Okay, so today we're going to be looking at this paper about GPQA.
And it sounds like it's a pretty interesting way to like benchmark AI.
Yeah, yeah, it is.
It's a really cool new benchmark.
You see a lot of the AI benchmarks kind of focus on things that are, you know,
kind of like easy to find information on like facts or common knowledge.
Right.
But GPQA is different.
It really focuses on these questions that are what they call Google proof.
Google proof.
Yeah.
So we're talking about questions that like, even if you were like the most
hardcore internet sleuth you would have trouble with.
Exactly.
These are questions in like physics, chemistry and biology that are really
meant to be challenging even for people with PhDs in those areas.
Wow.
There are questions that really require deep understanding and reasoning.
So it's not like just being able to like find the answer on Wikipedia.
It's more like actually like understanding it and like applying it.
Exactly.
The researchers wanted to see if AI could go beyond simple information
retrieval and actually like solve problems that even highly skilled
humans find difficult.
That's pretty cool.
Yeah.
I'm already intrigued.
Yeah.
Um, so how did they even go about creating these Google proof questions?
Well, they had a pretty intense process.
First they recruited, you know, PhD level scientists from physics,
chemistry and biology to actually craft the questions.
Okay.
But it wasn't just like those scientists coming up with their own pet questions
or anything.
They had like a whole four stage process to make sure these questions were legit.
Gotcha.
So what was like the first round like?
The first round was all about expert validation.
So other experts would review the questions and make sure they were actually
accurate and challenging.
Makes sense.
Yeah.
Gotta make sure the questions are actually, you know, difficult enough for
the right people.
Yeah.
So what about like round two?
Round two, they took all the feedback they got and like revised the questions
even more.
They really wanted to like make sure these questions were like, you know,
bulletproof, but then around three things got really interesting.
Oh, right.
They took the questions and they gave them to other PhDs, but from different fields.
Oh, so like someone who's an expert in like physics might have gotten questions
that were in chemistry.
Yeah.
And even with like the entire internet at their disposal.
That sounds right.
These PhDs still struggled.
Like the average time they spent per question was over 30 minutes.
Wow.
So that really is a good way to show that these questions were truly Google proof.
Exactly.
It wasn't just about like, you know, looking stuff up.
It was about like really understanding the concepts.
So what did they do with these questions once they had them?
Well, they actually created three different levels of difficulty.
Yeah.
So there's like GPQA extended, which is like the whole enchilada.
Yeah.
All 546 questions.
Okay.
So that's like the ultimate challenge.
Yeah.
Then there's the main set, which is just called GPQA.
And that one has 448 questions.
And these are the ones where at least one of the original experts could answer it.
Okay.
But most of the non experts in round three couldn't.
So this became like the main benchmark for the AI.
Gotcha.
So it's like challenging, but still like possible.
Exactly.
And then finally, there's GPQA diamond.
Ooh, diamond.
So this is where it gets really interesting.
This one's intense.
It has 198 questions that both of the experts aced.
Okay.
But most of the non experts couldn't answer.
So like the super hard ones?
Yeah.
These are like the real brainbusters.
Okay. So we've got like this tiered system of ultra difficult science questions.
Can we like get into some actual examples of what these questions are like?
Sure.
So one of the questions in organic chemistry was about a compound called methylcyclo-pentadine.
And it involved this whole cascade of reactions.
Okay.
And the challenge was to figure out how many possible isomers the final product would have.
But you weren't allowed to count stereoisomers.
I'm already lost.
But I'm assuming that's the point.
Right.
And the answer was 16. Like there are 16 possible isomers.
16. Wow.
Yeah.
That sounds pretty tricky to keep track of.
Even if you have like a background in chemistry, I can see how that would be really challenging.
What other examples are there?
There was also one in molecular biology about a scientist who's trying to create a heat tolerant wheat cultivar.
And it got into all these complex mechanisms of like gene expression and protein synthesis.
Genetics.
Always a fascinating topic.
And then there was also one in astrophysics, right?
Yeah.
This one was about determining a star's surface gravity using the spectral lines of different elements.
So they really covered a lot of ground with these questions.
Yeah.
They wanted to make sure it was like a diverse range of topics.
Yeah.
And all the questions were designed to be like really tricky, not something you could just Google.
Right.
So we've established that these questions are incredibly difficult, even for like highly educated humans.
Yeah.
But the real question is can AI actually solve these?
Well, that's what they wanted to find out.
They tested a bunch of different AI models on these questions.
Okay.
Like which ones?
So they tested Lama 2, GPT 3.5 and even GPT 4.
Oh, wow.
The big guns.
Yeah.
And they tested the AI in two different scenarios.
One where they didn't have access to the internet and one where they did.
So it's kind of like a closed book versus open book test.
Yeah.
I like that analogy.
So what happened?
Did the AI manage to conquer these questions?
Well, the results were pretty interesting.
Even the best model, which was GPT 4 with search capabilities, only got 39% accuracy on the main GPQ A set.
Wow.
So better than those PhDs who weren't experts in the specific field.
Right.
But still nowhere near as good as the experts who actually designed the questions.
Exactly.
And that's what makes this research so fascinating.
It shows us that AI has come a long way, but there's still so much room for improvement.
Yeah.
So AI can access all this information and process it really quickly.
But it still struggles with the kind of like deep understanding and reasoning that humans are really good at.
Right.
And that's one of the key takeaways from this research.
We need AI that can not only like solve problems, but also explain how it got to the solution.
That's a really good point, especially when we're talking about problems that even human experts find challenging.
You want to make sure that the AI isn't just like making lucky guesses or something.
Exactly.
We need to know that it's actually reasoning its way to the answer and that it understands the underlying concepts.
So explainability is really important.
Yeah.
And it's a big challenge right now.
A lot of these really powerful AI models are basically like black boxes.
We know it goes in and we know what comes out.
We don't know what happens in between.
So it's like having a student who gets all the answers right on a test.
Yeah.
But can't explain how they got them.
Yeah.
You start to wonder if they're actually learning or just like cheating somehow.
Right.
And that's not good enough, especially when we're talking about
like scientific research.
Exactly.
Yeah.
We need AI that can explain its reasoning in a way that humans can understand so that we can trust the results.
So this kind of ties into that idea of scalable oversight that you mentioned earlier.
It does.
As AI gets more powerful and starts tackling more and more complex problems.
They need to be able to keep up with it.
Yeah.
We need ways to like monitor and guide these systems.
Even if we don't fully understand the problems ourselves.
It's like sending explorers into uncharted territory.
Right.
We got to make sure they have the tools and the communication systems they need so they don't get lost or do something dangerous.
Exactly.
So it's not just about building a really smart AI.
It's about building a smart AI that we can trust and that we can understand.
And that we can work with.
This research is really helping to lay the groundwork for that.
It's not just about celebrating what AI can do, but also about being realistic about its limitations.
And figuring out how to overcome them.
I think that's a great way to put it.
This research really paints a nuanced picture of AI's potential in science.
It's exciting to see how these systems are already being used to tackle really tough problems.
But it's also clear that we need to be careful.
And we need to make sure that we're developing and using AI in a way that benefits everyone.
Absolutely.
It's a collaborative effort.
It's not just up to the AI researchers to figure this out.
We need scientists, ethicists, policymakers all working together.
That's a good point.
This isn't just a technological challenge.
It's a societal one.
We need to be having these conversations now.
So that we're prepared for a future where AI plays an even bigger role in our lives.
But before we go too far down that road, I want to come back to something you mentioned earlier about the different versions of the GPQA benchmark.
Oh, yeah.
Why did they decide to create three different versions with varying levels of difficulty?
Well, it's a really smart approach, actually, because it allows researchers to test different AI models and different approaches as they become more sophisticated.
So you can start with the easier questions.
And then as the AI gets better, you can move up to the harder ones.
It's like having a series of increasingly difficult obstacle courses for our AI athletes.
They can see how far they can get.
And we can figure out what they're good at and what they need to work on.
And it's also important to remember that this benchmark doesn't just test AI's overall problem-solving abilities.
It also looks at how AI performs across different scientific domains.
So did they find that the AI was better at some types of questions than others?
Like, were the chemistry questions easier than the physics questions or vice versa?
Yeah, they did find some variation.
Even with a really powerful model like GP24, the performance varied depending on things like the specific prompting technique,
whether it had access to internet search, and, yeah, even the scientific domain of the question.
So it's not just about how smart the AI is.
It's also about how we interact with it and how we set it up for success.
Exactly.
And that's one of the most exciting things about this research.
It shows us that there's still so much we can do to improve how we use AI.
And to tailor it to specific tasks.
Yeah.
It's like realizing that AI isn't just one thing.
It's a whole set of tools.
And we need different AI specialists for different jobs.
And this research gives us valuable insights into how we can create those specialists and how we can train them to excel in their fields.
That's awesome.
It also highlights the importance of evaluating AI performance carefully.
Yeah, definitely.
It's not enough to just look at the overall accuracy score.
We need to really dive deep and understand where the AI is succeeding, where it's failing, and why.
Right, because even the most advanced AI can still make mistakes.
Especially when we're talking about complex scientific concepts.
Exactly.
If we're going to trust these systems to help us with groundbreaking research, we need to be aware of their limitations.
And potential biases.
Yeah, for sure.
This paper does a really good job of encouraging a balanced perspective on AI.
I think so too.
It acknowledges the incredible potential of AI, but it also emphasizes the importance of responsible development, rigorous evaluation, and continuous oversight.
Absolutely.
It's like saying, hey, AI is an amazing tool, but let's not get carried away.
We need to use it wiser.
We need to make sure that it remains a force for good in the scientific world.
I couldn't agree more.
So this brings up a really important question.
What does all of this mean for the average person?
That's a great question.
Like if you're not a scientist and you're not an AI researcher, how does this research impact your life?
Well, even if you're not directly involved in those fields.
AI is becoming more and more a part of everyone's lives.
It is.
And this research raises some really crucial questions about the future of knowledge, the role of AI in society, and the importance of critical thinking in a world where technology is becoming increasingly powerful.
So it's like saying that AI is becoming so important that everyone needs to understand it, not just the experts.
Exactly.
We need to be informed citizens.
Right.
We need to participate in these conversations about AI and help shape the future in a way that benefits everyone.
I completely agree.
We all have a stake in this.
We need to make sure that AI is developed and used ethically and responsibly.
Yes.
And in a way that aligns with our values as a society.
This research is a good reminder that we're living in a really exciting time.
We are.
Technology is advancing so rapidly.
Yeah.
And it's up to us to make sure that those advancements lead to a better world.
A more just and equitable world.
Exactly.
But I think we need to pause here and come back for the third and final part of our deep dive.
It's a really good point.
It's like saying that even if AI can access all this information and process it really fast, it still struggles with that deep understanding.
Right.
Like humans are still much better at that.
Yeah.
And that actually leads to one of the other really big takeaways from this research, which is this idea of explainability in AI.
Explainability.
So like even if the AI gets the right answer, we need to be able to understand how it got there.
Exactly.
Especially when we're dealing with problems that even human experts find challenging.
We don't want the AI just making lucky guesses or, you know, taking advantage of some weird quirk in the data.
Right.
We need to know that it actually understands what it's doing.
Exactly.
And that's a big challenge right now because a lot of these really powerful AI models are essentially black boxes.
Black boxes.
So we can see what goes in and what comes out.
Right.
And we really know what's happening in between.
Yeah.
It's like having this brilliant student who aces every test but can't explain how they got the answers.
Oh, I see what you mean.
Like are they actually learning or are they just really good at finding shortcuts?
Exactly.
And in the world of scientific research, that's not good enough.
Right.
We need to be able to trust the results.
Yeah.
We need AI that can not only solve the problems but also explain its reasoning in a way that humans can understand.
So that we can trust it.
And that all ties back to that idea of scalable oversight.
It does.
As these AI systems become more powerful and we start giving them these more and more complicated problems to solve,
we need to make sure that we can keep up.
Like we don't want to get to a point where AI is so advanced that we can't even understand what it's doing anymore.
Exactly.
We need to be able to monitor and guide these systems even if we don't fully understand all the details ourselves.
It's kind of like sending explorers into uncharted territory.
We need to make sure they have the tools and the communication systems they need to stay on track.
Exactly.
So it's not just about building a really smart AI.
It's about building a smart AI that we can trust and that we can understand and that we can collaborate with.
So this research is really laying the groundwork for that.
It's not just about celebrating what AI can do but also about recognizing its limitations and figuring out how to address them.
Absolutely.
And I think that's a really important message to take away from all of this.
It is.
Research paints a really interesting picture of AI's potential in science.
It's exciting to see how these systems are already being used to tackle some really complex problems.
But it's also a reminder that we need to be cautious and make sure that we're developing and using AI in a way that ultimately benefits humanity.
I agree.
It's a collaborative effort.
It's not just up to the AI researchers.
We need scientists, ethicists, policymakers, everyone working together to make sure that AI is used responsibly.
That's a really important point.
This isn't just a technological challenge.
It's a societal challenge.
It is.
And we need to be having these conversations now so that we're prepared for a future where AI plays an even bigger role in our lives.
Right.
We need to think about the ethical implications of AI and develop guidelines for its responsible development.
And make sure that human oversight remains a top priority.
Exactly.
AI should be a tool that empowers humans, not a tool that replaces them.
It's about collaboration, not competition.
I agree.
So before we get too philosophical here, I want to come back to something you mentioned earlier about the different versions of the GPQA benchmark.
Oh, yeah.
The fact that they created three different versions with varying levels of difficulty.
Can you talk a little bit more about why they did that?
Sure.
It's actually a really clever approach because it allows researchers to test different AI models and different approaches as the AI gets more sophisticated.
Okay.
So you can start with the easier questions and then as the AI improves, you can move up to the harder ones.
So it's like having different levels of difficulty for our AI athletes.
Exactly.
You can see how far they can go and identify their strengths and weaknesses at each level.
I like that analogy.
And it's important to remember that this benchmark isn't just testing AI's overall problem-solving abilities.
It's also looking at how well it performs across different scientific domains.
Right.
So we find that the AI was better at certain types of questions than others.
Like were the chemistry questions easier than the physics questions, for example?
Yeah, they actually did find some variation in performance.
Even with a really powerful model like GPT-4, the accuracy vary depending on things like the prompting technique they use,
whether it had access to internet search and even the specific scientific domain of the question.
So it's not just about how smart the AI is.
It's about how we use it and how we set it up for success.
Yeah.
And that's one of the exciting things about this research.
It shows us that there's still so much we can do to improve how we use AI and how we tailor it to specific tasks.
It's like realizing that AI isn't just one thing.
It's a whole toolkit.
Exactly.
And we need different AI specialists for different jobs.
Right.
And this research is helping us figure out how to create those specialists and how to train them to excel in their respective fields.
It also highlights the importance of carefully evaluating AI performance.
Yeah, for sure.
It's not enough to just look at the overall accuracy score.
We need to dig deeper and understand where the AI is succeeding, where it's failing, and why.
Exactly, because even the most advanced AI can still make mistakes, especially when it's dealing with really complex scientific concepts.
And if we're going to rely on these systems to help us with important research, we need to be aware of those limitations.
And potential biases.
This paper really emphasizes the need for a balanced perspective on AI.
It does.
It acknowledges the incredible potential.
But it also stresses the importance of responsible development, rigorous evaluation, and continuous oversight.
I think that's a really important message.
It's like saying, hey, AI is an amazing tool, but let's not get carried away.
We need to use it wisely and make sure it remains a force for good in the scientific world.
I agree.
It's about finding that balance between pushing the boundaries of what's possible and making sure that we're doing it in a way that benefits humanity as a whole.
So all of this leads to a really important question.
What does this research mean for the average person?
That's a great question.
Like if you're not a scientist or an AI researcher, how did this research impact your life?
Well, even if you're not directly involved in those fields, AI is becoming increasingly integrated into our lives.
It's everywhere.
It is.
And this research raises some really important questions about the future of knowledge, the role of AI in society, the importance of critical thinking in a world where technology is becoming more and more powerful.
So it's like saying that AI is becoming so important that we all need to understand it.
Exactly.
Not just the experts.
We all need to be informed citizens so we can participate in these conversations and help shape the future of AI in a way that benefits everyone.
I couldn't agree more.
We all have a stake in this.
We need to make sure that AI is developed and used ethically and responsibly and in a way that aligns with our values as a society.
I agree.
This research is a reminder that we're living in a really exciting time.
Technology is advancing so rapidly and it's up to us to make sure that those advancements lead to a more just and equitable and sustainable world.
Absolutely.
It's up to all of us to guide these advancements in a positive direction.
I think that's a perfect place to pause for now.
We'll be back for the third and final part of our deep dive after a quick break.
Yeah, it really is amazing to think about how quickly this field is moving.
It wasn't that long ago that the idea of AI solving complex scientific problems seems like pure science fiction.
Right.
It's incredible to see how far we've come.
And this research really gives us a glimpse into what might be possible in the future as AI continues to evolve.
Yeah, I mean imagine a future where AI is helping us unlock the mysteries of dark matter or designing life-saving drugs.
Or even coming up with like whole new theories of physics that like change our understanding of the universe.
Exactly.
The possibilities are really mind-blowing.
But of course with all this potential comes a huge responsibility.
Oh, definitely.
Yeah.
We have to make sure that we're developing and using AI in a way that benefits humanity, not in a way that harms us.
Absolutely.
We need to be having open and honest conversations about the ethical implications of AI.
And we need to establish clear guidelines for its development and use.
And we can't forget about human oversight.
Right. Human oversight is crucial.
AI should be a tool that empowers us.
Not a tool that replaces us.
It's about collaboration, not competition.
Exactly.
And that's something we need to keep in mind as we continue to explore the potential of AI in scientific discovery.
Well, this has been a really fascinating deep dive into the world of AI and its potential to revolutionize scientific research.
It has.
It's clear that we're just at the beginning of this journey.
But it's an exciting and challenging one.
And I'm really curious to see where it takes us.
Me too.
I think this research is a great reminder that AI has incredible potential.
But it's up to us to guide its development and ensure that it's used responsibly to benefit humanity and to expand the frontiers of knowledge.
So to wrap things up, I want to leave you with one final thought.
If AI can help us solve problems that we're currently struggling with, what new questions might it help us ask?
Hmm. That's a really interesting question.
What mysteries might it uncover that we haven't even thought of yet?
I think that's a question that will drive a lot of research and exploration in the years to come.
And I, for one, am excited to see what we discover.
And on that note, we'll leave you to ponder that thought.
Thanks for joining us for this deep dive into the world of AI.
Until next time, keep exploring, keep learning, and keep asking those big questions.