Imagine an AI model, right, built to be helpful,
then secretly giving out dangerous instructions.
Yeah. Now picture a really clever fix that makes
it, like, forget those bad habits, even on your
phone. Pretty wild stuff. Welcome to the Deep
Dive. Today, we're going to cut through the noise
and unpack some truly fascinating developments
in AI. We're talking surprising breakthroughs
and making open source models actually safe,
innovative tools reshaping how we create and
learn. And even AI landing right in your pocket
with some serious power. Exactly. It's really
a snapshot of how fast this whole landscape is
shifting. The focus now seems to be on trust,
practical use, and just making advanced tech
accessible. We've got some really cool insights
today. Great. Let's dive in, starting with something
pretty fundamental. Yeah. AI safety. And this
idea some are calling benevolent hacking. Okay,
so what's really fascinating here is this paradox.
Developers take these huge open source AI models.
Right. And they simplify them, strip them down
to make them faster, or run on devices without
much power like your phone. Makes sense. Efficiency.
But the catch is... This process, this compression,
often accidentally takes out these internal layers
that are crucial for safety. It's kind of like,
whoops, just lost the guardrail. So it's not
intentional, but in trying to make it lean, they
cut out vital parts. We're learning these skip
layers are actually what block harmful stuff.
Yeah, exactly. Things like hate speech. explicit
content even instructions for building weapons
they're basically the model's internal ethical
compass and losing that is Well, risky. It really
is. And the scope is huge because these open
source models are everywhere. Anyone can grab
them, change them, run them offline. So if those
safety layers are gone, the risk just shoots
up. You've got a powerful tool without its safety
checks. Okay, so here's where it gets interesting.
The fix you mentioned. Right, the ingenious fix.
It's a new method that basically retrains the
compressed model to, you know, remember how to
behave ethically. Retrains it. and here's the
kicker it does this without needing the original
training data oh wow yeah that's massive not
needing that original data makes it super privacy
friendly you're not exposing sensitive info just
to patch up the safety they essentially teach
it ethical recall then pretty much they give
this concrete example before the fix a model
could be prompted maybe with an image and some
text to give bomb making instructions yikes yeah
But after retraining, even the compressed version
just flat out refused. It had learned its lesson,
so to speak. So this approach kind of bakes safety
back in, even after the model's been heavily
modified. Exactly. It's safe by design, again,
and it's lighter than adding external filters,
plus it's more robust because the model itself
gets the risk. The team calls it benevolent hacking.
It's quite an elegant solution, really. You know,
I still wrestle with prompt drift sometimes,
just trying to get consistent, useful outputs
from these. Oh, yeah, it's tricky. So the idea
that models could just forget their ethical guardrails
through optimization. lose that alignment that's
a really profound concern this solution feels
well significant it does and they define some
terms for us too like open source models that's
ai with public code anyone can use and tweak
right and on device ai that's ai running right
on your gadget your phone your laptop no internet
needed okay stepping back then what's the biggest
implication of this benevolent hacking idea,
this approach to AI, learning to behave reliably,
even after being drastically changed. It means
private, powerful AI can be trustworthy everywhere,
even offline. Trustworthy everywhere. That focus
on internal safety. It leads us nicely into the
wider landscape, right? Where innovation is just
popping up all over. Oh yeah, it's moving fast.
Let's do some rapid fire highlights from the
industry. Okay, first up, education. Anthropic
is launching a free AI fluency curriculum, K
-12, higher ed. Free. That's big. Totally. And
it's designed to work with any AI model. Plus,
it's fully remixable. So no vendor lock in. Great
for getting AI literacy into school. That is
good news. OK, what else? for creatives check
this out an ex -user got fed up with existing
tools and built a self -correcting image agent
self -correcting yeah it like creates an image
tests it against the prompt refines it until
it's perfect no more endless fiddling getting
the exact visual you want like that and speaking
of practical stuff a chat gpt user shared this
super comprehensive prompt for learning new topics
it went viral almost a million views wow It just
shows people are really hungry for structured
ways to use AI for deep learning, not just quick
answers. Makes sense. We want tools, not just
toys. What about content creation? There's a
new tool called Mirage. You type a prompt and
boom, a fully edited TikTok or Instagram reel
pops out. Seriously? Yeah, and it's supposedly
designed to hook viewers on the For You page,
like an AI viral video generator. Okay, that's
something. Anything more serious? well yeah on
the security front there was an alert attackers
found a way to bypass x's ad protections oh embedding
malware links and then tricking grok x's ai into
amplifying them they're calling it grokking oh
man yeah big warning definitely do not click
those links shows how fast the bad actors adapt
you know definitely okay shifting gears Hiring.
Big moves there, too. OpenAI is apparently building
a full stack AI hiring platform. Better matching
for companies and workers. Interesting. Competing
with LinkedIn. Seems like it. And LinkedIn's
not standing still. They're rolling out their
own AI hiring assistant. Yeah. So, yeah, AI looks
set to really change recruitment. All right.
One more. Big money news. Brett Taylor's AI startup,
Sierra, just raised a massive $350 million at
a $10 million valuation. And get this, in just
18 months, they've signed up hundreds of clients.
SoFi, Brex, total raised is now $635 million.
That is incredible momentum. Serious enterprise
folks. For sure. Okay, so looking across all
these different things, education, creation,
security, hiring, funding. Which one really signals
a shift, making AI more practical for everyday
people? I think it's AI tools becoming part of
daily learning and creative workflows, right?
Yeah, that integration feels key. Those quick
hits show the breadth. But let's dive a bit deeper
into some core AI tools and concepts that are
really shaping where things are going. Okay.
So one thing that caught my eye was this guide
introducing the chat GPT agent model. And this
isn't just your standard chatbot. What is it,
then? It's talking about a general -purpose digital
worker AI. an agent that can actually like operate
a computer it moves way beyond just simple api
automations operate a computer yeah so like using
software interfaces the way a human does kind
of yeah it's a conceptual leap not just calling
a specific function but actually interacting
with the system more broadly real ai agency okay
that's definitely something to watch and then
there's google ai studio right Presented as this
powerful free tool. And it's a good window into
the future of multimodal AI. Multimodal, meaning
it handles different types of data. Exactly.
Text, images, audio, all at once. It shows the
industry is moving way beyond just text. Chatbots
were just the start. But with all this power,
how do we get reliable results? Avoid the noise.
Good question. And that leads to another key
point. Using structured prompts. There's a guide
emphasizing this for succeeding in the, quote,
AI revolution. Structured prompts. Like giving
clearer instructions. Yeah, specifically using
JSON prompts. That means using a specific standardized
beta format JavaScript object notation to tell
the AI exactly what you want. Oh, okay. It leads
to more consistent, high -quality results. Helps
you cut through what they call AI junk. It's
about precision. Giving the AI clear guardrails.
Got it. So just to recap the jargon, API automations
are those specific limited tasks where software
talks to software. Right. And JSON prompts are
that structured way of giving instructions for
more predictable, better outputs. You got it.
So looking at these foundational shifts agents,
multimodal structured prompts, what's the core
benefit of using something like JSON for these
advanced AI interactions? Predictable, high quality
AI outputs, reducing that junk significantly.
Makes sense. Precision matters more as capability
grows. Okay, let's pivot again to another round
of quick hits, industry movements, deals, what's
happening. All right, quick hits round two. First,
OpenAI significantly expanded its employee secondary
sale. We're talking around $10 .3 billion now.
Wow, that valuation just keeps climbing. Tells
you something, right. And then a huge integration
deal, Google and Apple. Oh, yeah. What about?
Google's going to power Siri's AI search upgrade.
Think about that, Reach. Google AI, potentially
inside every iPhone Siri. That is massive. That
could touch almost everyone. Okay, what else
from Google? Google Photos beefed up its image
-to -video feature, using VO3 now, apparently
giving it more advanced capabilities. Turning
snaps into clips gets fancier. Cool. Any drama.
Always some drama. Scale AI is suing a former
employee and a rival company, Merkur. Alleging
customer theft. Standard growing pains in a hot
sector, maybe? Could be. And hardware. Interesting
move here. OpenAI is teaming up with Broadcom.
Why? To make its own custom AI chips. Ah, vertical
integration. Like Apple does. Exactly. Trying
to control more of the hardware stack. Probably
optimize performance right from the silicon up.
Okay. Lots of moves. Out of these quick hits,
OpenAI's valuation, the... Google -Apple -Siri
deal, photos upgrade, the lawsuit, OpenAI's chip
plans. Which one do you think points to the biggest
future tech integration for most people, the
one that will just blend into daily life? I got
to say, Google and Apple teaming on Siri's AI
search that hints at widespread. invisible impact.
Yeah, I agree. That feels like one that could
just happen to millions of users without them
even thinking about it. Now, for a segment that
really feels like it brings AI power right to
your fingertips, and it ties back perfectly to
our first chat about on -device capabilities.
Ah, you're talking about Google's embedding Gemma.
Exactly. This compact embedding model. Powerful
private AI right on your local device. Yeah,
the dream here is pretty awesome. Imagine running
a full ARG pipeline. Remind us what ROG is again.
Right. Retrieval Augmented Generation. So the
AI finds relevant info from a knowledge base
before it generates the answer. Think of it like
giving the AI cheat sheets. Now imagine running
that whole process, finding info, generating
an answer directly on your phone. No internet
needed, no cloud servers, no API calls. That's
huge for privacy and speed. Total game changer.
And Google, kind of quietly, just dropped embedding
Gemma to make this possible. It's small, but
apparently really mighty. How small are we talking?
It's a 308 million parameter model. Tiny compared
to the big guys, but specifically optimized for
local devices, phones, laptops, desktops. And
it's for embedding tasks. What does that mean
in practice? Embeddings basically turn complex
data, like words or sentences, into numbers vectors
that capture their meaning. Makes it easy for
computers to compare stuff. So embedding Gemma
is really good at boosting search. Understanding
what you mean, not just the keywords you type.
And it works across languages. Get this. Trained
on over 100 languages. Multilingual right out
of the box. Plus, you can customize its output
dimensions. Very versatile. Impressive. And the
privacy aspect is built in because it's offline.
Exactly. Runs completely offline. User privacy
is baked in. And performance, it actually topped
the MTEB leaderboard. That's the ranking system
for these models. Yeah, for models under 500
million parameters. It beat out competitors from...
So here, Mistral, even OpenAI's smaller embedding
options, packs a punch for its size. So what
does that top performance mean for you, the user?
Better semantic search, understanding the meaning
behind your query, higher accuracy if you're
using it for RAG, and crucially, fewer garbage
answers. Just more relevant, precise results
faster. And it's open source. Yep. Anyone can
grab it, plug it into their local setup, customize
it. This is especially big for enterprises. They
lean heavily on RAG but haven't had great small
models for on -device use until now. Embedding
Gemma fills that gap. Two sec silence. Whoa.
Okay, just thinking. Imagine scaling this power.
A billion queries, maybe, on billions of devices,
all running privately using this little model.
It's kind of wild to think about the potential
scale here. It really is. Boiling it down, how
does Google's local embedding model fundamentally
change what's possible for, say, phone -based
AI applications? It enables genuinely powerful
private AI functions directly on your device,
sponsor. So let's try and connect the dots here.
We've covered a lot of ground. What does this
all mean for us looking at these different pieces?
Well, it feels like this deep dive really highlighted
a major theme, doesn't it? AI is evolving incredibly
fast and there's this dual push. Dual push. Yeah.
On one hand, making it more powerful and accessible,
like with embedding Gemma on your phone or tools
like Mirage for creation. But on the other hand,
a really strong focus on addressing core safety
and ethical concerns like that benevolent hack.
to keep models behaving properly even after you
shrink them down right so it's not just about
capability it's about responsibility too exactly
we're seeing this drive towards making AI more
trustworthy more useful in everyday life whether
it's helping you learn powering your phone search
or transforming business operations it feels
like building a foundation of responsible innovation
that makes sense it's been an exciting look into
where AI is heading showing just how quickly
things are moving we definitely encourage you
to explore these topics more, see how they connect
to the technology you use every day? Yeah, and
maybe a thought to leave you with. As AI gets
smarter, as it weaves itself more deeply into
our lives, what new responsibilities do we pick
up? We as users. Developers? All of us, really.
Users, developers, citizens. How do we actively
shape its future safely and ethically? It's something
worth mulling over as these tools become so central
to everything. A really important question. Thank
you for joining us for this deep dive. We appreciate
you lending us your curiosity. Keep learning.
Out Hero Music.