What is today’s beat?

• Nvidia Omniverse blueprint

• AGI benchmark - non-profit started to drive it's progress

• A few tools to help dev and product experience.

  
  
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🎯 RELEASES 🎯

Nvidia
Omniverse blueprint - an ecosystem for robotic systems

Synopsis

NVIDIA has unveiled "Mega," an Omniverse Blueprint designed to transform the development, testing, and optimisation of industrial robot fleets through advanced digital twin simulations. First released in GTC 2024, Nvidia showed how Omniverse helps create synthetic data for model training, environments for robotic testing, and heavy simulation interfaces with controllable, visual agents (yes you can use AI inside the environment with gaurd rails and logs its responses).

Core Observations

1. NVIDIA Omniverse and OpenUSD Integrations

   • NVIDIA Omniverse serves as the foundational platform for Mega, enabling real-time collaboration and advanced simulation capabilities for digital twins.

   • OpenUSD (Universal Scene Description) is the default interface, and is a file format to store a physical models (like a solidworks model of a cup, or robotic arm).

   • The integration of OpenUSD with industry-standard tools (e.g., CAD systems, robotics software like ROS) ensures streamlined workflows, making it easier for teams to develop, visualize, and deploy robotics systems.

2. Simulation Capabilities

   • These simulations enable precise replication of real-world conditions, including physics-based modeling for kinematics, dynamics, and environmental interactions - and in real-time.

   • The Omniverse Cloud Sensor RTX API allows for the simulation of sensor outputs (e.g., LiDAR, cameras, ultrasonic sensors) in a variety of scenarios, producing synthetic data to validate robotic performance under diverse conditions. 🤯

3. Integration with AI Models:

   • Mega supports direct integration with NVIDIA's AI frameworks, such as NVIDIA Isaac for robotics and NVIDIA TAO for training AI models.

   • This allows AI models to be trained and tested in virtual environments. With the recent release of world foundation models like Cosmos (Github) the dev cycle will rapidly speed up.

   • By combining simulated training with real-world validation, Mega accelerates the development of AI algorithms for perception, decision-making, and control.

Broader Context

The Mega Omniverse Blueprint reflects NVIDIA’s commitment to advancing robotics and AI through digital twin technology. Omniverse is huge, and it's blueprints are quick starters to streamline your design. Mega’s reliance on OpenUSD and its interoperability with industry tools ensures its applicability across sectors such as manufacturing, logistics, and warehousing, paving the way for AI-driven transformation in industrial operations.

If you are a start up, you may consider apply for the Nvidia Inception Program to get discounts, added resources, and exclusive access to their leading edge tech.

Article
Francois Chollet Co-Founds Nonprofit to Establish AGI Benchmarks with ARC

Synopsis

Renowned AI researcher and former Google engineer François Chollet is co-founding a nonprofit organization to develop benchmarks for artificial general intelligence (AGI) using the Abstraction and Reasoning Corpus (ARC). The ARC test, already a critical metric in evaluating AI systems, will serve as a foundation to assess the reasoning and generalization abilities of AGI models. This initiative addresses the need for robust benchmarks to advance and objectively measure progress in AGI research.

Core Observations

1. ARC-AGI Benchmark Overview: The ARC (Abstraction and Reasoning Corpus) test is a comprehensive evaluation tool designed to measure an AI model's ability to generalize and reason. Unlike task-specific tests, ARC focuses on human-level problem-solving by presenting abstract reasoning puzzles requiring logic, pattern recognition, and transfer learning.

2. Importance of ARC Testing: ARC is significant because it assesses cognitive flexibility—a key attribute of AGI. It is the only AGI test at the moment!

3. Constraints and Challenges: The ARC benchmark presents several constraints, including its reliance on qualitative evaluation, as the problems are abstract and often subjective.

Broader Context

Backed by big players, this is the only test for AGI. Unlike existing AI performance metrics focused on domain-specific tasks, ARC emphasizes generalization, reasoning, and adaptability—capabilities essential for AGI. This initiative has broader implications for the AI industry, including the potential to guide research priorities, identify gaps in current models, and drive innovation in creating human-like intelligence systems.

Check out arc prize to read about it's origin, and $1M prize.

Here is the GitHub

Trending

• Hubspot acquired Frame AI - CHAT is coming

• Microsoft Announced release of PHI-4

• Remote-driving startup Vay expands its Last Vegas fleet to 100 vehicles

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⚙️ BUILDERS BYTES ⚙️

What will you learn today?

This week is Langchain week, and today we’ll look at part 3 of a series on creating a data extractor with LangGraph tools.

Today, we look at how to correct an LLM response by using tools to extract components of a transcript using JSONPatch. It reduces yesterday’s run_2 code by almost 100 lines of code (view patched.py)

Key Takeaways

1. JASONpatch: Used to review reasoning, and quickly update parts as retires are made

2. Extraction: The state used tools to extract parts of the transcript for reasoning

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Trending

• Model - SmallThinker-3B-Preview, a fine tune of the Qwen2.5-3b-Instruct

• Model - Kokoro is a frontier TTS (text to speech) model

• Model - Nvidia Cosmos-1.0-Diffusion-7B-Video2World

🤩 COMMUNITY 🤩

TOOLS

• TestSprite - Easiest AI Agent for Software Testing

• Athina - a collaborative AI dev platform,build, test and monitor AI features

These are two SUPER useful databases for both prototyping and fully managed deployments in production.

• Supabase - open source back-end as a service (free with limited storage)

• Qdrant - High-Performance Vector Search at Scale

THANK YOU

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