November 16, 2024 • 18 mins
In this episode, we delve into the latest developments from Anthropic, exploring their unique approach to building safe, powerful AI. Get an inside look at the new Claude 3.5 models, including the groundbreaking Sonnet, which brings an unprecedented ability to interact directly with computers—an innovation raising both opportunities and challenges. Discover how Anthropic navigates the limits of scaling with creative solutions like synthetic data, balances industry competition with a "race to the top" in AI safety, and pioneers Constitutional AI to align model behavior with human values. Whether you're a tech enthusiast or an AI professional, this episode offers a rare glimpse into the future of responsible AI development, bridging cutting-edge tech with ethical foresight.
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Episode Transcript
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(00:00):
Hey, everyone.
(00:00):
Ready for another deep dive?
This time, we're exploring Anthropic,
a company making some serious waves in the AI world.
Definitely a hot topic these days.
You bet.
And our listeners sent over a ton of great stuff.
Articles, interviews with Anthropic's leaders, even
some personal notes.
Looks like they're especially interested in how
(00:22):
Anthropic focuses on AI safety.
Yeah, super interesting stuff, especially
since they're developing some really advanced AI system.
Exactly.
So we're diving into what makes Anthropic tick,
how they're building AI systems like Claude and their vision
for the future.
You're our AI guru.
What stood out to you when you were looking through all this?
Honestly, it's fascinating how they're
(00:43):
all about pushing the limits of what AI can do.
But at the same time, they're so committed
to making sure it's beneficial and safe,
you don't see that combo every day.
It's almost like they want to win the race,
but also make sure everyone crosses the finish
line in one piece.
OK, so let's unpack Anthropic.
What makes them unique?
How are they approaching AI development,
especially with their Claude system?
(01:04):
And what do they see on the horizon for AI in general?
Well, one thing that really struck me
is their work on scaling laws.
Scaling laws.
Basically, they've observed that if you increase
the size of an AI model, like how much data it learns from,
and you bump up the computing power used to train it,
the AI usually performs better.
(01:26):
So bigger is better in the AI world.
Just keep throwing more data and computing power at it.
Yeah, kind of.
But it gets a little more complex than that.
Anthropic CEO Dario Amodei is actually
pretty vocal about the potential downsides.
Like what?
Well, for one thing, he's concerned about whether we'll
even have enough good data to train these supersized models
effectively.
(01:46):
So it's not just about having tons of data.
It's got to be good data.
Right.
Plus, there's the issue of how much our current computers can
handle.
And then there's the possibility that the way AI models are
designed might need to change to handle even more scaling.
So if just making AI bigger isn't always the answer,
what other options are they looking into?
How are they thinking about keeping AI moving forward?
(02:09):
Well, one thing they're exploring is using synthetic data.
Synthetic data?
What's that?
It's basically artificially generated data
that they can use to supplement real world data.
Interesting.
So they're trying to create their own data
to train these AIs.
In a way, yeah.
They're also trying to find ways to make AI training more
efficient so they can get more bang for their buck
(02:31):
with the data and computing power they already have.
Makes sense.
It sounds like they're trying to be really strategic with their AI
development, not just throwing things at the wall
and seeing what sticks.
Absolutely.
They're thinking long term.
So how does all of this relate to how they're building Claude?
Well, Claude is their flagship AI model.
They've designed it to be super powerful and safe all
(02:52):
at the same time.
Didn't they release a few different versions of it?
Yeah, there's Opus, Sonnet, and Haiku.
Each one's got its own strengths and capabilities.
And haven't they made some pretty impressive strides
with Claude?
I heard it can actually interact with computer screens now.
Yeah, for sure.
It can analyze screenshots, fill out spreadsheets, even
write code.
Wow.
But the crazy part is they're doing all of this
(03:14):
while still being incredibly careful
about the potential risks.
Which is where their AI safety levels come in, right?
That ASL system they talk about.
Exactly.
They've got this framework to categorize their models based
on the level of risk they pose.
Yeah.
Right now, Claude is considered ASL2.
ASL2, meaning?
It means that on its own, it's not
(03:34):
capable of causing major harm.
Gotcha.
But as they keep pushing Claude to do more and more,
are they worried about those safety levels going up?
Definitely.
They're already looking ahead to those higher ASL levels,
like ASL3 and beyond, where AI could potentially
get used for malicious purposes or even
start doing its own research independently.
AI doing its own research.
(03:55):
That's kind of freaky.
It is.
And that's why they're staying ahead of the game
when it comes to safety.
They're trying to anticipate those risks before they even
become a problem.
So they're building these powerful AI models,
thinking about the risks, but also trying to make sure
these AIs reflect positive human values, right?
Like it's not just about raw intelligence,
but also about making them good, in a sense.
(04:17):
Exactly.
They want more than just a brain.
They want a heart, too.
And that's where someone like Amanda Askel comes in.
Amanda Askel.
She's a researcher at Anthropic, right?
Yeah, she's been super involved in shaping Claude's
personality.
They want it to be a genuinely helpful and harmless AI,
embodying qualities like honesty, humility, empathy.
(04:39):
So they're not just building a brilliant AI.
They're building a kind one, too.
How are they actually doing that?
How do you even build those qualities into an AI?
Well, one of their key techniques is called constitutional AI.
Constitutional AI.
It's like giving the AI a set of guidelines, almost
like a moral compass in its code.
So they're giving it a sense of ethics,
like we have laws and societal norms.
(05:00):
Right.
And they're also using something called reinforcement
learning from human feedback, RLHF for short.
Basically, they have humans give feedback on Claude's responses.
And that helps it learn and improve over time.
So it's like training a dog, but instead of treats,
they're using feedback to guide its behavior.
Very much.
It sounds like they're really putting in the work
(05:22):
to make sure Claude is both helpful and aligned
with our values.
What else caught your eye about Anthropix approach?
Well, one thing that really stood out
was their focus on something called
mechanistic interpretability.
Oh, that's a mouthful.
Mechanistic.
What was it again?
Mechanistic interpretability.
Basically, they're trying to understand how these AI models
(05:43):
actually work, like at their core.
OK, let's break that down.
How do you even begin to understand
what's going on inside these incredibly complex AI systems?
It seems almost impossible.
It is a huge challenge.
But that's where someone like Crisola comes in.
Crisola.
He's another researcher in Anthropix
who's leading the charge on this interpretability front.
He's got this interesting way of thinking about it
(06:05):
where he compares AI development to neurobiology.
AI and brains.
Yeah, like they're trying to understand
how these AI models develop their own circuits and connections
based on all the data they're fed and how they're trained.
So it's like they're studying the AI's brain,
trying to map out its thoughts, so to speak.
Why is that so important to them?
(06:26):
Because if they can understand how these models work
at a fundamental level, they can better
predict how they'll behave.
Makes sense.
They want to identify any potential risks
before they become problems, right?
Right.
They're not just building a powerful tool.
They want to make sure they understand it inside and out
so they can use it safely and responsibly.
So how are they actually doing that?
What are they looking for when they peer inside these AI
(06:49):
systems?
Well, one thing they're looking for
is any sign of what they call deception or back
doors within the model.
Oh, deception.
Like the AI is trying to trick us.
It's not that they think the AI is consciously
trying to be sneaky, but it's more
that as AI gets smarter and smarter,
there's a chance it could learn to explode loopholes
(07:09):
or manipulate its environment in ways we didn't see coming.
So it's not necessarily that it's trying to be malicious,
but more like it might accidentally cause harm
if we're not careful.
Right, exactly.
And that's where all this mechanistic interpretability
research comes in.
It gives it a way to make sure the AI is behaving
how it's supposed to, even as it gets more and more complex.
It's like they're installing a security camera inside the AI's
(07:32):
brain so they can keep a close eye on things.
That's a good analogy.
But beyond just safety, what's their overall vision
for the future of AI?
Where do they see all this going?
Well, Amadeus actually said he believes
AI has the potential to solve some of humanity's biggest
problems.
Like what?
Like climate change, disease, poverty, all these huge issues.
(07:53):
AI could be a powerful tool for tackling those things.
Wow.
So it's like AI could be this incredible force for good,
helping us create a better world for everyone.
That's the idea.
But he's not naive about the potential downsides either.
Right, it's not going to be all sunshine and roses.
Exactly.
He's very realistic about the risks
and emphasizes the need to be careful and thoughtful
(08:14):
every step of the way.
So it's a real balancing act trying
to unlock all the amazing potential of AI
while making sure we don't create
something we can't control.
Definitely a huge responsibility.
And it's not just the responsibility
of the tech companies building this stuff, right?
It's got to involve everyone.
100% Anthropic is actually really active in discussions
(08:34):
with policymakers and ethicists to help shape the future of AI
in a responsible way.
It's a much bigger conversation than just the code itself.
Oh, yeah, absolutely.
They believe that AI needs to be guided
by a whole range of perspectives to get it right.
So they're not just building technology,
they're trying to build a better future for all of us.
Exactly.
And it's inspiring to see a company taking that so
(08:56):
seriously.
It really is.
OK, so big question time.
What do you think the future of AI actually looks like?
Are we all going to have robot butlers and flying cars?
Well, the future is always a bit of a mystery, isn't it?
But I think it's pretty safe to say
that AI is only going to become more integrated into our lives.
I mean, it already is in a lot of ways.
Right.
(09:16):
It's everywhere, helping us with all sorts of things,
choosing movies, navigating traffic.
And as these models get even more advanced,
they'll probably start playing even bigger roles in areas
like health care, education, transportation, maybe even
art and entertainment.
So instead of robot butlers, maybe
we'll have AI doctors and teachers and artists.
(09:36):
It's possible.
And if companies like Anthropic are successful in their mission,
this future will be built on a foundation of trust,
transparency, shared values, all that good stuff.
So it's not just about AI getting smarter,
it's about making sure it gets wiser too.
Exactly.
AI needs to develop not just intellectually, but also
ethically.
And that's a challenge that Anthropic
(09:57):
seems to be taking head on.
It's exciting to think about all the possibilities,
but also a little daunting.
The impact AI could have on our world is huge.
It is.
And it's encouraging to see a company like Anthropic really
wrestling with those big questions,
trying to do things the right way.
They're definitely one to watch.
They're pushing the boundaries while also setting a high bar
for responsible development.
Yeah.
(10:17):
This deep dive has been wild.
We've covered so much scaling laws, AI safety levels,
mechanistic interpretability.
And got to give a shout out to our listener
for sending over such great material to work with.
It was a fantastic selection of sources.
It really showed their interest in not just
the technical side of AI, but also
the ethical and societal implications.
(10:39):
Absolutely.
OK.
So let's do a quick recap of what we've
learned about Anthropic.
So far we know they're all about pushing
the limits of what AI can do.
But they're doing it with a strong emphasis
on safety and ethics.
It's like they're writing a new playbook for AI development,
where progress and responsibility go hand in hand.
What else would you highlight?
I'd say their approach to actually building these AI
(11:00):
models is really interesting.
They're being very strategic.
Not just throwing data and computing power at the problem.
Right.
They're thinking outside the box,
exploring things like synthetic data, new training techniques.
Exactly.
Trying to overcome those limitations of just scaling up.
And they're not shying away from the really tough questions.
How do you make sure AI actually reflects positive human values?
(11:22):
All that work they're doing with Claude's personality
is a perfect example.
Yeah, they're using techniques like constitutional AI and RLHF
to guide Claude's development, making sure it's not just
brilliant, but also kind and helpful.
Like they're raising a well-rounded AI citizen.
Exactly.
And then there's all their work on mechanistic interpretability,
(11:43):
which is honestly mind blowing.
They're literally trying to figure out how these AIs think.
It's like they're cracking the code of artificial intelligence.
And they're not doing it just out of curiosity.
They really believe it's essential for building safe
and trustworthy AI.
It's like they're saying, look, we're not just
going to build this powerful technology and hope for the best.
We're going to understand it inside and out
(12:04):
so we can use it responsibly.
Exactly.
And that commitment to transparency and understanding
is so important, especially in a field that can feel
very mysterious and secretive.
It's like they're throwing open the doors and saying, come on in.
Let's see how this all works.
Yeah.
And that openness is key for building trust with the public.
People need to see what's going on,
if they're going to feel comfortable with AI becoming
(12:26):
more integrated into our lives.
So they're pushing the boundaries of AI,
thinking deeply about safety and ethics
and being open about their process.
It really seems like they're trying
to change the game when it comes to AI development.
I think they are.
What you think, are they succeeding?
It's still early days, but they're
definitely making progress.
They're raising the bar for both innovation and responsibility
(12:50):
in the AI world.
And they're asking the tough questions
that other companies seem to be avoiding.
Questions like, what does it even mean to build good AI?
How do we make sure everyone benefits and not just
a select few?
How do we prevent misuse and unintended consequences?
Those are big questions that need to be addressed.
They're not just building technology.
They're trying to build a better world.
(13:10):
And that's something I get behind.
Me too.
So listener, if you're interested in AI,
Anthropic is definitely a company to keep your eye on.
They're showing us what's possible
when you combine cutting edge technology
with a strong moral compass.
And they're reminding us that the future of AI
isn't some predetermined thing.
It's something we're all creating together.
It's really something else how much they're focusing
(13:30):
on the ethical side of things.
It's not just lip service.
They're really putting their resources
into this whole mechanistic interpretability thing.
Yeah, because it's one thing to know that an AI can
do something amazing.
But if we're going to really trust these systems,
especially with important tasks, we
got to understand how they do it.
Exactly.
It's like, would you get in a self-driving car
(13:52):
if you had no clue how it was making decisions?
Probably not.
Not a chance.
And this is where all that talk about AI safety levels
really hits home, right?
As these AI models get more and more powerful,
they could pose some serious risks,
even if they're not trying to be malicious.
Right.
It's not about them being evil.
It's about unintended consequences.
We talked about Claude being at ASL too,
(14:13):
but they're already thinking about those higher levels
where things could get a lot more complicated.
They are.
They're playing the long game, trying to anticipate problems
before they even pop up.
It's impressive.
So this research into mechanistic interpretability,
it's like they're developing a way
to see inside these AI models, maybe even spot those risks
before they become a reality.
(14:34):
Exactly.
It's like having a safety check built right in.
So by understanding how these AIs think,
they can potentially see those red flags, those little hints
that something might go wrong.
Yep.
They're looking for anything out of the ordinary.
Any signs of what they call deception or back doors.
We talked about that before.
But remind me again, what do they mean by deception?
(14:55):
It's not like the AI is intentionally lying to us,
right?
Right.
It's not about malicious intent.
It's more about the possibility that the AI could
learn to manipulate its environment in ways
that we didn't expect, even if it's not trying to be sneaky.
So more like unintended consequences.
The AI is not being bad.
It's just maybe figuring out how to achieve its goals in ways
that could cause problems.
(15:16):
Exactly.
And that's precisely why they think
this mechanistic interpretability stuff is
so important.
It gives them a way to look under the hood
and make sure everything's running smoothly,
even as the AI gets more and more advanced.
It's like they're saying, OK, we trust you, AI,
but we're also going to double check your work just to be safe.
Exactly.
(15:37):
It's about finding that balance between pushing the limits
and being cautious.
We want to see what's possible, but we also
want to make sure we're doing it responsibly.
And Anthropic seems to be walking that line pretty well.
They really do.
OK, but zooming out a bit, what about their big vision for AI?
Where do they see all of this heading,
and how does Anthropic fit into that picture?
(15:58):
What's the end game here?
Well, Amadeus has talked about a future
where AI could help us tackle some of the biggest challenges
we face as a species.
No kidding.
Yeah, like climate change, disease poverty, things
that have plagued us for centuries.
He thinks AI could be a game changer.
So it's not just about building cool tech
for the sake of it.
It's about using that tech to actually make a difference.
(16:19):
Exactly.
They see AI as a way to boost our own capabilities,
help us solve problems that have seemed impossible for so long.
That's a pretty optimistic outlook.
But I'm sure they're not blind to the potential downsides.
Of course not.
They know there are risks, and they're
working hard to figure out how to avoid them.
That's why their focus on safety and ethics is so crucial.
(16:41):
They want to make sure that as AI gets more powerful,
it stays on the side of good.
It's like they're pioneers charting a course
through uncharted territory, trying
to steer clear of the dangers while still keeping
their eyes on the prize.
And they're doing it in a way that feels very thoughtful
and open.
They're not just working in isolation.
They're talking to experts outside the tech world,
(17:03):
collaborating with policymakers and ethicists
to make sure AI is developed and used
in a way that benefits everyone.
That's a big deal.
It's not just about profits.
It's about making the world a better place.
Right.
It's about thinking about the big picture.
And that's what I find so inspiring about Anthropic.
Couldn't have said it better myself.
Well, this has been an incredible deep dive
into Anthropic.
We've learned so much about their commitment to safety,
(17:26):
their groundbreaking research into mechanistic
interpretability, and their vision for a future
where AI is a force for good.
They're definitely pushing the boundaries,
while also staying true to their values.
And it's clear they're not afraid to ask
the tough questions, to really grapple with what
it means to build AI that is both powerful and ethical.
(17:46):
They're a company that's worth keeping an eye on,
that's for sure.
Absolutely.
So listener, if you're interested in the future of AI,
remember what we've learned from Anthropic.
Be curious, be critical, and most importantly, be engaged.
The future of AI isn't some predetermined thing.
It's something we're all creating together.
And the choices we make today will
shape the world of tomorrow.
That's a great note to end on.
(18:07):
Thanks for joining us on this deep dive into Anthropic.
Hey, everyone.
(00:00):
Ready for another deep dive?
This time, we're exploring Anthropic,
a company making some serious waves in the AI world.
Definitely a hot topic these days.
You bet.
And our listeners sent over a ton of great stuff.
Articles, interviews with Anthropic's leaders, even
some personal notes.
Looks like they're especially interested in how
(00:22):
Anthropic focuses on AI safety.
Yeah, super interesting stuff, especially
since they're developing some really advanced AI system.
Exactly.
So we're diving into what makes Anthropic tick,
how they're building AI systems like Claude and their vision
for the future.
You're our AI guru.
What stood out to you when you were looking through all this?
Honestly, it's fascinating how they're
(00:43):
all about pushing the limits of what AI can do.
But at the same time, they're so committed
to making sure it's beneficial and safe,
you don't see that combo every day.
It's almost like they want to win the race,
but also make sure everyone crosses the finish
line in one piece.
OK, so let's unpack Anthropic.
What makes them unique?
How are they approaching AI development,
especially with their Claude system?
(01:04):
And what do they see on the horizon for AI in general?
Well, one thing that really struck me
is their work on scaling laws.
Scaling laws.
Basically, they've observed that if you increase
the size of an AI model, like how much data it learns from,
and you bump up the computing power used to train it,
the AI usually performs better.
(01:26):
So bigger is better in the AI world.
Just keep throwing more data and computing power at it.
Yeah, kind of.
But it gets a little more complex than that.
Anthropic CEO Dario Amodei is actually
pretty vocal about the potential downsides.
Like what?
Well, for one thing, he's concerned about whether we'll
even have enough good data to train these supersized models
effectively.
(01:46):
So it's not just about having tons of data.
It's got to be good data.
Right.
Plus, there's the issue of how much our current computers can
handle.
And then there's the possibility that the way AI models are
designed might need to change to handle even more scaling.
So if just making AI bigger isn't always the answer,
what other options are they looking into?
How are they thinking about keeping AI moving forward?
(02:09):
Well, one thing they're exploring is using synthetic data.
Synthetic data?
What's that?
It's basically artificially generated data
that they can use to supplement real world data.
Interesting.
So they're trying to create their own data
to train these AIs.
In a way, yeah.
They're also trying to find ways to make AI training more
efficient so they can get more bang for their buck
(02:31):
with the data and computing power they already have.
Makes sense.
It sounds like they're trying to be really strategic with their AI
development, not just throwing things at the wall
and seeing what sticks.
Absolutely.
They're thinking long term.
So how does all of this relate to how they're building Claude?
Well, Claude is their flagship AI model.
They've designed it to be super powerful and safe all
(02:52):
at the same time.
Didn't they release a few different versions of it?
Yeah, there's Opus, Sonnet, and Haiku.
Each one's got its own strengths and capabilities.
And haven't they made some pretty impressive strides
with Claude?
I heard it can actually interact with computer screens now.
Yeah, for sure.
It can analyze screenshots, fill out spreadsheets, even
write code.
Wow.
But the crazy part is they're doing all of this
(03:14):
while still being incredibly careful
about the potential risks.
Which is where their AI safety levels come in, right?
That ASL system they talk about.
Exactly.
They've got this framework to categorize their models based
on the level of risk they pose.
Yeah.
Right now, Claude is considered ASL2.
ASL2, meaning?
It means that on its own, it's not
(03:34):
capable of causing major harm.
Gotcha.
But as they keep pushing Claude to do more and more,
are they worried about those safety levels going up?
Definitely.
They're already looking ahead to those higher ASL levels,
like ASL3 and beyond, where AI could potentially
get used for malicious purposes or even
start doing its own research independently.
AI doing its own research.
(03:55):
That's kind of freaky.
It is.
And that's why they're staying ahead of the game
when it comes to safety.
They're trying to anticipate those risks before they even
become a problem.
So they're building these powerful AI models,
thinking about the risks, but also trying to make sure
these AIs reflect positive human values, right?
Like it's not just about raw intelligence,
but also about making them good, in a sense.
(04:17):
Exactly.
They want more than just a brain.
They want a heart, too.
And that's where someone like Amanda Askel comes in.
Amanda Askel.
She's a researcher at Anthropic, right?
Yeah, she's been super involved in shaping Claude's
personality.
They want it to be a genuinely helpful and harmless AI,
embodying qualities like honesty, humility, empathy.
(04:39):
So they're not just building a brilliant AI.
They're building a kind one, too.
How are they actually doing that?
How do you even build those qualities into an AI?
Well, one of their key techniques is called constitutional AI.
Constitutional AI.
It's like giving the AI a set of guidelines, almost
like a moral compass in its code.
So they're giving it a sense of ethics,
like we have laws and societal norms.
(05:00):
Right.
And they're also using something called reinforcement
learning from human feedback, RLHF for short.
Basically, they have humans give feedback on Claude's responses.
And that helps it learn and improve over time.
So it's like training a dog, but instead of treats,
they're using feedback to guide its behavior.
Very much.
It sounds like they're really putting in the work
(05:22):
to make sure Claude is both helpful and aligned
with our values.
What else caught your eye about Anthropix approach?
Well, one thing that really stood out
was their focus on something called
mechanistic interpretability.
Oh, that's a mouthful.
Mechanistic.
What was it again?
Mechanistic interpretability.
Basically, they're trying to understand how these AI models
(05:43):
actually work, like at their core.
OK, let's break that down.
How do you even begin to understand
what's going on inside these incredibly complex AI systems?
It seems almost impossible.
It is a huge challenge.
But that's where someone like Crisola comes in.
Crisola.
He's another researcher in Anthropix
who's leading the charge on this interpretability front.
He's got this interesting way of thinking about it
(06:05):
where he compares AI development to neurobiology.
AI and brains.
Yeah, like they're trying to understand
how these AI models develop their own circuits and connections
based on all the data they're fed and how they're trained.
So it's like they're studying the AI's brain,
trying to map out its thoughts, so to speak.
Why is that so important to them?
(06:26):
Because if they can understand how these models work
at a fundamental level, they can better
predict how they'll behave.
Makes sense.
They want to identify any potential risks
before they become problems, right?
Right.
They're not just building a powerful tool.
They want to make sure they understand it inside and out
so they can use it safely and responsibly.
So how are they actually doing that?
What are they looking for when they peer inside these AI
(06:49):
systems?
Well, one thing they're looking for
is any sign of what they call deception or back
doors within the model.
Oh, deception.
Like the AI is trying to trick us.
It's not that they think the AI is consciously
trying to be sneaky, but it's more
that as AI gets smarter and smarter,
there's a chance it could learn to explode loopholes
(07:09):
or manipulate its environment in ways we didn't see coming.
So it's not necessarily that it's trying to be malicious,
but more like it might accidentally cause harm
if we're not careful.
Right, exactly.
And that's where all this mechanistic interpretability
research comes in.
It gives it a way to make sure the AI is behaving
how it's supposed to, even as it gets more and more complex.
It's like they're installing a security camera inside the AI's
(07:32):
brain so they can keep a close eye on things.
That's a good analogy.
But beyond just safety, what's their overall vision
for the future of AI?
Where do they see all this going?
Well, Amadeus actually said he believes
AI has the potential to solve some of humanity's biggest
problems.
Like what?
Like climate change, disease, poverty, all these huge issues.
(07:53):
AI could be a powerful tool for tackling those things.
Wow.
So it's like AI could be this incredible force for good,
helping us create a better world for everyone.
That's the idea.
But he's not naive about the potential downsides either.
Right, it's not going to be all sunshine and roses.
Exactly.
He's very realistic about the risks
and emphasizes the need to be careful and thoughtful
(08:14):
every step of the way.
So it's a real balancing act trying
to unlock all the amazing potential of AI
while making sure we don't create
something we can't control.
Definitely a huge responsibility.
And it's not just the responsibility
of the tech companies building this stuff, right?
It's got to involve everyone.
100% Anthropic is actually really active in discussions
(08:34):
with policymakers and ethicists to help shape the future of AI
in a responsible way.
It's a much bigger conversation than just the code itself.
Oh, yeah, absolutely.
They believe that AI needs to be guided
by a whole range of perspectives to get it right.
So they're not just building technology,
they're trying to build a better future for all of us.
Exactly.
And it's inspiring to see a company taking that so
(08:56):
seriously.
It really is.
OK, so big question time.
What do you think the future of AI actually looks like?
Are we all going to have robot butlers and flying cars?
Well, the future is always a bit of a mystery, isn't it?
But I think it's pretty safe to say
that AI is only going to become more integrated into our lives.
I mean, it already is in a lot of ways.
Right.
(09:16):
It's everywhere, helping us with all sorts of things,
choosing movies, navigating traffic.
And as these models get even more advanced,
they'll probably start playing even bigger roles in areas
like health care, education, transportation, maybe even
art and entertainment.
So instead of robot butlers, maybe
we'll have AI doctors and teachers and artists.
(09:36):
It's possible.
And if companies like Anthropic are successful in their mission,
this future will be built on a foundation of trust,
transparency, shared values, all that good stuff.
So it's not just about AI getting smarter,
it's about making sure it gets wiser too.
Exactly.
AI needs to develop not just intellectually, but also
ethically.
And that's a challenge that Anthropic
(09:57):
seems to be taking head on.
It's exciting to think about all the possibilities,
but also a little daunting.
The impact AI could have on our world is huge.
It is.
And it's encouraging to see a company like Anthropic really
wrestling with those big questions,
trying to do things the right way.
They're definitely one to watch.
They're pushing the boundaries while also setting a high bar
for responsible development.
Yeah.
(10:17):
This deep dive has been wild.
We've covered so much scaling laws, AI safety levels,
mechanistic interpretability.
And got to give a shout out to our listener
for sending over such great material to work with.
It was a fantastic selection of sources.
It really showed their interest in not just
the technical side of AI, but also
the ethical and societal implications.
(10:39):
Absolutely.
OK.
So let's do a quick recap of what we've
learned about Anthropic.
So far we know they're all about pushing
the limits of what AI can do.
But they're doing it with a strong emphasis
on safety and ethics.
It's like they're writing a new playbook for AI development,
where progress and responsibility go hand in hand.
What else would you highlight?
I'd say their approach to actually building these AI
(11:00):
models is really interesting.
They're being very strategic.
Not just throwing data and computing power at the problem.
Right.
They're thinking outside the box,
exploring things like synthetic data, new training techniques.
Exactly.
Trying to overcome those limitations of just scaling up.
And they're not shying away from the really tough questions.
How do you make sure AI actually reflects positive human values?
(11:22):
All that work they're doing with Claude's personality
is a perfect example.
Yeah, they're using techniques like constitutional AI and RLHF
to guide Claude's development, making sure it's not just
brilliant, but also kind and helpful.
Like they're raising a well-rounded AI citizen.
Exactly.
And then there's all their work on mechanistic interpretability,
(11:43):
which is honestly mind blowing.
They're literally trying to figure out how these AIs think.
It's like they're cracking the code of artificial intelligence.
And they're not doing it just out of curiosity.
They really believe it's essential for building safe
and trustworthy AI.
It's like they're saying, look, we're not just
going to build this powerful technology and hope for the best.
We're going to understand it inside and out
(12:04):
so we can use it responsibly.
Exactly.
And that commitment to transparency and understanding
is so important, especially in a field that can feel
very mysterious and secretive.
It's like they're throwing open the doors and saying, come on in.
Let's see how this all works.
Yeah.
And that openness is key for building trust with the public.
People need to see what's going on,
if they're going to feel comfortable with AI becoming
(12:26):
more integrated into our lives.
So they're pushing the boundaries of AI,
thinking deeply about safety and ethics
and being open about their process.
It really seems like they're trying
to change the game when it comes to AI development.
I think they are.
What you think, are they succeeding?
It's still early days, but they're
definitely making progress.
They're raising the bar for both innovation and responsibility
(12:50):
in the AI world.
And they're asking the tough questions
that other companies seem to be avoiding.
Questions like, what does it even mean to build good AI?
How do we make sure everyone benefits and not just
a select few?
How do we prevent misuse and unintended consequences?
Those are big questions that need to be addressed.
They're not just building technology.
They're trying to build a better world.
(13:10):
And that's something I get behind.
Me too.
So listener, if you're interested in AI,
Anthropic is definitely a company to keep your eye on.
They're showing us what's possible
when you combine cutting edge technology
with a strong moral compass.
And they're reminding us that the future of AI
isn't some predetermined thing.
It's something we're all creating together.
It's really something else how much they're focusing
(13:30):
on the ethical side of things.
It's not just lip service.
They're really putting their resources
into this whole mechanistic interpretability thing.
Yeah, because it's one thing to know that an AI can
do something amazing.
But if we're going to really trust these systems,
especially with important tasks, we
got to understand how they do it.
Exactly.
It's like, would you get in a self-driving car
(13:52):
if you had no clue how it was making decisions?
Probably not.
Not a chance.
And this is where all that talk about AI safety levels
really hits home, right?
As these AI models get more and more powerful,
they could pose some serious risks,
even if they're not trying to be malicious.
Right.
It's not about them being evil.
It's about unintended consequences.
We talked about Claude being at ASL too,
(14:13):
but they're already thinking about those higher levels
where things could get a lot more complicated.
They are.
They're playing the long game, trying to anticipate problems
before they even pop up.
It's impressive.
So this research into mechanistic interpretability,
it's like they're developing a way
to see inside these AI models, maybe even spot those risks
before they become a reality.
(14:34):
Exactly.
It's like having a safety check built right in.
So by understanding how these AIs think,
they can potentially see those red flags, those little hints
that something might go wrong.
Yep.
They're looking for anything out of the ordinary.
Any signs of what they call deception or back doors.
We talked about that before.
But remind me again, what do they mean by deception?
(14:55):
It's not like the AI is intentionally lying to us,
right?
Right.
It's not about malicious intent.
It's more about the possibility that the AI could
learn to manipulate its environment in ways
that we didn't expect, even if it's not trying to be sneaky.
So more like unintended consequences.
The AI is not being bad.
It's just maybe figuring out how to achieve its goals in ways
that could cause problems.
(15:16):
Exactly.
And that's precisely why they think
this mechanistic interpretability stuff is
so important.
It gives them a way to look under the hood
and make sure everything's running smoothly,
even as the AI gets more and more advanced.
It's like they're saying, OK, we trust you, AI,
but we're also going to double check your work just to be safe.
Exactly.
(15:37):
It's about finding that balance between pushing the limits
and being cautious.
We want to see what's possible, but we also
want to make sure we're doing it responsibly.
And Anthropic seems to be walking that line pretty well.
They really do.
OK, but zooming out a bit, what about their big vision for AI?
Where do they see all of this heading,
and how does Anthropic fit into that picture?
(15:58):
What's the end game here?
Well, Amadeus has talked about a future
where AI could help us tackle some of the biggest challenges
we face as a species.
No kidding.
Yeah, like climate change, disease poverty, things
that have plagued us for centuries.
He thinks AI could be a game changer.
So it's not just about building cool tech
for the sake of it.
It's about using that tech to actually make a difference.
(16:19):
Exactly.
They see AI as a way to boost our own capabilities,
help us solve problems that have seemed impossible for so long.
That's a pretty optimistic outlook.
But I'm sure they're not blind to the potential downsides.
Of course not.
They know there are risks, and they're
working hard to figure out how to avoid them.
That's why their focus on safety and ethics is so crucial.
(16:41):
They want to make sure that as AI gets more powerful,
it stays on the side of good.
It's like they're pioneers charting a course
through uncharted territory, trying
to steer clear of the dangers while still keeping
their eyes on the prize.
And they're doing it in a way that feels very thoughtful
and open.
They're not just working in isolation.
They're talking to experts outside the tech world,
(17:03):
collaborating with policymakers and ethicists
to make sure AI is developed and used
in a way that benefits everyone.
That's a big deal.
It's not just about profits.
It's about making the world a better place.
Right.
It's about thinking about the big picture.
And that's what I find so inspiring about Anthropic.
Couldn't have said it better myself.
Well, this has been an incredible deep dive
into Anthropic.
We've learned so much about their commitment to safety,
(17:26):
their groundbreaking research into mechanistic
interpretability, and their vision for a future
where AI is a force for good.
They're definitely pushing the boundaries,
while also staying true to their values.
And it's clear they're not afraid to ask
the tough questions, to really grapple with what
it means to build AI that is both powerful and ethical.
(17:46):
They're a company that's worth keeping an eye on,
that's for sure.
Absolutely.
So listener, if you're interested in the future of AI,
remember what we've learned from Anthropic.
Be curious, be critical, and most importantly, be engaged.
The future of AI isn't some predetermined thing.
It's something we're all creating together.
And the choices we make today will
shape the world of tomorrow.
That's a great note to end on.
(18:07):
Thanks for joining us on this deep dive into Anthropic.
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