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Category: robotics/AI – Page 5

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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https://www.clonerobotics.com/

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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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A Minnesota-based glazing firm is using a robotic system to install high-rise glass panel bracket. Harmon is installing the latest technology with the help of Raise Robotics and Universal Robots. For high rise fastener installation, the robotic system has improved worker safety, as well as consistency and precision.

The companies revealed that construction workers need extensive safety rigging to install glass panel brackets up to 1,000 ft. into the air.

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As artificial intelligence models become increasingly advanced, electronics engineers have been trying to develop new hardware that is better suited for running these models, while also limiting power-consumption and boosting the speed at which they process data. Some of the most promising solutions designed to meet the needs of machine learning algorithms are platforms based on memristors.

Memristors, or memory resistors, are electrical components that can retain their resistance even in the absence of electrical power, adjusting their resistance based on the electrical charge passing through them. This means that they can simultaneously support both the storage and processing of information, which could be advantageous for running machine learning algorithms.

Memristor-based devices could be used to develop more compact and energy-efficient hardware for running AI models, including emerging distributed computing solutions referred to as edge computing systems. Despite their advantages, many existing -based platforms have been found to have notable limitations, adversely impacting their reliability and endurance.

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