TOGETHER WITH

Good morning, human brains, and welcome back to your daily munch of AI news.

Here’s what’s on the menu today:

• Bot hits the streets 🤖 🛣️

  Google researchers unveiled Streetscapes.

• Bot ties the knot. Literally 🤖 👔

  University of Singapore researchers unveiled TieBot.

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Peep today's ‘What Would You Do?’ at the bottom. 👇

MAIN COURSE

Bot hits the streets 🤖 🛣️

Last week, Google researchers unveiled Streetscapes. It's an AI system that generates realistic street views of entire made-up cities.

How does it work?

The AI was trained on millions of real Google Street View images. It takes three inputs:

• A street map

• A height map of buildings

• A desired camera path

From there, it generates step-by-step video sequences with details like windows, cobblestones, lighting shadows, and more.

What’s under the hood?

• Motion Module: Ensures smooth movement and consistency between frames.

• Temporal Imputation: Each new image considers previous ones for better continuity.

• Long sequences: Can generate up to 100 frames covering 170+ meters of virtual travel.

Can I customize my virtual road trip?

Yupp. You can control the city's appearance with text prompts. You can generate Parisian streets in New York style, change the time of day/weather, and more.

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SIDE SALAD

Bot ties the knot. Literally 🤖 👔

On July 4, University of Singapore researchers unveiled TieBot. It's a framework that teaches robots to learn tasks from human demonstration videos.

What's the big deal?

It allows robots to learn complex tasks like tying a necktie by watching videos online. Most current methods need in-domain videos filmed in the same setting as the robot. TieBot learns from any video.

How does it work?

The framework has three main components:

• Real2Sim: Tracks object motion in the video and replicates it in a simulation.

• Learn@Sim: Uses reinforcement learning to figure out grasping and placing points.

• Sim2Real: Deploys the learned policy to a real dual-arm robot.

Why do I care?

It has the potential to solve the data scarcity problem in robotics and help transfer learned skills to real robots more efficiently.

YOUR DAILY MUNCH

Think Pieces 🧠

Is AI already sentient? 20% of Americans believe AI is already sentient, but AI currently relies on algorithms and data without true consciousness.

Startup News 💰

Apple's released DCLM on Hugging Face. It outperforms Mistral-7B and SmolLM, on the MMLU benchmark using 40% less compute.

Research 👨‍🔬

LazyLLM — accelerates large language model (LLM) inference by selectively computing only essential tokens during both the prefilling and decoding stages.

Shape of Motion — reconstructs dynamic 3D scenes and motion from a single video using a compact set of SE(3) motion bases and data-driven priors.

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WHAT WOULD YOU DO?

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Until next time 🤖😋🧠