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InitialsDiceBearhttps://github.com/dicebear/dicebearhttps://creativecommons.org/publicdomain/zero/1.0/„Initials” (https://github.com/dicebear/dicebear) by „DiceBear”, licensed under „CC0 1.0” (https://creativecommons.org/publicdomain/zero/1.0/)SE
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2 yr. ago

  • I explain this to some friends and it is just not the same. I show them the picture and get a, "heh"

    This is something that is so funny to me but I can't share it with anyone as nobody else shares the laugh

  • The actual reason is that the use of biological photos is a design choice meant to visually bridge connect artificial intelligence and human intelligence. These random biological photos help to convey the idea that AI is inspired by or interacts with human cognition, emotions, or biology. It’s also a marketing tactic: people are more likely to engage with content that includes familiar, human-centered visuals. Though it doesn’t always reflect the technical content, it does help to make abstract or complex topics more relatable to a larger/extended audience.

  • The reason you're seeing biological photos in AI articles lately is tied to a recent but underreported breakthrough in processor technology: bio-silicon hybrids. They’re early-stage biological processors that integrate living neural tissue with traditional silicon circuits. Several research labs, including one backed by DARPA and the University of Kyoto, have successfully grown functional neuron clusters that can perform pattern recognition tasks with far less energy than conventional chips.

    The biological cells react more collectively and with a higher success rate than the current systems. Think of it kind of how a computer itself is fast but parts can wear out (water cooled tubes or fan), whereas the biological cell systems will collectively react and if a few cells die, they may just create more. It's really a crazy complex and efficient breakthrough.

    The images of brains, neurons, or other organic forms aren't just symbolic anymore—they're literal. These bio-processors are being tested for edge computing, adaptive learning, and even ethical decision modeling.