I thank you for your critic but I’m not writing a research paper here and therefore wikipedia is a good ressource for the uniniated public. This is also why I think it’s sufficient to know a) what an artificial neural network is by talking about the simplest examples b) this field of research didn’t initiate 10 years ago as often conceived by public, when first big headlines were made. These tradeoffs are always made: correctness vs simplification. I see your disagreeing with this PoV but that’s no reason to be condescending.

The specifics are a bit different, but the main ideas are much older than this, I’ll leave here the Wikipedia

"Frank Rosenblatt, who published the Perceptron in 1958,[10] also introduced an MLP with 3 layers: an input layer, a hidden layer with randomized weights that did not learn, and an output layer.[11][12] Since only the output layer had learning connections, this was not yet deep learning. It was what later was called an extreme learning machine.[13][12]

The first deep learning MLP was published by Alexey Grigorevich Ivakhnenko and Valentin Lapa in 1965, as the Group Method of Data Handling.[14][15][12]

The first deep learning MLP trained by stochastic gradient descent[16] was published in 1967 by Shun’ichi Amari.[17][12] In computer experiments conducted by Amari’s student Saito, a five layer MLP with two modifiable layers learned internal representations required to classify non-linearily separable pattern classes.[12]

In 1970, Seppo Linnainmaa published the general method for automatic differentiation of discrete connected networks of nested differentiable functions.[3][18] This became known as backpropagation or reverse mode of automatic differentiation. It is an efficient application of the chain rule derived by Gottfried Wilhelm Leibniz in 1673[2][19] to networks of differentiable nodes.[12] The terminology “back-propagating errors” was actually introduced in 1962 by Rosenblatt himself,[11] but he did not know how to implement this,[12] although Henry J. Kelley had a continuous precursor of backpropagation[4] already in 1960 in the context of control theory.[12] In 1982, Paul Werbos applied backpropagation to MLPs in the way that has become standard.[6][12] In 1985, David E. Rumelhart et al. published an experimental analysis of the technique.[7] Many improvements have been implemented in subsequent decades.[12]"

To the second question it’s not novel at all. The models used were invented decades ago. What changed is Moores Law striked and we got stronger computational power especially graphics cards. It seems that there is some resource barrier that when surpassed turns these models from useless to useful.

I use Arsch btw

Small test: Dio porco Cunt Vaffanculo Hurensohn Dickhead

It’s not dumb it’s just a different take on reality (bayesian thinking). I have a model and accordingly a belief belonging to it: “given the data, how likely is it, that my current model is true?”.

So maybe you’re starting of believing an orbit has a spherical shape because it describes best your observations. But as you’re collecting data you’re noticing that an elliptic shape is more probable. Therefore you now believe the shape is elliptical.

In physics it’s often different. People start of writing down a law from first principles and see if it agrees with the data. E.g. Kepler writing down his three laws of planetary motion in an act of epiphany. Then he sees this fits the data well and is happy.

The question is philosophical: Do you believe there is some fundamental laws nature obeys or do you say I just take the model which is the most probable given the data.

But I agree that only very few people are belonging to the latter group and even fewer people in theoretical physics, where people are obsessed with “beauty” - e.g. believing orbits are being described by ellipses, not just some shape my data suggests me.

I don’t agree. It depends really on what you’re using it for. I have one machine with Xubuntu installed and it’s been used just for browsing or putting movies to the tv and it’s been just plug and play so far.

There was the one issue, of not being able to use some streaming services, because of drm protection meaning you can only stream via their windows desktop app not via browser. I took it as an invitation for streaming the stuff from somewhere else for free shrug

On my labs cluster they are named after famous physicists

I was providing Linux distros and Machine Learning datasets some time ago, because official servers where slow. I’m the meme I guess

Ah yes, like last year. And the year before. And …