This article explores the critical intersection of knowledge graphs and data lineage in the context of modern AI and machine learning. It examines how combining these two technologies can provide the transparency and traceability required to build trustworthy AI systems. By mapping the origins, transformations, and movements of data, organizations can ensure better data quality, regulatory compliance, and improved model interpretability.
This research introduces a novel robot operating system (ROS) framework designed to seamlessly integrate large language models (LLMs) into embodied artificial intelligence. The framework enables robots to interpret and execute natural language instructions with greater versatility and reliability.
Key features include automatic translation of LLM outputs into robot actions, support for both code-based and behavior tree execution modes, and the ability to learn new skills through imitation and automated optimization.
Extensive experiments demonstrate the robustness and scalability of the framework across diverse scenarios, including complex tasks like coffee making and remote control. The complete implementation is available as open-source code, utilizing open-source pretrained LLMs.
Key features include automatic translation of LLM outputs into robot actions, support for both code-based and behavior tree execution modes, and the ability to learn new skills through imitation and automated optimization.
Extensive experiments demonstrate the robustness and scalability of the framework across diverse scenarios, including complex tasks like coffee making and remote control. The complete implementation is available as open-source code, utilizing open-source pretrained LLMs.
This is an open, unconventional textbook covering mathematics, computing, and artificial intelligence from foundational principles. It's designed for practitioners seeking a deep understanding, moving beyond exam preparation and focusing on real-world application. The author, drawing from years of experience in AI/ML, has compiled notes that prioritize intuition, context, and clear explanations, avoiding dense notation and outdated material.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
This article details the rediscovery of the source code for AM and EURISKO, two groundbreaking AI programs created by Douglas Lenat in the 1970s and early 80s. AM autonomously rediscovered mathematical concepts, while EURISKO excelled in VLSI design and even defeated human players in the Traveller RPG. Lenat had previously stated he no longer possessed the code, but it was found archived on SAILDART, the original Stanford AI Laboratory backup data, and in printouts at the Computer History Museum. The code was password protected until Lenat's passing, and has now been made available on Github.
This essay argues that the economics of context engineering expose a gap in the Brynjolfsson-Hitzig framework that changes its practical implications: for how enterprises build with AI, which firms centralize successfully, and whether the AI economy will be as centralized as their framework suggests. It explores how the cost and effort required to make knowledge usable by AI—context engineering—creates a bottleneck that prevents complete centralization, preserving the importance of local knowledge and human judgment. The article discusses the implications for SaaS companies, knowledge workers, and the future of work in an AI-driven economy, predicting that those who invest in context engineering capabilities will see the highest ROI.
NVIDIA GTC is the premier AI conference and exhibition. Learn about the latest advancements in AI, deep learning, and accelerated computing. Includes keynote speakers, sessions, workshops, and an exhibit hall.
We’ve been experimenting with using large language models (LLMs) to assist in hardware design, and we’re excited to share our first project: the Deep Think PCB. This board is designed to be a versatile platform for experimenting with LLMs at the edge, and it’s built using a combination of open-source hardware and software. We detail the process of using Gemini to generate the schematic and PCB layout, the challenges we faced, and the lessons we learned. It's a fascinating look at the future of hardware design!
Sipeed’s MaixCAM2 is a powerful, open-source AI camera designed for makers, offering significant performance improvements over Raspberry Pi and OpenMV solutions. It features the Axera Tech AX630 AI SoC with up to 12.8 TOPS and supports training-free vision models and vision-language models.
3D simulations and movement control with PyBullet. This article demonstrates how to build a 3D environment with PyBullet for manually controlling a robotic arm, covering setup, robot loading, movement control (position, velocity, force), and interaction with objects.
Hierarchical Reasoning Model (HRM) is a novel approach using two small neural networks recursing at different frequencies. This biologically inspired method beats Large Language models (LLMs) on hard puzzle tasks such as Sudoku, Maze, and ARC-AGI while trained with small models (27M parameters) on small data (around 1000 examples). HRM holds great promise for solving hard problems with small networks, but it is not yet well understood and may be suboptimal. We propose Tiny Recursive Model (TRM), a much simpler recursive reasoning approach that achieves significantly higher generalization than HRM, while using a single tiny network with only 2 layers. With only 7M parameters, TRM obtains 45% test-accuracy on ARC-AGI-1 and 8% on ARC-AGI-2, higher than most LLMs (e.g., Deepseek R1, o3-mini, Gemini 2.5 Pro) with less than 0.01% of the parameters.
