BuilderBoi, if you just used a kitchen scale, it would have resolved the ambiguity by indicating that N($3,456, $478) is in the form of \(\mathbb{N}(\mu, \sigma)\) in this case there is no need for pot-laced candy or mushroom tea (though it might help ).
Recently, I saw a demonstration of very sophisticated kitchen scale at university. This scale connects to an AI-nerualnetwork. The scale’s sensors along with the AI identify the foods, caloric values, glycemic index, and other parameters. The AI also calculates the mean probable weight gain (with a standard deviation), based on the metabolic parameters of the consumer. It’s easy to program the parameters: the consumer need only to stand and then sit on the scale for 8 seconds, and then spit on a sensor. The demonstrated scale had a mass/weight limit of 450Kg –well above the weight of the largest tub-of-lard in attendance.
During the demonstration, one of the spectators plopped a bag of pot-laced candy on the scale. The AI correctly identified the contents and caloric value, but indicated an 85% probability of weight gain at a mean of (28.7) times the maximum value for the calorie count.
The engineer-tech, who was demonstrating the scale, queried the AI for an explanation. The AI extrapolated the stats for \(\Delta9THC \) causing the munchies in a standard population; from this, a probability of weight gain is calculated beyond the caloric value of the weighed food.
It is amazing how innovative technology can create a kitchen scale that can construct and solve statistical problems. You should get one as soon as they are on the market.
GA
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