There were more than one system proven to “cheat” through biased training materials. One model used to tell duck and chicken apart because it was trained with pictures of ducks in the water and chicken on a sandy ground, if I remember correctly.
Since multiple medical image recognition systems are in development, I can’t imagine they’re all this faulty trained with unsuitable materials.

I’m fully aware that those are different machine learning models but instead of focussing on LLMs with only limited use for mankind, advancing on Image Recognition models would have been much better.

AI models can outmatch most oncologists and radiologists in recognition of early tumor stages in MRI and CT scans.
Further developing this strength could lead to earlier diagnosis with less-invasive methods saving not only countless live and prolonging the remaining quality life time for the individual but also save a shit ton of money.

He is taking a time out with a friend in an involuntary hotel room.

Even at this price point isn’t it more economic and sustainable to emulate a RISC-V device with already owned hardware. Especially since it’s so low powered.

Remember the Cars movies? That’s the dystopia leading to a man made world inhabited by sentient Cars /s