Programmable photonic latch memory https://opg.optica.org/oe/fulltext.cfm?uri=oe-33-2-3501&id=567359
Researchers have unveiled a programmable photonic latch that speeds up data storage and processing in optical systems, offering a significant advancement over traditional electronic memory by reducing latency and energy use.
Fast, versatile volatile photonic memory could enhance AI, sensing, and other computationally intense applications.
Programmable Photonic Latch Technology
AI tools are analyzing images and tissue samples to detect cancer sooner and more precisely. Doctors hope to improve and accelerate patient care.
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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.
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 artificial intelligence, 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.
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.
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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More than 10,000 organizations, including Comcast, Block, Rivian and Shell, rely on the company’s Databricks Data Intelligence Platform to manage and analyze data for AI applications.
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.
