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Category: robotics/AI – Page 25

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Continue reading “Microsoft CEO: “Agents Will Replace ALL Software”” | >

Artificial neural networks, central to deep learning, are powerful but energy-consuming and prone to overfitting. The authors propose a network design inspired by biological dendrites, which offers better robustness and efficiency, using fewer trainable parameters, thus enhancing precision and resilience in artificial neural networks.

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The chemical composition of a material alone sometimes reveals little about its properties. The decisive factor is often the arrangement of the molecules in the atomic lattice structure or on the surface of the material. Materials science utilizes this factor to create certain properties by applying individual atoms and molecules to surfaces with the aid of high-performance microscopes. This is still extremely time-consuming and the constructed nanostructures are comparatively simple.

Using , a research group at TU Graz now wants to take the construction of nanostructures to a new level. Their paper is published in the journal Computer Physics Communications.

“We want to develop a self-learning AI system that positions individual molecules quickly, specifically and in the right orientation, and all this completely autonomously,” says Oliver Hofmann from the Institute of Solid State Physics, who heads the research group. This should make it possible to build highly complex molecular structures, including logic circuits in the nanometer range.

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Summary: A new AI model, based on the PV-RNN framework, learns to generalize language and actions in a manner similar to toddlers by integrating vision, proprioception, and language instructions. Unlike large language models (LLMs) that rely on vast datasets, this system uses embodied interactions to achieve compositionality while requiring less data and computational power.

Researchers found the AI’s modular, transparent design helpful for studying how humans acquire cognitive skills like combining language and actions. The model offers insights into developmental neuroscience and could lead to safer, more ethical AI by grounding learning in behavior and transparent decision-making processes.

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Summary: A study reveals that London taxi drivers prioritize complex and distant junctions during their initial “offline thinking” phase when planning routes, rather than sequentially considering streets. This efficient, intuitive strategy leverages spatial awareness and contrasts with AI algorithms, which typically follow step-by-step approaches.

The findings highlight the unique planning abilities of expert human navigators, influenced by their deep memory of London’s intricate street network. Researchers suggest that studying human expert intuition could improve AI algorithms, especially for tasks involving flexible planning and human-AI collaboration.

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Links From Todays Video:
https://www.clonerobotics.com/

Welcome to my channel where i bring you the latest breakthroughs in AI. From deep learning to robotics, i cover it all. My videos offer valuable insights and perspectives that will expand your knowledge and understanding of this rapidly evolving field. Be sure to subscribe and stay updated on my latest videos.

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Continue reading “Clone Robotics New Superintelligent Androids Sup[rise Everyone” | >

For over a decade, complexity scientist Peter Turchin and his collaborators have worked to compile an unparalleled database of human history – the Seshat Global History Databank. Recently, Turchin and computer scientist Maria del Rio-Chanona turned their attention to artificial intelligence (AI) chatbots, questioning whether these advanced models could aid historians and archaeologists in interpreting the past.

The study, which is the first of its kind, evaluates the historical knowledge of leading AI models such as ChatGPT-4, Llama, and Gemini.

The results, presented at the NeurIPS conference, reveal both potential and significant limitations in AI’s ability to grasp historical knowledge, especially at the nuanced, expert level.

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Recent advances in robotics and artificial intelligence (AI) are enabling the development of a wide range of systems with unique characteristics designed for varying real-world applications. These include robots that can engage in activities traditionally only completed by humans, such as sketching, painting and even hand-writing documents.

These robots could have interesting applications in both professional and creative contexts, as they could help to automate the creation of artistic renderings, legal papers, letters and other documents in real time. Most to date have considerable limitations, such as high production costs (around $150) and a large size.

Two researchers affiliated with the global student non-profit organization App-In Club recently developed a new cost-effective robotic handwriting system that could be more affordable for individual consumers, schools, universities and small businesses. This system, introduced in a paper on the arXiv preprint server, integrates a Raspberry Pi Pico microcontroller and other components that can be produced via 3D printing.

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