AI Met Fruit Fly

Hello, AI Enthusiasts!

When you're in doubt, just look to the insect kingdom for technological inspiration.

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NEW THINGS

AI Met Fruit Fly

Scientists have developed a virtual brain model based on the fruit fly’s visual system, offering new insights into both biological brain function and more energy-efficient artificial intelligence.

• Researchers at the Janelia Research Campus and University of Tübingen used over a decade of work to create a virtual brain model that simulates the fruit fly’s visual system, predicting the behavior of individual neurons.
• While a fruit fly's brain has only 100,000 neurons, it efficiently performs complex tasks like flying, detecting predators, and mating. In contrast, AI systems require billions of transistors and consume the same amount of energy as a small country to achieve comparable tasks.
• The study suggests that artificial intelligence, such as ChatGPT, could greatly reduce energy consumption by borrowing computational strategies from the fruit fly brain. This efficiency could transform AI’s environmental impact.

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NEW THINGS

Can AI Memory Match Human Memory?

A study from Hong Kong Polytechnic University reveals that large language models (LLMs) demonstrate memory capabilities similar to human memory, referred to as “Schrodinger’s Memory.” This memory is only observable when prompted by a specific query, akin to how humans recall information when asked specific questions.

• LLMs display a unique form of memory that researchers describe as “Schrodinger’s Memory.” Like human memory, LLMs' ability to recall information is only observable when prompted by a specific query, making the memory indeterminate until tested.
• Experiments showed that LLMs could recall almost 2,000 Chinese and English poems with high accuracy, a feat that would be difficult for humans to achieve without specialized training. The best-performing models, like Qwen2-1.5B-Instruct, remembered nearly 100% of the poems, demonstrating impressive memory capabilities.
• The study explains LLM memory through the Universal Approximation Theorem (UAT), suggesting that LLMs dynamically generate outputs based on input, similar to how human brains process information. This dynamic fitting of outputs forms the basis of LLMs' memory function.

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QUICK HEATS

• Lionsgate and AI startup Runway has announced a partnership to utilize generative AI in film production by developing a custom AI model using its media library.
• SiFive has launched its new SiFive Intelligence XM Series chip designs, featuring a scalable AI matrix engine for low-powered applications.
• YouTube announced the integration of Google DeepMind's AI video model, Veo, into YouTube Shorts, allowing creators to generate high-quality backgrounds and 6-second clips.
• The Biden administration will host a global safety summit on artificial intelligence in San Francisco on November 20-21 to address the technology's risks and capabilities.

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