Is he known outside of Germany?

Maybe OP could also try if he could contribute to existing projects.

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 did and I’m not getting why you’re so upset confused fish sounds

Thats why they lost the Emu war…

Yes, they ban moderators in the full knowledge in reducing the quality and well behavedness of users and to increase traffic by upset people. This is exactly what they want: upset people = engagement. This how Twitter works or maybe used to work, I didn’t follow the recent changes too closely.

Ah yes, here we go, comparing changing the color in some pixel to someone risking his life and that of his family.

Small test: Dio porco Cunt Vaffanculo Hurensohn Dickhead

What a joke is this the US? For europeans these swear words are fine. u/spez ist ein HÜR3ns0hn

He seems like the average reddit user raises nose

I didn’t go to university, because I wanted to learn useful stuff, but because I’m curiousity driven. There is so much cool stuff and it’s very cool to learn it. That’s the point of university that it prepares you for a scientific career where the ultimate goal is knowledge not profit maximisation (super idealistically).

Talking about Turing Machines it’s such a fun concept. People use this to build computers out of everything - like really - it became a Sport by this point. When the last Zelda was Released the first question for many was, if they can build a computer inside it.

Does it serve a practical purpose? At the end of the day 99% of the time the answer will be no, we have computing machines built from transistors that are the fastest we know of, lets just use these.

But 1% of the time people recognize something useful… hey we now found out in principle one can build computers from quantum particles… we found an algorithm that could beat classical computers in a certain task… we found a way to actually do this in reality, but it’s more proof of concept (15 = 5×3)… and so on

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 use it and it’s called Bixby. Thought most spam callers don’t make it after the first phrases.

Firstly running Android != running Google on it.

Secondly you prove exactly the point. They try to make you dependent on their proprietary technology, forcing you to use their app store, apps, chargers, repair shops, desktop OS, TVs etc. (you can circumvent each of these point, but it requires always some amount of technical time investment).

If privacy is your concern the better option could be e.g. some google free android variant. There are several other OSes that are specialised in this regard and I think it’s not a pro argument for apple.

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