There are many, many different variations, but they more or less all work on the same basic premise.

1. Begin with an initially random model.

2. Test the model. Give it a problem and ask for its response.

3. Modify. If the system didn't behave as intended, change something.

4. Repeat steps 2 and 3 until you run out of training data.

5. Pray that the model works.

The most obvious differences between AIs will be in the structure of the model (how big is it, how connected, how many layers, what kind of internal memory etc) but the real fun stuff is in how we do the modifying.

We can show that, for some problems, just tweaking the system randomly is enough to get okay solutions. But it's very far from ideal. Better is to be able to nudge the system "towards" the expected behavior. We've put a lot of focus into how to design these systems so that our modifications are more fruitful.

The term that would clear things up would be by-product. Adrenal production from glands of sheep and cattle would be a by-product of the meat industry. Extrapolating based on scale, extraction of adrenaline wouldn't significantly increase the environmental impact of the meat industry since I would be confident the supply of glands would be saturated by the supply of livestock.

Your brain does this with the blind spot in your eyes (where the retina joins the optic nerve). The brain fills in this blank spot. This is a favorite for things like demonstrating optical illusions.

https://www.brainfacts.org/for-educators/for-the-classroom/2021/blind-spot-illusion-011921

Put simply, it means you don’t want to over or undertighten it. Whatever you’re using it for has been identified as mission critical to the object. Undertightening would mean it’ll come loose during normal operation. Overtightening means you risk sheering the bolt or attached surface during normal operation.

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I totally saw a video about this. Here it is.

https://www.youtube.com/watch?v=WGKIjojADmg

More blades produce more power and more wind passing across the blades produces more power but more blades blocks more wind from passing. Then math led to the optimum design which you can imagine is what we build as modern wind turbines.

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For example, i forgot to retorque my alloys in the autumn when the mandatory winter tire months came on. When i finally did it, some of the bolts were already "finger tight".

Almost every garage i have worked at has reminded customers about retorque when they get their car back.

In the simplest case, you start with an untrained AI (some mathematical model with variable parameters) and training data for which you already know the desired output (supervised learning). Initially, the AI produces nonsense when given the training data, so you repeatedly make small changes to the parameters of the AI, making sure that the actual output gets closer and closer to the desired output. At some point, the actual output is close enough to the desired output that you stop - the AI has been trained, and when given data sufficiently similar to the training data will produce the desired output even though the AI has never encountered that specific data before.

It obviously gets more complicated, especially when you don't already know your desired output (unsupervised learning) or in more complex designs such as generative adversarial networks. Some machine learning approaches typically use specific algorithms for training, such as the Baum-Welch algorithm for Hidden Markov Models, while others may use generic optimization algorithms. In general though, the process of repeatedly making small changes and comparing the new result to the previous one is a largely universal part of training AI.

a photon detector IS a photon absorber

the photons are absorbed by the cells in your retina. basically, the energy of those photons excite some atoms. that excitation generates some electrical current. that current prompts some neurons to send an electro-chemical signal to your brain.

and yes - the photons are "destoryed" in that process (aka: it's energy is converted into another form of energy)

a photon is not some kind of miniature cannonball. it is kind of a packet of energy, that is transmitted in a wave over the electromagnetic field. take away the energy and the wave "goes flat".

Scientists are working on lab-grown beef. I wonder if lab-grown adrenal glands would be a viable alternative to how it's done today

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My intention was to say that it's a nonsensical way to explain how a giraffe copes with a long neck given the lungs really aren't that large compared to their mass.

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Weight, drag, and stability. The more blades you have, the more power is required to turn the rotor. With three blades, you're getting the most rotations out of the broadest wind speed range. More blades actually decreases power production.

I'd argue the old beam type torque wrenches were more accurate and reliable. They are so simple there's not much that could go wrong with it. They just aren't very convenient to use.

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The difference between the blood-types is the presence of certain antigens on the surface of the blood cell. These molecules are too small to be seen through a microscope. The "positive/negative" part is about another antigen on the surface of the cell, which is determined by another gene than the ABO system.

As someone with a herniated lower disc that presses up against my spine (and is doing so right now), it radiates to the actual receptors.

But those receptors can be anywhere and everywhere. I feel pain in different locations depending on "how" my disc is currently herniated, inflammation in the area affects this as well.

Right now my actual butt is in a lot of pain, but it can radiate all the way down to my toes sometimes. This is all from the disc pressing against the spinal cord in different micro locations, nothing is actually touching the pain receptors in my bum (other than my clothes, I guess).

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