Welcome back to the Elon Musk Podcast.
I'm thrilled to share some exciting news with you over the
next two weeks. We're evolving.
We'll be broadening our focus to cover all the tech Titans
shaping our world. And with that, our show will
become Stage 0. You'll still get the latest
insights on Elon Musk, plus so much more, so stay tuned for our
official relaunch at Stage 0 coming soon.
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years even bigger together. There's a link in the show notes
just for you. Why would Google train an AI to
talk to dolphins? That's not what you hear every
day, and it's exactly the kind of question that sums up what
might be the most unpredictable, chaotic, and fascinating week in
AI so far this year. Not only are researchers now
attempting to decode animal communication using lightweight
neural networks, but we also saw humanoid robots, runner of
literal half marathon, AI tools that can animate pets into
dancers or bring comic panels to life with one click, and Open AI
launching its most intelligent models yet.
Every one of these stories is a question in itself.
Why now, How does it work? And what does this mean for the
future of interaction, creativity, and intelligence
itself? Now to start, Google made
headlines this week with something called Dolphin Gemma,
a compact AI model trained to understand it even generate
dolphin vocalizations. Now, what makes this unusual
isn't just the implication, it's that the model runs directly on
your phone. Researchers use Google Pixel
devices to process real time dolphin chatter.
Using a framework based on audio tokenization, they recorded
every sound dolphins make, clicks, squawks, whistles, and
converted them into discrete audio tokens using Google's
Soundstream codec. This tokenized data set was then
used to train a smaller variant of Google's JEMA model, which
comes in at around 400 million parameters.
That's small enough to run efficiently on mobile hardware
without external computing and beyond understanding.
The model can also synthesize new dolphin like sounds, which
is a potential breakthrough for researchers aiming to translate
interspecies communication into something that humans can
eventually understand. Now the significance of this
extends well beyond dolphins. The architecture is general
enough that it could be retrained to understand and
emulate vocalizations of other animals.
And in theory we could eventually support real time two
way communication with certain animal species.
Could you imagine talking to your own pets?
Now that poses the question into strange territories.
If an AI can mimic a language like structure in dolphin
chatter, are we on the verge of developing machine driven cross
species translators now? While AI was speaking to marine
life, animation tools were speaking to the internet's
favorite content creators. Uni Animate DIT, which is a new
plugin built for the open source model Animate DIF 1.2 allows
users to animate any character image with a motion reference
video up like a static image or any photo of a person, a cartoon
or even a pet, and combine it with a short clip of someone
dancing or moving around. The tool extracts the pose data
from the video, then applies it to the static image, producing a
fully animated clip with smooth transitions.
And what stands out is that the model can guess unseen angles
like the back of the character and animate flowing fabric or
hand movements convincingly. All this runs locally with a
minimum of 14 gigs of VRAM, meaning creators can use a tool
without relying on cloud services.
And I've used it. The results are surprisingly
good. Even characters with complex
appearances or unusual anatomy, like fictional anime designs or
animals, can be animated with minimal artifacting.
And since everything is released open source, artists and
animators now have access to a new kind of puppetry.
This is accessible. That's downloading a GitHub repo
now. The companion tool emerged this
week from Tencent as well. It's called instant character.
It's focused is accuracy in reference based generation.
So you have an image of a fictional character.
Instant character can place the same character down to the
facial structure, outfit details and accessories into a new
scene. You can render them in a studio
playing piano or walking in a snowstorm in full anime style.
And the model is based on Flux, one of the highest fidelity open
source diffusion models available, and it uses Laura
adapters to style outputs in everything from Studio Ghibli to
Makato Shinkai's signature look. Now, unlike most existing
character transfer models, this one keeps attributes consistent
across varied scenes and does so across 2D3D and photo realistic
styles. So why this matters to you isn't
just for cosplay creators or fan artist.
In a world increasingly dominated by visualizations and
virtual characters, the ability to preserve identity across
generated media becomes critical, especially as avatars,
V tubers and AI generated influencers grow more complex
and integrated into media ecosystems.
Now there's a new thing called Parkfield.
It's a project from NVIDIA. It's focused on a very different
kind of segmentation. This time it's 3 dimensions.
It's a part segmentation model for 3D objects capable of
breaking complex meshes into individual label components.
Now think about 3D model of a robot or a car with part field.
Each part, arm, leg, wheel, mirror is isolated into its own
labeled region, enabling texture swaps, physical simulations, or
animations to be applied to each section independently.
This is obvious implications for anyone building physical
simulations, robotics, or gaming assets.
Compared to previous segmentation models, it not only
performs better, but is much faster completing tasks in a
fraction of the time thanks to more efficient tokenization and
inference architecture. Now one more thing.
A slightly surreal real world scene 1/2 marathon for humanoid
robots just took place in Beijing.
More than 20 companies from across China participated,
entering bipedal robots that walked, jogged and ran across
and around a race track in full view of cheering spectators.
Some entries were clunky, barely able to maintain balance, while
others like Unitaries G1 and Beijing Humanoid Innovation
Centers Chiang Jiang Ultra, manage smoother gates and even
completed longer runs now. Footage of this shows some
robots falling or freezing mid run, but others powering through
the full event now. Qiangyan Ultra in particular
drew attention for its speed and its stability, suggesting real
progress and bipedal locomotion design.
The event might sound like a novelty though, but it reflects
a changing reality. Robotic mobility is evolving
fast enough that humanoids may soon be deployed in public
facing or physical labor rolls. Holding a marathon may just be
like a publicity stunt, but it's also a benchmark for these
robots. Tests, endurance, adaptability,
and real world stability, these traits that are notoriously
difficult for machines to master.
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