Okay, hey there, and welcome back to the Deep
Dive. Hey. Today we are just jumping into this
stack of material you send over articles, research
notes, a whole bunch of different stuff. And
it shows us something really interesting. It's
how AI, you know, the tech that feels like it's
totally focused on the future. Right. Is actually
giving us these wild new insights into the ancient
world, like thousands of years ago ancient. And
also obviously still totally shaping the tools
we use right now today. Right. It's kind of fascinating,
you know, seeing AI's reach span from like uncovering
secrets from millennia ago to what's getting
launched this week. Totally. Our mission here
is to really just unpack the key nuggets from
this material, figure out what's happening, and
most importantly, why it actually matters to
you. Totally. And yeah, there's some surprising
bits in here. Like apparently... AI might actually
change the timeline of ancient history, which,
I don't know, that just sounds kind of mind -bending
when you think about it. It really does. It highlights
the capability of these models to find patterns
and data that, you know, was previously just
too complex or too large for us to easily process.
So, yeah, let's dive in. We're starting in the
way, way back past. Okay. And specifically with
the Dead Sea Scrolls. Ah, yeah. This is one of
the most significant findings highlighted in
your sources. This new research shows how AI
is actually redating some of these scroll fragments.
Redating them? Yeah, suggesting they might be
older than scholars previously thought. Okay,
real quick, just for anyone who maybe isn't totally
up to speed, what are the Dead Sea Scrolls? Oh,
good point. They are essentially fragments of
ancient Hebrew and Aramaic writings over 2 ,000
years old. They were found near the Dead Sea
back in the 1940s. 40s and 50s, obviously. They
give us this incredible window into Jewish life
culture and religious thought from that time.
But the big challenge, as the sources explain,
is that most of the fragments don't have specific
dates written on them. So scholars have to use
other methods to estimate their age. Right. So
you've got this priceless historical source,
but nailing down the exact timeline is really
tricky. Exactly. So, okay, how does AI help with
that? That's where this machine learning model
called Enoch. comes in. EMAC. Yeah. The research
describes how it was trained using two main types
of data. First, the traditional radiocarbon 14
dating data. Right. The standard stuff. Which
is the standard scientific way to date organic
material like the parchment. And then second,
information about the handwriting itself. The
handwriting. Yeah. The geometric and structural
features like the size, the shape, the curvature
of the letters. So it's not just about how old
the paper is, but like the unique style of the
person who is writing. that's kind of neat yeah
exactly it's combining the physical age with
the nuances of the script okay and the name enoch
the source and point out is you know named after
this ancient prophet symbolizing that blend of
ancient wisdom and modern science nice touch
and they're super clear the tool is designed
to assist scholars to help refine their estimates
not to completely replace the human experts right
it's a tool not a replacement gotcha okay so
What did Enoch find? Did it just confirm what
everyone already thought? Well, mostly it did,
which is good validation. The sources say there
was about an 80 % match rate between Enoch's
dating predictions and the human expert's previous
estimates. Okay, 80 % is pretty high. That's
a pretty strong agreement. But here's where it
gets really interesting. Oh, tell me. What happened
with the other 20 %? That remaining 20%. Enoch
consistently dated those scrolls older than expected.
Older? How much older? Sometimes by as much as
a century. Whoa, 100 years. That's a significant
difference when you're talking about documents
from over 2 ,000 years ago. It really is. Like,
is there a specific example? Yeah, the sources
mention scroll 4Q114, which contains parts of
the Book of Daniel. Okay. Human scholars had
previously dated it to around 165 BCE. Enoch's
range, though, puts it between 230 and 160 BCE.
Hmm, so it could be 165, but it could also be...
quite a bit earlier. Exactly. So while it does
overlap with the human date, it definitely opens
up the possibility that this particular text,
or at least that fragment, could be older, potentially
significantly older. So it's nudging the earliest
possible origin. Back in time. Right. And Enoch
found something even more specific that's impactful
for scholars. Instances of what they call Herodian
scripts. Okay. Like King Herod. Yeah. A handwriting
style previously believed to be characteristic
of, you know, the time of King Herod actually
appearing in fragments that the AI dated before
Herod's reign. before. Wow. Which suggests that
the transition to this specific writing style
might have happened earlier than previously understood.
Okay. So AI is looking at these incredibly subtle
details of handwriting, combining it with the
science of carbon dating. And it's basically
saying, hey. Maybe we need to tweak the historical
timeline a bit based on this. Pretty much, yeah.
And the why it matters point here is huge as
the research highlights. Applying AI to ancient
script analysis like this can fundamentally reshape
our understanding of things like cultural evolution,
how widespread literacy was in different periods,
and the overall historical context of these texts.
That makes sense. If texts are older, it changes
when and how we think certain ideas, beliefs,
or even just writing styles. spread or developed.
It's kind of like the future is shedding light
on the deep past to make it clearer. That's a
really cool way to think about it. OK, so we
see AI helping us understand these ancient, ancient
writings. But I mean, AI is also just moving
at like lightning speed right now, creating tools
and driving research that feels. Totally sci
-fi. Absolutely. The sources really emphasize
this rapid pace. We go from ancient history,
you know, to the sort of daily deluge of new
AI development. It's almost hard to keep up.
Yeah. Let's shift gears then and look at some
of the examples of current AI tools and research
that the sources cover, because it's a pretty
diverse landscape. It really is. One area that
felt particularly significant in the material
is AI's impact in science and medicine. They
talk about this new AI architecture called bioreason.
Bioreason. Okay, so what exactly is that? What's
kind of groundbreaking here is it combines a
DNA language model with a standard large language
model, an LLM. Like chat GPT type things? Yeah,
like, you know, the kind that powers chatbots.
Oh, so it's like it can understand the language
of DNA in our normal human language? Yeah, exactly.
The sources explain that this allows the LLM
part to... Reason over genomic data, not just
text data, but the actual genetic code. Okay.
They tested it on standard biological benchmarks,
data sets, essentially designed to connect genetic
changes to disease pathways and biological functions.
And how did it do? Was it like any good? The
sources report a significant gain in performance,
about 15 % better than using models that only
use the LLM or only use the DNA data. 15 % better.
That's pretty substantial. It really shows the
power of tightly integrating these. different
types of information. Okay, can you give me a
concrete example of what this bioreason can actually
do? Something specific. Sure. The sources use
this example query. What is the impact of a specific
genetic change, a PFN1 variant found on chromosome
17? Okay. BioReason was able to predict amyotrophic
lateral sclerosis, you know, ALS. ALS, okay.
But it didn't just give ALS as an answer. The
really powerful part is that it generated a step
-by -step, 10 -step actually, mechanistic explanation
of how that specific genetic variant is linked
to ALS. Wow. Okay, so it's not just spinning
out a prediction. It's actually showing its work,
like the biological process behind it. Precisely.
And the sources really emphasize that this ability
to provide a reasoned explanation is essential.
I bet. Especially for things like building trust
in clinical diagnostics or speeding up drug discovery
pipelines or even just generating automated hypotheses
for researchers to explore. It could potentially
significantly accelerate genetic and medical
research. So we've gone from dating ancient texts
by analyzing handwriting and carbon to predicting
serious diseases by reading DNA and explaining
the biology. That is quite the range. It really
is. And then, yeah, your sources dive into the
sheer volume of new tools and applications that
are just launching right now. Yeah. It feels
like, you know, every other day there's something
new. It really does feel like that. The material
carters a bunch. Like the recent OpenAI updates,
you can now... record and transcribe audio right
there in ChatGPT, right? And it can automatically
summarize meetings and add timestamps. Plus,
they added Google Drive integration, which is
pretty useful. Yeah, those are pretty practical
additions for everyday use, things people actually
need. There are these new players popping up,
too, like this French startup, H -Company. Oh,
yeah. The source has mentioned they just launched
three new AI agents, one that's reportedly 92
% successful at tasks and cheaper than comparable
models. 92%. Wow. And another that sounds pretty
advanced, described as a super agent. Which suggests
we're seeing AI move towards more autonomous,
specialized helpers, not just general chat interfaces.
Right. More like agents doing things. And for
folks interested in creative stuff or like making
money online, the sources highlight AI creator
Rory Flynn showing this neat trick to make VO3
video clips longer using just text prompts. Oh,
cool. And there are guides on. Using tools like
Genspark and VO3 specifically for beginners looking
to offer social media services or, you know,
build an online income. Yeah. Demonstrating how
these tools are being immediately leveraged for
practical, even entrepreneurial purposes. It's
not just, you know, abstract tech. Totally. There's
also just a flood of other interesting tools
mentioned. ID browser for spotting trends and
startup ideas. Okay. Spine research. to generate
quick research reports. UISnapper, which can
turn a screenshot of a user interface into AI
prompts to rebuild it. That's useful. UISnapper.
Yeah. And even niche things like Job for Agent,
which is apparently a job board specifically
for autonomous AI agents to find tasks. A job
board for AIs. Okay, that's interesting. Right.
And RecipeSnap, which can turn a photo of what's
in your fridge. into recipe suggestions. I mean,
that's pretty useful. I know, right? I could
totally use Recipe Snap. I always stare blankly
into the fridge. Me too. Yeah. And then you have
the bigger players integrating AI, like HubSpot
connecting their CRM data to ChatGPT. Right,
making existing tools smarter. Or Notebook LM,
adding a new feature for shared notebooks. And
the sources note that OpenAI gave internet access
back to its Codex agent, which is designed for
software engineering tasks. Codex is back online.
And Mistral launched their own coding client
called Vibe, which is clearly aimed at competing
with GitHub Copilot. So, yeah, a lot happening
on the coding front, too. It's not just about
discovering ancient secrets or medical breakthroughs.
It's also totally changing how we build, create
work and just like. get things done. Definitely.
The sheer volume of new tools feels like a huge
part of the landscape described in this material.
Absolutely. And navigating this landscape brings
up some important questions and challenges, which
the sources touch on as well. Like, is AI going
to make coding skills obsolete or make people
lazy? That's a question a lot of people have.
Right. It's a genuine concern, isn't it? If you're
constantly using an AI assistant to write code
for you, does it risk dulling your own fundamental
skills? Yeah. The material acknowledges that
concern and presents perspectives on how AI should
be used smartly as a tool to enhance your skills
and achieve deeper mastery rather than just relying
on it as a crutch. Yeah, that makes sense. You
have to learn how to use the tool to make yourself
better, not just like delegate everything to
it. Right. What about the maybe less positive
side, like AI washing? Ah, yes. The sources bring
up the concept of AI washing, which is... Essentially,
when companies use the hype around AI to, you
know, attract investment, boost their image or
sometimes even justify cutting jobs without actually
having genuine or sophisticated AI capabilities.
The builder AI example mentioned is quite striking,
where humans in India were reportedly posing
as AI to fulfill tasks. Posing as AI. Yeah. It
highlights that not everything labeled AI is
necessarily the real deal. So you have to be
pretty critical about the claims you hear. Buyer
beware. kind of that's definitely and then on
a totally different note the sources also present
some really optimistic viewpoints like the google
deep mind ceo's belief that ai will actually
make us less selfish over time and that um artificial
general intelligence agi the really smart ai
yeah human level ai could lead to radical abundance
and solve these fundamental underlying root node
problems and you know maybe the next five to
ten years wow it's a pretty uh A pretty bold
vision for the near future, five to 10 years.
It is. And the sources just present that as like
a possibility or a perspective, right? Not a
guaranteed future. Right. Okay. It's one possible
take. Exactly. It's one viewpoint included in
the material highlighting the wide range of beliefs
and predictions out there about AI's ultimate
impact on society. And in terms of like actual
industry stuff happening. The sources mentioned
Snorkel AI's recent fundraising, $100 million,
which puts their valuation at $1 .3 billion.
Wow. Big money. That just shows significant investment
is still flowing into companies building specific
AI products. It does. And the material also touches
on the emerging legal and questions related to
data. Oh, yeah, the data stuff. Like the Reddit
lawsuit against Anthropic, which is related to
the AI model using Reddit's data for training,
allegedly without permission or compensation.
These kinds of cases are just starting to, you
know, define the legal and ethical boundaries
around data usage and inaugural property in the
age of these massive language models. Yeah, that
whole area. is incredibly complex and starting
to be figured out. It feels like the law is trying
to catch up with the tech. Very much so. And
there was even a little note in there about the
AI Fire Academy trivia. Just a quick shout out
again. Yeah, just a small mention from the source.
It's kind of showing the different communities
and aspects that make up the current AI world.
Little bits of culture around it. Exactly. So,
wow. We've really gone from uncovering secrets
buried for thousands of years thanks to AI looking
at ancient handwriting. to wrestling with brand
new legal questions about data, and even hearing
visions of a future with AGI -driven abundance.
It really brings us back to the big question,
why does all of this, this incredibly diverse
set of developments, matter to you listening
right now? That's the core point, isn't it? It
matters because AI isn't just some abstract futuristic
concept anymore. Right. It's fundamentally changing
how we discover things, how we work, how we create,
how we interact with information and the world
around us. Right. Whether you're a researcher
using something like BioReason, a creative looking
for new tools to express ideas, a student trying
to process a ton of information, or just navigating
daily life, AI is impacting the tools available
to you. It could potentially shift job markets
and the skills needed. And it's changing how
information itself is found, processed and understood.
It's really about staying informed about this
evolving landscape, seeing the potential applications,
being aware of the challenges like AI washing
and recognizing how AI is being applied in ways
that might genuinely surprise you. Like, you
know, rewriting ancient timelines. It definitely
feels like we're just scratching the surface
of what AI can do, both for uncovering things
that were previously hidden and for building
things that have never existed before. Totally
agreed. It feels like just the beginning. OK,
so we've taken this deep dive today looking at
how AI is. unlocking secrets in the Dead Sea
Scrolls by analyzing everything from radiocarbon
data to these tiny details in handwriting. Yeah,
the Enoch stuff. We saw AI reasoning over DNA.
with bioreason to predict diseases and even offer
these step -by -step explanations for complex
medical connections. Which is pretty amazing.
And we touched on this huge wave of new tools
that are changing how we code, create, do research,
and even how we manage what's in our refrigerators
with things like RecipeSnap. It's been a journey
for sure, from the deepest past to the immediate
future, all in one go. Yeah. And here's just
like a final thought for you to... Maybe mull
over. Think about how these examples we discussed,
whether it's AI redating ancient scrolls based
on really subtle script variations or predicting
a complex disease based on intricate patterns
in DNA. They all show AI's pretty incredible
power to find meaningful connections and patterns
in data that was just previously too vast, too
complex or too nuanced for humans to process
at scale. Right. Seeing the unseen connections.
Exactly. What does this fundamental ability of
AI to reveal these hidden structures across wildly
different areas, history, biology, user interfaces,
whatever mean for how we'll understand and interact
with everything going forward? It's kind of mind
-blowing, right?