OK, so imagine you could suddenly just like look
inside an AI's brain, you know, really trace
how it's thinking step by step. Or maybe you
caught that news about AI predicting hurricanes
like super accurately. Or there's another one
that somehow can't even tell you what year it
is. Yeah, that contrast is pretty, pretty stark,
isn't it? Between the really groundbreaking stuff
and well. Kind of baffling. Totally. And honestly,
that pretty much captures what we're going to
dig into today. Yeah. Welcome to your deep dives.
We've got the whole stack of sources. You shared
some research papers, news bits, notes on new
tools popping up. And it's all about the latest,
most interesting things happening in AI. It's
a really great snapshot, actually. It shows where
the field is making these huge leaps and maybe
also where it's still kind of finding its feet.
Yeah, exactly. So our mission here is just to
unpack all of this with you, pull out the most
important bits, the surprising stuff, maybe some
real aha moments, and sort of give you the shortcut
to understanding what's actually going on in
this super fast -moving world. This is your custom
deep dive into your material. Think of us as
guides, maybe, helping you navigate the info
you brought to the table. Love that. Yeah. And
right at the heart of these sources, there's
this one piece that feels like, you know, the
main event. It's about anthropic and what they've
just, well, they've open sourced something pretty
big. A tool that people are describing as giving
us X -ray vision into that AI black box everyone
talks about. Which is huge. Yeah. Because for
years, everyone's kind of said that box is basically
sealed shut. So seeing a real move towards cracking
it open. That's a big deal. It really is. We're
definitely going to get into that. But your sources
cover a bunch of other stuff too, right? It's
not just the super technical breakthroughs. We've
also got some of the more, let's say, quirky
applications and just the general industry buzz
you picked up on. Yeah. And what's fascinating,
I think, is how these seemingly different pieces
actually connect. They're not totally random.
They kind of show these underlying trends, maybe
some tensions, and really paint a picture of
where AI is heading right now. Right. It's like
different sides of the same, I don't know. rapidly
changing crystal or something. Okay, let's start
with that big one then, the anthropic noose.
The source talks about this new tool they've
open sourced and they call the main technique
circuit tracing. Yes, circuit tracing. And they're
using something they call attribution graphs
to do it. This is basically their deep interpretability
work, understanding how the model works. But
now it's out there for others to use. Attribution
graphs. Okay, so what does that actually show
you? What are you looking at when you use it?
Well, they're designed to show you the internal
reasoning steps, right, that a language model
takes. When you give it a prompt and it spits
out an answer, these graphs visualize the path
the information took, which bits of the model
lit up, and how they actually contributed to
that specific response. Okay. Wow. So it's not
just seeing, like, input goes in, answer comes
out. You're literally watching the AI sort of
thing. think its way through in a very structured
way yeah a graph based representation you can
take a specific bit of the output and trace it
right back through the model's layers you see
the sequence of calculations and connections
that led right to it and the source mentioned
you can even like mess with it, edit things in
the graph to see what happens. Precisely. Yeah,
that's the experimental part. You can spot a
specific internal feature or like a pathway the
model used, and then you can tweak it or even
switch it off and see the direct cause and effect
on the final answer. It lets you investigate
really deeply. And this isn't just for Anthropic's
own models, is it? The source said it works with
some big open models too. Yeah, that's right.
They specifically mentioned support for models
like Gemma and Llama. And importantly, they've
put out demo notebooks and even some graphs they
haven't fully figured out themselves yet, kind
of inviting the whole research community to jump
in and explore. See, this is where it gets...
I think really significant because for years,
the biggest knock against advanced AI, especially
these huge language models, has been that black
box issue. We train them. They do amazing things,
but we don't really get why they make certain
choices or how they get to their conclusions.
Exactly. And this release feels like a major
step towards cracking that open. It's a very
different vibe compared to labs that tend to
keep these kinds of internal tools super secret.
you know, proprietary. Anthropix seems to be
making a statement here like transparency is
key, which is probably also a competitive thing,
too. It's a huge signal. And the source toss
out this really interesting idea. What if? down
the road, we could actually edit the model's
reasoning the same way we edit prompts now. That
does raise a big question. If you can clearly
trace, say, a flawed line of reasoning or see
a really good one, could you potentially go right
into the model's internal workings and correct
or reinforce those patterns? This tool feels
like a first step towards maybe having that kind
of capability someday. Yeah, that's definitely
something to think about. Okay, so shifting gears
a bit. From seeing inside the AI mind to predicting
hurricanes. Your sources also brought up NASA's
GAIA model. Ah, yes, GAIA. This is a fantastic
example of these big foundation models. The really
flexible, powerful ones moving completely beyond
just language and text. GAIA stands for Geospatial
Artificial Intelligence for Atmospheres. Right,
the atmosphere. So it's like an AI weather model.
different somehow. Much, much more than just
a standard weather model, really. It's a generative
AI model built just for our planet's atmosphere.
It was trained on something like 25 years of
Earth data. It can do things like predict hurricanes.
Yeah. But also spot wildfires, estimate rainfall,
potentially all in real time. 25 years of data.
Yeah. Wow. That sounds like a massive project.
What makes this different from, you know, the
super complex weather models we already have?
A couple of key things based on the source. One
is the detail and speed. It apparently has a
four kilometer spatial resolution that's really
fine grained and updates every 30 minutes, which
is incredibly detailed for real time use over
big areas. But it's also it's generative power.
It can intelligently like patch up big holes
in satellite data or reconstruct full high res
weather maps, even if the input is noisy or incomplete.
Whoa, that's amazing for places where you might
not have perfect sensor coverage or, you know,
if data drops out. Exactly. And it's designed
to be, you know, a foundation, something other
labs and organizations can build on top of. It
really shows these powerful AI methods aren't
just for writing code or text anymore. They're
starting to tackle really complex physical systems.
So, like, could cities or maybe disaster relief
groups actually use something like this? The
source suggested that within maybe the next couple
of years, GAA -like models could realistically
be powering real -time dashboards. You know,
for critical infrastructure, disaster response,
it seems to be moving pretty fast from research
to potential real -world, maybe even life -saving
uses. Wow. Okay. So we have this absolutely groundbreaking
stuff, seeing inside AI predicting massive storms
with incredible detail, and then... Your sources
also had that little snippet about Google's AI
assistant apparently telling people the current
year is 2024. Yes, that little detail was interesting,
wasn't it? It definitely highlights the current
state of things that even while we're making
these massive leaps like GAIA, really basic factual
slip ups can still. It is kind of humbling, right?
Like any kid knows the year, but a cutting edge
AI occasionally gets it wrong. It certainly brings
up that important point about reliability and,
you know, how much we can really trust these
tools for simple, checkable facts, especially
when they might be baked into systems we depend
on. The source even tied it back to some critics
who worry about relying on AI for the kind of
factual info we used to just get from the Web.
Right. And speaking of Google. Your sources also
mentioned those kind of weirdly realistic, sometimes
unsettling videos from VO3. People were sharing
them showing impossible challenges or physics
being broken. Yes, the sheer ability and realism
in AI video generation is just advancing incredibly
fast. Those examples really drive that home.
And then there was Odyssey, which showed off
something called an interactive video AI model.
Right. They described it like stepping into a
playable movie. That's a different angle, again,
taking AI video creation beyond just making a
fixed clip towards potentially creating dynamic,
responsive visuals that you can actually influence
somehow. That's a whole other thing for like
content creation or maybe training simulations
or something. And then you have tools like Opera's
new browser, Neon. They're calling it an AI browser
that can act on your behalf. Yeah, that sounds
like more of an interface shift. An AI browser
that can chat, sure, but also fill forms, maybe
book trips. The source even suggested it could
build symbol apps for you. The browser itself
becomes, you know, an AI assistant layered on
top of everything. So instead of just finding
the flight info, you could potentially just tell
it, hey, book me the cheapest flight next Tuesday.
Yeah. And I might just do it. That seems to be
the direction they're looking at. Yeah. Your
sources also caught some of that. broader industry
pulse, like Grammarly, the AI writing helper,
getting a billion dollars in funding. That's
huge. A billion dollars. Yeah, that is serious
cash flowing into AI productivity tools. And
some other quick hits, Perplexity, having a tool
to make spreadsheets from conversations, the
Netflix co -founder joining Anthropix board.
It all just shows the whole ecosystem is buzzing,
you know, evolving really fast, technically,
commercially, even in terms of who's leading
things. Oh, and there were mentions of layoffs
at business. insider, too, maybe hinting at wider
economic stuff or how AI might start shaking
up certain jobs. Yeah, there's definitely a lot
going on on all these different fronts at the
same time. It's kind of dizzying. Yeah. So, OK,
we've covered quite a bit here from trying to
peek inside the AI's mind with Anthropic's new
tool to predicting massive weather events with
NASA's GAIA model to. The surprising little glitch
of a major AI forgetting what year it is and
all these new ways AI is popping up in video
and even how we browse the web. Yeah. And if
we try to like connect the dots here, see the
bigger picture. What this collection of news
really signals is the incredible, almost kind
of jarring speed of AI evolution right now. You
see this fundamental push for transparency, for
understanding with Anthropic. But that's happening
at the same time as we're building these incredibly
powerful foundation models for really complex
physical systems like the atmosphere with GAIA.
And then you have that really stark contrast,
right, between that super cutting edge stuff,
the fact that, you know. Some basic reliability
things like an AI getting the year wrong are
still cropping up. Exactly. It shows the tech
is both incredibly advanced and still kind of
immature. Or maybe just unpredictable in some
ways. It also clearly demonstrates how fast AI's
application space is expanding. I mean, it's
not just about generating text or code anymore,
not by a long shot. It's moving firmly into physical
systems like GAIA and to creative media like
VO and Odyssey. And it's fundamentally changing
how we interact with information, like with these
new AI browsers. OK, so what does all this mean
for you listening right now? Why does knowing
about circuit tracing or a hurricane model actually
matter? Well, I think understanding these specific
points gives you a much better feel for where
AI is actually heading, you know, beyond just
the general hype you hear everywhere. Knowing
about something like circuit tracing, for instance,
it fundamentally changes that idea of the AI
black box. Maybe it's becoming less opaque. And
that's crucial if we want to build trust and
make sure these things are safe. The GIA model
shows that AI isn't just changing how we use
computers. It's starting to potentially change
our understanding and ability to deal with the
physical world around us in pretty significant
ways. And seeing that mix the incredible breakthroughs
right alongside the basic errors, that gives
you a more realistic view of where things stand
today, where it's powerful and where the limitations
still are. Right. It helps you maybe anticipate
how AI might show up next in your own life or
your work or just out in the world. and what
you can realistically expect from it, or maybe
what you shouldn't expect from it just yet. Precisely,
yeah. It's about being informed, grounded, and
what the actual research and the real -world
applications are showing us. Yeah, that really
is something to think about. Okay, so based on
these sources you brought us, here's a final
thought to kind of leave you with. We saw with
Anthropix tool... that we can now start to see
AI reasoning, right? And they even hinted at
maybe being able to edit that reasoning directly
in the future. So what happens when you combine
the ability to look exactly at why an AI made
a certain decision with the potential power to
directly go in and change its internal logic?
What does that mean for AI safety, for how we
control these systems, or maybe even for how
we think about intelligence itself down the line?
Yeah, definitely a lot to ponder as these capabilities
keep developing so so quickly. Indeed. Thanks
again for bringing your sources for this deep
dive.