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Craig Van's Deep Dives
AI Deep Dives on developing ourselves through health, wealth, and connection. This is the easiest way for me to listen to the AI Deep Dives that I generate before diving deeper into a field of knowledge, or alongside. If you find them interesting and useful too, enjoy.
Craig Van's Deep Dives
Beyond Task Masters: The Future of True AI Intelligence | Ep 09
We explore a mind-bending perspective on artificial intelligence that challenges conventional evaluation methods and proposes a new framework for understanding true machine intelligence. This fascinating discussion reveals how current AI systems might be mere task completers rather than genuinely intelligent entities, similar to someone who's memorized a cookbook but can't actually cook.
• The difference between two incomplete views of intelligence: task-specific skills versus general learning ability
• Three levels of generalization: local (variations within a domain), broad (related tasks), and extreme (entirely new situations)
• Why current AI excels at specific tasks but struggles with the extreme generalization that humans perform naturally
• Introduction to "skill acquisition efficiency" as a better measure of true intelligence
• The Abstraction and Reasoning Corpus (ARC) dataset: visual puzzles designed to test genuine intelligence
• Using algorithmic information theory to mathematically measure learning efficiency
• The importance of developing AI that's not just smart but wise – aligned with human values and goals
• Why this shift in perspective could unlock solutions to humanity's greatest challenges
We challenge you to think critically about your own hopes and fears for AI's future and what role you believe it should play in our lives. Maybe you'll be inspired to explore this fascinating field further or even contribute to the next breakthrough in artificial intelligence.
All right, get ready, because we're diving into something pretty mind-bending today.
Speaker 2:Yeah, this paper you sent over 1911.01547v2.pdf.
Speaker 1:Oh, it really makes you rethink how we understand intelligence, especially when it comes to AI.
Speaker 2:What's really interesting is, you know, the authors actually suggest that the way we currently evaluate AI might be misleading. Oh like are we really creating truly intelligent machines or just building really really good task completers?
Speaker 1:It's a good question, right, like the paper argues, that this whole focus on you know, ai, beating humans at specific tasks like chess or go, could be kind of leading us down the wrong path.
Speaker 2:It's like imagine someone memorizing a cookbook oh good, but having no idea how to actually cook oh interesting. You know, they might know all the steps, but they haven't really grasped the art of cooking right Right. And that's kind of where we are with AI right now.
Speaker 1:So are we saying that current AI is all smoke and mirrors, then Not necessarily the paper kind of highlights. Yeah, Two dominant views of intelligence. Okay, one sees it as like a collection of very specific task skills. Okay, like mastering chess.
Speaker 2:Yeah.
Speaker 1:The other one views intelligence as this general ability to learn and adapt to new situations.
Speaker 2:Okay.
Speaker 1:Like a child figuring out a new game, right, and the authors are saying both views are kind of missing something. So they're both incomplete in a way, yeah exactly Right, and they actually bring in some insights from developmental psychology.
Speaker 2:Oh, interesting.
Speaker 1:They point out that we humans are born with certain core knowledge, but we also learn and adapt throughout life.
Speaker 2:Of course, yeah.
Speaker 1:So it's a combination of both nature and nurture.
Speaker 2:I see, I see you know, Okay, so we need to look beyond just how well AI performs at specific tasks.
Speaker 1:Right.
Speaker 2:And consider its ability to generalize.
Speaker 1:Yes, exactly, and the paper breaks down generalization into three types.
Speaker 2:Okay.
Speaker 1:First, there's something called local generalization, which is like mastering these nuances.
Speaker 2:Within a very specific area, for example, like an AI that's trained to spot different types of flowers.
Speaker 1:Right.
Speaker 2:If it can recognize a slightly different image of a rose, maybe from a different angle or with different lighting, that's considered local generalization.
Speaker 1:So it's working within with different lighting. Yeah, that's considered local generalization. So it's working within that same domain, yeah, but handling sort of variations within it.
Speaker 2:Yeah, exactly.
Speaker 1:It's like becoming an expert in a particular field.
Speaker 2:Yeah, yeah, exactly.
Speaker 1:What's the next level up then?
Speaker 2:So the next level up is broad generalization, okay, which is about taking those skills and applying them to new but related tasks.
Speaker 1:Okay.
Speaker 2:So, for example, our flower identifying AI.
Speaker 1:Okay.
Speaker 2:If it could then use some of that knowledge.
Speaker 1:Right.
Speaker 2:To recognize, say, different types of trees.
Speaker 1:Yeah.
Speaker 2:That would be broad generalization.
Speaker 1:Okay.
Speaker 2:So it's transferring knowledge from one area to another Right Within a similar domain.
Speaker 1:Okay, and that makes sense.
Speaker 2:Yeah.
Speaker 1:So it's kind of like taking your expertise in botany and applying it to, you know, a slightly different but related field like forestry.
Speaker 2:Yeah, perfect example.
Speaker 1:This makes me think of all the times I've tried to use my limited coding skills to fix problems with my website.
Speaker 2:Yeah.
Speaker 1:Sometimes it works.
Speaker 2:Yeah.
Speaker 1:Sometimes it's a disaster.
Speaker 2:Yeah, exactly.
Speaker 1:Yeah.
Speaker 2:Sometimes it works, sometimes it's a disaster. Yeah, exactly, that's a great example. Actually, it highlights the limits of just having specific skills.
Speaker 1:Okay.
Speaker 2:Right Now. Imagine if your coding knowledge could adapt to any website issue you encountered, even ones you've never seen before. That's where this idea of extreme generalization comes in.
Speaker 1:Okay.
Speaker 2:And this is what makes human intelligence so remarkable.
Speaker 1:All right, bring on the extreme part.
Speaker 2:Yeah.
Speaker 1:This is where things get interesting.
Speaker 2:So extreme generalization is this ability to tackle entirely new tasks that may only share abstract similarities with past experiences?
Speaker 1:Okay.
Speaker 2:Think about a human child learning to ride a bike.
Speaker 1:Yeah.
Speaker 2:They've never done anything quite like it before, but they can draw on their understanding of balance. Yeah, coordination, spatial awareness from other activities.
Speaker 1:That's incredible when you think about it.
Speaker 2:Yeah.
Speaker 1:We humans do this kind of extreme generalization all the time.
Speaker 2:All the time.
Speaker 1:Without even realizing it. Yeah, exactly it's just you know part of how our brains work.
Speaker 2:And the paper points out that current AI excels at local Okay, sometimes broad generalization.
Speaker 1:Yeah.
Speaker 2:But struggles with this extreme kind Right, and that raises a big question.
Speaker 1:Yeah.
Speaker 2:Are we measuring AI intelligence correctly?
Speaker 1:So the paper is suggesting that our current benchmarks might be flawed correctly. So the paper is suggesting that our current benchmarks might be flawed.
Speaker 2:Well, they argue that focusing solely on AI beating humans at these very specific, complex games could be a little misleading. You could achieve mastery in a task by just feeding the AI tons of data or fine-tuning its programming without achieving genuine intelligence.
Speaker 1:Like that cookbook example again.
Speaker 2:Exactly.
Speaker 1:You know, just because you can recite recipes. Yeah, Doesn't mean you understand the nuances of flavor and technique. Precisely Right.
Speaker 2:So what do the authors propose? Okay, they introduce a new concept. Okay, called skill acquisition, efficiency, skill, acquisition efficiency.
Speaker 1:Now that sounds like something we should all be striving for, right, not just AI.
Speaker 2:Exactly.
Speaker 1:What exactly does it mean?
Speaker 2:It's essentially how effectively a system, whether it's an AI or a human, learns new skills from knowledge and experience.
Speaker 1:Okay.
Speaker 2:It's not just about how well you perform on a specific task, but how efficiently you can adapt and learn something entirely new.
Speaker 1:Imagine learning new skills as effortlessly as you mastered Mention, a skill the listener is good at.
Speaker 2:That's a great way to put it.
Speaker 1:That's the potential this concept unlocks.
Speaker 2:Exactly.
Speaker 1:Yeah.
Speaker 2:The authors even offer a mathematical formula.
Speaker 1:Oh, wow.
Speaker 2:Using something called algorithmic information theory to actually calculate this efficiency. But we won't dive into that just yet.
Speaker 1:Okay, good, I need a little more coffee before we go down that rabbit hole. But before we do tell me, how do you actually test this skill acquisition efficiency in the real world?
Speaker 2:Well, the authors put their money where their mouth is.
Speaker 1:OK.
Speaker 2:By introducing a new benchmark. Ok, called the ARC data set.
Speaker 1:OK.
Speaker 2:Which stands for abstraction and reasoning corpus.
Speaker 1:So a new test designed to assess how well AI can learn.
Speaker 2:Yeah.
Speaker 1:Adapt and generalize. Yes To new situations Exactly and it uses visual puzzles. Ok.
Speaker 2:That require the AI to understand abstract concepts from very few examples.
Speaker 1:Hold on, this is getting tricky.
Speaker 2:Yeah.
Speaker 1:So we're not talking about complex calculations or writing code or anything.
Speaker 2:Not in this case.
Speaker 1:We're talking about visual puzzles.
Speaker 2:Exactly, and these puzzles are designed to rely on those innate core knowledge principles we talked about earlier.
Speaker 1:Right.
Speaker 2:To really level the playing field between humans and AI.
Speaker 1:So both humans and AI are starting with the same basic building blocks of knowledge.
Speaker 2:Yeah.
Speaker 1:It's like a mental obstacle course for intelligence.
Speaker 2:That's a great way to put it.
Speaker 1:Tell me more about these puzzles.
Speaker 2:All right, so imagine a grid of colored squares. Okay, that's your input. Okay, then you imagine a grid of colored squares okay, that's your input okay then you see another grid of colored squares, right.
Speaker 1:That's the output okay your task is to figure out the rule okay that transformed the input into the output right based on just a few other input output examples hmm, sounds simple enough, but I have a feeling, yeah, anything but simple you're catching on yeah the rules can be incredibly complex. Oh.
Speaker 2:Involving spatial reasoning, pattern recognition, even basic understanding of concepts like counting or sorting.
Speaker 1:So it's like a logic puzzle.
Speaker 2:On steroids, exactly Wow, and they require this kind of flexible thinking and adaptation.
Speaker 1:Yeah.
Speaker 2:That's characteristic of extreme generalization.
Speaker 1:Okay, I am officially intrigued.
Speaker 2:Yeah.
Speaker 1:Let's take a closer look at these puzzles and see what they can tell us about the potential of AI.
Speaker 2:I think that's a great place to start. You know, these puzzles might seem like these abstract games, but they represent a crucial shift in how we think about AI.
Speaker 1:Okay.
Speaker 2:You know, instead of just focusing on these task-specific skills, we're challenging AI to learn and adapt like humans do.
Speaker 1:And if we can kind of crack that code, the implications are huge.
Speaker 2:Yeah.
Speaker 1:We're not just talking about better game-playing AI here.
Speaker 2:Oh, absolutely not.
Speaker 1:Yeah.
Speaker 2:The authors actually argue that this kind of shift in focus could unlock solutions to some of humanity's biggest challenges.
Speaker 1:Okay.
Speaker 2:Imagine AI that can help us address climate change.
Speaker 1:Yeah.
Speaker 2:Develop new life-saving medical treatments.
Speaker 1:Wow.
Speaker 2:Or even explore the vastness of space more efficiently.
Speaker 1:It's like we've been trying to teach AI to sprint yeah, before it can even walk. I love that to teach AI to sprint before it can even walk. I love that this focus on skill acquisition efficiency feels like teaching AI those fundamental steps.
Speaker 2:I think that's a great analogy, and the paper also emphasizes that this approach isn't about replicating the human brain exactly. It's about understanding these principles behind human intelligence and seeing if those principles can be translated into AI.
Speaker 1:So it's less about copying the human brain.
Speaker 2:Right.
Speaker 1:And more about distilling the essence of what makes it such an efficient learning machine.
Speaker 2:Exactly, precisely. Yeah, you know, think about it this way.
Speaker 1:Okay.
Speaker 2:You wouldn't necessarily need to rebuild an entire bird to understand the principles of flight. Okay to rebuild an entire bird to understand the principles of flight. You could study the aerodynamics of wings, the structure of feathers, the mechanics of bird flight to build something that flies right.
Speaker 1:Right.
Speaker 2:Even if it doesn't look exactly like a bird.
Speaker 1:I see the parallel there.
Speaker 2:Yeah.
Speaker 1:So we're not trying to create a carbon copy of the human mind. Exactly we're trying to understand those core principles that make it so adaptable and effective at learning.
Speaker 2:Yeah, yeah, absolutely.
Speaker 1:This is all incredibly fascinating.
Speaker 2:Yeah.
Speaker 1:But I have to admit I'm also wondering about the potential downsides, Right? What happens if we create AI that's too human-like in its intelligence?
Speaker 2:That's a really important question. And to be honest, science fiction has given us plenty to worry about.
Speaker 1:Right, yeah.
Speaker 2:When it comes to super intelligent AI.
Speaker 1:Killer robots anyone Exactly? Yeah, super intelligent AI Killer robots anyone Exactly?
Speaker 2:Yeah, but the authors emphasize that this focus on skill acquisition efficiency. You know, it also means building AI.
Speaker 1:Okay.
Speaker 2:That's aligned with human values and goals, right? So it's not just intelligence for intelligence's sake.
Speaker 1:Yeah.
Speaker 2:It's about intelligence that's actually beneficial to humanity.
Speaker 1:So it's not just about making AI smarter Right, it's about making it wiser. Yes, exactly.
Speaker 2:And that means building AI systems that are transparent, okay, explainable Right Accountable for their actions.
Speaker 1:Because if we're going to trust AI, Exactly. To help us solve these complex problems.
Speaker 2:Right.
Speaker 1:We need to understand how it arrives at its solutions.
Speaker 2:Yes.
Speaker 1:And ensure that those solutions are ethically sound.
Speaker 2:Absolutely yeah. And that actually requires a shift.
Speaker 1:Okay.
Speaker 2:Not just in how we build AI Right, but also in how we think about its role in society.
Speaker 1:We need to kind of view it as a collaborator.
Speaker 2:Yeah.
Speaker 1:A partner in problem solving Right, not just a tool that blindly follows orders, exactly, exactly.
Speaker 2:And while this paper focuses primarily on the technical aspects of AI intelligence, it also hints at these broader societal implications.
Speaker 1:Because ultimately, the goal of AI research shouldn't just be to create machines that can outperform humans.
Speaker 2:Exactly.
Speaker 1:It should be to create a future.
Speaker 2:Yeah.
Speaker 1:Where humans and AI can work together.
Speaker 2:Yes.
Speaker 1:To build a better world for everyone. Well said, I like that. Now. You mentioned earlier, yeah, that the authors even provide a mathematical formula. They do For calculating this skill acquisition efficiency.
Speaker 2:Yeah.
Speaker 1:Using something called.
Speaker 2:Algorithmic information theory.
Speaker 1:Algorithmic information theory. I have to admit, my inner geek is very curious about this.
Speaker 2:Mine too.
Speaker 1:Let's try to break it down without getting too lost in the technical weeds.
Speaker 2:All right. So the basic idea is to measure how much information a system needs to achieve a certain level of skill on a new task and the less information it needs.
Speaker 1:Yeah.
Speaker 2:The more efficient it is at learning.
Speaker 1:Okay, that makes sense intuitively, right, but how do you actually measure that information?
Speaker 2:Right. This is where algorithmic information theory comes in.
Speaker 1:Okay.
Speaker 2:It provides a way to actually measure the complexity of information.
Speaker 1:Okay.
Speaker 2:Imagine you have a computer program. Okay, imagine you have a computer program. Okay. The shorter the program, the less complex the information it contains.
Speaker 1:Okay.
Speaker 2:So a very short program might be something like you know print hello world Right. Very simple instruction.
Speaker 1:Yeah, very straightforward.
Speaker 2:Now imagine a much longer, more complex program that does something like playing a game of chess.
Speaker 1:Okay.
Speaker 2:That program would contain a lot more information and therefore be more complex. So the shorter the program, the simpler the information, the less complex it is.
Speaker 1:How does this apply to measuring AI intelligence then?
Speaker 2:Well, the authors argue that you can actually gauge the intelligence of an. Ai system by looking at how much information it needs to learn to solve a new task and they equate that information to the complexity of the shortest program that could solve that task.
Speaker 1:So if an AI can learn to solve a complex task using a very simple program, that means it's highly intelligent.
Speaker 2:Exactly.
Speaker 1:Oh, wow.
Speaker 2:Because it would mean it's able to extract the essential information from that task and discard anything unnecessary.
Speaker 1:So it's learning efficiently. Yeah, just like that human child who quickly grasps the key concepts of a new game.
Speaker 2:I like that. Yeah, perfect analogy.
Speaker 1:Okay, my brain is definitely working overtime here.
Speaker 2:I know it's a lot to process.
Speaker 1:But I think I'm starting to get it.
Speaker 2:Good, good.
Speaker 1:It's not just about how well an AI performs on a task Right. It's about how efficiently it learns.
Speaker 2:Precisely.
Speaker 1:Yeah.
Speaker 2:And the authors believe this shift in focus could actually lead to a whole new generation of AI that's not just powerful but truly intelligent, in a way that resembles human intelligence.
Speaker 1:Which brings us back to this fascinating ARC data set they've created Right. It's designed to test this skill acquisition efficiency using those visual puzzles we talked about earlier.
Speaker 2:Exactly, and so far it's proving to be quite a challenge. Ok For current AI systems.
Speaker 1:Which is actually a good thing. I think it's a great thing yeah.
Speaker 2:It means we're pushing the boundaries of AI research Right and forcing ourselves to think differently about how we approach intelligence in machines. How we approach intelligence in machines.
Speaker 1:Because if we're truly going to create AI, yeah. That can help solve humanity's biggest challenges Right. We need to make sure it's not just smart but also wise.
Speaker 2:And that wisdom comes from understanding not just how to solve problems Right, but why those solutions matter Exactly and how they fit into the bigger picture of human values and goals.
Speaker 1:This is a deep dive I can get behind. Yeah, you know, it feels like we've been so preoccupied with building. Ai that can outsmart us. Yeah, specific tasks that we've kind of overlooked.
Speaker 2:Right.
Speaker 1:The importance of teaching it how to learn and adapt like we do.
Speaker 2:That's a great point, and what's exciting about this ARC data set? Is it's not just some abstract theoretical exercise it really has the potential to reshape the future of AI research in very practical ways.
Speaker 1:I'm all ears, tell me more.
Speaker 2:Okay, so, for starters, it provides this common benchmark that researchers can use to evaluate the intelligence of their AI systems in a much more meaningful way.
Speaker 1:You know, we can finally move beyond those apples to oranges comparisons we've been stuck with it's like having a standardized test for AI intelligence right Exactly A way to actually measure progress accurately.
Speaker 2:And because the ARC data set is publicly available, it means anyone can contribute to improving it and developing new AI systems that can actually tackle these challenging puzzles.
Speaker 1:So it's like this giant open source project for the future of AI, yeah, with everyone working together to kind of push the boundaries.
Speaker 2:I like that way of thinking about it.
Speaker 1:Of what's possible.
Speaker 2:Yeah, and by focusing on this skill acquisition efficiency, we might just unlock a whole new era of AI innovation.
Speaker 1:You know.
Speaker 2:Imagine AI systems that can learn new skills.
Speaker 1:Yeah.
Speaker 2:As easily as humans do. Right Adapting to new situations.
Speaker 1:Yeah.
Speaker 2:Solving problems in ways we can't even fathom right now.
Speaker 1:That's a pretty mind-blowing concept.
Speaker 2:It's exciting.
Speaker 1:And exciting and a little bit daunting.
Speaker 2:I agree.
Speaker 1:At the same time.
Speaker 2:Yeah, but like any powerful technology, ai can be used for good or for ill. It's our responsibility as humans to guide its development in a direction that benefits all of humanity.
Speaker 1:Which brings us back to why this paper and this new way of thinking about AI intelligence is so important.
Speaker 2:Absolutely.
Speaker 1:It's not just about building smarter machines Right. It's about building a smarter future for everyone.
Speaker 2:Well said.
Speaker 1:And that's why I think it's crucial for everyone not just. Ai researchers to really engage in these conversations about the future of AI.
Speaker 2:Absolutely.
Speaker 1:You know, because, whether we realize it or not, AI is already shaping our world in some really profound ways.
Speaker 2:Yeah.
Speaker 1:And as it continues to evolve, it's going to impact all aspects of our lives.
Speaker 2:Absolutely. The more we understand about how AI works, the better equipped we'll be.
Speaker 1:Yeah, to make these informed decisions about its role in society.
Speaker 2:So to our listener. We challenge you to keep thinking critically about these ideas. What are your hopes and fears about the future of AI?
Speaker 1:Yeah, what role?
Speaker 2:What role do you think it should play in our lives?
Speaker 1:Exactly. These are all questions we need to grapple with and, who knows, maybe you'll be the one to come up with the next breakthrough in AI lives Exactly?
Speaker 2:These are all questions we need to grapple with.
Speaker 1:Yeah, and who knows, yeah, maybe you'll be the one to come up with the next breakthrough in AI intelligence.
Speaker 2:Or maybe you'll just be inspired to delve deeper into this fascinating field. That's right Either way. We hope this deep dive has sparked your curiosity Absolutely and left you with a few things to ponder.