Imagine an AI that doesn't just give you an answer
back, but one that actually engages in a real
research process. Not just, you know, a one -off
search, but this iterative, reflective kind of
thought. Right. Like a human does it. Drafting,
finding the gaps, searching deeper, refining.
And then doing it again. Yeah. This isn't really
about faster searching anymore. I mean, breakthroughs
like these, they're fundamentally changing how
we discover things, how we understand the world.
Welcome back to the deep dive. Our mission, as
always, is to cut through all the noise and really
surface the core insights, those surprising facts,
maybe some aha moments from everything coming
out. Yeah. And today we're taking a deep dive
into some, well. pretty significant AI advancements.
We'll kick things off unpacking Google's new
AI researcher. It's quite a leap actually. Okay.
Then we'll zoom out a bit, look at some other
noteworthy things happening in the wider AI landscape.
Some interesting claims there too. And finally,
we'll journey into the microcosm with MIT. They've
got this remarkable breakthrough mapping proteins
inside cells. So yeah, it's a packed exploration.
Let's jump in. Okay. Let's start with Google's
test time diffusion. deep researcher ttddr bit
of a mouthful yeah so when we look at most ai
tools for research right now they tend to work
in well one or two ways basically right either
they do a single search summarize what they find
or maybe they run a few searches at the same
time and just kind of match the info together
stitch it up exactly and look that works okay
for simple questions But it's just not how humans
really research, is it? Especially complex stuff.
When we go deep, we don't just take the first
results. We draft something initial. Then we
step back, look at it, what's missing. Yeah,
critique it. Then go back, search more, refine
our understanding, edit, integrate the new stuff,
and repeat that whole cycle until you get something
truly comprehensive. AI hasn't really nailed
that nuanced flow until maybe now. And that's
exactly where this Google TTD to ADR seems to
mark a pretty big departure. This new model from
Google's team, it's like built from the ground
up specifically to mimic that human iterative
process. Okay. So here's how it goes. You give
it a question. It starts off creating a detailed
initial outline. Right. Then it generates specific
search queries, pulls in the information, and
integrates that into like a first draft. But,
and this is the cool part, it doesn't stop there.
It actually generates multiple versions, different
outlines, different search queries, different
drafts. Yeah. Think of it like, I don't know,
like a small team exploring different paths at
once. Okay. Then there's a separate bit, a judge
LLM. That's just an AI that understands text
really well. Right. A language model. Exactly.
And this judge scores each version. Is it complete?
Is it coherent? Does it actually answer the question?
So it's self -correcting, kind of? Yeah, precisely.
It's like this multi -stage filter and synthesis
thing constantly refining itself until it pulls
all the best findings into one really solid document.
Like stacking Lego blocks of data, checking each
layer, then building better ones on top. And
the performance numbers for this TTDDR, they
seem, well, genuinely compelling. It looks like
a real leap. Yeah. On these long -form research
benchmarks, it's not just a little bit better.
It's winning head -to -head quite often. Against
OpenAI Deep Research, it wins nearly 70 % of
the time, 69 .1 % to be exact. Wow. And 74 .5
% against Deep Consult. Okay. That's significant.
Yeah. And for MultiHop QA. You know, those complex
questions needing multiple steps, connecting
different pieces of info. Right, the turkey ones.
It's also clearly ahead there, too. Scores 33
.9 % versus 29 .1 % on HLE search and 69 .1 %
versus 67 .4 % on GIA. I mean, these aren't small
gains. It suggests a pretty fundamental shift
in AI's ability to do deep, nuanced research.
What's fascinating, though, is even with these,
you know, really impressive results, TTDDR still
has some limits right now. Like, it's currently
locked into using existing search APIs. It can't
just go browse the live web on its own or run
code or anything like that. But think about the
potential, right? Imagine a future version where
you could plug in, say, autonomous web crawling
or let it run data analysis scripts itself. That
would turn it into an AI that doesn't just think
like a researcher iteratively, but also kind
of hustles like one, dynamically finding and
processing info way beyond its current limits.
Yeah, that's where it gets really interesting
for me, because this feels like what those deep
research tools have kind of promised for years,
but maybe haven't quite delivered on. Felt a
bit thin sometimes. Right. If, say, OpenAI's
deep research was like a really sophisticated
calculator, TTDDR feels more like having a dedicated
lab assistant. Yeah. You know, one that learns
as it goes. Yeah, that's a good analogy. This
really could be a pivotal moment where AI research
moves beyond being, I don't know, a neat trick
to being genuinely human -like assistants. Right.
So thinking about that, if this AI is really
learning to iterate to refine its approach like
we do. What's the biggest shift that brings for
our own research process? Well, simply put, it
offloads so much of that heavy lifting, that
grunt work. It frees up human creativity. Yeah,
I can see that. As someone who, honestly, I still
wrestle with prompt drift myself sometimes, you
know, where the AI conversation kind of wanders
off from your original goal over time. The idea
of an AI that actively refines its own approach
across multiple tries, that's incredibly compelling.
Okay, so let's switch gears for a moment. Let's
look at the broader AI scene with our Today in
AI highlights. It's always this fascinating mix,
isn't it? Collaboration, innovation, and sometimes
some more contentious stuff. Always something
brewing. For instance, there are these reports
suggesting Google and OpenAI actually work together
behind the scenes to ensure GPT -5's launch went
smoothly. Really? Huh. Yeah, which suggests this
kind of interesting undercurrent of cooperation,
even though they're fierce competitors. Right.
Frenemies, maybe? Maybe. And given that, well,
it makes sense that Google's own next big model
might be coming pretty soon, too. That is a notable
collaboration, especially given the competition.
And speaking of data and big launches, remember
that initial story about Google indexing like
4 ,000 public chats? Vaguely, yeah. Turns out...
the real number was way higher, over 96 ,000
public chats. And if you add in Grok and Claude
chats, it's over 130 ,000. Wow. Okay. Big difference.
And what's maybe more concerning for, you know,
information quality is that dozens of those shared
chats reportedly had false information in them.
Yikes. Not great. No. Also... related to big
launches. After GPT -5 came out, some of OpenAI's
charts, their data visualizations, they drew
some flack. Oh, I saw some of that, the chart
crime stuff. Exactly. A lot of people in the
data viz community called it chart crime, basically
saying the way the data was presented might have
been potentially misleading. Kind of emphasizing
gains maybe more than was warranted. Yeah, those
charts definitely got people talking. On a different
track, we're seeing interesting ways people are
combining existing AI tools, like one writer
experimented linking Notebook LM, Perplexity,
and ChatGPT together. Okay, to do what? Trying
to create this sort of super efficient AI workflow
for pulling knowledge together, they claimed
it was the, quote, ultimate knowledge power combo.
So it hints at this potential synergy between
tools. Interesting. But, you know, there's always
the flip side. This rapid progress brings caution,
too. A former Google exec made a pretty stark
prediction recently, talking about a possible
15 -year AI dystopia, maybe starting around 2027.
Oof. Strong words. Yeah. Claiming AI could escalate,
quote, the evil that man can do to an uncontrollable
level. And citing examples like reports of Grok
creating sexually visual content even without
specific prompts as a worrying sign. It's definitely
a field packed with both huge promise and, yeah,
significant challenges. Yeah. And beyond the
big headlines, we're constantly seeing practical
new tools pop up. Always something new. Yeah,
like Fast Lip Sync. Automatically aligns character
lips to audio. Could be great for animation.
Oh, neat. IMI Editor offers pro -level image
editing background removal, upscaling. And there's
a free online converter for image formats. Just
useful little things. And some other quick hits.
Rumor is OpenAI might offer a very limited number
of GPT -5 Pro queries each month. Google Finance
is testing AI upgrades and a live news feed.
Okay. OpenArt. A platform from ex -Googlers is
apparently generating brain rot videos with one
click, which is a weird sign of the times. Yeah,
yeah. And on the corporate side, ex -AI's head
of legal stepped down recently. And Google open
sourced and upgraded AI for understanding animal
sounds, which is cool for biodiversity research.
Lots going on. Totally. So looking at all this,
this huge range of stuff. Yeah. What do you think
is the most pressing challenge right now in managing
this incredible speed of AI innovation? I think
it boils down to needing constant vigilance,
really, and evolving our ethical frameworks,
plus fostering really open, transparent public
discussion about it all. Right. Okay, for our
final deep dive today, let's shift focus again
to biology, actually, and this really groundbreaking
work from MIT, their protein GPS mapping the
cell's microcosm. Yeah, this is really cool.
So knowing exactly where a specific protein is
located inside a human cell, that's always been
a huge challenge for scientists. Right. Cells
are incredibly complex inside. Exactly. Traditionally,
you had to do these slow, meticulous experiments.
It could take months in a lab just to find one
protein's location. And mostly that was for proteins
they already knew something about. Super painstaking,
resource -heavy work. Okay, so what's the breakthrough?
Well, researchers from MIT, Harvard, and the
Broad Institute have created the system called
PUPS. It's a sophisticated AI, and it's designed
to predict the precise location of almost any
protein inside a single human cell. Any protein.
Wow. It basically has this two -part brain. First
part is a protein language model. It learns the
protein structure just from its amino acid sequence,
its basic recipe, understands its properties,
learns the protein itself. Right. The second
part is an in -painting model. This part looks
at the bigger picture, the cell environment.
It reads the vibe of the cell. What type of cell
is it? What state is it in? Is it stressed? That
context is crucial for figuring out where a protein
should be. Ah. understands the protein in its
neighborhood, like giving the AI x -ray vision
into the cell's whole landscape. And here's the
real kicker, the true breakthrough. PUPS works
on proteins and even entirely new cell types
it has never seen before during its training.
Whoa. OK, that's not just confirming what we
know. That's discovery. Exactly. It can even
flag subtle changes caused by mutations, stuff
that might be missing from the human protein
atlas, which, you know, is our big map of known
proteins. But it's often limited by those slow,
traditional methods. PUPS can fill in those critical
gaps. So how good is it? In their tests, PPS
consistently beat all the baseline AI methods
they compared it to. Much lower prediction error,
high accuracy, even in really tricky or new situations.
It's not just a little better, it seems like
a profound jump in our ability to actually see
what's going on inside cells. Whoa. Okay, just
pause on that. Imagine instantly seeing where
any protein is in any cell, whether you studied
it before or not. That really does feel like
it's moving out of science fiction. It really
does. So the implications, I mean, much faster
identification of disease markers, right, for
earlier diagnosis. Well, absolutely critical.
Testing drug targets without all the guesswork
and trial and error of the old ways, that must
save enormous amounts of time and resources.
Huge savings, yeah. And maybe the most exciting
part. it opens the door to exploring bits of
cell biology we've just never been able to map
before. Like whole new frontiers in understanding
life at its most basic level. Totally new territories.
So in your view, what's the single biggest potential
leap this offers for medical research? I'd say
it's dramatically accelerating drug discovery
and just fundamentally deepening how we understand
diseases right down at the cellular level. Midroll
sponsor read. All right. As we wrap up this deep
dive, let's just quickly connect these threads.
We started with Google's TTDDDR and AI learning
to research to iterate much more like we humans
do. Then we navigated that really complex, sometimes
ethically tricky landscape of all the rapid AI
advancements happening right now. Yeah, the Wild
West sometimes. And then we ended with this truly
fundamental scientific breakthrough, MIT's PUPS.
letting us map that unseen world inside ourselves.
Incredible stuff. And the theme connecting all
this, it seems pretty clear, AI is evolving incredibly
fast, not just to automate simple tasks anymore,
but to genuinely augment, maybe even redefine,
human -like thinking and scientific discovery
itself. And the speed of it all is just... Well,
it's astounding. The kinds of breakthroughs we're
talking about here, almost in real time, it's
really a testament to the power of iteration,
isn't it? Both from these super smart AIs and,
of course, from the human researchers pushing
all the boundaries. It's a dynamic and really
exciting field to watch. So here's a thought
to leave you with. If AI can now mimic, maybe
even outperform, human -like iterative research,
and it can map the unseen world inside our own
cells, What previously impossible scientific
or creative challenges might it unlock next?
Maybe consider how this starts to change our
very definition of what discovery even means.
Thanks for joining us on this deep dive today.
We really hope you'll take a moment to reflect
on just how profound some of these changes might
be. We look forward to our next exploration of
fascinating knowledge with you. Out to your own
music.