actual reasoning.  Like, can AI really think?

Host-1: It’s a big question.  And honestly, it’s a question that’s kind of at the heart of all the AI research that’s happening right now.

Host-2: Right, because it’s more than just those old school AI systems that were basically just like giant databases, right?

Host-1: Exactly.  I mean, now we’re talking about AI that can solve problems and understand like context.  You know, all those things that we think of as being like, uniquely human.

Host-2: Yeah.  And one of these articles I’ve got here talks about this really interesting experiment where they use riddles to figure out how well AI can actually reason.

Host-1: Ohh, that’s a cool idea.

Host-2: Because apparently even those simple logic puzzles like the ones we did as kids, they can be surprisingly hard for AI to figure out.

Host-1: That is fascinating.  So which AI did they test?

Host-2: Well, this article focuses on GPT 3.

Host-1: OK yeah, GPT 3.  That’s a pretty powerful language model.

Host-2: Right.  But it turns out it’s not exactly a riddle master.

Host-1: Really.  So you’re telling me even GPT 3 can’t solve a Riddle?  What kind?  Of riddles did they use?

Host-2: Well, they gave it all sorts, but there was one in particular.  It really struggled with.  What is full of holes but still holds water?  Like, come on, that’s a classic.

Host-1: Wait, seriously, it couldn’t get that one even I know.

Host-2: That one right?  Sponge

Host-1: A sponge, of course.  OK, so that’s interesting.  That does kind of highlight something important though, right?  I mean, we’ve talked before about how AI it can process language and stuff, but.

Host-2: But there’s this whole other level of, like, common sense that it’s still missing.

Host-1: Exactly like with that riddle, there’s that visual element, you know, picturing a sponge and maybe even a tactile element too, like remembering what it feels like to hold a wet sponge.  And it seems like AI, at least for now, just doesn’t quite grasp those things the way humans do.

Host-2: So maybe GPT 3 won’t be winning any riddle contests anytime soon, but were there other riddles that struggled with like?  Was there a pattern?

Host-1: There totally was a pattern.  They noticed that GPT 3 consistently struggled with riddles that weren’t about concrete things.  You know, riddles where the answer was an idea or a concept.

Host-2: So interesting.

Host-1: Right?  Like another one it missed was.  What can you break even if you never pick it up or touch it?

Host-2: Oh, interesting.  Oh, that’s a good one.

Host-1: The answer, of course, is a promise, but for GPT 3 it’s like the concept of a promise was just too abstract you know.

Host-2: Yeah, because it’s more than just knowing the definition of promise, right.  It’s about understanding.  What a promise means in like a social context, the weight it carries.

Host-1: Exactly.  And that’s where things get really tricky for AI, because how do you teach machines about things like social constructs, you know, human emotions, all those nuances?

Host-2: It’s almost like, OK, this is gonna sound weird, but it’s almost like they know the words but not the music, right?  Like they can use language, but they don’t necessarily feel the meaning behind it the way we do.

Host-1: That’s a really good way to put it actually.  And it gets at this fundamental challenge in AI, which is, you know, how do we bridge that gap?  How do we get AI to understand not just the what but the why?

Host-2: It makes you wonder, like, how do we even measure true AI intelligence you know.

Host-1: Well, that’s where something like the Turing test comes in, right?

Host-2: Ohh yeah, the Tuning test.  That’s the one where like if you can’t tell if you’re talking to a machine or a person.  Then, the AI is basically considered intelligent, right?

Host-1: Exactly.  And it’s interesting because one of the articles I was reading was talking about how they’re actually trying to come up with even harder Turing tests like ways to really push AI to its limit.

Host-2: Harder.  How do you even make it harder?

Host-1: Well, for example, they were talking about asking AI to do something that sounds really simple, but it’s actually pretty tricky for machines.  Counting the number of a specific letter in a word.

Host-2: Wait, what?  Like how many A’s are in Banana?

Host-1: Yeah, exactly.  Something like that sounds easy enough, right?  But think about it.  To do that, you need to be able to, like, focus your attention on very specific details.  And then, like, filter out the rest.  And that can be surprisingly challenging for AI systems.

Host-2: Huh.  Yeah.  I never thought of it like that because it’s one of those things that our brains just kind of do automatically.

Host-1: Right.  And so, this article was making the point that even seemingly simple tasks like counting letters in a word can actually tell us a lot about how far AI has come and how far it still has to go.

Host-2: It’s funny how something that seems so basic can actually be so revealing.

Host-1: Totally.  And you know, this whole idea of being able to tell the difference between a human and a machine.  It’s not just some abstract thought experiment.  I mean, it has real world implications too.

Host-2: Ohh right.  Like what about those CAPTCHA things you see online?

Host-1: Exactly those things where you have to, like click, on all the pictures with crosswalks or whatever.

Host-2: Yeah, those things drive me crazy, but they’re meant to stop bots.

Host-1: Right, exactly.  Because those CAPTCHA tests they exploit those subtle differences in perception and cognition that AI still struggles with, like a human can look at a distorted image and pretty easily recognize what it is.  But for a bot it’s way more difficult.

Host-2: Makes sense, but as AI gets better, I mean eventually it’s going to be able to get past those CAPTCHA things, right?

Host-1: Oh, absolutely.  And that’s where researchers are always trying to come up with even more sophisticated tests and detection methods because the stakes are getting higher, right?  Think about all the concerns about bots being used to spread misinformation or to manipulate online conversations.

Host-2: It’s kind of scary, honestly.

Host-1: It is, and the more advanced AI becomes, the more important it’s going to be to be able to tell the difference between a real.  Person and a machine.

Host-2: So it’s like even if AI can have a conversation that seems totally human or can write a poem or even solve a ride.  That doesn’t necessarily mean it’s truly intelligent in the same way that a human is.

Host-1: Great.  And that kind of brings us to another question, which is how will we know when we’ve actually created artificial general intelligence.  That AGI everyone’s talking about?

Host-2: Because AGI that’s like the next level, right?  Like true artificial intelligence that can think for itself, learn new things on its own.  How would we even know when we’ve gotten there?

Host-1: Well, that is the $1,000,000 question, isn’t it?  And I mean, there are a lot of different theories out there, but one of the articles I was reading it had this really interesting thought experiment.

Host-2: Don’t tell me.

Host-1: They said, imagine asking an AI this question: Why are humans creative?

Host-2: Wow.  Yeah, I don’t know if I can answer that.

Host-1: Exactly.  And that’s exactly the point they are making in the article.  They are saying that even some of the most advanced language models we have today, like ChatGPT.  They might be able to mimic creativity like they can generate text that sounds creative, but when you really get down to it, they can’t actually explain why humans are creative.

Host-2: Because they don’t really understand the underlying reasons and motivations behind it.

Host-1: Exactly.  And so the article was arguing that maybe this is a key difference between True AG.  Way and even the most.  Sophisticated AI we have today.

Host-2: Just they can like, go through the motions, but they don’t really get it.

Host-1: Yeah.  And that’s a really important distinction to make, right, because it gets at this question of, like, what does it actually mean to be creative?

Host-2: Right.  More than just being able to like rhyme words or whatever, there’s something deeper there.

Host-1: Exactly.  And the article was arguing that until we can create an AI that understands that deeper, something that can actually grasp the why behind human creativity, we haven’t really achieved AGI.

Host-2: So instead of asking an AI to write a poem, we should be asking it like, what is the meaning of art?

Host-1: Well, there you go.  Maybe that’s the real Turing test.  But seriously, it’s these bigger questions, these philosophical questions about consciousness and creativity and the nature of reality that might be the key to understanding whether or not a machine can truly think.

Host-2: Wow!  It’s pretty mind blowing when you think about it.

Host-1: It really is because it forces us to confront not just the limitations of AI, but also the mysteries of our own minds.

Host-2: Totally.  And you know, who knows?  Maybe in trying to create AI that can think like us, we’ll end up learning even more about how we think.

Host-1: Now that would be the ultimate irony, wouldn’t it?

Host-2: It would.  Well on that note, I think we’ve reached the end of our deep dive into AI and the quest for true reasoning.

Host-1: It’s been quite a journey.

Host-2: It has.  So, for all our listeners out there, we want to know what you think.  If you were designing a Turing test, what one question would you ask to determine if you were talking to a machine or a real person?  Leave us a comment and let us know.

As we’ve uncovered in this episode, while AI has made incredible strides in language processing and problem-solving, there are still gaps in its ability to grasp the deeper meanings behind human experiences and concepts.  But the future of AI is filled with potential, and innovations like the Kognetiks Chatbot are at the forefront of this journey.  If you’re ready to bring AI-powered intelligence to your own WordPress website, download the Kognetiks Chatbot for WordPress plugin today.  Take the next step in transforming how your site interacts with users – let AI do the heavy lifting for you.

This podcast was generated using NotebookLM, a new and experimental product from Google.  The transcription for this podcast was generated using Microsoft Word.

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