3 ms·

RNN's are 80's technology at least: https://en.wikipedia.org/wiki/Recurrent_neural_network#History https://en.wikipedia.org/wiki/Recurrent_neural_network#Histo.

by cat199 6y ago

RNN's are 80's technology at least: https://en.wikipedia.org/wiki/Recurrent_neural_network#History https://en.wikipedia.org/wiki/Recurrent_neural_network#Histo... With neural network use in image recognition (if not facial recognition) being used at least as early as 1983 (I'm not in the field, so just did 5s of research and found something related) - https://ieeexplore.ieee.org/document/6313076 https://ieeexplore.ieee.org/document/6313076 "A recognition with a large-scale network is simulated on a PDP-11/34 minicomputer and is shown to have a great capability for visual pattern recognition. The model consists of nine layers of cells. ..." another paper from 1990 using neural networks for facial sex identification: https://www.researchgate.net/publication/221619290_SEXNET_A_Neural_Network_Identifies_Sex_From_Human_Faces https://www.researchgate.net/publication/221619290_SEXNET_A_... and software facial recognition itself dates to the 60s. despite what the 'data science and AI and by AI I really only mean the subset of AI referring to ML' hypetrain tells you, these things are not new, the processors are just faster, and the tools are more accessible, which means the relevant application space is larger.

WorldMaker 6y ago
> these things are not new, the processors are just faster, and the tools are more accessible, which means the relevant application space is larger. The core to my argument that most of contemporary ML isn't new or different in an "algorithmic breakthrough" sense, but in that we've massively sped up GIGO. An AI researcher in the 60s/70s teleported to today isn't going to have a rough time figuring out what's going on, they are going to whistle in shock at how massively bloated our "sample sets" and data corpuses have grown, how fast we're churning through that data (regularly), and how much of it we are leaving unsupervised or "self-supervised", and with maybe some questions as to what we did with our (lack of) QA/QR processes along the way.

by cat199 6y ago

WorldMaker 6y ago

> these things are not new, the processors are just faster, and the tools are more accessible, which means the relevant application space is larger. The core to my argument that most of contemporary ML isn't new or different in an "algorithmic breakthrough" sense, but in that we've massively sped up GIGO. An AI researcher in the 60s/70s teleported to today isn't going to have a rough time figuring out what's going on, they are going to whistle in shock at how massively bloated our "sample sets" and data corpuses have grown, how fast we're churning through that data (regularly), and how much of it we are leaving unsupervised or "self-supervised", and with maybe some questions as to what we did with our (lack of) QA/QR processes along the way.