Ready for a deep dive.
Always.
Well, today we're diving into the world of AI
and specifically is potential
to like revolutionize scientific discovery.
Sounds ambitious.
Yeah, and to help us kind of navigate
this complex landscape,
we're turning to the insights of Demis Hasabis.
Oh, yes.
The co-founder of DeepMind.
Exactly.
And we're pulling from a fascinating conversation
that he had with Chris Anderson from TED.
I'm familiar with that one.
It's a good one.
Lots of interesting insights.
Yeah.
So our mission for today, I guess you could say,
is to kind of explore Hasabis' journey,
how he got started,
what led him to these groundbreaking AI breakthroughs.
And maybe, just maybe, we'll uncover some clues
about unlocking the secrets of the universe along the way.
You never know, right?
You never know.
So Hasabis' background, it's really fascinating.
Kind of all seems to stem from this childhood obsession he had.
Oh, you mean chess.
Yeah.
Chess.
The guy was basically a prodigy.
I'm captaining England's under-11 chess team
at just nine years old.
Nine years old?
Nine years old.
I mean, that's insane.
Most kids at that age are just figuring out
how the pieces move.
Yeah.
You know?
Totally.
But he's already leading a national team.
Like, wow.
Right.
That early fascination, I think, with strategy, you know,
with the intelligence that's programmed
into those early chess computers.
It's like, that's what really, I think,
ignited this lifelong passion.
Understand intelligence.
Yeah, I understand intelligence.
And ultimately, to recreate it.
It looks like his brain was kind of wired for strategy
from the get-go.
It seems that way.
So speaking of strategy, this kind of strategic thinking
led DeepMind to some really interesting early work
using games.
Oh, you're talking about the Atari stuff.
Yeah, like breakout.
Ah, breakout of classic.
Classic, right?
So they trained in AI to play it.
And the results were, well, they were surprising,
to say the least.
OK, I'm intrigued.
What happened?
Did the AI just become super fast at bouncing the ball?
Well, yeah, it won.
But the way it won, that's what was unexpected.
So the AI actually discovered this strategy.
Well, this is a strategy.
This strategy that most human players wouldn't even think of,
it learned to tunnel behind the wall.
Wow.
Creating this kind of shortcut to victory.
So it wasn't just following pre-programmed rules.
No, it was coming up with its own solution.
It's learning.
It was adapting.
Wow.
And it was coming up with solutions
that even its creators hadn't considered.
OK, so that's one of those aha moments.
I love in AI research.
It's like, wait a minute.
The machine is outsmarting us already.
Right.
And this was just a glimpse of what was to come.
A glimpse.
Yeah.
So they took these learnings from breakout,
and they developed AlphaGo, which then
went head to head with the world champion, Lisa Doll,
in the game of Go.
OK, now Go.
This is where it gets really interesting.
Go is incredibly complex.
It is.
I mean, there are more possible moves in Go
than there are atoms in the universe.
That's what they say.
It's just mind-boggling.
It really is.
And I've heard it said that Go is a game of intuition
as much as it is strategy.
So when AlphaGo was first announced,
I remember there was a lot of debate
about whether an AI could truly grasp those nuances,
those subtle aspects of the game.
Yeah.
I mean, Go was considered this grand challenge in AI,
something that experts thought was decades away,
before a machine could even come close.
But AlphaGo didn't just win.
It does.
No, it didn't just win.
It invented entirely new strategies.
Really?
Yeah, revealing weaknesses in traditional Go thinking
that had gone unnoticed for centuries.
For centuries.
So it's not just playing the game.
It's pushing the boundaries of the game itself.
Right.
It's like it made people realize that AI wasn't just
about brute force calculation.
It was showing signs of creativity.
Yeah, creativity, strategic thinking.
It was exhibiting a level of, I don't know,
it seemed almost human.
OK, so if you can master something as complex as Go,
what else can AI achieve?
Well, they didn't stop there.
They created AlphaZero.
Oh, AlphaZero, OK.
Which took this whole concept of self-learning
to a whole new level.
OK, so if AlphaGo was like a prodigy,
AlphaZero was like the ultimate grandmaster.
Pretty much, yeah.
AlphaZero learned to play not just Go, but also chess
and shokie.
All from scratch.
Entirely from scratch.
No pre-programmed strategies.
No, no human guidance.
Just pure learning.
From the ground up, and here's the kicker,
it reached superhuman level play in these games
within just a few hours.
A few hours?
I mean, come on.
These are skills that take humans years, decades to achieve.
And it did it in hours.
That's the power of AI learning.
That's the power.
It makes you reconsider the limitations we put on learning
and skill development.
It's like, what else could we crack
with this kind of learning power?
What other seemingly insurmountable problem?
Well, one of those insurmountable problems
might just be next on Deep Mind's list.
What's that?
Protein folding.
Yes.
This is where Deep Mind's work takes a giant leap,
from the world of games into real-world scientific breakthroughs.
Yeah, because proteins, those tiny building blocks of life,
have been a bit of a mystery.
Oh, for a long time.
Yeah.
We know their 3D shape determines their function.
But predicting that shape from its amino acid sequence.
That's been a challenge, a challenge for decades.
Yeah, for over 50 years, researchers
have been painstakingly unraveling protein structures one by one.
And this process could take years for a single protein.
Years.
Yeah.
Incredibly time-consuming, resource-intensive.
And that's where Deep Mind's alpha fold comes in.
The AI specifically designed to crack the protein folding code.
And crack it, it did.
It did.
OK, I have to ask, how did alpha fold change the game?
It'll imagine a billion years.
A billion.
A billion years of PhD research time,
compressed into a single year.
That's essentially what alpha fold achieved.
It predicted the structure of all 200 million.
200 million.
200 million known proteins.
In a year.
That's just mind-blowing.
It is.
But we're talking about tiny, complex structures here.
How do we even know if those predictions are accurate?
That's a good question.
They compared alpha fold's predictions
to experimentally determined structures.
And?
The results.
Astonishingly accurate, matching experimental data
down to the atomic level.
So alpha fold isn't just spitting out random shapes.
Nope.
It's generating these really precise models
that reflect reality.
Exactly.
And to really grasp the magnitude of this,
we need to visualize it.
We've got some images of these protein structures,
both as predicted by alpha fold and as determined
through experiments.
OK, I'm looking at these images now.
And wow, they're beautiful.
Like intricate works of art.
I can see why it would take a billion years to figure this out
using traditional methods.
The complexity is breathtaking.
And the fact that alpha fold can predict these structures
so accurately, well, it's a game changer.
For biology.
And medicine.
Yeah.
I mean, this has to have huge implications for drug
discovery through understanding diseases,
and maybe even curing diseases that have plagued us
for centuries.
Absolutely.
It's the perfect example of how AI
can accelerate scientific progress in ways
we never thought possible.
And the crazy thing is this might just be the beginning.
Yeah.
Alpha fold could just be one example
of what AI can unlock in the scientific world.
So if you take a step back and look at the big picture,
alpha fold is just one piece of the puzzle
when it comes to AI's potential to solve what
Hasabah calls root node problems.
Root node problems.
He uses this really interesting analogy
of a tree of knowledge.
I like that analogy.
Yeah.
So how does this tree of knowledge work?
OK.
So imagine all the knowledge in the universe
represented as this vast sprawling tree.
OK.
With branches reaching out in countless directions,
representing different fields of knowledge,
the knowledge we have today, just a tiny fraction
of this vast tree.
So we've only explored a few branches,
and it's a whole mass of trees still waiting to be discovered.
Exactly.
OK.
And Hasabah sees AI as the tool that
can help us explore that entire tree,
uncovering branches of knowledge.
That we haven't even imagined yet.
And root node problems are the keys
to unlocking those new branches.
Exactly.
Solving a root node problem opens up
a whole new branch of that tree, leading
to vast new areas of research and discovery.
So like the protein folding problem, right?
By solving that, AlphaFold essentially unlocked
a whole new branch of biology and medicine.
Precisely.
And Hasabah believes there are countless other root node
problems out there waiting to be solved.
In all sorts of fields.
In all sorts of fields, physics, cosmology, energy,
materials, science.
I mean, who knows what breakthroughs await us.
This is getting me really excited about the possibilities.
AI could revolutionize the way we approach scientific discovery.
It could.
But you know, with great power comes great responsibility,
right?
Of course, yeah.
As we delve deeper into this world of AI-driven science,
there are some ethical considerations we can't ignore.
That's a good point.
We can't just get caught up in the excitement of all this
discovery and forget about the potential downsides.
Exactly.
And Hasabah himself highlights one of these concerns,
something he calls the Mollok trap.
The Mollok trap.
OK, that sounds a little ominous.
What is that?
Well, it refers to the risk that companies driven
by intense competition may be pushed to develop AI
in potentially harmful ways.
Even if it goes against their better judgment.
Even if those actions go against the better judgment
of the individuals involved.
So it's like a runaway train where everyone is on board,
but no one's quite sure how to apply the brakes.
That's a great analogy.
Thanks.
And this is precisely why Hasabah emphasizes the need
for collaboration, thoughtful development, ethical frameworks.
To ensure that AI is developed and used safely for all of humanity.
For all of humanity.
Yeah, that makes a lot of sense.
But there's another factor that worries me.
The sheer scale of resources being poured into AI development,
particularly by those tech giants we were talking about,
we're talking billions, even hundreds of billions of dollars.
I know.
Massive data centers, supercomputers.
It's just staggering.
And I wonder if this intense competition might lead to someone
taking a shortcut.
Pushing an AI system too far, too fast,
in an attempt to gain an edge.
It's like a high stakes poker game,
where the players are tempted to go all in,
even if they're not sure what cards the other players are holding.
That's a good analogy.
And Hasabah recognizes this risk.
He does.
He does, yeah.
He suggests we need to be proactive in establishing safeguards
to prevent those kinds of scenarios.
OK, so how do we do that?
He proposes that instead of trying to bolt on safety measures
after the fact, we should focus on developing safe and robust AI
architectures from the outset.
So building in safety from the ground up?
From the ground up.
Ensuring that these systems are inherently designed
to operate within certain ethical and safety parameters.
He even suggests it might be possible to develop AI
architectures with mathematical or practical guarantees
around their behavior.
So almost like a guarantee that they'll operate as intended.
Right.
Providing a higher level of assurance?
That would certainly alleviate some of the fears
about AI going rogue or something like that.
Right.
It's a complex challenge, but one
that Hasabah believes we can overcome.
Through collaboration.
Through collaboration, careful planning,
and a constant focus on those ethical considerations.
Yeah.
He remains optimistic about the future of AI.
Seeing it as this powerful tool that
can help us unlock extraordinary possibilities.
So what are some of those possibilities?
What's the big picture vision that fuels his optimism?
Well, remember that Tree of Knowledge analogy we talked about?
Yeah.
He sees AI as the ultimate explorer,
charting these uncharted territories of knowledge,
pushing the boundaries of human understanding.
Right.
The idea that AI can help us discover whole new branches
of knowledge that we haven't even imagined yet.
Exactly.
And he believes AI can help us tackle those root node
problems, those fundamental challenges that once solved
open up whole new fields of research and discovery,
like alpha fold solving the protein folding problem.
Which has the potential to revolutionize medicine.
Exactly.
And Hasabah believes there are countless other root node
problems out there in fields ranging from physics
and cosmology to energy and material science.
He even talks about using AI to tackle
some of the biggest questions humanity has ever pondered.
Like what kinds of questions?
Questions about the fundamental nature of reality.
Whoa.
The origins of the universe.
Maybe even the meaning of life itself.
Wow.
Those are some truly grand aspirations.
It's inspiring.
But also a little daunting to think about the scale
of what he's envisioning.
It is.
But Hasabah truly believes that AI, if developed and used
responsibly, can be the key to unlocking answers
to these profound questions.
He talks about a future of radical abundance,
a future where disease is eradicated,
consciousness expands beyond Earth,
and human potential reaches new heights.
That's a vision that sparks both awe and, I have to admit,
a bit of healthy skepticism.
Of course.
It's important to be excited about the possibilities,
but also to remain grounded and aware
of the potential risks.
So as we stand at this crossroads,
with AI rapidly advancing and its potential impact
on humanity becoming increasingly clear,
what's the takeaway for our listeners?
What should they be thinking about as they navigate
this complex and ever-evolving landscape?
That's a great question.
I think the key is to approach AI with a sense
of informed optimism.
Informed optimism.
Yeah.
We need to be aware of the potential risks,
but also open to the extraordinary possibilities
that AI offers.
So embrace the potential while also advocating
for responsible development and those ethical considerations.
Exactly.
It's a balancing act.
But a crucial one if we want to harness the power of AI
for the benefit of all humankind.
Yeah.
It's like finding that balance between pushing
the boundaries of AI, but also ensuring it's
developed responsibly.
That seems like it's going to be one of the big challenges
moving forward.
Oh, it's a tight rope walk, for sure.
And the rapid release of these powerful AI models
like ChatGPT, that really brought this whole tension
into focus, I think.
Yeah.
It felt like it came out of nowhere.
And all of a sudden, everyone's talking about AI,
experimenting with it.
Realizing its potential in a whole new way.
It was a turning point, for sure.
I mean, AI went from this kind of niche topic
to a mainstream phenomenon almost overnight.
And it definitely shook things up in the tech world.
Like Microsoft, with its backing of open AI,
was looking to, I don't know, challenge
Google's dominance in search.
Oh, yeah.
And they certainly made Google scramble to catch up.
Google definitely danced as Microsoft's CEO put it.
Yeah.
Suddenly, they're rushing to release their own AI products
like Gemini.
It felt like that MoLok trap in action.
With these tech giants being driven by competition
to push the boundaries of AI development,
maybe faster than they would have otherwise.
Yeah, it's an interesting dilemma, isn't it?
This rapid progress, it's exciting.
But it also raises some valid concerns.
Even Hasada acknowledges that these systems,
as impressive as they are, still have flaws.
Like what?
Like generating incorrect or misleading information,
those what people call hallucinations.
Right.
And those hallucinations can be a real problem,
especially if people start relying on these AI
systems for critical information without understanding
their limitations.
Exactly.
And then there's the whole open source debate,
like Meta, for example, released its own large language model
as open source, making the technology freely available
for anyone to use and modify.
That sounds both incredibly democratizing
and potentially terrifying, all at the same time.
Yeah, it's a double-edged sword, for sure.
On one hand, you have the potential
to accelerate innovation, make this powerful technology
accessible to a wider range of people.
But on the other hand, you have concerns about safety,
control, the potential for misuse.
It's a lot to grapple with.
So given all these complexities, like how do we move forward?
How do we ensure that AI is developed and used responsibly
as we enter this new era of increasingly powerful systems?
Well, Hussabus believes that collaboration is key.
Collaboration.
Especially as we get closer to developing
artificial general intelligence, or AGI.
AGI, that's the idea of AI that can perform
any intellectual task a human can.
So essentially, AI that's as smart as or even smarter
than humans.
Exactly.
And Hussabus argues that as we get closer to that point,
we need to shift our mindset from competition
to cooperation.
I could see why that would be important.
But is it realistic?
In a world that's so driven by technological advancement
and economic competition, can we really
expect companies, countries even, to just put aside
their rivalries and work together?
Well, Hussabus believes it's not just idealistic,
it's essential for the safe and beneficial development of AGI.
He points out that many of the leading AI scientists
around the world, they already know each other.
They communicate.
They collaborate to some extent.
So there's already a foundation of trust and shared
understanding in place.
Right.
And Hussabus argues that that foundation needs
to be strengthened and expanded to include governments,
civil society, academia.
On the stakeholders.
All the stakeholders.
He sees this as a crucial step towards establishing
those ethical frameworks, guidelines for AI development.
So it's like creating a global set of rules of the road for AI,
ensuring that everyone's driving on the same side,
following the same traffic signals.
Exactly.
We need to ensure that the development and deployment
of these powerful AI systems align with human values
and prioritize safety and well-being.
That makes a lot of sense.
But there's another factor that worries me.
The sheer scale of resources being poured into AI development,
particularly by those tech giants we were talking about.
We're talking billions, even hundreds of billions of dollars
being invested in massive data centers and supercomputers.
Yeah, the scale of investment is staggering.
And it's understandable to wonder
if all this intense competition might lead to someone
taking a shortcut, pushing an AI system too far too fast
in an attempt to gain an edge.
It's like a high stakes poker game
where the players are tempted to go all in,
even if they're not sure what cards the other players are
holding.
That's a good analogy.
And Hussabus recognizes this risk.
He does.
He suggests that we need to be proactive in establishing
safeguards to prevent those kinds of scenarios.
Like what kinds of safeguards?
Well, he proposes that instead of trying
to bolt on saty measures after the fact,
we should focus on developing safe and robust AI
architectures from the outset.
So building in safety from the ground up?
From the ground up.
Ensuring that these systems are inherently designed
to operate within certain ethical and safety parameters.
He even suggests that it might be possible to develop
AI architectures with mathematical or practical
guarantees around their behavior.
So almost like a guarantee that they'll
operate as intended.
Providing a higher level of assurance.
That would certainly alleviate some of the fears
about AI going rogue or something like that.
It's a complex challenge, but one
that Hussabus believes we can overcome
through collaboration, through careful planning,
and a constant focus on those ethical considerations.
He remains optimistic about the future of AI,
seeing it as this powerful tool that can help us unlock
these extraordinary possibilities.
So what are some of those possibilities?
What's the big picture vision that fuels his optimism?
Well, remember that Tree of Knowledge analogy.
He sees AI as the ultimate explorer,
charting these uncharted territories of knowledge,
pushing the boundaries of human understanding.
Right.
The idea that AI can help us discover whole new branches
of knowledge that we haven't even imagined yet.
Exactly.
And he believes that AI can help us tackle those root node
problems, those fundamental challenges that, once solved,
open up whole new fields of research and discovery,
like alpha fold, solving the protein folding problem.
Which has the potential to revolutionize medicine.
Precisely.
And Hussabus believes there are countless other root node
problems out there and feels ranging from physics and cosmology
to energy and material science.
He even talks about using AI to tackle
some of the biggest questions humanity has ever pondered.
Like what kinds of questions?
Questions about the fundamental nature of reality,
the origins of the universe, maybe even
the meaning of life itself.
Wow, those are some truly grand aspirations.
It's inspiring, but also a little daunting
to think about the scale of what he's envisioning.
It is.
But Hussabus truly believes that AI, if developed and used
responsibly, can be the key to unlocking answers
to these profound questions.
He talks about a future of radical abundance,
a future where disease is eradicated,
consciousness expands beyond Earth,
and human potential reaches new heights.
It's a vision that sparks both awe
and I have to admit a bit of healthy skepticism.
Of course.
It's important to be excited about the possibilities,
but also to remain grounded and aware
of the potential risks.
So as we stand at this crossroads,
with AI rapidly advancing and its potential impact
on humanity becoming increasingly clear,
what's the takeaway for our listeners?
What should they be thinking about as they navigate
this complex and ever-evolving landscape?
That's a great question.
I think the key is to approach AI
with a sense of informed optimism.
Informed optimism.
Yeah.
We need to be aware of the potential risks,
but also open to the extraordinary possibilities
that AI offers.
So embrace the potential while also advocating
for responsible development and those ethical considerations.
Exactly.
It's a balancing act, but a crucial one
if we want to harness the power of AI
for the benefit of all humankind.
It's like we're standing on the edge of a whole new era
of exploration.
And AI is the ship that's going to take us there.
I like that.
It's a powerful image.
But as we embark on this journey, it's important to remember,
we all have a role to play in shaping the future of AI.
Right.
It's not just about the big tech companies or research labs.
It's about all of us.
You're engaging in this conversation,
thinking critically about the implications.
It's not just about passively observing.
It's about actively shaping the direction that this all takes.
We need to ask questions, challenge assumptions,
demand transparency and accountability
from those who are developing and deploying these systems.
It's about being informed citizens, not just bystanders.
In this technological revolution that's
happening all around us.
And I think sometimes it's easy to forget
that AI isn't some far-off futuristic concept.
It's already here.
It's already woven into our lives in so many ways.
From the recommendations we see online
to the medical diagnosis we receive,
it's shaping our world whether we realize it or not.
And its influence, it's only going to grow in the years to come.
So we better start paying attention.
We better start paying attention.
We need to ensure that AI is used to enhance human well-being,
to promote fairness, justice, to create a more
sustainable and equitable world.
Those are some pretty lofty goals, but they're essential ones.
If we want to create a future where AI benefits everyone.
They are.
And you know, Sasabas, despite all the challenges
and all the complexities, he remains optimistic.
He really believes that AI, if developed and used wisely,
can be the key to solving some of humanity's greatest
challenges and unlocking this future of extraordinary
possibilities.
So as we wrap up this deep dive into Dimus'
Sasabas' world, his vision for the future of AI,
I'm left with a question for our listeners.
If AI has the potential to be this incredible tool
for exploration and discovery, what root node problems
might it help you solve?
What breakthroughs are waiting to be unlocked in your field,
in your life, in the world around you?
Keep your eyes open because the future of AI
is unfolding right now.
And it's going to be an incredible journey.