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- Google researchers unveiled Streetscapes.
Google researchers unveiled Streetscapes.
PLUS: Bot ties the knot. Literally
TOGETHER WITH
Good morning, human brains, and welcome back to your daily munch of AI news.
Hereβs whatβs on the menu today:
Bot hits the streets π€ π£οΈ
Google researchers unveiled Streetscapes.
Bot ties the knot. Literally π€ π
University of Singapore researchers unveiled TieBot.
Sponsor Bot Eat Brain and reach over 20,000 readers.
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Peep today's βWhat Would You Do?β at the bottom. π
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MAIN COURSE
Bot hits the streets π€ π£οΈ
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.
Whatβs 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 π€ π
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.
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YOUR DAILY MUNCH
Think Pieces π§
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 π°
Apple's released DCLM on Hugging Face. It outperforms Mistral-7B and SmolLM, on the MMLU benchmark using 40% less compute.
Research π¨βπ¬
LazyLLM β accelerates large language model (LLM) inference by selectively computing only essential tokens during both the prefilling and decoding stages.
Shape of Motion β 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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AI ART SHOW
WHAT WOULD YOU DO?
This band sucksβ¦ π
Who are we listening to instead? π
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