Imagine a future where AI spots a neurological
disease maybe weeks before symptoms even show
up. And then it actually guides a therapy using
light. Yeah, a light therapy to literally help
save brain cells. Well, that future. We're going
to dive into some research today that suggests
it might be closer than you think. Welcome to
the Deep Dive. We're here again to unpack your
latest stack of sources. Yeah, pulling out those
key insights, the important facts, maybe some
surprising stuff too. Today, we've got a really
fascinating breakthrough. It combines AI and
light therapy for Parkinson's research. And then
we'll swing wide, look at AI out in the wild.
Some useful things, some, well, some controversies
too. Right. And finally, we'll dig into what
makes AI seem creative, what's actually going
on there. Our goal, as always, is to give you
that clear path, help you grasp these complex
topics. And get those aha moments without feeling
overloaded. So let's just jump right in. So this
first deep dive, it takes us into something I
think is truly groundbreaking. It's a recent
study pairing AI with optogenetics. Right. Optogenetics.
That's using light to control cells, basically.
Exactly. And it's not just about spotting Parkinson's
early in these mouse models. It's about a targeted
treatment. Yeah. The potential there is huge.
What's really cool is the strategy they used.
Kind of two parts. Okay. First, the AI part.
They built this incredibly detailed 3D pose estimation
system. So like a motion tracker, but super detailed.
Exactly. Super, super detailed. For mice, it
tracks over 340 different movement features.
Wow, 340. That's a lot of detail for tracking
tiny mouse movement. It is. And that huge amount
of data lets the AI learn subtle patterns. Which
leads to... The accuracy, I guess. Precisely.
The system hits 90 % accuracy for spotting early
stage Parkinson's. 90%. And this is the key part.
It does it. weeks before the standard tests could
even detect anything. Weeks earlier, that early
detection window, that changes everything, potentially.
Especially with neurodegenerative diseases, time
is critical. So that's the detection side. But
then there's the therapy part. That's where the
optogenetics comes in. Right. So the researchers
use these special wireless LED cages. Wireless
cages. Yeah, to deliver pulses of blue light
directly into specific brain regions in the mice.
Okay. And they targeted mice with mild Parkinson's
symptoms? Mm -hmm. They applied this light therapy
on an alternate day schedule. And the results
for those mice? Genuinely profound is the word
the source used. It actually prevented the disease
from getting much worse. Prevented severe progression.
That's significant. It's huge. They saw about
90 % of the dopamine neurons preserved in the
mice that got the treatment. 90%. Compared to
what in the untreated ones? Only on 38%. So a
massive difference. Wow. And it wasn't just the
neurons. Their gait went back to normal. Coordination
got better. Tremors reduced. Real functional
improvements. That really paints a picture of
what intervention could look like. What about
mice with more severe PD? The effects were more
limited there, understandably. But it still showed
some positive impact, suggesting potential even
later on. So this whole setup, AI detecting,
light treating, feels like a blueprint. Exactly.
They're calling it closed -loop neuromedicine.
Meaning? Meaning the AI diagnoses, the optogenetics
delivers targeted therapy, and then crucially,
both parts adapt over time. The system learns.
It gets smarter. Like stacking Lego blocks of
data and light to fight the disease. That's a
great analogy. Yeah. Stacking data and light.
It's intelligent. It's responsive. Yeah. Now,
obviously, this is early days. It's mouse models,
not humans. We have to stress that. Of course.
Big jump from mice to people. But it really does
highlight the potential, doesn't it? Combining
AI and bioengineering for these really tough
diseases. It absolutely does. So stepping back
a bit, what's the biggest takeaway for you seeing
AI and this light therapy working together so
precisely? Precisely, so adaptively. For me,
it signals a shift towards truly responsive medicine,
AI guiding adaptive, highly targeted treatments.
Okay, so from the microscopic world inside the
brain, let's zoom out. Let's look at AI's impact
kind of everywhere else in our daily lives. Yeah,
the sources cover a real mix this time. Some
really useful tools, some pretty big controversies,
and some, well, ambitious visions for the future.
It really is touching everything. One source
mentioned a Redditor using AI. Oh, yeah, that
was pretty cool. They built this tool with AI
to filter LinkedIn job postings much more effectively.
How many jobs did it scrape? Over 4 .1 million
directly. And they made the tool free, trying
to tackle that whole ghost jobs problem, help
people find real active roles. That's a fantastic
example of AI being used for something practical,
kind of democratizing access. Definitely. And
speaking of practical value, Andrew Ng. you know,
from Google Brain. Right. He laid out five big
opportunities where he sees potential for, well,
creating significant wealth using AI, a clear
sign of the economic shift happening. It's definitely
changing the landscape. On the maybe stranger
side of things. Light laugh. Yeah. Yeah, the
AI ASMR food challenges are apparently back and
going viral. Wait, AI eating? Yeah, generating
clips of, like, digital food crunching and sizzling.
Tools like BaseLab's AI making these millions
of views. The Internet's a weird place sometimes.
It certainly is. Okay, but back to useful resources.
Anthropic released a guide. Yeah, a really detailed
step -by -step guide on prompt engineering. Which
is basically... How to talk to an AI effectively.
Exactly. How to craft good instructions to get
the results you want. Super valuable for anyone
really using these tools. Definitely sounds useful,
but it's not all positive applications, is it?
There are challenges. No, definitely not. We're
seeing real issues emerge, like these AI crawlers,
the bots from Meta, OpenAI. What are they doing?
They're just hammering websites with traffic.
One source mentioned a bot hitting a site 39
,000 times a minute. 39 ,000 per minute. Yeah.
It's overwhelming smaller sites, making it hard
for them to even stay online. Huge strain. That's
a serious unintended consequence. And speaking
of things happening without much transparency,
YouTube. Ah, yeah. YouTube apparently used AI
to silently edit something like 20 billion shorts
videos. Silently edited. Without creators knowing.
Seems that way. Subtle edits, maybe stabilization
or something, but done without explicit consent.
And once you notice it, it feels kind of off.
Yeah. That raises big questions about control
and transparency, doesn't it? Absolutely. Where
is that line? How much transparency do creators
deserve when platforms use AI on their work?
It's a critical question. It really is. On a
totally different scale, though, the investment
is just pouring in. Field AI. Right. Raised $405
million. Yeah. Backed by huge names, Bezos, Intel.
And their goal is... Ambitious. Very. Building
a universal AI robot brain. One AI that can work
across basically any type of robot. Incorporating
physics too for safety. Exactly. Physics -based
understanding for smarter, safer decisions in
the real world. Whoa. Just imagine scaling that.
One AI brain adaptable to like... a billion different
robot tasks manufacturing logistics maybe even
disaster response it's a moment where you really
see the potential scale yeah mind -boggling implications
it is things are moving so fast we had some quick
hits too right yeah rapid fire grok 2 .5 is open
source now grok 3 maybe in six months okay meta's
partnering with mid journey on image and video
ai Big combo there. OpenAI warned people about
dodgy investments like unauthorized SPVs. Good
to know. AI art is getting scarily good tests.
Show aesthetic AI pieces are basically indistinguishable
from human work now. Wow. And some really cool
research looking at tiny movements in bee brains.
Might hold clues for smarter AI design. Bee brains.
OK, that's a lot happening all at once. It really
shows the speed. So with all these amazing tools,
but also these potential downsides. How do we
strike that balance? How do we manage responsible
use? It demands constant vigilance, really clear
ethical guidelines that can adapt as fast as
the tech does. Okay, let's shift gears one last
time to something, oh, maybe a bit philosophical,
the idea of AI creativity. Ah, yes. Models like
Daolati, stable diffusion, they feel creative,
don't they? They really do. They improvise, they
blend patterns, they make things that seem genuinely
new. But the sources suggest it's mine. Well,
it's more like an illusion. A very, very clever
illusion based on math. Not magic, just math.
Kind of takes the mystery out of it, maybe. Slight
laugh, a little bit. But the math itself is fascinating.
These are diffusion models, right? Explain that
again simply. They basically start with random
noise, like static on our old TV. Okay. And then
they gradually remove the noise step by step,
following patterns they learned, until a clear
image emerges, denoising. Got it. Start with
chaos and with order, but how does that lead
to creativity? It comes down to two key quirks
in how they're designed. The first one is called
locality. Locality. Yeah, it means the AI doesn't
see the whole picture at once. It focuses on
generating just one small patch of pixels at
a time. Like building a mosaic tile by tile without
seeing the full design initially. Exactly like
that. It forces the model to synthesize the bigger
picture from lots of small local decisions. Okay,
that's one quirk. What's the second? Equivariance.
This basically means if you shift the input,
the output shifts the same way. Like if I ask
for a cat on the left and then ask for a cat
on the right, the catness moves. Pretty much.
The generated features move predictably with
changes in the input position or orientation.
So locality and equivariance. Yeah. How do those
two things make it creative? Because together
they actually stop the model from just perfectly
copying images from its training data. Ah, they
constrain it. Right. It can't just reproduce.
It's forced to assemble those little patches
based on the patterns it knows, but in ways that
fit the prompt and the neighboring patches. That
assembly process leads to novel combinations.
So the novelty, the apparent improvisation, comes
from the constraints of building it piece by
piece. That seems to be the idea. Originality
emerges as a sort of byproduct of the architecture
itself. That's actually quite profound. And researchers
tested this. They did. They built a simplified
mathematical model called an ELS machine to mimic
just these core principles. And its output was
90 % identical to the actual complex trained
AI models. 90 % identical, just from the core
mathematical principles. Yeah. It strongly suggests
that this creativity we see isn't some emergent
consciousness, but a determined... So it's following
rules, very complex rules, that happen to produce
outputs we perceive as novel and creative. Precisely.
But then the source takes it a step further,
drawing a parallel to human creativity. How so?
It suggests maybe our own creativity isn't so
different. Maybe we also largely recombine things
we've seen and learned in new ways. That's a
big thought, that we get originality for free
just by how our own minds piece things together.
You know, I still wrestle with prompt drift myself
sometimes trying to get these models to produce
something truly unexpected, something beyond
recombination. It's harder than it looks. It
really is. Which leads to the big question here.
Yeah. If AI creativity is essentially this deterministic
process of recombination based on its structure,
does that change how we think about human creativity
at its core? It definitely suggests human and
AI creativity might share more fundamental mechanisms
than we previously thought. A lot to chew on
there. So wrapping things up for today, we've
gone from neurons and light therapy. All the
way to the mechanics of digital art creation.
It's really spanned the breadth of AI's impact
right now. Yeah, we've seen AI as this incredibly
precise potential guide for medicine. And as
a powerful force online. useful tools, but also
some real problems and controversies. And then
this dive into AI as a surprisingly creative
artist, making us question originality itself.
You know, the thread connecting all of this for
me is AI's power to process information and make
connections. Yeah, often in ways that still genuinely
surprise us. It keeps opening doors we didn't
even know were there. This deep dive really shows
just the incredible scope of what AI is doing.
It's transformative, truly. Changing science,
definitely changing how we work, and maybe even
changing how we understand art, creativity, originality.
So thinking back on everything we covered, what's
the one thing that really stands out to you today?
Maybe it's this. If AI's creativity stems from
combining existing pieces in new ways because
of its structure, what does that suggest about
the ultimate source of innovation? Even human
innovation. Is it all just clever recombination
at some level? That is definitely a great question
to ponder. Thank you for joining us on the Deep
Dive. We'll be back soon, ready to explore another
stack of sources. Until next time, keep digging
deeper. Out to your own music.