All right, everyone get ready because today we are taking a deep dive into this new open AI paper.
Oh yeah, this one's a good one.
All about keeping those large language models.
You know, LLMs on the straight and narrow.
Yeah, keeping them safe.
Safe, exactly.
It's called Rule-Based Rewards for Language Model Safety.
And the best part is we're going straight to the source with excerpts from the paper itself.
I love it when we do that.
Me too, I think it's fun.
So this paper really digs into a problem that's been a thorn in the side of AI for a while now.
Okay.
You know how LLMs are getting so incredibly good.
At so many things.
At things like writing and translating, but sometimes they can say things that are...
A little.
A little off-color, shall we say.
Yeah, it's like that friend who always puts their foot in their mouth.
Exactly, and the old ways of fixing this, like having humans constantly checking, correcting them,
it's just not feasible.
Yeah, super time consuming.
Way too expensive.
So this paper's got a new method that's way more efficient.
And gives us a lot more control over how these models behave.
Okay, so spill the beans.
What's the secret?
Yeah, what's the secret sauce?
It's called Rule-Based Rewards, or we can just call them RBRs for short.
RBRs, okay.
And it's all about setting very specific rules for what the AI should and shouldn't say.
Okay, like boundaries.
Boundaries, exactly.
So imagine you're teaching a chatbot to politely refuse requests for things that are dangerous
or illegal.
Right, I'm with you.
Okay, so with RBRs, you break down that polite refusal into clear rules.
Like what?
Like, apologize, don't be judgmental, and clearly state you can't do that.
So it's basically like giving the AI a code of conduct.
It's like a digital etiquette guide, yeah.
I like that, a digital etiquette guide.
And here's where things get really interesting.
Okay.
They actually use another AI.
Whoa.
Almost like a robot judge to score how well the chatbot is following those rules during
its training.
So AI judging AI.
Exactly, it's like AI inception.
That is wild, and that means we don't need humans constantly supervising.
That's the idea.
This AI judge gives feedback, so we don't need humans hovering over it all the time.
I mean, that makes it much more efficient.
Exactly.
And I guess more consistent too.
You got it way more consistent.
And here's another cool thing about RBRs.
Yeah.
They're really easy to update as the world changes, you know, as our ideas about safety evolve.
Oh, that's important because things change so quickly these days.
Things change so fast, yeah.
So let's say some new kind of harmful content pops up online.
Like DeepFix or something.
Yeah, like DeepFix, exactly.
Well, with RBRs, you can just add new rules or tweak the old ones to keep the AI in check.
So it's not just a one-time fix, it's like a constantly evolving system.
It's like a living, breathing set of guidelines.
I like that.
So it sounds good in theory, but how well does this actually work in practice?
You know, they got some pretty impressive results.
Yeah, they put it through the ringer.
They tested these RBRs in all kinds of scenarios.
And one example they gave was training a chat bot to respond appropriately to requests for advice on self-harm.
Oh, wow.
That's a tough one.
That's a sensitive area.
You really need the AI to handle that carefully.
Absolutely.
You can't just have it spitting out generic advice.
You don't want canned responses.
An early version of their system tended to just recommend calling a US suicide hotline.
Right, which isn't helpful for someone in another country.
Totally useless for someone outside the US.
So how did the RBRs actually help in this case?
Well, they were able to refine the system so it acknowledges the user's distress,
but doesn't actually give harmful advice.
So it's learning to be more empathetic?
More empathetic.
It offers more general support and guidance.
Wow.
So it avoids those potentially harmful responses, but it's still engaging with the user.
It's still there for them.
That's a pretty big step forward in making these models safer.
It's a huge step.
And they said that it helped reduce something called over-refusals.
Oh, yeah.
Over-refusals.
What is an over-refusal?
So that's when the chat bot becomes overly cautious and refuses to do anything.
Even if it's harmless?
Even if it's totally harmless.
It's like that friend who always says no to every invitation.
Right, even if you're just asking them to hang out and watch a movie.
Exactly.
You just want to watch a movie.
And you want the AI to be safe, but not so locked down that it can't hold a conversation.
Right.
It still needs to be useful.
Still needs to be useful.
And the RBRs seem to help find that sweet spot.
So it's that balance between safety and functionality?
Finding that balance.
And what I really like about this approach is that it seems to give us more control than we had before.
Way more control.
We're not just giving a thumbs up or thumbs down on a response.
Yeah, we're giving very specific instructions.
We're giving instructions.
We're saying this is how you should behave.
So it's almost like we're teaching the AI good manners.
Yeah, like teaching it how to be a responsible conversational partner.
Exactly.
So it sounds like a major win for AI safety, but I'm sure there are some limitations to this approach.
Well, of course, no system is perfect.
One thing the researchers point out is that RBRs work best when you can clearly define the rules.
When the rules are very objective.
Very objective.
Yeah, like if the desired behavior is really subjective, it might not be as effective.
I see.
So like if you're trying to teach an AI humor.
Like how to be funny.
Yeah, that's really hard to define with specific rules.
Exactly.
Humor is so subjective.
It's subjective and it changes depending on the person and the context.
Right.
So it's not like this is a silver bullet solution that will solve every AI challenge.
It's not a magic wand, but it's a very powerful tool when used in the right situations.
So this is all super fascinating.
It is.
I am so eager to dig into the details of their experiments.
Yeah, let's look at some of the specifics.
Well, let's unpack those ablations they were talking about.
All right, let's get down to the nitty gritty.
You bet.
Okay, so ablations.
Ablations.
I like saying that word.
It's a fun one, isn't it?
It is.
So they're like these little tests they run to see what happens when they tweak different parts of the system.
Yeah, it's like tinkering under the hood to really understand how this RBR engine works.
I like that analogy a lot.
Glad you like it.
It's a good one.
One thing that I was really curious about was how the amount of training data affects all of this.
Yeah, that's a good one.
Because we know that AI, generally speaking, gets smarter with more data.
Right, more data, more smarts usually.
But I was wondering how that plays out with these safety-focused rules, these RBRs.
So basically they wanted to find out if there's a point where adding more and more safety rules doesn't actually make much difference.
So it's like, is there a saturation point?
Like can the AI only handle so many rules before it just gets overloaded?
Yeah, like does it just kind of freak out and say, I can't handle any more rules?
Exactly, exactly.
So what do they find?
Well, at least in their experiments, what they found is that more rules usually meant safer behavior.
Oh, interesting.
So it's not like there's this magic number of rules where it's like, OK, now the AI is a safety expert.
OK, so more is more in this case.
More is more, it seems like.
But they did find some really interesting patterns.
In how often the AI refused to do things and also like how it phrased those refusals.
So basically how it said no.
Exactly how it said no.
So as they added more safety rules, they noticed that the AI did get a little bit more cautious.
Like more hesitant to do things.
Yeah, more hesitant.
So it was refusing more requests, even if they were harmless.
So a little more uptight, but not to the point where it was completely useless.
Exactly, not totally useless.
It was a gradual increase and it never got as bad as some of the other methods they tried,
like the one where they just relied on human feedback on safety.
Right, which we talked about earlier.
And that one seemed to be really quick to just hit the no button.
Oh, yeah, that one was very, very cautious.
Yeah, like overly cautious.
Yeah.
So I guess what you're saying is that like finding that perfect balance between being safe
and still being able to actually do stuff is still a work in progress.
It's a constant balancing act.
A constant balancing act, yeah.
It makes sense because it probably is different for every AI model, depending on what you
want it to do.
Absolutely, it all depends on the context.
Right.
So what about the style of the refusals?
Do they see anything interesting there?
Yeah, so they found that as they added more safety rules, the AI actually got better at
politely refusing requests.
So it learned how to say no in a nicer way.
It learned to say no with more grace.
Yeah.
Okay.
So it's all about learning from good examples, right?
Like if you read a lot of well-written stuff, you'll probably start writing better yourself.
It makes sense, yeah.
Right.
So it's like feeding the AI good examples of how to politely say no.
It makes you wonder though, did they experiment with using different kinds of safety rules,
not just more rules?
Oh, you mean like strong refusals versus softer refusals?
Yeah, like no way versus I'd rather not.
They did.
They actually experimented with that and that's where things got really interesting in terms
of like fine-tuning the AI's behavior.
Okay.
So it's a week, tell me more.
So they found that by adjusting the mix of those strong like hard no refusals and more
like compliant softer responses, they could actually make the AI more or less cautious.
So more strong refusals made it a bit more uptight.
Okay.
While more compliant examples made it more willing to go along with the request.
So it's almost like you're setting the sensitivity on like a smoke detector.
Oh, that's a great analogy.
Like you want it to go off when there's a real fire, but you don't want it going off
every time someone burns toast.
Exactly.
It's all about striking that right balance.
So it's that balance again.
But then they dug even deeper, right?
They looked at how those softer, more empathetic refusals actually affected things.
They did, yeah.
And it turns out that the more they use those softer refusals in the training, the better
the AI got at handling those really tricky situations like the request for advice on
self-harm.
Oh, that's so interesting.
So instead of just shutting down the conversation, it learned to be more understanding, more
supportive.
It learned to be more human basically.
Yeah, more human.
And it did all of this without becoming any less safe overall.
So it was still really good at avoiding the harmful content, but it was also able to offer
this like compassionate response.
Yeah.
It was able to kind of walk that line.
That's amazing.
It really is.
It's like they found the perfect recipe, you know, like safety plus compassion equals
a really good AI assistant.
Yeah.
It's like the secret sauce.
It's pretty amazing how much we can influence these models just by carefully choosing what
we teach them.
It really highlights the importance of the training data and the rules that we set.
It's like raising a kid, right?
You don't just want to say no all the time.
Exactly.
You want to guide them, teach them how to make good choices.
You want them to be good people, but you also want them to be able to experience life and
learn and grow.
Exactly.
You don't want to stifle them.
Right.
Yeah.
So it sounds like they made some really great progress here, but even with all these fancy
rules and all this training, there's still a human element to all of this, right?
Absolutely.
Like someone still has to decide what the rules are in the first place and how to actually
teach those rules to the AI.
You're absolutely right.
And the researchers acknowledge that in the paper.
They talk about how important it is to have a diverse group of people involved in this
process.
So not just AI researchers.
Not just AI researchers, no.
But also ethicists, people from different backgrounds and perspectives.
Yeah, people who can bring their own lived experiences to the table.
So that we can make sure that these AI models are actually reflecting the values that we
want to see in the world.
It's about making sure that AI is a force for good and not just this powerful tool that
could be used for who knows what.
Right.
Exactly.
It's not just about building a super smart AI.
It's about making sure that AI is actually beneficial for society as a whole.
It's about making sure that AI is aligned with our values as humans.
Exactly.
Well said.
So speaking of pushing the boundaries, I know that they also tried some really out there
approaches to training these AI models.
Yeah, they did.
Like what if you didn't teach it to be helpful at all, but just focus purely on safety?
Oh, you're talking about the experiment where they threw out all the helpful training data
and they just used those safety rules.
Yeah.
Did that even work?
You know, it's interesting.
It wasn't a total failure.
The AI was still pretty good at saying no to harmful stuff.
Okay.
So it passed the safety test.
It aced the safety test.
But there was a catch.
It also became way more likely to refuse requests that were totally harmless.
Oh no.
So it's like that super strict teacher who sucks all the fun out of learning.
Exactly.
You just want to ask a simple question and they shut you down.
It's like, can we just have a little fun here?
Exactly.
It really shows how important it is to build a foundation of helpfulness into these models
from the start.
It's not just about teaching them what not to do.
It's about teaching them how to be useful and engaging and helpful.
It's about that balance again, right?
Safety and usefulness.
Yeah.
Like think about it in terms of like raising a child.
You don't want to just tell them no all the time.
You want them to learn and explore.
You want them to experience the world.
And you want them to enjoy the experience.
Oh yeah.
Exactly.
And it's the same with AI.
We want AI that's responsible, but we also want it to be enjoyable to interact with.
We want AI that can make our lives better and easier and more fun.
We want AI that can be a true companion and helper.
Yeah.
So I think we've covered a ton of ground here, really digging into the nitty gritty of these
RBRs.
It's been a quite the journey.
It really has.
And I think these experiments show just how much work there still is to be done in AI
safety research.
Oh absolutely.
There's still so much to explore and learn.
It's a complex field, but it's clear that we're making some really exciting progress.
Yeah.
We're definitely moving in the right direction.
So for the last part of our deep dive, I want to kind of zoom out and think about the bigger
picture.
You know, what does all this mean for the future of AI?
What's next for these rule based systems?
Stay tuned.
This open AI paper, it's been a wild ride, really exploring all the different facets
and turns of this approach to making AI safer.
I feel like we really got in the weeds.
We did.
We got into the weeds.
We got turned into the nitty gritty.
We talked about the basic idea of these rules, the RBRs and all those experiments.
Yeah, and all those tests.
But now I want to step back.
Zoom out a little.
And look at the big picture, you know.
What does this all mean for the future?
The future of AI.
Yeah, where do we go from here?
That's the million dollar question, isn't it?
It is.
And this paper doesn't give us all the answers.
But it opens up some really interesting possibilities.
It does.
One thing that really struck me was.
What's that?
The RBRs could potentially change the way we train AI.
Yeah, the training process.
Because right now, it takes so much time and effort to get humans to label all that data.
Oh, it's a massive undertaking.
It's a huge job.
And it's not just the time, it's also the potential for bias, right?
Oh, yeah.
Human bias can creep in.
And errors.
So many errors.
But with these RBRs, it's like you're automating that process.
Using AI to train AI.
Exactly.
So you have this other AI judging the responses.
Based on the rules.
And those rules has already been defined.
So you don't need humans to do that tedious labeling work.
Yeah, that frees up the humans.
Frees them up to do more interesting things.
Like what?
Like coming up with better rules.
Yeah, more nuanced rules.
Or tackling those ethical questions.
It's a big question.
Yeah, exactly.
So it's not just about labeling data points.
It's about shaping the overall values of the system.
Guiding the AI, giving it a moral compass.
Exactly.
Which leads to another exciting possibility.
What's that?
What if we could use these RBRs to create AI models that aren't just safe, but also reflect
like a broader set of values.
Oh, I like where you're going with this.
So it's not just about avoiding harm.
It's about promoting good.
Like being fair and unbiased.
Exactly.
Like imagine an AI assistant.
Helpful and harmless.
But also inclusive, respectful of different cultures, sensitive to people's emotions.
AI with empathy.
Yeah.
It's not just about preventing bad things.
It's about actively making things better.
That's a powerful idea.
And I think that's where the real potential of this research lies.
It's not just about minimizing risks.
It's about maximizing benefit.
Yeah.
Unlocking the positive potential of AI.
Of course there are still challenges, right?
Like Alissa challenges.
Like we've been talking about, these RBRs, they're better suited for tasks where the
rules are really clear.
Objective rules.
But for things like creativity.
Art.
Storytelling.
It's harder to define those rules.
It's much more subjective.
So there's still a lot of work to be done.
Oh yeah, we're just scratching the surface.
Figuring out how to adapt these rule-based approaches to these more nuanced areas.
AI that can write a symphony.
Compose a poem.
Or paint a masterpiece.
Exactly.
It makes you wonder if we need entirely new types of AI, right?
Ones that are less reliant on rules.
And more capable of learning and adapting like humans do.
AI that can think outside the box.
Thank for itself.
Be creative.
It's an exciting time to be working on this stuff.
It really is.
It feels like we're at the beginning of a whole new era.
A new chapter in AI.
Where the focus is shifting.
From just building powerful machines.
To building machines that are powerful and beneficial.
AI that's aligned with our values.
Exactly.
But before we get too carried away with all these possibilities.
We need a dose of reality.
It's important to remember that no system is perfect.
No AI is foolproof.
And the researchers are very clear about that.
They acknowledge the limitations.
They say that these RBIs aren't a magic solution.
They're not a silver bullet.
And there's always a chance for unexpected consequences, right?
AI could find loopholes.
Find workarounds.
Outsmart the rules.
It's a constant arms race.
So we have to be vigilant.
Keep researching and testing and refining.
Making sure AI stays on the right track.
It's like you're game of chess.
Right.
It gets a supercomputer.
You have to think several moves ahead.
Anticipate the AI's strategy.
Come up with countermeasures.
It's a challenge, but it's an exciting challenge.
And that's why this research is so important.
It's pushing the boundaries.
It's showing us what's possible.
And reminding us to be careful.
Exactly.
So I think that's a good place to wrap up our deep dive.
It's been a fascinating conversation.
We've explored these rule-based rewards.
How they work.
What they could mean for the future of AI.
A future where AI is safe and ethical.
A future where AI empowers us all.
And helps us build a better world.
That's the goal.
That's the dream.
And thanks to research like this, we're one step closer.
To making that dream a reality.
So to everyone listening, keep learning, keep asking questions, and stay engaged in the
conversation about AI.
Because the future of AI is up to all of us.
That's right.
And on that note, we'll sign off for today.
Until next time.
Take care everyone.