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Digital Inbreeding: What Happens When AI Runs Out of “Surprise”?

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Read Time:4 Minute, 16 Second

We are living through a massive experiment in information dynamics, and I’m starting to worry about the results.

Initially, Large Language Models (LLMs) were trained on the “wild” internet—a chaotic, messy, and deeply human repository of text. It was a library written by people. But today, the internet is fundamentally changing. More and more of the content we consume (and that future models will train on) is heavily influenced, if not completely generated, by AI. read more

Posted On : Published By : Shawn Harris
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My object detection and classification model, running on Raspberry Pi 4. Progress!

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Read Time:33 Second

Fun little project his weekend, building a object detection and classification solution for less than $100. Though this pic only shows “person” and “book” classifications, the model can classify some 90 objects! The Tensorflow Lite model is running on a 4GB Raspberry Pi 4 w/ 128GB Sdcard. The camera is a Arducam, which I need to work on the resolution for but it didn’t impact the detection or classification, and ran at ~2.0 fps. Running on a Pi I have a give and take between model performance and accuracy, given the limited resources, but will push to see how resource hungry a model I can run on it.  More to come… read more

Posted On : Published By : Shawn Harris