All right, strap in everyone, because today we're taking a deep dive into some really
cool new AI research.
Oh yeah.
Yeah, this one is from OpenAI.
And it's, well, it's making some serious waves in the world of AI safety.
I'm intrigued.
What is it?
Well, they're calling it Deliberative Alignment.
And it's basically like, it's all about making AI models think before they act.
You know, like really think through those safety guidelines before they go and answer
your requests.
Like giving them a moment to pause you mean.
Exactly.
Like giving them time to think about, you know, the consequences, what they're about to say.
I see.
So kind of like adding in a layer of ethical reasoning.
Right.
Exactly.
Because even with all the safety measures they have in place, you know, like fine tuning
with human feedback.
Right.
AI can still trip up sometimes.
It can, it can.
Like they might accidentally generate harmful content or, you know, or refuse a perfectly
fine request.
Yeah.
And the worst is when they get tricked into misbehaving.
Ugh.
Those shell breaks, right?
Yeah.
Those are no good.
And this paper, this research, it points out two big reasons why this happens.
Okay.
What are they?
First, well, current models often have to respond like instantly, right?
Maybe do.
Yeah.
Even in those really complex situations where, where more thought is needed.
Right.
And second, well, they learn about safety in this roundabout way from, you know, like
tons and tons of labeled example.
Indirectly.
And instead of directly learning the actual safety rules.
That makes sense.
So it's a quantit over quality thing.
Yeah.
Kinda.
Okay.
So how does deliberative alignment change that?
Well, it's all about, you know, teaching the AI to actually reason through those safety
rules.
Interesting.
Like almost as if they have, you know, a mental checklist.
Okay.
Yeah.
Before generating any response.
So I'm picturing this, like if I try to be sneaky and ask the AI something that's,
you know, not allowed.
Right.
How would deliberative alignment stop me?
So imagine, let's say you try to be super sneaky and hide your request in code or something.
The AI, you know, trained with this deliberative alignment, it decode the message and then
it would realize, wait a second.
This is a trick.
I better check the safety policy.
Yeah.
And then, you know, say, nope, not going to do it.
The researchers actually have this great example of it in the paper.
They use figure one.
I'll have to check that out.
Pretty cool.
So it's like the AI is literally stopping to think, you know, like, hold on a minute.
This seems fishy.
Right.
I better check my rules before I do anything.
It's pretty impressive.
I mean, how do they actually make it work though?
Well, they use a two stage training process.
Okay.
So in the first stage, the AI learns to think about safety.
Right.
They use a method called context distillation.
Context distillation.
Yes.
So, you know, basically they feed it like safety rules and examples of prompts.
Okay.
And they ask it to come up with responses that take those rules into account.
So it's learning like the rules of the road.
Yeah, exactly.
But, but here's the thing.
Where do those initial responses come from?
Hmm.
Well, I'm assuming from human trainers or something.
Actually, no, it's way more interesting than that.
They use another AI model.
Another AI model.
One that's trained like just for helpfulness.
Okay.
Not for safety.
Yeah.
To generate those initial responses.
So one AI is what?
Training another AI to be safe.
Yeah.
That's wild.
It is.
So at this stage, they use reinforcement learning, you know, to fine tune the AI's reasoning
skills.
Right.
So they use this like judge AI.
Okay.
And this judge AI, it has like access to all the safety specs.
So it's kind of like overseeing the training.
Yeah.
And it evaluates the responses from the AI that's learning.
Right.
And then it provides feedback.
Like, you know, okay, you almost had it, but let's work on your reasoning a bit more.
I like that.
And then the AI is back and forth a constant improvement process.
Exactly.
And this two step process, it helps the AI not only understand the rules, but also learn
how to apply them in, you know, different situations.
Got it.
So the big question now is, does this actually work?
Well, that's what I was going to say.
They tested this whole approach on open AI's new O-Series models.
Okay.
The O-Series.
Yeah.
And the results are pretty impressive.
Oh, really?
Let's hear it.
What kind of improvements did they see?
Well, across a bunch of safety benchmarks, these new models, they showed, you know, significant
improvements.
Significant?
Yeah.
Like, especially when it came to, you know, those jail breaks we were talking about earlier.
Yeah.
All right.
Turns out these O-Series models are way harder to trick.
Oh, that's good.
They're much harder to trick into breaking the rules.
That is definitely reassuring because, you know, we've all seen those headlines about
AI going rogue.
We have.
Right.
And if anyone could just manipulate these, you know, these powerful models into doing
something harmful.
Exactly.
And on top of that, they saw a huge reduction in what they call over-refusals.
Over-refusals?
What's that?
So it's like when the AI refuses a perfectly good request, you know?
Yeah.
Because it's being, like, overly cautious.
I see.
Yeah, I see.
Like, refusing to translate a phrase because it's got a word that might be associated
with a sensitive topic.
Right.
Even if, like, the user's intention is totally harmless.
Yeah, it's being a little too uptight.
Right.
Missing the context.
Yeah, missing the context.
And the research found that these O-Series models, they were, like, way less likely to
do that.
That's great.
They were much better at understanding the context, you know, and applying those safety
rules appropriately.
So it's actually using its judgment instead of just having a knee-jerk reaction.
Right.
And then, like, the refuse button every time whenever something seems, you know, even a
little bit sensitive.
Yeah.
So it's finding that balance between being safe but still being useful.
Exactly.
That's a huge deal because, I mean, what's the point of having an AI assistant if it's,
like, constantly shutting down conversations?
Totally.
You want it to be helpful, not a roadblock.
Exactly.
So it sounds like this deliberative alignment, it's helping these models find that, you
know, that sweet spot.
Yeah.
And there's another interesting finding.
The researchers found that the amount of time the AI had to think actually made a difference
in its safety performance.
Wait, really?
So giving it, like, more time to process.
Yeah, more time to process.
Actually made it safer.
But it's safer.
That's counterintuitive.
It is.
But their findings, they suggest that the O-Series models do better on those really tough, like,
safety challenges when they have more time to think.
So it's not just, like, about how much the AI knows.
It's about how it uses that knowledge.
Exactly.
It's like, you know, if you give a student enough time to, like, really think through
a problem.
Yeah, they're going to come up with a better solution.
Right.
They're more likely to get to a well-reasoned solution.
Exactly.
And that really highlights the power of this chain of thought reasoning that deliberative
alignment uses.
It's like, the AI is, it's not just spitting out an answer.
It's actually thinking through those implications before it acts.
This is really cool stuff.
And it sounds like, you know, it could have some huge implications for the future of AI
safety.
Yeah, it really does.
I mean, it's almost like a fundamental shift in how we're approaching AI safety.
It's not just about adding more rules and restrictions.
It's about changing the way AI thinks.
Exactly.
It's about teaching them to think about safety.
About safety, yeah.
Which is really important because, let's be honest, rules can be broken.
They can.
But if the AI understands why certain actions are harmful, like if it has, like, its own
sense of responsibility, then it's going to be, like, way harder to manipulate.
Exactly.
It's like the difference between teaching someone to just blindly follow orders versus,
like, teaching them to actually understand the values.
Right, the reasoning behind it.
And the reasoning.
Yeah, yeah.
That's a great point.
And it makes me think about, you know, those jailbreak attacks again.
Like it seems like this deliberative alignment is, it's giving AI a much stronger defense.
It is.
Against those exploits.
Yeah, because we're essentially teaching it to reason.
We're giving it a more robust, like, way to protect itself from malicious actors.
And it's not just about, you know, preventing harm.
Right.
It's also about making sure that the AI can be, like, helpful.
Right, right.
In a wider range of situations.
Yeah.
Without being too cautious.
Exactly.
Like those over-refusal cases.
Yes.
So, deliberative alignment seems to help them find that sweet spot.
Yes, exactly.
Like, finding that balance.
That balance.
Where it can navigate, you know, complex conversations.
Yeah.
And, like, assist with sensitive topics.
Right.
Without shutting down entirely.
Totally, totally.
It's about giving them the tools to make those, like, nuanced judgments.
Right.
Based on the context.
Okay, so we've talked a lot about the benefits, right?
Yeah, we have.
But part of me, I have to admit, is still a little wary.
Can we really trust, you know, AI to make these ethical decisions?
I mean, that's a great question.
It's a big question.
And it's one that we, like, need to approach with a lot of thought and care.
Yeah.
I think it's important to remember that, you know, AI systems, they're created by humans.
They reflect the values and the biases of their creators.
Right.
So it's not just about the AI itself.
It's about the people behind it.
Yeah, the people developing it, deploying it.
We need to make sure they're doing things, you know, ethically and responsibly.
Exactly.
And that's why research like this is so crucial.
It is.
You know, it's not just about making AI safer.
It's about, like, starting this bigger conversation.
Yeah.
About the ethics.
About the ethics of it all.
Of AI and, like, our role in shaping its future.
You know, speaking of the research, there was one finding I thought was really fascinating.
Oh, yeah.
It's the impact of thinking time on safety performance.
Thinking time.
Yeah.
It's almost like giving the AI a chance to, like, to pause.
Yeah, to reflect.
To reflect.
Yeah.
Before it acts.
Yeah.
It's a tough spot.
Exactly.
And they found, you know, the researchers found that when they gave the models, the O-Series
models, more time to process a request, they actually did better on those tricky safety
challenges.
So, you know, it suggests that slowing down the process can actually lead to better outcomes.
So it's not just about how fast it can respond.
No, not at all.
It's about how effectively it uses that time to, like, reason through things.
To think it through.
Yeah, the consequences of its actions.
Absolutely.
And that's, you know, that's a big shift from how we've traditionally thought about AI.
It is.
Where it's all about, you know, speed and efficiency.
Yeah, yeah, yeah.
Always.
It makes you wonder if by rushing it, we've actually been, like, limiting its capacity
for ethical reasoning.
Maybe, maybe, you know, by giving it that space to think, we can unlock something new.
Yeah, like a whole new level.
A whole new level of responsibility and awareness.
It's a really interesting thought.
It is.
Like, maybe we've been, you know, underestimated.
Estimated.
AI's potential.
Yeah.
For ethical decision-makers.
Because we're so focused on speed.
Right, right.
That we're not giving it the space to actually deliberate.
It's like, it's like we've been trying to train a sprinter when maybe it should be a
marathon runner.
Yeah, yeah, that's a great analogy.
Start blowing down, helps you, you know, see things more clearly.
Absolutely.
And make better choices.
And that, that actually leads us to another really cool finding from this research.
Oh yeah.
What is that?
Their ability to generalize, like, these O-series models, they can take their safety knowledge
right and apply it to new situations, like things they haven't even been trained on.
Yeah, that was a big one.
Yeah, they tested this, you know, with non-English languages, they used encoded data.
And it still worked.
It worked.
It was amazing.
Which suggests that, like, they're not just memorizing the rules.
They're actually learning to reason about it in, like, a more fundamental way.
Exactly.
It's not rote memorization.
It's about understanding those principles.
And being able to apply them flexibly.
So it's not just about, you know, teaching them to follow the rules.
Yeah.
It's teaching them to, like, understand why.
The why, yeah.
The reasoning behind it.
So that they can adapt to new situations, new challenges.
Exactly.
That's really impressive.
It is.
And that ability to generalize, you know, that's going to be so important.
For AI safety moving forward, right?
In a world that's constantly changing.
And where we're facing new ethical dilemmas all the time.
Right.
And I think that, you know, beyond just preventing harm.
Yeah.
Beyond just preventing harm.
Imagine, like, the possibilities in fields like, like, healthcare.
Healthcare.
Education.
Yeah.
Or even, like, law enforcement.
Absolutely.
If AI can learn to reason this well about safety and ethics.
Right.
I mean, it could totally change how we approach decision making in all of those fields.
Imagine, like, an AI system that could, you know, assist doctors.
Yeah.
And making these, like, life or death decisions.
Right.
Taking into account not just, you know, medical history.
Yeah.
But also, like, their ethical considerations and personal values.
Exactly.
Or think about, like, an AI tutor.
Yeah.
That could, like, personalize education.
Right.
For each student, you know.
Making sure the content is engaging and ethical.
Yeah.
That's amazing.
The possibilities are.
They're so exciting.
They are.
But I think it's important, as with any powerful technology, to proceed carefully.
Right.
You know, to make sure that AI development is guided by ethical principles.
Absolutely.
And commitment to human well-being.
I couldn't agree more, you know.
As we're pushing these boundaries of AI, it's crucial that we put safety first.
We have to.
Responsibility, transparency, and, you know, making sure that AI is a force for good in
the world.
A force for good.
Well said.
So, on that note, I think that wraps up our deep dive.
Yeah.
Into deliberative alignment.
It has been a fascinating one.
It really has.
You know, it's given us this glimpse into a future.
Yeah.
Where AI is not only powerful, but also, you know, responsible.
Yeah.
Ethical and aligned with human values.
And an exciting future to imagine.
It is.
And it's one that we all have a role in shaping.
You do.
So, we've reached the end of our deep dive for today.
Thanks for joining us on this exploration, you know, of this cutting-edge AI safety
research.
It's been a pleasure.
We hope this episode has maybe sparked some curiosity.
Yeah, I hope so.
And got you thinking more deeply about the ethical implications of AI.
Ethical implications.
That's key.
So, as always, we encourage you to keep exploring these questions.
Yeah, keep thinking about it.
And stay engaged in the conversation about, you know, the future that we want to build
with AI.
That's right.
Until next time.
Welcome back, everyone.
I think we were talking about how this deliberative alignment is helping AI get smarter and safer.
Yeah.
You know, the more I think about this research, the more it seems like a fundamental shift.
I know, right?
In how we're approaching this whole, you know, AI safety thing.
Yeah.
It's not just about adding more and more rules and restrictions.
No, it's not.
It's about actually changing how the AI thinks about safety.
That's a great way to put it.
Yeah.
Right.
It's like we're moving from, you know, telling the AI, don't do this or don't do that, to
actually explaining why certain actions are harmful and helping it develop its own, like,
sense of responsibility.
Exactly.
And that's so important because, I mean, rules can be broken, right?
They can, of course, yeah.
But if the AI understands, you know, those underlying principles, it's going to be a
lot harder to manipulate or trick it into doing something it shouldn't.
That's the hope at least.
Right.
Yeah.
It's like, it's kind of like a...
It's like the difference between teaching someone to blindly follow orders.
Blindly.
Versus teaching them, like, you know, to understand the values.
The values behind it.
The reasons behind those orders.
Yeah.
Perfect analogy.
But those jailbreak attacks again.
Oh, yeah.
The jailbreak stuff.
It seems like deliberative alignment is giving AI a much stronger defense.
They think so.
Yeah.
Against those exploits.
Absolutely.
Yeah.
Because we're essentially teaching it how to reason through things.
So we're giving it, you know, a way more robust, flexible way to protect itself from
bad actors, essentially.
And it's not just about preventing harm, is it?
No.
No, not at all.
It's also about making sure that the AI can be helpful.
Helpful, yeah.
You know, in a wider range of situations.
In more situations.
Without being overly cautious.
Yeah.
Without being overly cautious.
Right.
Exactly.
Think about those over-refusal cases we talked about earlier.
Deliberative alignment is really helping them to find that balance.
Yeah.
Between, you know, being safe but still being useful.
Yeah.
And that sweet spot.
It's that sweet spot.
Where the AI can, you know, navigate these complex conversations and, like, assist with
sensitive topics.
Right.
Without, you know, just shutting down completely.
That shutting down entirely, yeah.
Right.
Yeah.
It's about giving the AI the tools and the training to be able to make those judgment
calls.
Those nuanced judgments.
The nuanced judgments, yeah, based on, you know, the context and the rules.
Yeah.
Okay.
So we've talked about, you know, the benefits.
Yeah, we have.
But I gotta say, I'm still a little wary.
I understand that.
You know, can we really trust AI to make these kinds of ethical decisions?
I mean, that's a big question.
And I don't, you know, I don't think there's an easy answer.
Right.
But I think, you know, it's important to remember that AI systems, they're created by humans.
Right.
And they reflect the values and the biases of their creators.
So it's not just about the AI itself.
No.
It's about, you know, the people who are developing it.
People developing it, deploying it.
Yeah.
Deploying it.
Making sure that they are, you know, operating ethically and responsibly.
Right.
Exactly.
And that's why, you know, research like this.
Research like this, yeah.
Is so crucial.
Absolutely.
Because it's, it's sparking that conversation about the ethical implications of AI and our
role in shaping it.
You know, and speaking of the research, there was one finding I thought was really interesting.
Oh yeah.
Which one?
It was the impact of thinking time.
Thinking time.
On safety performance.
Oh, that's a great point.
Yeah.
You know, it's almost like giving the AI like a moment to pause.
A moment to pause.
To reflect.
Yeah.
Just like, like we would.
Before it acts.
Right.
Before it acts.
Exactly.
Like we would in, you know, a challenging situation.
A challenging situation.
And the, and the researchers, they found.
You found.
That, you know, when they gave those O-Series models more time.
More time.
To like process a request.
To think it through.
They did better on those really difficult challenges.
They did.
Right.
Which, which is kind of amazing.
It is.
It suggests that, that slowing down the process.
Yeah.
Can actually lead to, you know, safer outcomes.
So, so it's not just about how fast the AI can respond.
No, not at all.
It's about like how effectively it uses that time.
Uses that time.
To, to reason through.
To reason through.
To the potential consequence.
Right.
Exactly.
Yeah.
And that's, that's a big difference.
Yeah.
You know, from how we traditionally view AI.
It is.
Where speed and efficiency are, you know, everything paramount.
It makes you wonder if, if, you know, by rushing AI.
By rushing it.
Yeah.
We've been like limiting its capacity.
Yeah.
For ethical reasoning.
Right.
We might be, we might be underestimating AI's potential for ethical decision making
because we're so focused on speed.
It's like, it's like we've been trying to train it to be a sprinter.
A sprinter.
When maybe it's better suited to be like a marathon runner.
Yeah.
When I'm slowing down.
Yeah.
Helps you see things more clearly.
Yes.
Make better choices.
And that, that actually brings us to another really interesting finding from this research.
It does.
Which is, the ability of these models.
Yeah.
To generalize their safety knowledge.
In new situations.
Yeah.
They tested this, you know, with, with different languages.
Yeah.
They used encoded data.
And it worked.
It worked.
It was amazing.
Which, which suggests that like they're not just, you know, memorizing rules.
No, they're, they're learning.
They're learning.
You're learning.
To reason about safety in a, in a much more, uh, a fundamental way.
In a more fundamental way.
Yeah.
Right.
It's about equipping the AI with that, with that deeper understanding of ethical principles
that it can apply flexibly.
So it's not just about, you know, teaching it to follow the rules.
No.
It's, it's teaching it to understand the reasoning.
The reasoning.
Behind those rules.
So that it can adapt, right.
So it can adapt.
So it can adapt to new situations, new challenges.
Now that's impressive.
That is impressive.
And, and that ability to generalize, that's going to be so important.
That can be critical.
Yeah.
For ensuring AI safety.
Yeah.
You know, in a world that's constantly changing.
Yeah.
You know, presenting all these, these new ethical dilemma.
Ethical dilemmas.
Yeah.
Right.
Speaking of, speaking of new challenges, it makes me think about, you know, the potential
applications of this.
Oh yeah.
The application.
Beyond just preventing harm.
It's just preventing harm.
Yeah.
And there's possibilities in fields like healthcare.
Yeah.
Education.
Even like law enforcement.
Law enforcement.
Yeah.
If, if AI can learn to reason this well.
Right.
I mean, it could revolutionize how we, how we approach all these really complex decisions.
Imagine an AI system that could like, you know, help doctors.
Help doctors.
Yeah.
With these, you know, life or death decisions.
Yeah.
Taking into account, you know, not just medical history.
Not just medical history.
But also like ethical considerations.
Ethical considerations.
Personal values.
Right.
Or, or think about an AI tutor.
An AI tutor.
Yeah.
Could like, personalize education.
And personalize education.
That's right.
For each student.
I could do it.
For each student.
Yeah.
Making sure that the content is, is not only engaging.
Thank you.
But also ethically sound.
Ethically sound.
Yeah.
It's, it's amazing to think about.
Right.
The possibilities are, they're so exciting.
They are exciting.
Yeah.
But, but of course, you know, as with any powerful technology, we, we got to be careful.
We do.
We need to make sure that, you know, AI development is, is guided by those ethical principles.
Guided by ethical principles.
You know, that commitment to human well-being.
Human well-being.
That's right.
I couldn't agree more.
Yeah.
As we, as we push these boundaries of AI, it's, it's crucial that, that we prioritize
safety.
Prioritize safety.
Yeah.
Responsibility.
Transparency.
You know, making sure that AI is, is a force for good.
A force for good in the world.
Yeah.
Well said.
On that note, I think we should take a quick pause.
That's good.
Gather our thoughts.
And when we come back, we'll, we'll dive into the, the broader implications of this research.
Yeah.
And what it might mean for the future of AI.
The future of AI.
Stay tuned.
Welcome back everyone. I'm ready to keep unpacking this research.
How about you?
Definitely.
This deep dive into deliberative alignment.
You know, it's really got me thinking about the future of AI.
Me too.
Like, you know, a future where AI isn't just powerful, but also like, you know, responsible.
Responsible.
Ethical.
Yeah.
And really aligned with, you know, with human values.
It's a, it's a really exciting possibility.
It is.
And I think this research, you know, this deliberative alignment.
Yeah.
It's just a really solid foundation to build that future on, you know?
Absolutely.
And as we've talked about, it's showing like real promise in tackling those big AI safety
challenges, right?
It is.
Yeah.
Like those, those pesky jailbreak attacks.
Yeah.
This is jailbreaks.
And, and the problem of, you know, AI being a little too cautious.
Right.
The over refusal.
The over refusal, exactly.
Yeah.
It seems like, you know, giving AI this ability to actually reason through those safety guidelines
before it takes action rather than just, you know, reacting based on, on pre-programmed
rules.
Yeah.
It's, it's really a game changer.
It is.
It's like, we're giving it the tools.
The tools.
To make those more, you know, informed decisions.
Get formed.
Yeah.
Those nuanced judgments.
Guaranteed judgments, exactly.
And what I find really fascinating is, is that this ability.
Yeah.
To reason about safety.
It's, it's not just limited to those specific situations, right?
Right.
That the AI has been trained on.
Right.
The research showed that these models, they can actually generalize their knowledge.
Yeah.
To new situations.
To new and unfamiliar challenges.
Unfamiliar challenges even in, in different languages.
Different languages, yeah.
Or with like encoded data.
Right.
That's, that's huge.
It is huge, which, which means, you know, we're not just teaching them to follow the
rules.
Yeah.
No.
We're, we're teaching them to understand.
The reasoning behind it.
The reasoning behind those rules, which is, which is amazing.
It is.
And that's what makes this approach so, so promising.
It does.
You know, it suggests that we're not just building rule followers.
Right.
We're building AI that can adapt.
Yeah.
To new situations, new ethical dilemmas.
So as, as we wrap up this deep dive.
Yeah.
I'm curious to hear, you know, your thoughts on the big picture.
Okay.
Yeah.
Like where do you see this research going?
What are some of the possibilities.
Yeah.
That this, that this deliberative alignment opens up.
Well, I think, you know, one really exciting possibility is, is the potential for AI.
Yeah.
To, to become a more active partner in ethical decision making.
So, so instead of just, you know, passively following rules.
Yeah.
Right.
AI could actually contribute to the discussion.
Yeah.
Contribute to ethical debates, help us navigate these, these really complex dilemmas.
Wow.
That's, that's a powerful thought.
It is a powerful thought.
You know, think about fields like, like bioethics.
Yeah.
Environmental policy, even like international relations.
I mean, AI could, could really.
It could really help us weigh those different perspectives.
Right.
Think about the consequences, you know, and, and arrive at more ethically sound decisions.
That's incredible.
But, but of course with any powerful technology.
Of course.
There are risks, right?
There are risks.
There are challenges.
Yeah.
For sure.
That we need to be aware of.
We need to be very mindful of those.
The AI becomes, you know, more sophisticated, more integrated into our lives.
Integrated.
Yeah.
And we have like good systems in place for accountability.
Accountability.
Over oversight, you know.
Right.
We can't just blindly trust these systems.
No, no, we can't.
We need to be able to like understand.
How they're making decisions.
Valid and making decisions.
And, and hold them accountable.
Accountable for their actions.
Yeah.
Transparency is key.
Transparency is absolutely key.
Right.
Like we need to be able to see how.
To look under the hood.
Yeah.
Look under the hood.
See how it's, how it's getting to those conclusions.
Especially when those conclusions have, you know, ethical implications.
Yeah.
Absolutely.
And, and we also need to be aware of, you know, the potential for bias.
Bias.
Yes.
That's a big one.
Right.
These, these systems, they're trained on data created by humans.
Created by humans.
And that data can reflect, you know, existing societal biases.
And that can be, you know, really harmful.
It can be.
Yeah.
And we need to be vigilant in making sure that these systems are fair.
Fair.
Equitable.
And they're not perpetuating harmful stereotypes.
Absolutely.
It's, it's a lot to think about.
It is a lot to think about.
Yeah.
But it's, it's clear that AI safety, you know, it's not just a technical challenge.
No, no, it's not.
It's a human one.
It's a deeply human challenge.
It is, it is.
And it requires, you know, collaboration.
Collaboration.
Careful consideration.
From all of us.
You know, researchers, developers.
The policymakers, the public, everybody.
Everybody.
Yeah.
Well, this deep dive into deliberative alignment has been, it's been really eye-opening.
It has.
You know, it's given us this glimpse into a future where AI is not just, you know, powerful,
but also.
But also responsible.
Responsible, ethical.
Oh, ethical, yeah.
And truly aligned with, with those human values.
It's a, it's a future worth striving for.
It is.
And it's one that, you know, as you said, we all have a role in shaping.
We all do.
Well, that brings us to the end of our deep dive today.
It does.
Thank you all so much for joining us on this, you know, expeleration of, of this really
cutting edge AI safety research.
Always a pleasure.
We hope this episode has, you know, maybe sparked your curiosity.
It sparked your curiosity.
And got you thinking more deeply about these, these really important ethical implications
of AI.
Yeah.
Because it's a conversation we all need to be a part of.
It is.
And always we encourage you to, to keep exploring these questions, you know.
Speak, learn and keep thinking.
Keep learning, keep thinking and, and stay engaged in this conversation.
Stay engaged.
Yeah.
About the future that, that we want to build with AI.
That's right.
Until next time.
Thanks a lot.