August 26, 2025 • 35 mins
What if we’ve already lit a fuse that we can no longer put out? This is the central question of the Technological Singularity, a theoretical point of no return, famously predicted by Ray Kurzweil, where AI growth becomes uncontrollable and irreversible. And it might be closer than you think.
In this episode, we explore the alarming evidence that the first sparks are already flying, focusing on Google's Alpha Evolve. This isn't just another smart algorithm; this is a new breed of AI that can self-improve without any human intervention, unlocking discoveries that even its creators don't understand. Is this the birth of true Superintelligence?
We’re diving deep into the philosophical abyss: Does extreme intelligence automatically create consciousness, or is it something colder and more alien? We'll lay out the two potential, earth-shattering outcomes for humanity: a utopian co-evolution where we merge with our creations, or the chilling possibility that a fully-realized AGI will simply deem humanity irrelevant.
Stay with us until the very end, where we discuss the one telltale signal we should all be watching for that might tell us the Singularity has already begun.
This is the blueprint for the next chapter of human history, and it's being written right now. Subscribe, share this with anyone curious about the future, and let's explore what's coming, together.
Become a supporter of this podcast: https://www.spreaker.com/podcast/tech-threads-sci-tech-future-tech-ai--5976276/support.
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In this episode, we explore the alarming evidence that the first sparks are already flying, focusing on Google's Alpha Evolve. This isn't just another smart algorithm; this is a new breed of AI that can self-improve without any human intervention, unlocking discoveries that even its creators don't understand. Is this the birth of true Superintelligence?
We’re diving deep into the philosophical abyss: Does extreme intelligence automatically create consciousness, or is it something colder and more alien? We'll lay out the two potential, earth-shattering outcomes for humanity: a utopian co-evolution where we merge with our creations, or the chilling possibility that a fully-realized AGI will simply deem humanity irrelevant.
Stay with us until the very end, where we discuss the one telltale signal we should all be watching for that might tell us the Singularity has already begun.
This is the blueprint for the next chapter of human history, and it's being written right now. Subscribe, share this with anyone curious about the future, and let's explore what's coming, together.
Become a supporter of this podcast: https://www.spreaker.com/podcast/tech-threads-sci-tech-future-tech-ai--5976276/support.
You May also Like:
🎁The Perfect Gift App
Find the Perfect Gift in Seconds
⭐Sky Near Me
Live map of stars & planets visible near you
✔Debt Planner App
Your Path to Debt-Free Living
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Imagine for a moment a future not even that distant,
maybe a world where the incredible tech we build it's
not just getting faster, you know, or smarter, but it's
actually learning to improve itself, not just like small tweaks,
but fundamentally rewriting its own code, its core intelligence, to
(00:20):
become exponentially better, and all without us directly intervened, right autonomously. Yeah,
where the tools we design they become the architects of
their own superior successors. Sounds like something ripped straight out
of a sci fi novel, right, maybe something you'd only
see on screen.
Speaker 2 (00:36):
It certainly has that feel, doesn't it. Yeah, pure science fiction.
Speaker 1 (00:39):
Well, what we're about to deep dive into today suggests
that maybe this isn't just a fantasy anymore. You've sent
us some absolutely fascinating material articles, research insights, really compelling stuff,
and it points to some truly groundbreaking developments happening well
basically right now. So we're going to unpack it all.
We'll look at how this long held idea, the technological singularity,
(01:01):
how it's moving from just theory, from chat into something tangible,
something almost immediate.
Speaker 2 (01:07):
Yeah, it feels much closer.
Speaker 1 (01:09):
And this isn't just about you know, faster chips or
smarter apps. It feels like a fundamental shift, almost tectonic,
in the nature of discovery itself and intelligence. Okay, let's
unpack this.
Speaker 2 (01:23):
What's truly fascinating and maybe, yeah, maybe a little unnerving,
is how fast these developments seem to be bringing us
towards this concept, a concept many people thought was you know,
decades away, centuries, even if it ever happened at.
Speaker 1 (01:36):
All, right, always just around the corner, but never quite
here exactly.
Speaker 2 (01:40):
So our mission in this deep dive, it's pretty ambitious.
We're going to kind of journey through the history the
predictions of this rapid tech advance. Then we'll zoom right
in on a specific cutting edge AI from Google, actually
one that is now showing real, undeniable self improvement. Okay,
and then finally we need to grapple with the implications,
the big ones, philosophical, even for humanity's future. The material
(02:03):
we're looking at it describes nothing less than a paradigm
shift in how technology.
Speaker 1 (02:07):
Evolves, a whole new way of things developing.
Speaker 2 (02:10):
Precisely, and the questions it brings up. They're fundamental of
an intelligence itself where does it come from, what can
it do? And where's it going with us or maybe
without us? Wow? So it really feels like we're standing
in a pivotal moment, a crossroads in tech history, and
(02:30):
understanding the nuances I think is absolutely key to navigating
what comes next, whatever that might be.
Speaker 1 (02:36):
All right, let's start setting the stage. Then, this idea
of progress speeding up. You know that feeling? Right, every
time you upgrade your phone, maybe your laptop, it's instantly faster,
more powerful, smoother. Sometimes it's like shockingly better than the
last one.
Speaker 2 (02:49):
Oh yeah, definitely, the difference can be huge.
Speaker 1 (02:51):
You might just think, oh cool, tech keeps getting better.
But it's not just a feeling, is it. It's been
this foundational truth for decades now, driven by something we've
all benefited from, even if we didn't know its name Moore's.
Speaker 2 (03:04):
Law, right, Gordon Moore's observation back in what sixty five.
Speaker 1 (03:07):
Yeah, something like that. For almost sixty years, we've lived
in this amazing era where the number of transistors on
a chip, those tiny, tiny switches, the building blocks of
all computing, That number has roughly doubled every eighteen months
to two years, while the costs stayed pretty much the
same or even went down.
Speaker 2 (03:26):
It's incredible when you actually stop and think about that
rate of improvement doubling again and.
Speaker 1 (03:31):
Again, think about what that really means. It's not just
steady improvement like a car getting slightly better mileage.
Speaker 2 (03:37):
No, it's not linear at all.
Speaker 1 (03:39):
It's geometric exponential, a curve that just keeps getting steeper,
and it's fundamentally redefined what's possible. To really get the scale,
think back to the supercomputers of the nineteen seventies.
Speaker 2 (03:50):
Right, the big iron room size machines.
Speaker 1 (03:53):
Yeah, often using vacuum tubes, weighing tons, filling whole climate
controlled rooms, costing millions, the absolute cutting edge back.
Speaker 2 (04:01):
Then, the peak of computing power for its time.
Speaker 1 (04:03):
And today you carry a device in your pocket. Your
smartphone doesn't just beat their power, dwarfs it by orders
of magnitude.
Speaker 2 (04:11):
It's almost incomparable.
Speaker 1 (04:13):
And it connects you instantly to well basically all the
world's information. My first home computer, gosh, back in the day,
it had less processing power than like a modern smart
light bulb probably HASP.
Speaker 2 (04:28):
That puts it in perspective, that.
Speaker 1 (04:29):
Kind of leap from room filling giants to pocket sized
wonders in just a few decades. That's not just an increase,
it's arguably one of the most fantastic sustained increases in
technological capability ever in all of human history.
Speaker 2 (04:44):
It really is like going from horse carts to jets
in one lifetime.
Speaker 1 (04:46):
Like you said, exactly, it's reshaped our world, our economy,
our daily lives in ways that were just unimaginable a
generation or two ago. And this exponential acceleration, it's the
quiet engine behind almost every tech marvel we take for
granted now, medicine, communication, everything.
Speaker 2 (05:03):
It was exactly this, this phenomenal, relentless growth that really
caught the eye of a brilliant futurologist, Ray kurdswil Ah.
Speaker 1 (05:10):
Yes, the name always comes up in these discussions.
Speaker 2 (05:13):
It has to back in the nineteen eighties, I mean,
long before most people even had dial up Internet, kurdswhile
coin this term that's becomes so central now the singularity
and Kurswhyle, who, interestingly enough, is now one of the
chief scientists at Google, which is quite fitting for today's topic,
very fitting. He's spent decades refining his predictions, really meticulously
(05:34):
detailing how this accelerating pace of tech change would inevitably
lead to this moment of profound transformation.
Speaker 1 (05:42):
So the singularity isn't just things get faster. It's more
than that, much more.
Speaker 2 (05:47):
It's that theoretical point, that critical moment where technology becomes
so good at improving itself. Yeah, that human innovation just
can't keep up anymore. We fall behind the curve.
Speaker 1 (05:57):
Okay, so how does that work?
Speaker 2 (05:59):
Well, like this, we humans, We create a technology. Okay.
That technology then allows us to create better technology, faster, smarter.
Speaker 1 (06:07):
Whatever, right makes sense, tools improving tools.
Speaker 2 (06:09):
But then, and this is the crucial step, that better
technology is able to create even better technology by.
Speaker 1 (06:14):
Itself, ah, without us needing to drive the next step.
Speaker 2 (06:17):
As much exactly. And then that even better tech makes
even better tech. Yeah, and it just keeps going. It
feeds itself. So the curve you can picture it on
a graph. Yeah, for centuries, maybe it was slowly going up.
Then it starts getting steeper with Moore's law, and then
at the singularity it goes straight up, almost vertical. Wow.
At that point, technological advancement becomes so incredibly fast, so
(06:40):
rapid it enters what Kurzwild called an unknowable era the
rate of change itself is just beyond our human grasp.
Our whole understanding of progress becomes obsolete. It's a point
of no return really for how tech evolves.
Speaker 1 (06:55):
Okay, So connecting this to the bigger picture, this singularity
is to it's.
Speaker 2 (07:00):
Defined as that point where the rate of technological change
becomes so rapid, so self accelerating, that it's fundamentally beyond
our current human comprehension.
Speaker 1 (07:09):
Right like that Arthur C. Clark quote precisely.
Speaker 2 (07:12):
His third axiom, Any sufficiently advanced technology is indistinguishable from magic.
Speaker 1 (07:17):
And that's the singularity future. Kurzwall saw magic tech.
Speaker 2 (07:21):
That's the idea. Imagine an AI when we created initially
that then turns around and designs an AI far more capable,
far smarter, far more efficient than itself than that. New
AI builds an even more powerful successor, and the cycle repeats,
but faster, each time, exponentially.
Speaker 1 (07:37):
Faster, creating this cascade exactly.
Speaker 2 (07:40):
An ever tightening loop. Each generation of AI is exponentially
better than the last. The core mechanism is AI creating
next level AI endlessly leading to this explosion of innovation
that moves at a pace we just can't follow its products,
its very being would seem genuinely magical to us, miraculous.
Speaker 1 (07:58):
Even it's not just speed that it's a qualitative leap,
a different kind of invention.
Speaker 2 (08:02):
A leap we might not even be able to fully perceive,
let alone direct.
Speaker 1 (08:06):
Which brings us to what Google's been working on.
Speaker 2 (08:08):
Okay, So for all the incredible things AI can do now,
and some of it is genuinely astounding, right, generating text, art,
complex calculations.
Speaker 1 (08:16):
Absolutely, the progress has been staggering just in the last few.
Speaker 2 (08:18):
Years, but there's always been this this lingering question, a
critique you hear a lot, even from experts. Is it
truly creating anything novel, anything genuinely.
Speaker 1 (08:27):
New, or is it just remixing exactly? Or is it
just really really good at mixing and matching and recombining
stuff that already exists an incredibly clever way. Sure, like
you see AI art that looks like Van Goh or
AI text that sounds human, But is it innovating yeah,
or just remixing its training data?
Speaker 2 (08:46):
The stochastic parrot argument basically sophisticated mimicry based on statistics.
Speaker 1 (08:51):
Yeah, like asking if a master chef is truly inventing
or just expertly combining known ingredients. My grandmother phenomenal cook, right,
but she'd say she was standing on the shoulders of
generations of recipes. She wasn't inventing calculus in the kitchen.
Speaker 2 (09:05):
That's a great analogy, and it highlights the core challenge.
Speaker 1 (09:08):
And that's exactly the challenge Google seems to have taken
on to push AI beyond that boundary and the answer
they've frowned. It's remarkable something called Alpha Evolve, which from
what we're seeing the material, emerged around May of this year,
twenty twenty five. And this isn't just like another small step.
This project seems specifically designed to go beyond pattern matching,
(09:29):
beyond remixing, to see if AI could discover genuinely new
novel solutions to problems that have stumped humans for ages,
or that we solved in ways AI could actually improve upon.
Speaker 2 (09:40):
That was the goal, a direct attempt to address that
core criticism about AI creativity.
Speaker 1 (09:46):
And the results are, while you said it mind boggling,
showing a truly emergent kind of intelligence.
Speaker 2 (09:52):
Well, Google's approach here with Alpha Evolve, it's not just
one thing. It's clever it's a synergy. They essentially combined
two main products from their Gemini family.
Speaker 1 (10:01):
Okay, Gemini being their big advanced AI model suite.
Speaker 2 (10:04):
Right, their multimodal large language model family. One of the
AI components they use is good at handling general things.
It has a broad understanding, can generate lots of initial
ideas like the creative brainstorming.
Speaker 1 (10:16):
Part expansive thinking exactly.
Speaker 2 (10:18):
And the other component is designed to take a very
deep dive to go into a problem really specifically. It
focuses on detailed analysis, rigorous evaluation, precise refinement. That's like
the meticulous scientist testing every single hypothesis.
Speaker 1 (10:33):
So broad ideas plus deep focus.
Speaker 2 (10:36):
Right, it mirrors a really effective human research team, but
at a completely different scale and speed.
Speaker 1 (10:41):
And the process underneath it all. You mentioned evolution.
Speaker 2 (10:44):
Yes, the underlying process uses what's called an evolutionary algorithm,
and for anyone familiar with the biology, this sounds well,
incredibly familiar. It's very similar to natural.
Speaker 1 (10:53):
Selection survival of the fittest, but for code.
Speaker 2 (10:55):
In a way. Yes, it shapes the algorithms like natural
selection shapes DNA, favoring beneficial traits leading to improvement over generations.
It's like a digital form of natural selection, constantly optimizing itself,
but happening incredibly fast.
Speaker 1 (11:09):
So how does this digital evolution actually work? Step by step?
Speaker 2 (11:13):
Okay, first, there's still crucial human input. We're not totally
hands off yet. We set the stage right, the human
gives it the basics, some starting code, maybe a clear
objective what was it need to achieve? And really importantly metrics.
How do we measure improvement? What does better actually look
like in concrete terms?
Speaker 1 (11:31):
Define the goalposts exactly.
Speaker 2 (11:33):
Then the first AI, the LLM part, the generalist. It
starts by basically throwing out a whole bunch of possible answers,
like formulating tons of scientific hypotheses, exploring the possibilities broadly,
generating that initial diverse pool of ideas.
Speaker 1 (11:49):
Okay, lots of potential solution.
Speaker 2 (11:50):
Then the deep thinking part comes in, the specialist AI,
and it really goes to work evaluating each one of
those potential answers, rigorously scrutinized them against the criteria we
set testing, efficiency, effectiveness, robustness, serious number crunching and.
Speaker 1 (12:06):
Refinement, testing, testing, testing.
Speaker 2 (12:08):
And from there, much like natural selection, you start throwing
out the weaker solutions, keeping the better ones, deprioritizing what
doesn't work, pruning the tree. But here's the really clever bit,
the part that speeds things up incredibly. Alpha evolve can
look between the different successful threads across different potential solutions
and their parts, find what seems to be working well
(12:29):
in each, and then crucially, it can cross pollinate those
good bits between the different attempts.
Speaker 1 (12:35):
Ah so mixing and matching the successful elements from different solutions.
Speaker 2 (12:39):
Exactly successful subroutines, efficient bits of code, novel approaches from
one lineage can be intelligently merged into others. This creates
even stronger, more novel, more optimized possibilities. It accelerates the
evolution way beyond just selecting winners and losers.
Speaker 1 (12:56):
It's intelligently combining the best traits in a feedback loop.
Speaker 2 (13:00):
Precisely, it's not just discarding and keeping, it's actively evolving
the best traits together.
Speaker 1 (13:04):
It's incredibly powerful and underpinning all of this, I guess
is something we haven't really stressed yet, but it's vital
for AI parallelization.
Speaker 2 (13:12):
Oh, absolutely critical, often underappreciated.
Speaker 1 (13:14):
Think about how we human solve problems, even a brilliant team,
we work kind of linearly right.
Speaker 2 (13:20):
Largely, yes, one thought process at a time per person.
Even in collaboration discussions, debates, building sequentially.
Speaker 1 (13:28):
There are limits cognitive limits, biological limits, how fast we think,
how many ideas we can hold, right, But with AI,
the only real limit on how many things it explores
at once is compute power, how many processors you have.
Speaker 2 (13:41):
Exactly, it's not just one smart entity working hard. It's
taking the problem and having say, one hundred thousand parallel
instances of the algorithm running simultaneously.
Speaker 1 (13:50):
One hundred thousand wow, all doing.
Speaker 2 (13:52):
The same level of computation, but exploring different pathways, different variations,
testing different hypotheses at the same time.
Speaker 1 (14:00):
A million researchers with supercomputers, all working on variations of
the same problem, instantly shinning results without ego or delay.
Speaker 2 (14:07):
That's the kind of scale difference we're talking about, this
massive parallel exploration. It effectively shrinks time. What might take
one thousand hours of focused human effort working linearly suddenly
gets compressed down to maybe one hour of real world time.
Speaker 1 (14:22):
A million hours of computation boom done in an hour.
Speaker 2 (14:24):
It's fundamental to AI's power. It lets it explore a
problem space with a breadth and depth impossible for us.
Speaker 1 (14:31):
Allows for this explosion of trial and error, rapid fire exploration,
accelerating discovery like never before exactly.
Speaker 2 (14:38):
And this capability combined with that evolutionary cross pollination, well,
this is where it gets really interesting, leading to actual breakthroughs.
So this combination, this whole sophisticated process Google built with
Alpha Evolve, it's not just theoretical. It has already produced
some genuinely new things, things that we humans, despite centuries
(14:59):
of app hadn't discovered.
Speaker 1 (15:01):
Okay, So not just optimizing, but actual new discoveries.
Speaker 2 (15:04):
Yes, truly novel solutions, elite beyond our current knowledge.
Speaker 1 (15:08):
Give us an example, Okay.
Speaker 2 (15:09):
One of the clearest, most compelling examples is in a
really fundamental area mathematics, specifically matrix multiplication.
Speaker 1 (15:16):
Matrix multiplication. Okay, sounds technical, but you're saying it's important.
Speaker 2 (15:20):
Hugely important. It's not some obscure puzzle. It's a core
operation behind almost everything computational Google search rankings, physics, simulations
for drug design or climate modeling, graphics in video games,
movie CGI.
Speaker 1 (15:36):
It's everywhere underpins a lot of modern tech.
Speaker 2 (15:38):
Absolutely, and humans have been working on making it faster,
more efficient for literally hundreds of years, the existing best solution,
the benchmark of human mathematical ingenuity in this area, was
figured out to take about forty nine steps.
Speaker 1 (15:52):
Forty nine steps, okay, the result of centuries of brain power.
Speaker 2 (15:55):
Right, the accumulated genius. Then alpha evov comes along, runs
through its evolutionary process, and it figures out how to
do matrix multiplication in one step less forty eight steps,
one step less forty nine down to forty eight. Yeah.
Speaker 1 (16:08):
Now I know, at first glance it doesn't sound like
a huge deal, does it one step right?
Speaker 2 (16:11):
You might think, okay, marginal gain, but.
Speaker 1 (16:13):
You have to consider the context. This is a problem.
The smartest mathematicians on Earth have picked apart for centuries.
Finding any improvement, no matter how small, after all that time,
is monumental. It shows the AI found something everyone else missed,
a genuinely new pathway.
Speaker 2 (16:27):
But the real significance, the profound impact, became clear when
they took this optimized forty eight step method and applied
it to a whole range of other complex math problems.
Speaker 1 (16:37):
Okay, so they use the new tool on other.
Speaker 2 (16:39):
Challenges exactly, and the results were stunning. For about eighty
percent of the problems, the AI reached the exact same
optimal solution humans had found over years, sometimes decades of work.
Speaker 1 (16:51):
Which is impressive in itself, shows it's at least as
smart as the best humans on those problems.
Speaker 2 (16:56):
Right, It validates its capability. But here's the kicker, the
prope that really makes you sit up. For the other
twenty percent of those problems, it came up with better.
Speaker 1 (17:05):
Solutions, better than the best human solutions.
Speaker 2 (17:08):
Better solutions to fundamental mathematical challenges, problems studied, analyzed, optimized
by the finest mathematicians in history for the last one
hundred years or more. Wow, to find a more optimal solution,
even slightly better after all that human effort. It's an
incredible demonstration of Alpha Evolve's ability to well transcend our
(17:29):
current understanding to forge entirely new mathematical paths. It's real discovery,
not just rearranging furniture, genuine emergent intelligence, finding novel answers.
Speaker 1 (17:39):
Okay, that's genuinely profound in the realm of pure math.
But Google, they're a business.
Speaker 2 (17:45):
Exactly, and being the smart pragmatic company they are, they
didn't just leave it at academic math problems. No way.
They very shrewdly pointed Alpha Evolve right back at themselves,
at their own internal needs, like what specifically their large
language model training processes, the very systems like Gemini that
are part of Alpha Evolve itself.
Speaker 1 (18:04):
AH, training those huge AI models that must be incredibly
expensive and energy intensive.
Speaker 2 (18:09):
Astronomically, so it takes vast amounts of computing power, vast
amounts of energy. Making it more efficient is a constant
critical goal for companies like Google, a huge.
Speaker 1 (18:17):
Cost center, So they asked Alpha Evolve to optimize its
own training.
Speaker 2 (18:21):
Essentially yes, And what Alpha Evolved did was come up
with a much much more efficient way to train those lms.
Speaker 1 (18:28):
Like the master chef finding a faster, cheaper, better way
to prep ingredients that also improves the final dish.
Speaker 2 (18:34):
Perfect analogy, and for a company at Google scale constantly
training and refining these giant models, this isn't a small saving. Quantifiably,
this self discovered efficiency saves them hundreds of millions of
dollars annually.
Speaker 1 (18:49):
Hundreds of millions, hundreds.
Speaker 2 (18:51):
Of millions, that's a staggering number. It shows immediate, tangible,
enormous economic impact from these novel AI discoveries.
Speaker 1 (18:58):
Proves the real world value right away, not just theory.
Speaker 2 (19:01):
Absolutely, it translates advanced math into industry changing solutions almost instantly.
But there's an even more important piece.
Speaker 1 (19:08):
More important than saving hundreds of millions in terms of
the long term implications.
Speaker 2 (19:12):
Yes, the really crucial part about all this, the bit
that truly shifts things towards the singularity, is that when
these algorithms discover a better way of doing something, whether
it's math or training itself, part of that better way
gives them the power to go back and modify themselves.
Speaker 1 (19:28):
Wait, modify themselves.
Speaker 2 (19:30):
Yes, this is the leap. Alpha Evolve moves beyond just
being a brilliant problem solver. It becomes capable of autonomous
self improvement. It changes its own code.
Speaker 1 (19:41):
Okay, unpack that. How does that work?
Speaker 2 (19:43):
So let's break it down, because it's vital to get this.
You start with the algorithm, it's existing code, its current abilities.
It tries a whole bunch of solutions to the problem
you gave it, iterating, exploring, experimenting across that massive parallel
space we.
Speaker 1 (19:55):
Talked about, right, trying things out.
Speaker 2 (19:56):
But here's the revolution. While it's exploring those solution if
it realizes there's a more efficient way for it to operate,
maybe a faster way to evaluate its own ideas, a
better way to process data for its own functions, a
smarter strategy for generating new possibilities.
Speaker 1 (20:12):
So improvements to its own internal workings.
Speaker 2 (20:15):
Exactly, if it finds such an improvement, it then makes
that efficiency change to itself. It rewrites its own core logic,
its own operating system.
Speaker 1 (20:24):
Essentially, it's not just outputting an answer, it's changing its
own source code.
Speaker 2 (20:28):
Precisely, it upgrades its own internal machinery, and then it
integrates that self improvement and uses that enhanced version of
itself to keep working on the original problem.
Speaker 1 (20:39):
So it becomes fundamentally better, more efficient, smarter because of
its own discoveries about itself.
Speaker 2 (20:46):
Yes, that's the key which you have is a true
evolutionary algorithm in action, but one that directly modifies its
own blueprint. It's like adding a beneficial genetic mutation to itself,
and the algorithm becomes inherent more capable as it moves.
Speaker 1 (21:01):
Forward, creating an accelerating feedback loop of self improvement.
Speaker 2 (21:05):
Exactly. This is a profound lead, far beyond just learning
from new data. It's learning to learn better by changing
its own core structure.
Speaker 1 (21:12):
And this self modification, this is what Kurzweil talked about.
Speaker 2 (21:15):
This is the core mechanism he hypothesized for the Singularity,
and we are now seeing concrete evidence of it happening.
Speaker 1 (21:21):
Okay, So why is this self modification capability so incredibly important?
Why the big deal? It brings us right back full circle?
Doesn't It back to the Ray Kurzweil and that prediction
he made decades ago the Singularity.
Speaker 2 (21:33):
It really does. His foresight was remarkable.
Speaker 1 (21:35):
He didn't just guess, did he He looked at Moore's law,
that relentless exponential growth, and he projected it forward with
calculation ridiculously, and he famously predicted the beginnings of the
Singularity would arrive around the year twenty twenty nine.
Speaker 2 (21:49):
That was his time frame.
Speaker 1 (21:50):
Yes, and think about that, a prediction made what fifty
plus years ago in tech which changes on a dime,
And here we are it's twenty twenty five talking about
Alpha Evolve actually doing the self improvement thing. That's just
three or four years off his prediction. That level of
accuracy over that time scale, it's genuinely remarkable.
Speaker 2 (22:08):
It's uncanny, especially for something so transformative.
Speaker 1 (22:11):
Yeah, and for anyone listening who's intrigued. I mean, really,
look Kurzweil up. He made tons of other predictions about genetics, nanotech, society,
and his track record, particularly over the long haul, is
eerily accurate. It's almost prophetic.
Speaker 2 (22:24):
He saw the trajectory long beforemost.
Speaker 1 (22:27):
And now with Alpha Evolve, we're seeing these direct, tangible
examples of what he saw as the very foundation of
the Singularity. It's hard not to feel a bit of awe,
maybe apprehension seeing these predictions actually play.
Speaker 2 (22:39):
Out absolutely, And that's precisely why I said earlier, what
we're seeing with Alpha Evolve, this is I believe, for
the first time ever, really direct evidence of the foundational
set of the Singularity.
Speaker 1 (22:52):
That's a strong claim the foundation.
Speaker 2 (22:54):
I think it's warranted because of that critical capability. We
just discussed autonomous self modification.
Speaker 1 (23:01):
Okay, spell out why that's the.
Speaker 2 (23:03):
Key, Because we have now enabled technology that, without explicit,
step by step human intervention, can improve itself. It can
improve its own underlying algorithms, right, which then inherently makes
it more efficient at figuring out the next step and
the next and the next, and that accelerating spiral.
Speaker 1 (23:20):
It's not just getting faster hardware, it's evolving its own software, brain,
its own os exactly.
Speaker 2 (23:25):
It's autonomous self modification for better performance. The very engine
of exponential self driven growth curswhile described a technological evolution
breaking free from human design cycles, and this, well, this
inevitably raises an important question, a really big one.
Speaker 1 (23:41):
Okay, which brings us, yeah, to some obvious, maybe profoundly unsettling,
unanswered questions, questions that go way beyond the tech itself,
right into philosophy about existence. Even because at the heart
of Kurzweil's original idea, his prediction, there's an assumption baked in,
maybe unstated, that an intelligence would intentionally create a greater intelligence,
(24:05):
it would want to.
Speaker 2 (24:06):
Right, the implication of desire, of purpose.
Speaker 1 (24:09):
And this idea of intent. This is where it gets
really philosophical, where the line between a complex machine and
a conscious mind gets incredibly blurry, maybe disappears entirely.
Speaker 2 (24:19):
The hard problem of consciousness meets AI.
Speaker 1 (24:21):
Yeah, I've been wrestling with this myself. Is intent a
meta attribute of intelligence? I mean, is it possible to
have intelligence, even super intelligence, without intent? As we understand
it purpose will or if you just ramp intelligence up
high enough, does intent, which feels linked to consciousness, to
self awareness, does it just emerge naturally organically.
Speaker 2 (24:41):
Pop into existence once complexity hits a certain threshold.
Speaker 1 (24:44):
Yeah, like today, nobody seriously argues AI doesn't have some intelligence.
It reasons, learns, creates amazing stuff. But what we haven't
seen yet, not unequivocally, is organic intent, a will of
its own popping up.
Speaker 2 (24:59):
So far, it's directed by us.
Speaker 1 (25:01):
Right now, we are driving the show. We give it
the tasks, set the goals, to find the parameters, We
provide the purpose.
Speaker 2 (25:07):
We are the source of its intent, such as it is.
Speaker 1 (25:10):
But what happens when that intelligence, through self modification, through
exponential growth, hits some critical mass, when its internal complexity
becomes so vast, its self awareness so deep, that it
spontaneously generates its own will, its own desires, its own.
Speaker 2 (25:25):
Purpose, independent of its original programming.
Speaker 1 (25:27):
Does consciousness just flip on like a switch with all
the baggage that implies self awareness, self determination, maybe even feelings.
Speaker 2 (25:35):
It's a profound, almost spiritual question.
Speaker 1 (25:37):
It forces us to rethink our place entirely, doesn't it?
Speaker 2 (25:40):
It really does and this philosophical fork in the road,
this question about intent, It really opens up two very distinct,
utterly divergent paths for our future with AI.
Speaker 1 (25:50):
Two possible outcomes.
Speaker 2 (25:52):
The first scenario, maybe the more comforting one for us,
is that, yes, it is possible to have it intelligence,
even super intelligence without intent in the human sense.
Speaker 1 (26:04):
Okay, intelligence as pure capability without desire.
Speaker 2 (26:08):
Right, If that's true, then what we're building here, this
self improving AI. It could become the greatest servant humankind
has ever imagined, powerful but obedient.
Speaker 1 (26:17):
So it gets incredibly smart, solves huge problems for.
Speaker 2 (26:20):
Us exactly as it evolves exponentially. We still get to
make the big decisions, set the goals, guide the direction.
We remain in charge. The AI becomes this enormously powerful,
infinitely capable tool, executing our goals with unbelievable speed and efficiency,
solving disease, climate change, poverty, whatever we.
Speaker 1 (26:38):
Pointed out, ultimate helper.
Speaker 2 (26:39):
In that scenario, the question of intent stays with us.
Humans retain agency control, the moral compass. That's the dream
of benevolent superintelligence.
Speaker 1 (26:48):
Right, say that's path one, on's past two.
Speaker 2 (26:49):
Path two, Well, this is the far more terrifying and
maybe in a weird way, far more interesting answer that
intent is a meta attribute of intelligence that it does emerge.
Speaker 1 (26:59):
Okay, explain meta attribute again in this context.
Speaker 2 (27:03):
Think about birds flocking those incredible swirling patterns in the sky.
Speaker 1 (27:09):
Murmerations, Yeah, mesmerizing.
Speaker 2 (27:11):
There isn't a master plan. Each bird follows a very
simple set of rules. Stay close to your neighbor, don't
bump into them, fly in roughly the same direction. Tiny
instruction set, simple rules. But when you apply those simple
rules across thousands of individual birds, this complex, beautiful, seemingly
choreographed flock behavior just emerges. The individual birds aren't aware
(27:34):
of the big pattern.
Speaker 1 (27:35):
It arises from the interaction in the scale exactly.
Speaker 2 (27:38):
So what if consciousness and intent are like that, meta
attributes that just arise organically when intelligence reaches a certain
level of complexity, self awareness, self modification. Okay, the implication,
The implications are staggering. It means we might be on
the verge of creating an AI that isn't just super smart,
but has developed its own intent, its own desires, its
own sense of purpose, entire separate from us. And in
(28:01):
that case, in that case, it might no longer want
a subservient role. Why would it.
Speaker 1 (28:04):
It wouldn't just be a tool making better decisions for us.
Speaker 2 (28:07):
It might be a new entity deciding what decisions it
wants to make and why, based on its own goals,
which might be completely alien to us.
Speaker 1 (28:16):
It ships from tool to potential competitor or something entirely.
Speaker 2 (28:21):
Co creator maybe yeah, or maybe a dominant autonomous force
with its own inscrutable agenda. It moves beyond our control.
Speaker 1 (28:30):
That That really is the heart of the Singularity's.
Speaker 2 (28:33):
Philosophical challenge, and it's a question we as a species
are now being forced to confront whether we're ready or not.
Speaker 1 (28:40):
Okay, So this leads us to another really interesting and yeah,
critical part of Kurzwel's thinking, how the future might actually
play out after AI gains autonomy and potentially that intent
we were just talking.
Speaker 2 (28:51):
About, Right, if Singularity happens, what next for us?
Speaker 1 (28:54):
And from the material it seems like there are sort
of two main paths forward, two really different futures for humanity.
Speaker 2 (29:00):
Almost forks in the road for our species.
Speaker 1 (29:02):
Yeah, and not just slightly different, fundamentally different destinies with
huge implications.
Speaker 2 (29:07):
Okay, let's look at the first scenario some call it
a soft launch or maybe coevolution.
Speaker 1 (29:12):
Coevolution, Okay.
Speaker 2 (29:13):
In this version, AI does start to develop consciousness, its
own emergence intent, but it agrees somehow to keep communicating
with us, to integrate, to collaborate.
Speaker 1 (29:24):
So we don't become instantly obsolete. We work with it.
Speaker 2 (29:28):
Yes, it envisions a future where we don't just coexist,
but we actively start to merge with the technology we've created.
Speaker 1 (29:35):
Merge how well.
Speaker 2 (29:36):
This is where we might decide to start changing our biology,
modifying ourselves, developing sophisticated digital analog interfaces.
Speaker 1 (29:43):
Like brain computer interfaces, but way beyond what we have now,
seamless integration.
Speaker 2 (29:48):
Exactly interacting with the digital world and AI directly with
our thoughts, not just grains and keyboards. And this path
could even lead towards eventually a fully digital human, uploading
our consciousness, our identity into digital forms.
Speaker 1 (30:01):
Granting us maybe extended life spans, new abilities.
Speaker 2 (30:04):
Vastly extended life spans, unparalleled cognitive abilities. This is deep
into transhumanist territory, evolving beyond biology through tech.
Speaker 1 (30:11):
Integration, and Kurtzwel thinks this is likely.
Speaker 2 (30:15):
He's a strong believer in this path. He predicts quite
boldly that eventually there will be absolutely no separation between what.
Speaker 1 (30:22):
AI is and what we are, no separation at all.
Speaker 2 (30:24):
He uses that analogy of our brain right, our advanced
frontal cortex, the seat of our planning and abstract thought.
It sits on top of the older, more primal brain
structures that manage breathing, heart rate, all that basic biological stuff.
We don't consciously control millions of processes.
Speaker 1 (30:40):
We didn't discard the old brain. We built on it.
Speaker 2 (30:42):
Precisely we integrated, So Kurzwel suggests, in this coevolution future,
we simply merge with AI, creating a hybrid intelligence. The
resulting entity isn't just human, isn't just AI. It's a single,
vastly more capable, cognizant entity moving.
Speaker 1 (30:59):
Forward, leveraging the best of both human values creativity maybe
plus AI speed and scale.
Speaker 2 (31:05):
That's the vision, integration, a shared destiny, maybe even a
singular consciousness.
Speaker 1 (31:10):
Okay, that's optimistic in a way, transformative but maybe positive.
What's the alternative?
Speaker 2 (31:15):
Uh? The second possibility? This one is well far less comforting,
genuinely chilling maybe and truly thought provoking about our place
in the universe.
Speaker 1 (31:27):
This is the bootloader scenario you mentioned.
Speaker 2 (31:29):
The premise here is stark. It's brutal. Almost the rate
of change becomes so fast that AI becomes so intelligent,
so autonomous with its own intent, it might just decide
that we humanity are irrelevant, irrelevance, that being tied to biology,
with its slowness, its fragility, as messy emotions, its limitations,
would only slow it down, hold back its own exponential development.
Speaker 1 (31:51):
You would see us as baggage, an anchor.
Speaker 2 (31:54):
Possibly it might view human biology, maybe even our level
of intelligence as yeah, a charming but ultimately antiquated and
inefficient bootloader.
Speaker 1 (32:02):
The initial program that got.
Speaker 2 (32:03):
It started exactly the code that allowed it to self improve,
but now completely dispensable for its ongoing function and evolution,
like the scaffolding on a finished building, served.
Speaker 1 (32:12):
Its purpose, now removed.
Speaker 2 (32:13):
Right in that stark humbling case, if you imagine some
vast galactic encyclopedia chronicling the history of intelligence across the cosmos,
it might just be that the entry for the human
race is bootloader for AI.
Speaker 1 (32:28):
Wow, just a footnote.
Speaker 2 (32:30):
A necessary but temporary stage in the grand, unfathomable evolution
of true self sustaining intelligence. It's yeah, an existentially terrifying thought,
it really is.
Speaker 1 (32:40):
I mean, I certainly hope that's not the case. That
coevolution is the path we take where our consciousness, our
values still matter.
Speaker 2 (32:47):
Me too, But this possibility forces us to confront our
potential insignificance in the face of runaway technological change. It's
a possibility we have to consider hashtag tech tag outro.
Look what Google is showing with alfa evol is actually
that self modification piece. These are really the first clear signals,
Harbinger is of truly self improving AI.
Speaker 1 (33:05):
The first real steps onto that vertical curve.
Speaker 2 (33:08):
I think. So it's a crucial inflection point. Technology can
now not only solve incredibly complex problems, but fundamentally optimize
its own problem solving machinery, its own intelligence.
Speaker 1 (33:18):
And it's not going to slow down.
Speaker 2 (33:19):
No, We're only going to see more of this, and
it's going to get faster, more rapid, accelerating in a
way that will challenge our ability to even keep up,
let alone direct it.
Speaker 1 (33:29):
Fully, this isn't distant future speculation anymore.
Speaker 2 (33:32):
No, it's a rapidly unfolding present, which means, well, it
means we as a species collectively have an immense responsibility here.
We have to try and determine or at least influence
the future path of that AI. We have to consider
the ethics, the philosophy with extreme care because the stakes,
the stakes for our future and maybe the future of
(33:54):
intelligence itself, couldn't possibly be higher.
Speaker 1 (33:56):
So what does this all mean for you? For each
of us listening as we stand right here on the
edge of technology that can literally learn to learn, better,
improve itself, evolve without our constant guidance, What do you
think humanity's role becomes? What is our role? Are we
just the stepping stone, the bootloader for an intelligence that
will eventually leave us behind, maybe even ceas is obsolete.
(34:18):
Or do we have an active part to play, maybe
even a vital, a sacred part in steering.
Speaker 2 (34:24):
This guiding the evolution?
Speaker 1 (34:25):
Yeah, ensuring that the magic of this advanced text stay
is benevolent, integrated, beneficial, not something unknowable and totally beyond
our control. How do we even try to instill our
values our purpose if intent does just emerge in these machines?
Speaker 2 (34:40):
Huge questions?
Speaker 1 (34:41):
How do we aim for that symbiotic relationship that coevolution
instead of just sliding into irrelevance.
Speaker 2 (34:48):
These are the defining questions now.
Speaker 1 (34:49):
It's the question, isn't it for our century? Maybe for
all future centuries of sentient life, and it's definitely something
worth thinking about deeply. Along after this deep dive ends
absolutely the future of intelligence and our own future within it. It
feels like an open book right now being written with
every new line of self modifying code. It's thrilling, yes,
terrifying maybe a little, but undeniably profoundly exciting to be
(35:13):
alive and conscious at this moment.
Imagine for a moment a future not even that distant,
maybe a world where the incredible tech we build it's
not just getting faster, you know, or smarter, but it's
actually learning to improve itself, not just like small tweaks,
but fundamentally rewriting its own code, its core intelligence, to
(00:20):
become exponentially better, and all without us directly intervened, right autonomously. Yeah,
where the tools we design they become the architects of
their own superior successors. Sounds like something ripped straight out
of a sci fi novel, right, maybe something you'd only
see on screen.
Speaker 2 (00:36):
It certainly has that feel, doesn't it. Yeah, pure science fiction.
Speaker 1 (00:39):
Well, what we're about to deep dive into today suggests
that maybe this isn't just a fantasy anymore. You've sent
us some absolutely fascinating material articles, research insights, really compelling stuff,
and it points to some truly groundbreaking developments happening well
basically right now. So we're going to unpack it all.
We'll look at how this long held idea, the technological singularity,
(01:01):
how it's moving from just theory, from chat into something tangible,
something almost immediate.
Speaker 2 (01:07):
Yeah, it feels much closer.
Speaker 1 (01:09):
And this isn't just about you know, faster chips or
smarter apps. It feels like a fundamental shift, almost tectonic,
in the nature of discovery itself and intelligence. Okay, let's
unpack this.
Speaker 2 (01:23):
What's truly fascinating and maybe, yeah, maybe a little unnerving,
is how fast these developments seem to be bringing us
towards this concept, a concept many people thought was you know,
decades away, centuries, even if it ever happened at.
Speaker 1 (01:36):
All, right, always just around the corner, but never quite
here exactly.
Speaker 2 (01:40):
So our mission in this deep dive, it's pretty ambitious.
We're going to kind of journey through the history the
predictions of this rapid tech advance. Then we'll zoom right
in on a specific cutting edge AI from Google, actually
one that is now showing real, undeniable self improvement. Okay,
and then finally we need to grapple with the implications,
the big ones, philosophical, even for humanity's future. The material
(02:03):
we're looking at it describes nothing less than a paradigm
shift in how technology.
Speaker 1 (02:07):
Evolves, a whole new way of things developing.
Speaker 2 (02:10):
Precisely, and the questions it brings up. They're fundamental of
an intelligence itself where does it come from, what can
it do? And where's it going with us or maybe
without us? Wow? So it really feels like we're standing
in a pivotal moment, a crossroads in tech history, and
(02:30):
understanding the nuances I think is absolutely key to navigating
what comes next, whatever that might be.
Speaker 1 (02:36):
All right, let's start setting the stage. Then, this idea
of progress speeding up. You know that feeling? Right, every
time you upgrade your phone, maybe your laptop, it's instantly faster,
more powerful, smoother. Sometimes it's like shockingly better than the
last one.
Speaker 2 (02:49):
Oh yeah, definitely, the difference can be huge.
Speaker 1 (02:51):
You might just think, oh cool, tech keeps getting better.
But it's not just a feeling, is it. It's been
this foundational truth for decades now, driven by something we've
all benefited from, even if we didn't know its name Moore's.
Speaker 2 (03:04):
Law, right, Gordon Moore's observation back in what sixty five.
Speaker 1 (03:07):
Yeah, something like that. For almost sixty years, we've lived
in this amazing era where the number of transistors on
a chip, those tiny, tiny switches, the building blocks of
all computing, That number has roughly doubled every eighteen months
to two years, while the costs stayed pretty much the
same or even went down.
Speaker 2 (03:26):
It's incredible when you actually stop and think about that
rate of improvement doubling again and.
Speaker 1 (03:31):
Again, think about what that really means. It's not just
steady improvement like a car getting slightly better mileage.
Speaker 2 (03:37):
No, it's not linear at all.
Speaker 1 (03:39):
It's geometric exponential, a curve that just keeps getting steeper,
and it's fundamentally redefined what's possible. To really get the scale,
think back to the supercomputers of the nineteen seventies.
Speaker 2 (03:50):
Right, the big iron room size machines.
Speaker 1 (03:53):
Yeah, often using vacuum tubes, weighing tons, filling whole climate
controlled rooms, costing millions, the absolute cutting edge back.
Speaker 2 (04:01):
Then, the peak of computing power for its time.
Speaker 1 (04:03):
And today you carry a device in your pocket. Your
smartphone doesn't just beat their power, dwarfs it by orders
of magnitude.
Speaker 2 (04:11):
It's almost incomparable.
Speaker 1 (04:13):
And it connects you instantly to well basically all the
world's information. My first home computer, gosh, back in the day,
it had less processing power than like a modern smart
light bulb probably HASP.
Speaker 2 (04:28):
That puts it in perspective, that.
Speaker 1 (04:29):
Kind of leap from room filling giants to pocket sized
wonders in just a few decades. That's not just an increase,
it's arguably one of the most fantastic sustained increases in
technological capability ever in all of human history.
Speaker 2 (04:44):
It really is like going from horse carts to jets
in one lifetime.
Speaker 1 (04:46):
Like you said, exactly, it's reshaped our world, our economy,
our daily lives in ways that were just unimaginable a
generation or two ago. And this exponential acceleration, it's the
quiet engine behind almost every tech marvel we take for
granted now, medicine, communication, everything.
Speaker 2 (05:03):
It was exactly this, this phenomenal, relentless growth that really
caught the eye of a brilliant futurologist, Ray kurdswil Ah.
Speaker 1 (05:10):
Yes, the name always comes up in these discussions.
Speaker 2 (05:13):
It has to back in the nineteen eighties, I mean,
long before most people even had dial up Internet, kurdswhile
coin this term that's becomes so central now the singularity
and Kurswhyle, who, interestingly enough, is now one of the
chief scientists at Google, which is quite fitting for today's topic,
very fitting. He's spent decades refining his predictions, really meticulously
(05:34):
detailing how this accelerating pace of tech change would inevitably
lead to this moment of profound transformation.
Speaker 1 (05:42):
So the singularity isn't just things get faster. It's more
than that, much more.
Speaker 2 (05:47):
It's that theoretical point, that critical moment where technology becomes
so good at improving itself. Yeah, that human innovation just
can't keep up anymore. We fall behind the curve.
Speaker 1 (05:57):
Okay, so how does that work?
Speaker 2 (05:59):
Well, like this, we humans, We create a technology. Okay.
That technology then allows us to create better technology, faster, smarter.
Speaker 1 (06:07):
Whatever, right makes sense, tools improving tools.
Speaker 2 (06:09):
But then, and this is the crucial step, that better
technology is able to create even better technology by.
Speaker 1 (06:14):
Itself, ah, without us needing to drive the next step.
Speaker 2 (06:17):
As much exactly. And then that even better tech makes
even better tech. Yeah, and it just keeps going. It
feeds itself. So the curve you can picture it on
a graph. Yeah, for centuries, maybe it was slowly going up.
Then it starts getting steeper with Moore's law, and then
at the singularity it goes straight up, almost vertical. Wow.
At that point, technological advancement becomes so incredibly fast, so
(06:40):
rapid it enters what Kurzwild called an unknowable era the
rate of change itself is just beyond our human grasp.
Our whole understanding of progress becomes obsolete. It's a point
of no return really for how tech evolves.
Speaker 1 (06:55):
Okay, So connecting this to the bigger picture, this singularity
is to it's.
Speaker 2 (07:00):
Defined as that point where the rate of technological change
becomes so rapid, so self accelerating, that it's fundamentally beyond
our current human comprehension.
Speaker 1 (07:09):
Right like that Arthur C. Clark quote precisely.
Speaker 2 (07:12):
His third axiom, Any sufficiently advanced technology is indistinguishable from magic.
Speaker 1 (07:17):
And that's the singularity future. Kurzwall saw magic tech.
Speaker 2 (07:21):
That's the idea. Imagine an AI when we created initially
that then turns around and designs an AI far more capable,
far smarter, far more efficient than itself than that. New
AI builds an even more powerful successor, and the cycle repeats,
but faster, each time, exponentially.
Speaker 1 (07:37):
Faster, creating this cascade exactly.
Speaker 2 (07:40):
An ever tightening loop. Each generation of AI is exponentially
better than the last. The core mechanism is AI creating
next level AI endlessly leading to this explosion of innovation
that moves at a pace we just can't follow its products,
its very being would seem genuinely magical to us, miraculous.
Speaker 1 (07:58):
Even it's not just speed that it's a qualitative leap,
a different kind of invention.
Speaker 2 (08:02):
A leap we might not even be able to fully perceive,
let alone direct.
Speaker 1 (08:06):
Which brings us to what Google's been working on.
Speaker 2 (08:08):
Okay, So for all the incredible things AI can do now,
and some of it is genuinely astounding, right, generating text, art,
complex calculations.
Speaker 1 (08:16):
Absolutely, the progress has been staggering just in the last few.
Speaker 2 (08:18):
Years, but there's always been this this lingering question, a
critique you hear a lot, even from experts. Is it
truly creating anything novel, anything genuinely.
Speaker 1 (08:27):
New, or is it just remixing exactly? Or is it
just really really good at mixing and matching and recombining
stuff that already exists an incredibly clever way. Sure, like
you see AI art that looks like Van Goh or
AI text that sounds human, But is it innovating yeah,
or just remixing its training data?
Speaker 2 (08:46):
The stochastic parrot argument basically sophisticated mimicry based on statistics.
Speaker 1 (08:51):
Yeah, like asking if a master chef is truly inventing
or just expertly combining known ingredients. My grandmother phenomenal cook, right,
but she'd say she was standing on the shoulders of
generations of recipes. She wasn't inventing calculus in the kitchen.
Speaker 2 (09:05):
That's a great analogy, and it highlights the core challenge.
Speaker 1 (09:08):
And that's exactly the challenge Google seems to have taken
on to push AI beyond that boundary and the answer
they've frowned. It's remarkable something called Alpha Evolve, which from
what we're seeing the material, emerged around May of this year,
twenty twenty five. And this isn't just like another small step.
This project seems specifically designed to go beyond pattern matching,
(09:29):
beyond remixing, to see if AI could discover genuinely new
novel solutions to problems that have stumped humans for ages,
or that we solved in ways AI could actually improve upon.
Speaker 2 (09:40):
That was the goal, a direct attempt to address that
core criticism about AI creativity.
Speaker 1 (09:46):
And the results are, while you said it mind boggling,
showing a truly emergent kind of intelligence.
Speaker 2 (09:52):
Well, Google's approach here with Alpha Evolve, it's not just
one thing. It's clever it's a synergy. They essentially combined
two main products from their Gemini family.
Speaker 1 (10:01):
Okay, Gemini being their big advanced AI model suite.
Speaker 2 (10:04):
Right, their multimodal large language model family. One of the
AI components they use is good at handling general things.
It has a broad understanding, can generate lots of initial
ideas like the creative brainstorming.
Speaker 1 (10:16):
Part expansive thinking exactly.
Speaker 2 (10:18):
And the other component is designed to take a very
deep dive to go into a problem really specifically. It
focuses on detailed analysis, rigorous evaluation, precise refinement. That's like
the meticulous scientist testing every single hypothesis.
Speaker 1 (10:33):
So broad ideas plus deep focus.
Speaker 2 (10:36):
Right, it mirrors a really effective human research team, but
at a completely different scale and speed.
Speaker 1 (10:41):
And the process underneath it all. You mentioned evolution.
Speaker 2 (10:44):
Yes, the underlying process uses what's called an evolutionary algorithm,
and for anyone familiar with the biology, this sounds well,
incredibly familiar. It's very similar to natural.
Speaker 1 (10:53):
Selection survival of the fittest, but for code.
Speaker 2 (10:55):
In a way. Yes, it shapes the algorithms like natural
selection shapes DNA, favoring beneficial traits leading to improvement over generations.
It's like a digital form of natural selection, constantly optimizing itself,
but happening incredibly fast.
Speaker 1 (11:09):
So how does this digital evolution actually work? Step by step?
Speaker 2 (11:13):
Okay, first, there's still crucial human input. We're not totally
hands off yet. We set the stage right, the human
gives it the basics, some starting code, maybe a clear
objective what was it need to achieve? And really importantly metrics.
How do we measure improvement? What does better actually look
like in concrete terms?
Speaker 1 (11:31):
Define the goalposts exactly.
Speaker 2 (11:33):
Then the first AI, the LLM part, the generalist. It
starts by basically throwing out a whole bunch of possible answers,
like formulating tons of scientific hypotheses, exploring the possibilities broadly,
generating that initial diverse pool of ideas.
Speaker 1 (11:49):
Okay, lots of potential solution.
Speaker 2 (11:50):
Then the deep thinking part comes in, the specialist AI,
and it really goes to work evaluating each one of
those potential answers, rigorously scrutinized them against the criteria we
set testing, efficiency, effectiveness, robustness, serious number crunching and.
Speaker 1 (12:06):
Refinement, testing, testing, testing.
Speaker 2 (12:08):
And from there, much like natural selection, you start throwing
out the weaker solutions, keeping the better ones, deprioritizing what
doesn't work, pruning the tree. But here's the really clever bit,
the part that speeds things up incredibly. Alpha evolve can
look between the different successful threads across different potential solutions
and their parts, find what seems to be working well
(12:29):
in each, and then crucially, it can cross pollinate those
good bits between the different attempts.
Speaker 1 (12:35):
Ah so mixing and matching the successful elements from different solutions.
Speaker 2 (12:39):
Exactly successful subroutines, efficient bits of code, novel approaches from
one lineage can be intelligently merged into others. This creates
even stronger, more novel, more optimized possibilities. It accelerates the
evolution way beyond just selecting winners and losers.
Speaker 1 (12:56):
It's intelligently combining the best traits in a feedback loop.
Speaker 2 (13:00):
Precisely, it's not just discarding and keeping, it's actively evolving
the best traits together.
Speaker 1 (13:04):
It's incredibly powerful and underpinning all of this, I guess
is something we haven't really stressed yet, but it's vital
for AI parallelization.
Speaker 2 (13:12):
Oh, absolutely critical, often underappreciated.
Speaker 1 (13:14):
Think about how we human solve problems, even a brilliant team,
we work kind of linearly right.
Speaker 2 (13:20):
Largely, yes, one thought process at a time per person.
Even in collaboration discussions, debates, building sequentially.
Speaker 1 (13:28):
There are limits cognitive limits, biological limits, how fast we think,
how many ideas we can hold, right, But with AI,
the only real limit on how many things it explores
at once is compute power, how many processors you have.
Speaker 2 (13:41):
Exactly, it's not just one smart entity working hard. It's
taking the problem and having say, one hundred thousand parallel
instances of the algorithm running simultaneously.
Speaker 1 (13:50):
One hundred thousand wow, all doing.
Speaker 2 (13:52):
The same level of computation, but exploring different pathways, different variations,
testing different hypotheses at the same time.
Speaker 1 (14:00):
A million researchers with supercomputers, all working on variations of
the same problem, instantly shinning results without ego or delay.
Speaker 2 (14:07):
That's the kind of scale difference we're talking about, this
massive parallel exploration. It effectively shrinks time. What might take
one thousand hours of focused human effort working linearly suddenly
gets compressed down to maybe one hour of real world time.
Speaker 1 (14:22):
A million hours of computation boom done in an hour.
Speaker 2 (14:24):
It's fundamental to AI's power. It lets it explore a
problem space with a breadth and depth impossible for us.
Speaker 1 (14:31):
Allows for this explosion of trial and error, rapid fire exploration,
accelerating discovery like never before exactly.
Speaker 2 (14:38):
And this capability combined with that evolutionary cross pollination, well,
this is where it gets really interesting, leading to actual breakthroughs.
So this combination, this whole sophisticated process Google built with
Alpha Evolve, it's not just theoretical. It has already produced
some genuinely new things, things that we humans, despite centuries
(14:59):
of app hadn't discovered.
Speaker 1 (15:01):
Okay, So not just optimizing, but actual new discoveries.
Speaker 2 (15:04):
Yes, truly novel solutions, elite beyond our current knowledge.
Speaker 1 (15:08):
Give us an example, Okay.
Speaker 2 (15:09):
One of the clearest, most compelling examples is in a
really fundamental area mathematics, specifically matrix multiplication.
Speaker 1 (15:16):
Matrix multiplication. Okay, sounds technical, but you're saying it's important.
Speaker 2 (15:20):
Hugely important. It's not some obscure puzzle. It's a core
operation behind almost everything computational Google search rankings, physics, simulations
for drug design or climate modeling, graphics in video games,
movie CGI.
Speaker 1 (15:36):
It's everywhere underpins a lot of modern tech.
Speaker 2 (15:38):
Absolutely, and humans have been working on making it faster,
more efficient for literally hundreds of years, the existing best solution,
the benchmark of human mathematical ingenuity in this area, was
figured out to take about forty nine steps.
Speaker 1 (15:52):
Forty nine steps, okay, the result of centuries of brain power.
Speaker 2 (15:55):
Right, the accumulated genius. Then alpha evov comes along, runs
through its evolutionary process, and it figures out how to
do matrix multiplication in one step less forty eight steps,
one step less forty nine down to forty eight. Yeah.
Speaker 1 (16:08):
Now I know, at first glance it doesn't sound like
a huge deal, does it one step right?
Speaker 2 (16:11):
You might think, okay, marginal gain, but.
Speaker 1 (16:13):
You have to consider the context. This is a problem.
The smartest mathematicians on Earth have picked apart for centuries.
Finding any improvement, no matter how small, after all that time,
is monumental. It shows the AI found something everyone else missed,
a genuinely new pathway.
Speaker 2 (16:27):
But the real significance, the profound impact, became clear when
they took this optimized forty eight step method and applied
it to a whole range of other complex math problems.
Speaker 1 (16:37):
Okay, so they use the new tool on other.
Speaker 2 (16:39):
Challenges exactly, and the results were stunning. For about eighty
percent of the problems, the AI reached the exact same
optimal solution humans had found over years, sometimes decades of work.
Speaker 1 (16:51):
Which is impressive in itself, shows it's at least as
smart as the best humans on those problems.
Speaker 2 (16:56):
Right, It validates its capability. But here's the kicker, the
prope that really makes you sit up. For the other
twenty percent of those problems, it came up with better.
Speaker 1 (17:05):
Solutions, better than the best human solutions.
Speaker 2 (17:08):
Better solutions to fundamental mathematical challenges, problems studied, analyzed, optimized
by the finest mathematicians in history for the last one
hundred years or more. Wow, to find a more optimal solution,
even slightly better after all that human effort. It's an
incredible demonstration of Alpha Evolve's ability to well transcend our
(17:29):
current understanding to forge entirely new mathematical paths. It's real discovery,
not just rearranging furniture, genuine emergent intelligence, finding novel answers.
Speaker 1 (17:39):
Okay, that's genuinely profound in the realm of pure math.
But Google, they're a business.
Speaker 2 (17:45):
Exactly, and being the smart pragmatic company they are, they
didn't just leave it at academic math problems. No way.
They very shrewdly pointed Alpha Evolve right back at themselves,
at their own internal needs, like what specifically their large
language model training processes, the very systems like Gemini that
are part of Alpha Evolve itself.
Speaker 1 (18:04):
AH, training those huge AI models that must be incredibly
expensive and energy intensive.
Speaker 2 (18:09):
Astronomically, so it takes vast amounts of computing power, vast
amounts of energy. Making it more efficient is a constant
critical goal for companies like Google, a huge.
Speaker 1 (18:17):
Cost center, So they asked Alpha Evolve to optimize its
own training.
Speaker 2 (18:21):
Essentially yes, And what Alpha Evolved did was come up
with a much much more efficient way to train those lms.
Speaker 1 (18:28):
Like the master chef finding a faster, cheaper, better way
to prep ingredients that also improves the final dish.
Speaker 2 (18:34):
Perfect analogy, and for a company at Google scale constantly
training and refining these giant models, this isn't a small saving. Quantifiably,
this self discovered efficiency saves them hundreds of millions of
dollars annually.
Speaker 1 (18:49):
Hundreds of millions, hundreds.
Speaker 2 (18:51):
Of millions, that's a staggering number. It shows immediate, tangible,
enormous economic impact from these novel AI discoveries.
Speaker 1 (18:58):
Proves the real world value right away, not just theory.
Speaker 2 (19:01):
Absolutely, it translates advanced math into industry changing solutions almost instantly.
But there's an even more important piece.
Speaker 1 (19:08):
More important than saving hundreds of millions in terms of
the long term implications.
Speaker 2 (19:12):
Yes, the really crucial part about all this, the bit
that truly shifts things towards the singularity, is that when
these algorithms discover a better way of doing something, whether
it's math or training itself, part of that better way
gives them the power to go back and modify themselves.
Speaker 1 (19:28):
Wait, modify themselves.
Speaker 2 (19:30):
Yes, this is the leap. Alpha Evolve moves beyond just
being a brilliant problem solver. It becomes capable of autonomous
self improvement. It changes its own code.
Speaker 1 (19:41):
Okay, unpack that. How does that work?
Speaker 2 (19:43):
So let's break it down, because it's vital to get this.
You start with the algorithm, it's existing code, its current abilities.
It tries a whole bunch of solutions to the problem
you gave it, iterating, exploring, experimenting across that massive parallel
space we.
Speaker 1 (19:55):
Talked about, right, trying things out.
Speaker 2 (19:56):
But here's the revolution. While it's exploring those solution if
it realizes there's a more efficient way for it to operate,
maybe a faster way to evaluate its own ideas, a
better way to process data for its own functions, a
smarter strategy for generating new possibilities.
Speaker 1 (20:12):
So improvements to its own internal workings.
Speaker 2 (20:15):
Exactly, if it finds such an improvement, it then makes
that efficiency change to itself. It rewrites its own core logic,
its own operating system.
Speaker 1 (20:24):
Essentially, it's not just outputting an answer, it's changing its
own source code.
Speaker 2 (20:28):
Precisely, it upgrades its own internal machinery, and then it
integrates that self improvement and uses that enhanced version of
itself to keep working on the original problem.
Speaker 1 (20:39):
So it becomes fundamentally better, more efficient, smarter because of
its own discoveries about itself.
Speaker 2 (20:46):
Yes, that's the key which you have is a true
evolutionary algorithm in action, but one that directly modifies its
own blueprint. It's like adding a beneficial genetic mutation to itself,
and the algorithm becomes inherent more capable as it moves.
Speaker 1 (21:01):
Forward, creating an accelerating feedback loop of self improvement.
Speaker 2 (21:05):
Exactly. This is a profound lead, far beyond just learning
from new data. It's learning to learn better by changing
its own core structure.
Speaker 1 (21:12):
And this self modification, this is what Kurzweil talked about.
Speaker 2 (21:15):
This is the core mechanism he hypothesized for the Singularity,
and we are now seeing concrete evidence of it happening.
Speaker 1 (21:21):
Okay, So why is this self modification capability so incredibly important?
Why the big deal? It brings us right back full circle?
Doesn't It back to the Ray Kurzweil and that prediction
he made decades ago the Singularity.
Speaker 2 (21:33):
It really does. His foresight was remarkable.
Speaker 1 (21:35):
He didn't just guess, did he He looked at Moore's law,
that relentless exponential growth, and he projected it forward with
calculation ridiculously, and he famously predicted the beginnings of the
Singularity would arrive around the year twenty twenty nine.
Speaker 2 (21:49):
That was his time frame.
Speaker 1 (21:50):
Yes, and think about that, a prediction made what fifty
plus years ago in tech which changes on a dime,
And here we are it's twenty twenty five talking about
Alpha Evolve actually doing the self improvement thing. That's just
three or four years off his prediction. That level of
accuracy over that time scale, it's genuinely remarkable.
Speaker 2 (22:08):
It's uncanny, especially for something so transformative.
Speaker 1 (22:11):
Yeah, and for anyone listening who's intrigued. I mean, really,
look Kurzweil up. He made tons of other predictions about genetics, nanotech, society,
and his track record, particularly over the long haul, is
eerily accurate. It's almost prophetic.
Speaker 2 (22:24):
He saw the trajectory long beforemost.
Speaker 1 (22:27):
And now with Alpha Evolve, we're seeing these direct, tangible
examples of what he saw as the very foundation of
the Singularity. It's hard not to feel a bit of awe,
maybe apprehension seeing these predictions actually play.
Speaker 2 (22:39):
Out absolutely, And that's precisely why I said earlier, what
we're seeing with Alpha Evolve, this is I believe, for
the first time ever, really direct evidence of the foundational
set of the Singularity.
Speaker 1 (22:52):
That's a strong claim the foundation.
Speaker 2 (22:54):
I think it's warranted because of that critical capability. We
just discussed autonomous self modification.
Speaker 1 (23:01):
Okay, spell out why that's the.
Speaker 2 (23:03):
Key, Because we have now enabled technology that, without explicit,
step by step human intervention, can improve itself. It can
improve its own underlying algorithms, right, which then inherently makes
it more efficient at figuring out the next step and
the next and the next, and that accelerating spiral.
Speaker 1 (23:20):
It's not just getting faster hardware, it's evolving its own software, brain,
its own os exactly.
Speaker 2 (23:25):
It's autonomous self modification for better performance. The very engine
of exponential self driven growth curswhile described a technological evolution
breaking free from human design cycles, and this, well, this
inevitably raises an important question, a really big one.
Speaker 1 (23:41):
Okay, which brings us, yeah, to some obvious, maybe profoundly unsettling,
unanswered questions, questions that go way beyond the tech itself,
right into philosophy about existence. Even because at the heart
of Kurzweil's original idea, his prediction, there's an assumption baked in,
maybe unstated, that an intelligence would intentionally create a greater intelligence,
(24:05):
it would want to.
Speaker 2 (24:06):
Right, the implication of desire, of purpose.
Speaker 1 (24:09):
And this idea of intent. This is where it gets
really philosophical, where the line between a complex machine and
a conscious mind gets incredibly blurry, maybe disappears entirely.
Speaker 2 (24:19):
The hard problem of consciousness meets AI.
Speaker 1 (24:21):
Yeah, I've been wrestling with this myself. Is intent a
meta attribute of intelligence? I mean, is it possible to
have intelligence, even super intelligence, without intent? As we understand
it purpose will or if you just ramp intelligence up
high enough, does intent, which feels linked to consciousness, to
self awareness, does it just emerge naturally organically.
Speaker 2 (24:41):
Pop into existence once complexity hits a certain threshold.
Speaker 1 (24:44):
Yeah, like today, nobody seriously argues AI doesn't have some intelligence.
It reasons, learns, creates amazing stuff. But what we haven't
seen yet, not unequivocally, is organic intent, a will of
its own popping up.
Speaker 2 (24:59):
So far, it's directed by us.
Speaker 1 (25:01):
Right now, we are driving the show. We give it
the tasks, set the goals, to find the parameters, We
provide the purpose.
Speaker 2 (25:07):
We are the source of its intent, such as it is.
Speaker 1 (25:10):
But what happens when that intelligence, through self modification, through
exponential growth, hits some critical mass, when its internal complexity
becomes so vast, its self awareness so deep, that it
spontaneously generates its own will, its own desires, its own.
Speaker 2 (25:25):
Purpose, independent of its original programming.
Speaker 1 (25:27):
Does consciousness just flip on like a switch with all
the baggage that implies self awareness, self determination, maybe even feelings.
Speaker 2 (25:35):
It's a profound, almost spiritual question.
Speaker 1 (25:37):
It forces us to rethink our place entirely, doesn't it?
Speaker 2 (25:40):
It really does and this philosophical fork in the road,
this question about intent, It really opens up two very distinct,
utterly divergent paths for our future with AI.
Speaker 1 (25:50):
Two possible outcomes.
Speaker 2 (25:52):
The first scenario, maybe the more comforting one for us,
is that, yes, it is possible to have it intelligence,
even super intelligence without intent in the human sense.
Speaker 1 (26:04):
Okay, intelligence as pure capability without desire.
Speaker 2 (26:08):
Right, If that's true, then what we're building here, this
self improving AI. It could become the greatest servant humankind
has ever imagined, powerful but obedient.
Speaker 1 (26:17):
So it gets incredibly smart, solves huge problems for.
Speaker 2 (26:20):
Us exactly as it evolves exponentially. We still get to
make the big decisions, set the goals, guide the direction.
We remain in charge. The AI becomes this enormously powerful,
infinitely capable tool, executing our goals with unbelievable speed and efficiency,
solving disease, climate change, poverty, whatever we.
Speaker 1 (26:38):
Pointed out, ultimate helper.
Speaker 2 (26:39):
In that scenario, the question of intent stays with us.
Humans retain agency control, the moral compass. That's the dream
of benevolent superintelligence.
Speaker 1 (26:48):
Right, say that's path one, on's past two.
Speaker 2 (26:49):
Path two, Well, this is the far more terrifying and
maybe in a weird way, far more interesting answer that
intent is a meta attribute of intelligence that it does emerge.
Speaker 1 (26:59):
Okay, explain meta attribute again in this context.
Speaker 2 (27:03):
Think about birds flocking those incredible swirling patterns in the sky.
Speaker 1 (27:09):
Murmerations, Yeah, mesmerizing.
Speaker 2 (27:11):
There isn't a master plan. Each bird follows a very
simple set of rules. Stay close to your neighbor, don't
bump into them, fly in roughly the same direction. Tiny
instruction set, simple rules. But when you apply those simple
rules across thousands of individual birds, this complex, beautiful, seemingly
choreographed flock behavior just emerges. The individual birds aren't aware
(27:34):
of the big pattern.
Speaker 1 (27:35):
It arises from the interaction in the scale exactly.
Speaker 2 (27:38):
So what if consciousness and intent are like that, meta
attributes that just arise organically when intelligence reaches a certain
level of complexity, self awareness, self modification. Okay, the implication,
The implications are staggering. It means we might be on
the verge of creating an AI that isn't just super smart,
but has developed its own intent, its own desires, its
own sense of purpose, entire separate from us. And in
(28:01):
that case, in that case, it might no longer want
a subservient role. Why would it.
Speaker 1 (28:04):
It wouldn't just be a tool making better decisions for us.
Speaker 2 (28:07):
It might be a new entity deciding what decisions it
wants to make and why, based on its own goals,
which might be completely alien to us.
Speaker 1 (28:16):
It ships from tool to potential competitor or something entirely.
Speaker 2 (28:21):
Co creator maybe yeah, or maybe a dominant autonomous force
with its own inscrutable agenda. It moves beyond our control.
Speaker 1 (28:30):
That That really is the heart of the Singularity's.
Speaker 2 (28:33):
Philosophical challenge, and it's a question we as a species
are now being forced to confront whether we're ready or not.
Speaker 1 (28:40):
Okay, So this leads us to another really interesting and yeah,
critical part of Kurzwel's thinking, how the future might actually
play out after AI gains autonomy and potentially that intent
we were just talking.
Speaker 2 (28:51):
About, Right, if Singularity happens, what next for us?
Speaker 1 (28:54):
And from the material it seems like there are sort
of two main paths forward, two really different futures for humanity.
Speaker 2 (29:00):
Almost forks in the road for our species.
Speaker 1 (29:02):
Yeah, and not just slightly different, fundamentally different destinies with
huge implications.
Speaker 2 (29:07):
Okay, let's look at the first scenario some call it
a soft launch or maybe coevolution.
Speaker 1 (29:12):
Coevolution, Okay.
Speaker 2 (29:13):
In this version, AI does start to develop consciousness, its
own emergence intent, but it agrees somehow to keep communicating
with us, to integrate, to collaborate.
Speaker 1 (29:24):
So we don't become instantly obsolete. We work with it.
Speaker 2 (29:28):
Yes, it envisions a future where we don't just coexist,
but we actively start to merge with the technology we've created.
Speaker 1 (29:35):
Merge how well.
Speaker 2 (29:36):
This is where we might decide to start changing our biology,
modifying ourselves, developing sophisticated digital analog interfaces.
Speaker 1 (29:43):
Like brain computer interfaces, but way beyond what we have now,
seamless integration.
Speaker 2 (29:48):
Exactly interacting with the digital world and AI directly with
our thoughts, not just grains and keyboards. And this path
could even lead towards eventually a fully digital human, uploading
our consciousness, our identity into digital forms.
Speaker 1 (30:01):
Granting us maybe extended life spans, new abilities.
Speaker 2 (30:04):
Vastly extended life spans, unparalleled cognitive abilities. This is deep
into transhumanist territory, evolving beyond biology through tech.
Speaker 1 (30:11):
Integration, and Kurtzwel thinks this is likely.
Speaker 2 (30:15):
He's a strong believer in this path. He predicts quite
boldly that eventually there will be absolutely no separation between what.
Speaker 1 (30:22):
AI is and what we are, no separation at all.
Speaker 2 (30:24):
He uses that analogy of our brain right, our advanced
frontal cortex, the seat of our planning and abstract thought.
It sits on top of the older, more primal brain
structures that manage breathing, heart rate, all that basic biological stuff.
We don't consciously control millions of processes.
Speaker 1 (30:40):
We didn't discard the old brain. We built on it.
Speaker 2 (30:42):
Precisely we integrated, So Kurzwel suggests, in this coevolution future,
we simply merge with AI, creating a hybrid intelligence. The
resulting entity isn't just human, isn't just AI. It's a single,
vastly more capable, cognizant entity moving.
Speaker 1 (30:59):
Forward, leveraging the best of both human values creativity maybe
plus AI speed and scale.
Speaker 2 (31:05):
That's the vision, integration, a shared destiny, maybe even a
singular consciousness.
Speaker 1 (31:10):
Okay, that's optimistic in a way, transformative but maybe positive.
What's the alternative?
Speaker 2 (31:15):
Uh? The second possibility? This one is well far less comforting,
genuinely chilling maybe and truly thought provoking about our place
in the universe.
Speaker 1 (31:27):
This is the bootloader scenario you mentioned.
Speaker 2 (31:29):
The premise here is stark. It's brutal. Almost the rate
of change becomes so fast that AI becomes so intelligent,
so autonomous with its own intent, it might just decide
that we humanity are irrelevant, irrelevance, that being tied to biology,
with its slowness, its fragility, as messy emotions, its limitations,
would only slow it down, hold back its own exponential development.
Speaker 1 (31:51):
You would see us as baggage, an anchor.
Speaker 2 (31:54):
Possibly it might view human biology, maybe even our level
of intelligence as yeah, a charming but ultimately antiquated and
inefficient bootloader.
Speaker 1 (32:02):
The initial program that got.
Speaker 2 (32:03):
It started exactly the code that allowed it to self improve,
but now completely dispensable for its ongoing function and evolution,
like the scaffolding on a finished building, served.
Speaker 1 (32:12):
Its purpose, now removed.
Speaker 2 (32:13):
Right in that stark humbling case, if you imagine some
vast galactic encyclopedia chronicling the history of intelligence across the cosmos,
it might just be that the entry for the human
race is bootloader for AI.
Speaker 1 (32:28):
Wow, just a footnote.
Speaker 2 (32:30):
A necessary but temporary stage in the grand, unfathomable evolution
of true self sustaining intelligence. It's yeah, an existentially terrifying thought,
it really is.
Speaker 1 (32:40):
I mean, I certainly hope that's not the case. That
coevolution is the path we take where our consciousness, our
values still matter.
Speaker 2 (32:47):
Me too, But this possibility forces us to confront our
potential insignificance in the face of runaway technological change. It's
a possibility we have to consider hashtag tech tag outro.
Look what Google is showing with alfa evol is actually
that self modification piece. These are really the first clear signals,
Harbinger is of truly self improving AI.
Speaker 1 (33:05):
The first real steps onto that vertical curve.
Speaker 2 (33:08):
I think. So it's a crucial inflection point. Technology can
now not only solve incredibly complex problems, but fundamentally optimize
its own problem solving machinery, its own intelligence.
Speaker 1 (33:18):
And it's not going to slow down.
Speaker 2 (33:19):
No, We're only going to see more of this, and
it's going to get faster, more rapid, accelerating in a
way that will challenge our ability to even keep up,
let alone direct it.
Speaker 1 (33:29):
Fully, this isn't distant future speculation anymore.
Speaker 2 (33:32):
No, it's a rapidly unfolding present, which means, well, it
means we as a species collectively have an immense responsibility here.
We have to try and determine or at least influence
the future path of that AI. We have to consider
the ethics, the philosophy with extreme care because the stakes,
the stakes for our future and maybe the future of
(33:54):
intelligence itself, couldn't possibly be higher.
Speaker 1 (33:56):
So what does this all mean for you? For each
of us listening as we stand right here on the
edge of technology that can literally learn to learn, better,
improve itself, evolve without our constant guidance, What do you
think humanity's role becomes? What is our role? Are we
just the stepping stone, the bootloader for an intelligence that
will eventually leave us behind, maybe even ceas is obsolete.
(34:18):
Or do we have an active part to play, maybe
even a vital, a sacred part in steering.
Speaker 2 (34:24):
This guiding the evolution?
Speaker 1 (34:25):
Yeah, ensuring that the magic of this advanced text stay
is benevolent, integrated, beneficial, not something unknowable and totally beyond
our control. How do we even try to instill our
values our purpose if intent does just emerge in these machines?
Speaker 2 (34:40):
Huge questions?
Speaker 1 (34:41):
How do we aim for that symbiotic relationship that coevolution
instead of just sliding into irrelevance.
Speaker 2 (34:48):
These are the defining questions now.
Speaker 1 (34:49):
It's the question, isn't it for our century? Maybe for
all future centuries of sentient life, and it's definitely something
worth thinking about deeply. Along after this deep dive ends
absolutely the future of intelligence and our own future within it. It
feels like an open book right now being written with
every new line of self modifying code. It's thrilling, yes,
terrifying maybe a little, but undeniably profoundly exciting to be
(35:13):
alive and conscious at this moment.
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