A wild week in AI
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The PC gave us computing power at home, the internet connected us, and mobile let us do it pretty much anywhere. Now generative AI lets us communicate with technology in our own language, using our own senses. But figuring it all out when you're living through it is a totally different story. Welcome to Leading the Shift.

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So let's make the next five 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 why

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 run a 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 Gamma, a compact AI model trained to understand and even generate dolphin vocalizations. Now, what makes this unusual isn't just the application. It's that the model runs directly on your phone.

Researchers used 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 Gemma 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, it could eventually support real-time two-way communication with certain animal species. Could you imagine talking to your own pets? 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. UniAnimate DIT, which is a new plugin built for the open source model. Animate Diff 1.2 allows users to animate any character image with a motion reference video. Upload an aesthetic image, 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 the 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 articulation.

artifacting. And since everything is released, open source artists and animators now have access to a new kind of puppetry. This is accessible as downloading a GitHub repo. Now, a companion tool emerged this week from Tencent as well. It's called Instant Character. It's focused as 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. It uses LoRa adapters to style outputs in everything from Studio Ghibli to Makoto Shinkai's signature look.

Now, unlike most existing character transfer models, this one keeps attributes consistent across varied scenes and does so across 2d 3d and photo realistic styles. So why this matters to you isn't just for cosplay creators or fan artists in a world increasingly dominated by visual

organizations 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 three dimensions.

It's a part segmentation model for 3D objects capable of breaking complex meshes into individual labeled components. Now think of a 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 has 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.

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Now, one more thing, a slightly surreal real world scene, a half 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 racetrack in full view of cheering spectators. Some entries were clunky, barely able to maintain balance, while others like Unitree's G1 in Beijing humanoid innovation centers

Qiangjian Ultra managed 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 Qiangjian Ultra in particular drew attention for its speed and its stability, suggesting real progress in 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 roles. 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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