Imagine an AI that can absolutely ace medical
exams. I mean, outscore real doctors. But then
when you or I try to use it for something simple
like a stomachache, it actually makes things
worse. That's not science fiction. That's the
intriguing paradox we're unpacking today. Welcome
to the Deep Dive. Yeah, we're really going to
explore some fascinating corners of AI in this
deep dive. We're pulling insights from a really
rich source, a recent newsletter packed with
the latest developments. Today, we're looking
at everything from why these AI tools, even the
brilliant ones, can be kind of double -edged
sword in the wrong hands. Yeah. All the way to
the quiet revolution of AI running locally right
there on your device. Right on your own machine.
Exactly. And then there's this flurry of new
tools changing, well, everything from how we
see ads to how robots learn. Okay. Oh, and just
so we're on the same page, when we talk about
LLMs, we mean large language models. Just think
of them as like advanced chatbots that learn
from truly massive amounts of text and data.
Yeah. They understand, summarize, generate stuff.
Got it. Okay, so let's really unpack this first
one. The AI doctor dilemma. If AI is so incredibly
smart, I mean, demonstrating near perfect knowledge,
why does it seem to fall apart when an everyday
person, you know, just needs help with a common
sickness? It feels counterintuitive. It absolutely
does. And the research highlighted in our source
material, it really drills into this. They ran
this study involved about 1300 people using advanced
LLMs like GPT -4 -0 trying to navigate. medical
scenarios. Now, when these LLMs were just doing
their thing, responding to clinical prompts,
their accuracy was astonishing. We're talking
90 % to 99%. Wow. Incredible, right? But here's
the twist. When humans got involved trying to
self -diagnose using these same tools, the accuracy
just plummeted. Plummeted how much? Only 34 .5
% of the scenarios were correctly assessed. And
here's the truly wild part. A control group just
using Google, they actually did better. Better
than the advanced AI. Yep. 47 % accuracy. The
data suggests adding this powerful AI actually
made people worse at self -diagnosing. That's,
yeah, that's pretty sobering. What's going on
there? Is the AI giving bad advice or is it how
people are using it? It's rarely about the AI
giving flat -out bad advice, not with these professional
-grade models anyway. The reasons the research
uncovered were quite clear and, honestly, very
human. Like what? Well, first, users often gave
incomplete information. They didn't have the
diagnostic discipline, you know. Second, frequent
misinterpretation of what the AI spat out. The
AI might list possibilities, but people kind
of latched onto one or just didn't get the nuances.
And maybe most frustratingly, people just ignored
good AI advice. Ignored it, even when it was
right. Even when the LLM correctly flagged relevant
conditions, only about one in three people actually
use that information. I have to admit, I still
wrestle with prompt drift myself sometimes, you
know, where the AI's answers kind of shift subtly
over time. So the idea of misinterpreting or
overlooking AI suggestions, well, it isn't totally
foreign to me either. So it's less about the
AI's raw intelligence and more about the human
interface. And maybe the training needed to use
that intelligence safely. Because like you said,
in professional settings, AI seems to be making
a huge difference. Exactly. It highlights that
critical gap between raw, super accurate AI data
and what you might call actionable, trusted guidance.
A doctor brings years of training, right? deep
context about a patient, the ability to ask precise
follow -ups. Right. An average user often lacks
that. They might not even know what to ask next,
or they might see a probability as a definite
diagnosis. Which it isn't. No. Professionals,
though, they have the framework to interpret
that data correctly. Take open evidence, for
example. It's a diagnostic engine trained only
on peer -reviewed medical literature. Not for
consumers. It's for pros. Okay. And the numbers.
One in four U .S. doctors use it. On average,
10 times a day. Especially valuable in complex
areas like oncology. It just shows how powerful
these tools are when they're in the right hand,
used the right way. So what does this all mean
for us then? This tension between consumer use
and professional success. It seems to hinge on
who's using it and why. It's not about needing
a robot doctor maybe, but something different.
Right. It means AI can save lives? Absolutely.
Only if we design it to genuinely help people
in a way that respects the complexity of human
interaction and expertise, not just design it
to ace a test. So if AI gives correct advice,
but people don't listen, what's the real barrier
then? It's about bridging that raw data with
actionable, trusted guidance. That's the core
challenge. Okay, shifting gears a bit, we often
hear about AI in these giant data centers, you
know, humming away in the cloud, needing constant
internet. But what if the next big shift is bringing
that power much closer? Right. Right onto your
device. That's exactly what's happening. There's
a really strong push for AI that lives directly
on your machine, your laptop, your phone, maybe
even your robot vacuum. Huh. Why the shift? Well,
a lot of users and developers, too, are getting
kind of tired of unpredictable cloud AI updates,
right? And the occasional outages. Local AI offers
much better stability. Your workflows stay consistent.
Okay. Stability makes sense. And you get far
greater control over the AI models themselves.
And, crucially, enhance privacy for your data.
Think of it like having your own personal AI
assistant. Always there, always ready, and your
info doesn't need to travel across the internet.
That's a big deal for privacy. It is. It's a
fundamental shift in how we might interact with
AI. Moving the processing from some distant server
farm right into your pocket or on your desk,
it gives you a level of consistency. Cloud services
just can't always guarantee. Huge benefit. So
what's the core advantage then for choosing local
AI over the cloud? Greater control, consistent
performance, and stronger data privacy, bringing
the power to you. Okay. So beyond these fundamental
shifts in where AI runs, the whole landscape
is just exploding. New tools, new applications
everywhere. It's not just helping doctors anymore.
Oh, absolutely not. It's reshaping creative industries.
It's influencing public opinion, changing how
we use tech every single day. The pace is incredible.
Give us some examples. What's catching your eye?
Okay, we'll look at voice AI. Eleven Labs, they're
known for voice stuff, right? They just launched
an AI assistant you interact with just using
your voice. And it connects easily to other apps
like Slack or Perplexity. So just talking to
your apps. Yeah, imagine just talking naturally
like they're another person. No typing, no clicking.
Then there's advertising. Did you see that Kelshi
ad during the NBA finals? I might have missed
that one. It was wild. 30 seconds. Made entirely
with AI. Reportedly cost a tiny fraction of a
normal ad shoot. It sparked this huge debate
about the future of advertising. I bet. Disrupting
traditional industries. Totally. Democratizing
it, maybe, but also challenging creative workflows.
Sounds powerful. That kind of power, it often
brings you challenges, right? Especially around
information. Absolutely. And this is more concerning.
We're seeing AI generate... misinformation on
a really alarming scale. The newsletter points
to two highly realistic AI generated pro -Iran
propaganda pieces. Oh, wow. Flooding TikTok,
Instagram, Facebook, YouTube. Over 30 million
views on TikTok alone. Posted hundreds of times.
It raises these really serious questions about
manipulation, misinformation, figuring out what's
even real online anymore. Yeah, that's a huge
ethical challenge. We're just starting to grapple
with that. We really are. And then on the productivity
side. AI is making a big push there, too. How
so? Well, ChatGPT just rolled out new features.
Real -time document collaboration. Speedy PDF
export with citations. Ah, so competing with
Google Docs, Microsoft Word. Exactly, a direct
play. The productivity sweep battle is definitely
heating up, and AI is right in the middle of
it. And connecting back to our first point about
professional use, look at a bridge. The medical
AI app. Yeah. It's now valued at a staggering
$5 .3 billion, just raised $300 million, $800
million total. It just reinforces the immense
value when AI is applied professionally. That's
serious money. It is. And Quick Hits on some
other tools. RunBear lets you use custom GPTs
inside Slack or Teams. Dubbing 3 .0 translates
videos into like 30 languages, one click. Sounds
natural. Great for creators. Overflow AI turns
questions into instant charts. makes data easy.
Lots of tools emerging. And one last thing, a
big legal and ethical point. Anthropic, using
copyrighted books to train its AI. It's currently
being argued as fair use in court. That's huge.
Yeah, the outcome could have massive implications
for how all AI models get trained in the future,
what data they can use. Okay, considering all
that advertising, misinformation, productivity,
ethics. What's the most surprising area AI is
impacting right now for you? Its ability to generate
media from ads to propaganda at scale and low
cost. That speed and accessibility is just transformative.
Okay, let's pivot to the physical world now.
Let's talk robots. Google, just kind of quietly,
dropped something pretty big, something that
could fundamentally shift how robots operate.
Yeah, this is potentially a huge lead forward,
a real game changer for robotics. It's called
Gemini Robotics on Device. On device. So running
locally, like we were talking about earlier.
Exactly. Robots running complex AI models locally.
Think about that. No cloud connection needed
constantly. No lag. No Wi -Fi dependency. That's
different. Most robots need that connection,
right? Until now, yeah. Most AI -powered robots
had to send signals back and forth to huge servers
just to, you know, move an arm or pick something
up. The computation happened off -board. This
new Gemini model lives inside the robot. So what
does that mean practically? Faster responses,
better privacy because the data stays on the
robot, and way more potential for real -world
use, especially where internet might be spotty
or non -existent. Google's saying this on -device
model performs almost as well as its bigger cloud
sibling and even beats some unnamed rivals in
benchmarks. And this isn't just theory. They've
shown it working. Oh, yeah. The live demos were
pretty impressive. These weren't just lab tricks.
Robots using this new Gemini brain were doing
surprisingly delicate adaptive things in real
time. Like what kind of things? Things like unzipping
bags, carefully folding clothes, even tackling
totally new tasks they hadn't seen before, like
assembling parts on a moving conveyor belt. It's
not just about force. It's about nuanced interaction,
adaptation. Wow. And what's really cool for developers,
Google's releasing a full Gemini Robotics SDK.
It's a software development kit. It means devs
can fine tune robot tasks using just like. 50
to 100 examples that few yeah you basically just
show the robot what to do a few times and it
gets it's like stacking lego blocks of data for
them teaching them fast okay connecting this
to the bigger picture yeah it feels like everyone
wants to build the gp2 of robotics right that
foundational model for truly smart autonomous
machines could this local ai be that leap whoa
yeah imagine scaling this robots that truly understand
and adapt in our homes our workplaces operating
reliably without needing that constant internet
umbilical cord. That's a massive game changer
for getting robots out into the real world, responding
instantly to unpredictable stuff. So how does
on -device AI fundamentally change how we'll
interact with robots day to day? It enabled true
autonomy and real -time responsiveness, freeing
them from network limits. More natural collaboration
becomes possible. Sponsor. So reflecting on our
discussion today, we've really explored this
fascinating tension at the heart of AI right
now. It's incredibly powerful, yes. Capable of
passing the hardest tests, mastering complex
data. Yet its actual value, its true worth, so
often depends on how we interact with it, how
we understand its limits, and maybe most importantly,
how we design it to genuinely help, not just
impress. Yeah, and we're definitely seeing that
clear shift towards AI living closer to us on
our devices or embedded right into robots. That
gives us more control, better privacy, and just
incredible stability. Right. And the sheer range
of new AI tools. I mean, from shaking up creative
fields and boosting productivity to raising these
really tough ethical questions around misinformation.
Yeah. And this shows how fast this field is moving
and touching, well, pretty much every corner
of our lives. Which brings up, I think, an important
question for all of us. As AI gets smarter and
as it moves physically closer running on our
phones, in our homes, via robots, what new responsibilities
do we take on? You know, the users, the developers.
How do we ensure it truly serves humanity, helps
us thrive responsibly? Two secs silence. Lots
to think about there. We hope this deep dive
gave you some new insights to mull over. Out
to your own music.