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Very tiny. A blood cell is around 7-8um across (there are 1000 um, micrometers or microns, in every millimeter). A surface protein is probably around the 10s of nm (nanometers, there are 1000 nm in every um) large. So roughly a thousand times smaller.

It is relatively easy to see cells with a microscope. With a light microscope you generally cannot see proteins because they are too small. Microscopes are limited by something called the diffraction limit, which is usually around 0.25um (or 250nm).

However you can see much smaller structures with an electron microscope and could presumably “see” surface proteins in that manner.

You could also use a stain or dye which colors the cell based on what surface proteins are present. You wouldn’t directly see the proteins, but you could differentiate blood type based on an indirect visualization (the color)

TL&DR: Damn this was ranty. Check out things like "perceptron", "Taylor series", and "Gradient descent"

Rant:

"AI" is a misnomer for a programming model where you don't describe the logic, but rather provide the data to "program" software. The method via which this data is generated, organized, and processed into logic is called "training" The steps to train will depend on the type of model and type of problem.

The "model" is the rules for how the data interacts to form logic, but is devout of problem specific logic. However models do model the possible logic by describing the data flow on how to search for a solution.D depending on the type of "training", supervised vs unsupervised, and the type of model you are using: A discriminator/classifier vs a transformer, training will entail some specific details and could even go as far as use another AI model to generate the training data itself. Ultimately it just a bunch of number crunching in order to calculate the coefficients (scalar weights) used at the various inputs of the model's internals. The task of training is typically massively parallel over a large data set, so SIMD (single instruction, multiple data) type processors like GPUs or TPUs have been tremendously successful since the relationship of weights to data can be represented as a multi dimensional vector (array, list, or whatever else)

There are two steps to training:

1 - black magic: selecting (or generating) the right training data, handcrafting/selecting a model (data flow) suitable for the problem, determining the source of the error signal and deciding on he cost function. The success criteria of one model vs another is biased by these factors.

2 - Crunching some numbers via Math: Linear algebra and Calculus (processing)

The weird stuff is in 1:

A raw "model" is a combination of mathematical functions that can execute on data and a description of all possible data flow between functions. When I say function, i literally mean things like "add". For example consider a basic linear function of f(x)=ax+by+cz+k in a 3d space... so 3 inputs... the job of training is to calculate a, b, c and k such that line used to connect the 3 variables in your specific problem class.... obviously i can make a subtract out of the add by training a negative coefficient, or I can recuse the function to form an exponent... the basic building block for a deep learning neural network is the "perceptron" (or the McCulloch-Pitts neuron) which was inspired by the function of neuron dendrites (weighted inputs), soma (add them up) and axon (output to other neurons)... an overly fancy way to in fact just say 1 if there is enough input to fire, and 0 if there isn't. Enough input is defined as the dot product between the input vector x (so [x y z] ) and the coefficient vector ( for our line [a b c] ) plus k (the constant) being greater than 1

A line is not a particularly sophisticated function, however by defining x as a line of some subset of other variables, you can get a generic polynomial. Engineers will recall the usefulness of Taylor series, where if you give me enough data, I can guess the right function pretty damn well. AI training is basically that, beating a polynomial into the shape of your data set without "over-fitting" (meaning, hard wiring your training data as a solution) the function.

These perceptions are wired up as necessary to create more complex functions. Additionally the model may also have some creative wiring, like "attention" which is message signalling back and forth between the neuron layers, which creates interesting behaviours in Transformer models that are oddly successful in processing human language. (until a model succeeds, this is the black magic, researchers are just chimping combinations of these structures till they find one that works for their problem)

Ultimately for training to be efficient the model must be differentiable, meaning you can find its derivative. This allows you to use calculus' gradient descent to find minima in the cost (or loss) function calculated on the errror signal.

Once you have defined a model, "training it" is easy

The process is simple...

• pick random values for coefficients
• from training data set pick the first sample set of inputs
• perform dot product (lots of them) this is why SIMD/GPU across all features (input layer and intermediate layers)
• generate output
• compare output to reference in training set (or against objective function if rule based in self play, or against a discriminator if using a GAN or other such unsupervised learning), calculate error
• use error as input to cost function
• perform gradient descent on the weights to reduce the cost of the error
• repeat until you have found global minimum (derivative of error vs stepping in your coefficients is as low as it is going to get with specific heuristics to ensure you didn't accidentally find a local minimum in the polynomial's error - "over-fit" the model) minimized the cost of error

So training requires a large amount of linear algebra and calculus processing of "training data" and "internal weights".

Once you have the model trained, and you are happy with it. Re-running it on new input data, without adjusting weights (coefficients) is called "inference" Since inference is basically calculating an already know polynomial (training discovered it, inferring it means I just have new values for the variables), the "algorithm" can be implemented as optimally as a physical circuit that requires very little computational resources, however that wouldn't be very "upgrade-able"... You are seeing "inference" engines that are basically programmable circuits like FPGAs for low power low latency realtime inference (running the algorithm derived by the model)

For what it is worth, AI is not a panacea, It is very likely that P is not equal to NP. Meaning that not all problems are solvable by a polynomial, no matter how big your calculator or how badly you beat the math into submission. Get into too many "if this then that" conditions and your model is not differentiable and you will struggle to generalize a loss function. Even in cases where you "found" a polynomial solution, you can't make the assumption real data will always fit. Humans can learn as they infer, AI typicallyu cannot for the simple reason that the computational tasks are different between learning and inference, and for a large model you'd have to packing quite the overkill of melted sand with jammed up lightning in it just so you can dynamically re-train.

The article says it rejects the sterile neutrino hypothesis quite strongly, although it doesn't say it rules them out altogether from existing.

I just want to say, that's a great question, and a lot of accurate answers 😁. I work in a related field as well, and many people cannot wrap their minds around 'yes, there are symptoms in you leg (for example), but the cause is located at the peripheral nerves that become the sciatic nerve. Your leg is actually physically okay, at least for now.' Obviously when the nervous system isn't functioning properly, all of the other bodily functions will be affected as well. Resulting in a multitude of other issues.

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That reads like they are ruling out the possibility that sterile neutrinos are the explanation for a particular anomaly seen in past experiments, and not that they rule out the possibility of sterile neutrinos in general.

Edit: see for example this figure from the article. They rule out parameters to the right of the red/blue curves, in particular ruling out the parameters that would have explained the "reactor antineutrino anomaly (RAA)".

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It has to do with with the turning force being applied to the head when tightening the screw/bolt/nut into position during assembly. Generally the higher the torque used to put the screw in, the better it will stay in position due to increased pressure on the threading. Putting too much pressure into turning the screw and you could damage the screw threading, the shaft threading, the screw head could snap off, you could strip the head, or damage the material you're fastening together.

So torque settings are recommended to both ensure the screw/bolt/lug will lock into place as tightly as possible, but not so tight that you will break something completely. The torque setting is determined by a variety of factors ranging from the materials being fastened, material of the screw, threading size, and physical size of both.

You set your tool to the torque setting. As you apply turning pressure with the tool it will hit that setting limit and click as the tool shifts/gives under the pressure. This is essentially the tool giving out before the screw does to ensure you don't over-torque and break something.

How well a screw stays in place is determined by threading size, original torque pressure, the material it's connected to, the environment, and other fastening additives like glue or locktite.

IIRC, when the energy in the photon is absorbed by the physical "detector", the "detector" changes shape, altering its output, but does NOT actually "send" a signal to the brain as a result, it actually STOPS sending a signal. That non-signal is then what is interpreted as visual meaning.

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Thank you for this!

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>a respiration rate 1/3 as slow as a human's.

You mean 1/3 that of a human?

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In a way; applying the torque translates into a force the screw/bolt head is applying axially down the screw/bolt into whatever it’s being driven into; this force is what holds the parts together, or in the sense of a gasket crushes the gasket a certain amount. (Think of the gasket like a spring not a solid).

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Yes, you're just multiplying the length of the arm by the force applied.

During phase one of drug trials the medication is administered to healthy volunteers in a controlled setting. These volunteers undergo regular, timed testing, particularly blood tests, to see how fast the drug is eliminated from their body. Once you know the half life of the medication you can make determinations of how often the drug has to be taken.

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