Imagine a world where we could map, I mean, every
single inch of our planet, basically in near
real time, or run an entire cutting edge science
lab instantly just using AI. Yeah, it sounds
like something straight out of science fiction,
doesn't it? But, you know, pieces of that future,
they're not just concepts anymore. They're actually
here. They're actively reshaping how we see the
Earth and just speeding up discovery like crazy.
We're about to take a deep dive into exactly
how. Welcome to the deep dive. Today, we're unpacking
some really, truly groundbreaking developments
in AI. We've got this fascinating newsletter
in front of us that just landed. Yeah, it's absolutely
packed, really dense. We'll kick off exploring
Google's new planetary co -pilot, Alpha Earth
Foundations. Honestly, it's kind of astonishing
stuff. Then we'll zoom out a bit, look at a whole
range of exciting new AI tools that are transforming
industries like right now. And then finally,
we'll shift our focus to something that, well,
it genuinely bends the mind a little. Virtual
AI scientists. Yes, you heard that correctly.
So our mission today is to help you get a handle
on this, gain knowledge quickly, maybe spot those
key aha moments and hopefully avoid feeling just,
you know, overwhelmed by how fast everything's
moving. Exactly. We want to help you understand
not just what's happening, but maybe why it matters,
what it could mean for you down the line. Okay,
so let's unpack this. Right. Let's begin with
Google DeepMind's Alpha Earth Foundations. This
system is already being called a planetary co
-pilot for Earth data. What, in your view, is
the real core innovation here? What's the big
deal? Well, what's truly fascinating, I think,
is its approach to just handling immense data.
Like mind boggling amount. So instead of using
raw pixel by pixel satellite images, which are
huge files, Alpha Earth compresses petabytes.
And that's, you know, thousands of terabytes
of this Earth observation data. It compresses
it into something they call embedding fields.
Think of these as like tiny, super efficient,
searchable summaries for every little 10 by 10
meter patch of the Earth's surface. 10 by 10
meters. Yeah. And the key thing is they're generated
in near real time. It's constantly updating.
Okay. That sounds like an incredible leap in
just pure efficiency. So when we talk about performance,
what kind of impact are we actually seeing? What
does this new architecture deliver? Oh, it's
profoundly faster and significantly cheaper to
run. The system cuts storage needs by, get this,
16 times, 16, which is just critical when you're
dealing with planetary scale information. It
also reduces error rates by about 24 % compared
to the older, more traditional models. 24%, wow.
Yeah. To put it in perspective, we're talking
about generating and making available like 1
.4 trillion of these embedding footprints. Every
single year. That's like a new snapshot of the
Earth's surface changing, what, roughly every
couple of seconds. It's wild. Wow. OK, so this
isn't just like a research paper or some theoretical
concept. It's live right now, integrated into
Google Earth Engine, which, you know, a lot of
people already use. Can you give us maybe a few
concrete examples? Like where is this being used
in the real world? Oh, absolutely. There are
plenty of applications already rolling out and
they're impacting some really critical environmental
work. For instance. Mapiomas down in Brazil.
They're actively using Alpha Earth Foundations
right now. They're monitoring Amazon deforestation
in near real time, which gives them crucial data
for conservation efforts. Then there's the global
ecosystems, Atlas. They're leveraging it to map
regions that were previously uncharted, really,
like vast shrub lands, remote deserts. It helps
us understand global biodiversity better. And
it even outperformed other systems in these things
called evapotranspiration tests. Okay, what's
that exactly? That's basically measuring how
water moves through ecosystems. You know, evaporation,
plant transpiration. It's super vital for understanding
climate cycles, planning agriculture, predicting
droughts, that kind of thing. Right, right. So
it sounds like this makes that kind of planet
-scale intelligence much more accessible. It's
moving beyond just the big players like NASA
or other national space agencies. What's the
bigger picture here? What does it mean that Google
is building something like this? If we connect
this to that bigger picture, Google is essentially
building an operating system for Earth. Just
imagine, like a single dynamic platform where
all this planetary data, weather patterns, deforestation
rates, water cycles, you name it, isn't just
collected, but it's processed, analyzed, and
then made accessible for pretty much anyone to
build applications on top of it. Kind of like
how an OS lets software run on your computer,
right? Okay, yeah. An OS for Earth. And it's
also a huge win for AI privacy design, which
is really important because instead of analyzing,
say, sensitive raw images directly, Alpha Earth
works with these anonymized embedding fields.
So you get the critical data insights without
compromising individual privacy or specific location
privacy. That's a really critical step for, you
know, responsible AI deployment, building trust.
Right. And if you're maybe open AI, well. Perhaps
you'd better move fast on this. Google seems
to have taken a big lead on this kind of planetary
data infrastructure. Seems that way. So if this
makes planet scale data so much more accessible,
what's the biggest shift this brings, say, for
smaller organizations or even individual users?
I'd say it really democratizes global mapping.
It's not just for the big labs anymore. It's
potentially for everyone. OK, so we've seen how
AI is giving us this. unprecedented new way to
see and understand the whole planet with Alpher.
That's the big picture, the macro view. But AI
is also revolutionizing things at a much smaller
scale, right? Individual tasks, specific industries.
Let's pivot now to that broader AI landscape.
What new tools or trends have kind of jumped
out at you recently? Yeah, it's been a real flurry
lately. So many developments. For instance, XAI,
you know, Elon Musk's company. They just opened
a wait list for Imagine. It's their new image
and video generation feature. And what's cool
is it apparently includes audio capabilities,
too, hinting at that multimodal creation future.
Image, video, and audio. Right. Then there's
Ideogram. They released something called Character.
It makes it super easy to just swap faces or
specific characters into generated images. You
can see how that has huge implications for creative
industries, right? Marketing, digital art, maybe
even entertainment. Definitely. And I saw something,
too, about specialized AI agents, almost like
virtual employees. Can you tell me more about
that? Exactly. That's a really significant trend
we're seeing emerge. There's this new open source
repository out there showcasing like 40 specialized
cloud agents. And each one is designed to streamline
very specific business functions. They can even
sort of mimic an entire workforce, handling stuff
from customer service bots to complex data analysis.
The potential there to automate tasks is just
enormous. Could free up human teams for more
complex creative thinking. And honestly, I still
wrestle with prompt drift myself sometimes. You
know that thing where your AI's answers start
to... subtly change or degrade over time. Even
if you get the same input, it makes consistency
a real headache. So these highly specialized
purpose -built tools, they're particularly interesting
because they aim to get rid of that variability,
make things more reliable. That's wild to think
about, an entire virtual workforce. And then
there's this thing called Showrunner. It's been
described as the Netflix of AI for film. What
exactly does that do? Yeah, Showrunner is pretty
remarkable, especially for creative professionals.
Film, video, it lets you take existing film scenes
and essentially clone them. You can replace actors
with AI -generated ones. You can control their
acting styles, their expressions, and even precisely
copy the whole cinematic look, the lighting,
the framing from a reference scene. It's kind
of like having an entire production studio just
sitting on your desktop. It makes professional
-quality video creation way more accessible,
potentially, and speeds up iteration like crazy.
It's pretty incredible for creative work. Beyond
these really fascinating individual tools, what
about the big industry movements, the strategic
plays? It feels like a high stakes chess game
sometimes out there. It certainly does. Huge
investments happening. So on the investment front,
Microsoft is reportedly spending a record like
$30 billion this quarter on AI investments. Just
shows their massive commitment. $30 billion in
one quarter. And then there's Groke, a relatively
new AI chip startup. They're in the process of
raising $600 million. Their goal is to directly
challenge NVIDIA's dominance in the AI hardware
market with their LPU chip architecture. So competition
heating up there. And on the more practical consumer
side, Google and YouTube, they're now using AI
for age checks on user accounts. Just a straightforward
application for safety and compliance. Okay.
And what about politically? Or strategically,
any interesting moves from the big tech players
regarding, say, regulation or how they plan to
control this tech? Definitely some maneuvering
there. Google, for example, publicly stated it
will sign the EU's voluntary AI code of practice.
So that indicates a willingness to align with
European regulatory thinking. Meta, on the other
hand, stated they would not sign it. So signaling
may be a different approach, maybe prioritizing
open source development differently. Mark Zuckerberg
also hinted that Meta won't open source all of
its most advanced superintelligence AI models
down the road, perhaps learning from past releases.
Plus, Microsoft secured this really interesting
open AI access deal. It basically ensures that
they'll have access to open AI's models even
after AGI, you know, artificial general intelligence
is potentially achieved. That's a huge strategic
hedge for them. guarantees access no matter what.
So with all these rapid developments, specialized
agents, massive investments, strategic corporate
moves, what's the core overarching trend here?
What's the one liner? I'd say AI is just expanding
rapidly, embedding itself into basically every
industry. It's driving both huge innovation and
really intense competition. OK, so we've covered
how AI is mapping the planet with this incredible
new detail and how it's sparking this explosion
of new tools across all sorts of industries.
Now, let's talk about something that kind of
unites these capabilities in a really profound
way. Virtual AI scientists. Stanford University
and the Chan Zuckerberg Biohub. They just launched
a system that acts like an entire AI research
lab. This is where it gets really interesting,
I think. It truly is. Yeah, this is mind -bending
stuff. Their setup isn't just like one AI tool.
It's a whole ecosystem. It includes an AI principal
investigator that's basically the lead scientist
making the calls. It has specialized AI agents
acting as the researchers doing the work. And
get this, they even have digital lab meetings
that literally finish in seconds. Just cuts out
all that administrative time, discussion overhead.
Meetings in seconds. Yeah, it's a real glimpse
into an entirely new way of doing scientific
discovery, a different paradigm. Okay, but what
have these virtual scientists actually discovered?
Are there tangible results from this? purely
virtual process? Or is it still theoretical?
No, the results are concrete and pretty impactful
already. They've successfully designed 92 distinct
COVID nanobody designs. Now, nanobodies, think
of them as like tiny, really effective antibodies.
They're much smaller, more stable than conventional
ones. Makes them easier to work with, potentially
more potent against diseases. Okay, 92 designs.
Right. And out of those 92 designs generated
by the AI system, two have already shown success
in actual real -world lab testing, which is a
remarkable outcome, frankly, for an entirely
virtual process, really shows AI's power to accelerate
biomedical research directly. That's astonishing.
How does this virtual lab actually function,
though? What are the mechanics behind it? How
does it generate these specific testable breakthroughs?
So the AI principal investigator, the PI, is
kind of the orchestrator. It identifies the research
problem, then it forms task -specific agent teams.
It assigns specialized roles to other AI models,
like data analyst or experiment designer. These
agents then run these rigorous internal debates,
almost like human scientists arguing over data
to refine hypotheses and design experiments.
And they even automatically call on external
specialized tools when needed, like AlphaFold,
Google DeepMind's system for predicting protein
structures. They call on tools like that all
by themselves, integrate that complex data seamlessly.
They basically operate autonomously through the
whole research cycle. from hypothesis all the
way to experimental design. This raises a really
important question then. Why does this matter
so profoundly for the future of science? What
core bottlenecks does it actually address? Well,
think about traditional science. Most of it is
inherently slow, right? It's often blocked by
endless meetings, coordinating schedules. It's
constrained by budgets, funding cycles, and frequently
limited by expert bottlenecks. You need that
one specific specialist who's always swamped.
These AI agents, they just skip all that. They
don't get tired. They don't need weekends off.
They certainly don't care if a lab is short -staffed
or someone's on vacation. And maybe crucially,
they're transparent. You can literally watch
their entire reasoning process unfold step by
step. That gives you an unprecedented level of
auditability. You can see how they reached a
conclusion. Whoa. Seriously, imagine scaling
this. Solving problems at that kind of speed,
like running a billion queries simultaneously
to find a cure or some new material. It's staggering.
Yeah. What are the current limitations, though?
It sounds almost too good to be true. Where does
it fall short right now? Yeah, it's definitely
not perfect yet. We're still in the early days
here. Really complex, long term studies that
required, you know. deep, nuanced, contextual
understanding over years, that's still hard.
Or those genuinely creative leaps, the kind that
involve really abstract conceptual breakthroughs,
that still largely seems to need human intuition,
human insight. But if this approach scales and
improves at the rate... It seems to be trending.
Well, the next major scientific breakthrough
may be a cure for a really challenging disease
or a solution to a complex environmental problem.
It might very well come from what essentially
looks like a slack thread between four bots and,
you know, a protein structure tool like AlphaFold.
So does this mean human scientists are, you know,
on a path to becoming obsolete? Is that the trajectory?
Not yet, I don't think so. Humans are still absolutely
crucial for that complex creativity, for asking
the right questions, for guiding the big picture
strategy. Mid -roll sponsor, Reed. So yeah, we've
covered some truly profound advancements today,
haven't we? I mean, from Google's Alpha Earth
mapping basically our entire planet in incredible
detail, near real time, to... Just this burst
of innovative new AI tools that are fundamentally
changing how we work, how we create, how businesses
even operate day to day. Yeah. And then that
that mind bending idea of virtual AI scientists
just autonomously conducting research and getting
real world breakthroughs like with those COVID
nanobodies. It really shows, I think, that AI
isn't just assisting us anymore. It's rapidly
becoming a genuine collaborative partner, maybe
even a primary driver in the actual process of
human discovery itself. Exactly. What's truly
fascinating. fascinating here I find is just
how AI is accelerating our basic fundamental
capacity, our capacity to understand and interact
with the incredibly complex world around us.
It feels like we're stacking Lego blocks of data
and insight, you know, but at an exponential
rate, building these knowledge structures way
faster than ever before. We are definitely, undoubtedly,
moving into an era where these incredibly complex
global problems, things like precise environmental
monitoring or accelerating critical disease research,
they can now be tackled with just unprecedented
speed and scale and efficiency. So what does
this all mean for you listening right now? We
are clearly entering an era where these profound
capabilities things once exclusively reserved
for, you know, National labs or massive supercomputers
that are rapidly becoming accessible. So the
question is, how will you use these powerful
new lenses to see the world around you? Or maybe
use these incredible new tools to solve the problems
that matter most to you? Yeah, the power of these
kinds of deep dives, just getting into this complex,
fast -moving information. It's hopefully helping
us all keep pace with this really exciting future
that's unfolding. We sincerely hope this deep
dive gave you some valuable new insights, maybe
sparked a few aha moments along the way. Thank
you so much for joining us. Until next time,
keep exploring Aotearoa music.