Imagine an AI that doesn't just write code for
you, but actually invents completely new scientific
methods. Methods that, you know, outperform human
experts. Not just by a little bit, but like significantly.
Yeah, and it does it in hours instead of months.
It's kind of wild to think about that speed.
Pretty wild acceleration, right? Welcome to the
Deep Dive. Today we're looking at a whole stack
of, frankly, fascinating insights into this accelerating
world of AI. Our mission, like always, is to
try and unpack how these systems aren't just
helping us anymore, but actively redefining what's
even possible in research, communication, creative
stuff. That's right. So first up, we're going
to dive into how Google's AI is now, well, out
-researching actual researchers. It's quite something.
Then we'll zip through a bunch of rapid fire
updates, you know, everything from AI making
films to new ways to sort out your digital life.
And finally, we'll look at a potential game changer
in speech recognition. Could totally reshape
how we talk to machines. It's a lot to cover,
so let's just jump right in. Okay, so this first
big story, it really feels like it pushes the
boundaries of what we thought AI could do. We're
looking at this Google system, and it's not just
writing essays or fixing code, it's actively
inventing scientific methods. Feels like a big
leap, you know? Moving AI from just an assistant
into genuine discovery. It absolutely does. And
what's really fascinating is how it works, the
mechanics of it. You basically feed it a very
specific problem. You need to clear a goal, some
data to work with, and this is key, a way to
score itself. Evaluation metric. Once it has
that, the system starts generating initial ideas.
Then it writes code based on those ideas. It's
not just a one -shot deal, right? It keeps refining.
Not at all. It tests thousands, literally thousands
of variations of that code. It uses something
called a tree search loop. which is think of
it like a really sophisticated trial and error
process. It systematically explores tons of possible
solutions, branching out, trying different paths,
always aiming for the best one. This whole approach,
by the way, it's inspired by AlphaZero, you know,
the AI that mastered Go and chess. So the system
keeps rewriting its code, constantly trying to
improve those scores, and it only keeps the very
best solutions as it goes along. Okay, and this
is where the results get. Well, you called it
wild earlier. Yeah. Pretty staggering stuff.
The system didn't just match human performance.
It blew past it in several areas, like genomics.
It generated 40 new methods for RNA -seq integration,
and the best one, 14 % better than the top tool
made by human experts. That's a serious jump.
And it wasn't just one field for COVID -19 forecasting.
He came up with 14 new models. And those models
outperformed the CDC's own gold standard system,
the COVID Hub Ensemble. That's directly relevant
to public health planning. Right. And neuroscience,
too, predicting neuron activity in zebrafish.
Yeah, 70 ,000 neurons. More accurately than any
of the baseline models they compared it to. Wow.
And math. It solved hard integrals. Solved 17
out of 19 tough integrals where the standard
methods just failed. Couldn't do it. Yeah. And
for time series data, it basically built its
own forecasting library from scratch, testing
it across 28 different data sets. The sheer breadth
of that is. Wow. It really is. Whoa. Just imagine
scaling that, scaling that kind of exploration
to, I don't know, a billion queries across every
single scientific field. The amount of new knowledge
just pouring out. It would be. genuinely transformative.
And that's why it matters so much. It signals
this fundamental shift, right? Most coding AIs
we talk about, they focus on getting the syntax
right, making sure the code runs without errors.
But this Google system, it's different. It's
idea generating and it's performance maximizing.
It's not just following orders. It's actively
finding new, better ways to do things. And it
never gets tired. It never stops exploring that
kind of relentless search. It pushes entire fields
forward at, well, warp speed. So thinking of
the immediate future. What's the biggest single
impact this could have on scientific discovery,
like right now? Yeah, it just shortens those
discovery cycles like crazy, accelerates breakthroughs.
Okay, all right. Let's pivot a bit from that
really deep foundational research to just the
sheer whirlwind of daily AI stuff happening.
It feels like there's so much going on, it's
almost impossible to keep up. What's caught your
eye recently? What feels like a sign of where
things are headed? It really is a whirlwind,
yeah. Let's maybe hit some rapid -fire highlights
just to show the range. Notebook LM, for instance.
That's really leveled up. Users can now auto
-generate custom reports, full blog posts, flashcards,
quizzes, even video summaries. And it works in
over 80 languages. So it's not just organizing
info anymore. It's actively synthesizing it into
new things for you. That sounds incredibly useful,
especially if you're drowning in information.
And on the creative side, OpenAI is getting into
movies. Yeah, they are. They're backing a film
called Critters. It's supposed to be an entirely
AI -generated animated movie slated for con in
2026. Apparently, yeah. They even showed a short
video bit back in 2023. So, yeah, they're showing
early signs of AI storytelling on a bigger scale.
And talking about creating content, Claude's
making some practical moves too, right? And like
office tools. Totally. Claude can now create
and edit PDFs, slide decks, reports with actual...
visual charts, right inside the chat. You can
just feed it raw data, maybe from a spreadsheet,
and it pulls together a whole write -up. That's
a huge time saver for anyone doing business reports
or presentations. It really feels like AI becoming
a direct co -pilot for office work. Very practical.
And then for the younger crowd, Google and Stanford
launched AI Quests. Right. It's a game series
for middle schoolers. Kids get to role play as
Google researchers. It's kind of edutainment
for the AI age, right? Making tricky AI concepts
interactive, fun, maybe inspires the next generation.
Clever way to introduce those ideas early. And
on a later note, apparently you shouldn't call
ChatGPT dumb anymore. Slight chuckle. Yeah, seems
like you'll get roasted. Not by the AI, but by
other humans defending it. Someone actually asked
if that makes them a bad person for criticizing
it. Kind of highlights the weirdly social, almost
emotional connection people are forming with
these tools. That's a funny little community
quirk. Shows how embedded they're becoming. But
back to content. Yeah. There's this company,
Inception Point, making waves or maybe causing
a bit of a stir. Definitely causing a stir. Their
plan, produce 5 ,000 AI -generated podcasts per
week with 3 ,000 new episodes weekly. And get
this. For just $1 per episode. So yeah, critics
are pretty understandably screaming soulless
content at that kind of volume. Raises huge questions
about authenticity, market saturation, the future
of stuff made by actual people. It certainly
does. And on the business end, some really eye
-popping fundraising numbers are coming out.
Shows massive investor confidence. Oh, absolutely.
Cognition AI, for example. They just raised,
what, $400 million. Reached a $10 .2 billion
valuation in just 18 months. And their tool,
Devin. Its annual recurring revenue jumped from
$1 million to $73 million. They've got clients
like Goldman Sachs, Cisco already using it, that
kind of growth. Yeah. It's just astronomical.
Shows the huge demand for these specialized AI
tools. Okay, so with all this happening just
incredibly fast, what's maybe one... key challenge
these rapid advances are creating that we really
need to watch. Yeah, I'd say maintaining content
authenticity. Yeah. Especially with that AI generated
volume exploding. Gotcha. Okay, let's keep this
pace. Let's do some AI quick hits. Just a quick
rundown of smaller but still important announcements
that maybe show subtle shifts. Microsoft, Azure,
OpenAI, they're offering new conversational AI
shopping experiences now. Yeah. It could genuinely
change how we interact with online stores, you
know, making shopping assistants feel more personal,
maybe more proactive. That's a big step for customers.
Customer service. Yeah. And definitely a sign
of AI getting deeper into everyday commerce.
And also Microsoft, they announced they'll be
using Clots on it 4. That's a pretty big shift
from relying so heavily on OpenAI's GPT models.
Yeah. Tells you a lot about the competitive scene,
right? And how companies are diversifying, looking
for the best AI for specific jobs, not just one
size fits all. Indeed. Yeah. Feels like a strategic
move, leveraging different AI strengths. Meanwhile,
Apple. They're quietly working on something called
World Knowledge Answers. Sounds like a potential
chat GPT rival. Always interesting to see what
Apple's cooking up in the background with their
resources. Very true. It really highlights how
every major tech player is scrambling to build
their own powerful conversational AI. And kind
of an interesting side note, within the AI research
community itself, apparently researchers often
highlight well -cited papers and certain big
names. It's a bit of a human thing, right? Recognizing
who influenced what, even in this super - automated
field. A little nod to the foundational work.
Huh. Bit of academic. Self -reinforcement, maybe.
And speaking of Apple, one detail that kind of
surprised me, their new iPhone 17 devices still
apparently don't have a fully AI -powered Siri
yet. That's kind of surprising given everything
we're talking about. Maybe suggest they're being
more careful, slower integration, perhaps. It
is surprising, yeah. Maybe suggest they're taking
a more cautious route. Or perhaps a more deeply
integrated approach for their on -device AI,
rather than just rushing out something superficial.
So how quickly do you think these quick hits,
these smaller things, become just mainstream
reality for us? Well, many are already in beta
testing, so mass adoption. It feels pretty imminent,
actually. Mid -roll sponsor Reed Placeholder
will be provided separately. All right, let's
switch gears again. Let's look at another area
that feels ripe for a big transformation, speech
recognition. Alibaba just put out something called
QEN3 ASR Flash. It's a new speech recognition
model built on their QEN3 Omni Foundation, but
with some really clever, unique tricks. Okay,
so what can it actually do that's different besides
just, you know, turning speech into text? Well,
first off, performance -wise, it outperforms
rivals in Chinese, English, and nine other languages.
broad capability. But here's where it gets really
interesting. It can accurately transcribe rap
music and songs, even with complex background
music playing. That's a huge leap handling really
nuanced, noisy audio like that. And here's another
potential game changer. You can feed it keywords
or whole documents or even just sort of general
text to bias its results without any complex
pre -processing. It's kind of like prompt engineering,
but specifically for speech recognition. You
mean like giving the automatic speech recognition,
the ASR system, specific instructions or examples
to guide how it understands things and what it
writes out. Exactly that. It's about giving the
AI context, telling it what's important to you
without needing to rebuild the whole model. And
apparently it works really well under noisy conditions.
It's like cars, crowd noise, even when people
are code switching. missing languages in one
conversation, it just automatically detects the
language being spoken and intelligently ignores
background noise or silence. It makes it seem
extremely robust. Okay, this is where the why
it matters really clicks for me. Most ASR models,
they're pretty good, sure, but they can be quite
rigid. They often struggle to adapt to your specific
context, your industry jargon, your company names
without a lot of engineering work or complex
fine -tuning. QEN 3 ASR sounds like it completely
changes that dynamic. Like you want transcripts
to always get your brand name right or pick up
specific technical terms. You just feed it the
relevant info once and boom, it's set. I'll admit
I still wrestle with prompt drift myself sometimes
trying to keep an AI focused and consistent.
So this biasing feature. That sounds like a huge
leap in control, real precision. It really is.
It potentially elevates ASR from just a generic
transcription tool to something much more like
a highly customized intelligent assistant, one
that actually adapts to your specific vocabulary
and context. Right now, OpenAI's Whisper, that
really dominates the open source ASR world. But
this Quen 3 ASR, it feels like Alibaba's direct
enterprise -ready challenger. Its ability to
handle singing, different accents, plus that
unique biasing feature, that's a major differentiator.
This looks like Alibaba making a serious play
to own how the world talks to AI. Seriously.
And if they actually nail this, voice interaction
could be the place where Quinn finally leapfrogs
some of the Western models in terms of adoption
and capability. It's a direct challenge for market
leadership in a really critical area. So to thinking
about that, what's maybe the most surprising
real world application you could see for this
new kind of speech AI? Maybe capturing really
nuanced legal discussions or diverse global meetings
with perfect clarity, finally getting all the
specific terms right. That could be huge. So,
OK, let's try and pull this all together. What
does this all mean for you listening right now?
Today's deep dive really shows us this landscape
where AI isn't just a tool anymore. It's increasingly
a collaborator, an inventor even, and a global
competitor. We've definitely moved beyond simple
automation into... genuine creation, sophisticated
understanding. And it's happening everywhere
all at once. Absolutely. We're seeing this really
profound shift. AI isn't just processing information
anymore. It's generating new knowledge, new capabilities.
And at a speed that was, frankly, previously
unimaginable. It's kind of like, you know, stacking
Lego blocks of data, but the blocks are also
building themselves faster than we can quite
grasp sometimes. And as these systems keep evolving,
they really challenge our basic definitions of
human ingenuity. They accelerate progress across
pretty much every field you can think of. So
how do you see AI reshaping your world in the
next few years? What new possibilities does all
this spark for you? personally, professionally.
Yeah, think about that. The speed is just incredible.
It really makes you wonder, how are we actually
going to collaborate with these intelligent systems
tomorrow? What will that look like? That is definitely
a deep dive for another day, perhaps. Thank you
for joining us. Yeah, thanks for tuning in. We
always appreciate your curiosity.