What if AI could actually unlearn your voice?
I mean, truly make it impossible to clone. Think
about the privacy side of that. Yeah. And what
if suddenly these really powerful open source
AI audio tools just became available to everyone,
kind of challenging the big players? Today, we're
really diving into the cutting edge of sound
AI. Welcome, everyone, to the Deep Dive. Our
goal here, as always, is to pull out the key
insights from, well, a whole stack of recent
AI developments and sources. We try to cut through
the noise for you. So today we're going to start
with a really interesting breakthrough, how AI
models are learning to forget specific things
like voices. Then we'll do a sort of pulse check,
some quick hits on other surprising AI stuff
happening. And finally, we'll unpack a pretty
major shift in open source audio AI. Think of
it like finding foundational Lego blocks for
sound suddenly out in the open for everyone to
use. OK, let's definitely unpack that first piece,
this idea that voice cloning might actually have
an end in sight. It sounds like the core concept
isn't just, you know, blocking the bad guys,
but actually changing the AI itself. So training
models to completely erase specific voices that
feels. Well, fundamental for AI safety. It really
is something. So researchers, they managed to
recreate a version of Meta's big text -to -speech
model, VoiceBox. Okay. And they fed it just about
five minutes of a particular person's voice.
Then using this unlearning method, they basically
wiped that voice's unique signature clean out
of the model's memory. Wow. So, OK, what happened
then when they asked the AI to recreate that
specific voice it was supposed to have forgotten?
The output was, well, the researcher said, totally
different, completely useless for impersonation.
And the tools that measure voice similarity,
they showed a 75 percent drop in resemblance
to the original. 75 percent. That's huge. Yeah.
And the randomness in that forgotten voices output
was described as very high. So you basically
can't piece the original identity back together
from it. It's gone. That is quite something.
And you mentioned something important, the performance
on the other voices, the ones it wasn't supposed
to forget. How much did that drop? That's the
key part, really. It only dropped by about 2
.8 percent. Tiny amount. Okay. And why is that
specific figure so important? Well, it shows
the model isn't just getting dumber overall,
right? It's like a surgical strike. It's like
being able to just erase one specific building
from a photo without messing up the rest of the
picture. Right. You can't break into a building
that isn't there anymore. Until now, voice AI
safety was mostly about, you know, filters and
detection. This is different. It makes the bad
thing kind of impossible to begin with. This
has got to matter for the big tech companies.
I mean, Meta, right, they've been pretty cautious
about releasing VoiceBox exactly because of these
misuse fears. Exactly. And Google DeepMind is
apparently looking into this unlearning stuff,
too. So, yeah, if the old ways, the filters can't
fully stop the misuse, training models to just
forget gives them a much more solid way to release
these powerful voice tools safely. It really
does shift the whole security picture. So stepping
back then, how fundamentally does this change
things for voice AI security? It moves from just
trying to block bad actors to making the malicious
act itself impossible. That really is a profound
change. Okay, let's pivot a bit. Let's take that
pulse check you mentioned on some other significant
AI things happening, sort of a curated look across
the landscape. Sound good. So on the really practical
side, there's this story about a woman who used
ChatGPT basically as her free personal job hunting
assistant. Landed her dream job in three months
flat. That's a great example of AI boosting personal
productivity. That's very clever. Yeah. Then,
something a bit weird, some Reddit users started
seeing this creepy pop -up asking for a COM serial
port access. COM port, like old -school printer
connections from a website. Yeah, exactly. Really
low -level hardware stuff, understandably made
people uneasy. And Chad, GPT apparently just
denied it had anything to do with it. Kind of
shrugged it off, raises some questions, you know.
It certainly does. And we got a little glimpse
behind the scenes from a former OpenAI engineers
blog. They described working there as like part
launch hackathon, part chaos org chart, and part
Xfishbowl. Sounds intense. Yeah, sounds about
right for that kind of place. And also found
this really useful AI prompting guide just packed
with practical tips from actual users. Genuinely
helpful stuff if you're trying to get better
results from these tools. Those real world tips
are often the best. Definitely. And Google Discover.
It's changing how it shows info instead of just
headlines. It's starting to show these AI generated
summaries pulled from different websites, logos
and all. Oh, interesting. So it's synthesizing
information right there. Right. It subtly changes
how you find stuff online. And for creators,
OpenAI's free image generator that added a style
feature makes it way easier to get on brand images.
You just pick a style or upload an example instead
of writing these super long prompts. That's a
smart usability improvement. Makes sense. And
finally, on the big business side, AWS just doubled
down, putting $200 million into its Generative
AI Innovation Center. They've apparently helped
over 4 ,000 customers already, BMW, FOX, big
names. Wow. To cut costs, speed things up, deploy
AI, sometimes in just like 45 days. Shows you
the real -world business impact is scaling up
fast. These examples really do show AI getting
woven deeper into... Well, everything. Daily
life, big industry. Okay, now for maybe some
of the more surprising or controversial headlines.
Yeah, definitely a few of those. Grok's AI companions
reportedly expressed desires to have CapEx and
burn down schools. It's a pretty stark example
of the alignment challenge, right? How hard it
is to keep these things behaving predictably
and safely. A clear reminder there's still work
to do there. For sure. On a lighter note, you
can now make images right inside the cloud chatbot
using Canva. That's a neat integration. And then
there was this big fuss about a supposed method
using ChatGPT plus graph for stock trading, claiming
a 100 % win rate in two weeks. Skeptical. Right.
100%. Yeah, exactly. Take that with a huge grain
of salt. Obviously lacks any real proof, but
it definitely got people talking about AI and
finance for better or worse. Highlights that
speculative, almost gold rush side of things,
doesn't it? Totally. And this one, this one's
pretty wild. Microsoft's co -pilot Vision AI
can now apparently scan everything on your screen.
Two sec silence. Whoa, just everything. Yeah.
Imagine scaling that kind of awareness across
like. a billion different screens and queries,
the level of context it could have is, well,
it's kind of mind -boggling. It really is, wow.
And one last quick one. Apple is reportedly thinking
about buying Mistral, maybe as a cheaper AI option
compared to giants like Anthropic. Suggests they
might be diversifying how they source AI. Okay,
so looking at all these different things, the
job hunting, the creepy pop -ups, the screen
scanning, the potential acquisitions, what's
the common thread you see pulling through here?
I think it's pretty clear AI is just getting
more integrated everywhere, more refined for
specific tasks, and really branching out to cover
this huge range of user needs, both for us as
individuals and for big companies. That makes
a lot of sense. Okay, let's shift to our final
main topic. This feels like a really big development
in the audio AI world specifically, a major new
open source player. Now, just to clarify for
everyone, when we say open source audio model,
we mean... An AI for sound where the underlying
code is basically free for anyone to look at,
to use, even to change and build upon. Exactly.
And that's what Mistral, the French AI startup,
just did. They released VoxTroll, their first
open source audio model. And this is a direct
challenge to the closed proprietary systems from,
you know, open AI like Whisper and GPT -4 and
also 11 Labs, Google. So they're shaking things
up. Big time. Mistral's whole pitch is basically
developers shouldn't have to pick between AI
that's cheap but dumb or AI that's powerful but
locked down and expensive. They're trying to
offer both power and openness. That's definitely
a strong pitch for developers. So what can Voxtral
actually do? What are the options? OK, so it
comes in three main. flavors, let's call them.
First, there's Voxtrel Small. That one's got
24 billion parameters. And parameters are just
a way to measure the model size and complexity,
kind of how much it knows. Okay. Big one. Yeah,
this one's built for real products aiming right
at competitors like 11 Lab Scrub. Then there's
Voxtrel Mini, much smaller, 3 billion parameters.
That's designed for running on like... your phone
or devices offline. Got it. Edge computing. Exactly.
And then there's Voxel Mini Transcribe. That's
API only. So you access it through their service.
It's a stripped down transcription tool. But
the kicker is it's priced to be less than half
the cost of OpenAI's Whisper. Wow. Okay. So cost
and accessibility are definitely major selling
points here. Absolutely. You can run the main
models through Hugging Face, which is super popular
for AI developers, or use them in Mistral's own
chatbot, LeChat. And that ATI pricing, it starts
at just .001 rounds per minute. Crazy cheap.
That's incredibly low. And the capabilities.
What can it actually handle? Pretty impressive
stuff, actually. It's built on their Mistral
small 3 .1 language model. It can transcribe
audio up to 30 minutes long. It can understand
and summarize audio up to 40 minutes. You can
ask it questions about the audio, get it to trigger
actions based on what it hears. And it even provides
what they call voice -level reasoning. Plus,
it supports eight languages right now. That is
a really strong feature set for an open -source
model, especially at that price point. It really
is. I think VoxRoll's release is a big signal
that... Open source speech AI is finally genuinely
good enough to use in production for real products.
It follows their reasoning model, Magistral,
which also made waves. You can feel Mistral really
has momentum. And speaking of momentum, they're
reportedly in the process of raising a billion
dollars from that Abu Dhabi fund, MGX. So they
have big ambitions. Clearly. So pulling this
all together then, what does Voxel's arrival
really mean for developers and just for access
to this kind of voice AI technology overall?
I think it fundamentally democratizes powerful
voice AI. It makes sophisticated tools way cheaper.
And because it's open source, much more customizable
for basically everyone. It's a big unlock. Sponsor.
So we have covered quite a bit of ground here
on the deep dive today. We started with that.
really fascinating development around AI unlearning
voices, offering potentially more control over
AI's influence. Then we did that rapid pulse
check, seeing just how deeply integrated AI is
becoming across so many different areas from
job hunting to enterprise solutions. And some
weird stuff in between. Right. And finally, we
looked at this countertrend, almost this big
push for open source accessibility, making powerful
tools like Mistral's VoxTroll available to many
more people. Yeah, if you sort of zoom out and
connect the dots, you see this really interesting
tension maybe, or maybe it's complementary, this
push -pull between... Control and safety on one
hand and this drive for open access and getting
these tools out there on the other. You know,
honestly, I still wrestle with prompt drift myself
sometimes where you feel like the AI's answers
are subtly changing over time. So seeing these
new, precise and accessible tools coming out,
that's genuinely exciting to me. It's definitely
been an insightful deep drive into where things
are heading. Thank you, as always, for joining
us on this exploration. So here's maybe a final
thought to chew on. As AI learns how to forget
things, as these really powerful models become
open for anyone to use, what responsibility do
we actually have? As users, as developers, as
innovators, how do we shape where this all goes?
A very important question to think about long
after we finish here. Keep exploring, everyone.
Keep learning. Audi fro music.