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I'm dabbling in that world right now. Along with the fact that, well, I'm dabbling in that world right now... no. They're doing different thing. You won't have

by jerf 4y ago

I'm dabbling in that world right now. Along with the fact that, well, I'm dabbling in that world right now... no. They're doing different thing. You won't have ML models creating UI code (and I mean, actual UI code to draw widgets, not just assembling UIs) or any number of other tasks. They're doing new things. And we have plenty of time to learn, just like I haven't been left behind by any number of other things. It won't be like the web. (In fact I've been generally unimpressed by the people I've seen so far. The ML math is complicated, but there's a whole lot of "just fire this at that without understanding why" in the actual practitioners. I've found I have a better understanding of what is and is not possible and why than they seem to rather often. This comes back to my extended education background, though; my training is old but less obsolete than you may think, a lot of modern ML stuff isn't some totally new thing just invented yesterday, but a couple of slight tweaks to established stuff that worked really well combined with finally having the CPU firepower to use it.)

hamburglar 4y ago
Anyone who has gone through the extreme pixel-level iteration process of honing a UI at a company that really cares about UX knows we are very, very far off from letting AI do it.
- splistud 4y ago
YeGoblynQueenne 4y ago
That has to be said again. Basically all of deep learning, which is what people mainly mean today when they say "ML", is at least 20 years old. Other data science techniques? Even older. The big spurt seen recently is mainly the result of a) faster hardware and b) larger datasets [1]. At some point the "low-hanging fruit" achievable by scaling up and crunching down MOAR DATA will be all taken and then soemone will need to do all the hard work of figuring out new techniques, or at least building bigger computers. Who's going to do that? All the people who are "passionate about machine learning" and are experts in Tensorflow and PyTorch? I wouldn't bet my money on that. Thirty years from now the people leading the field will be people with thirty years of experience in thinking real hard about real hard problems. As they usually are [2]. __________________ [1] My standard reference for this is a guy called Geoff Hinton. Some people in machine learning may have heard the name. Quoting: AMT: Ok, so you have been working on neural networks for decades but it has only exploded in its application potential in the last few years, why is that? Geoffrey Hinton: I think it’s mainly because of the amount of computation and the amount of data now around but it’s also partly because there have been some technical improvements in the algorithms. Particularly in the algorithms for doing unsupervised learning where you’re not told what the right answer is but the main thing is the computation and the amount of data. http://techjaw.com/2015/06/07/geoffrey-hinton-deep-learning-in-baby-steps-and-the-future-of-google-and-ai/ http://techjaw.com/2015/06/07/geoffrey-hinton-deep-learning-... [2] Check out the birth dates of the current generation of deep learning luminaries. Geoff Hinton: 1947. Yoshua Bengio: 1964. Yann LeCun: 1960. Jurgen Schmidhuber: 1963. It goes on. Go ahead and tell those guys they're greybeards who don't get what the kids today are doing.

by jerf 4y ago

hamburglar 4y ago

Anyone who has gone through the extreme pixel-level iteration process of honing a UI at a company that really cares about UX knows we are very, very far off from letting AI do it.

splistud 4y ago

YeGoblynQueenne 4y ago

That has to be said again. Basically all of deep learning, which is what people mainly mean today when they say "ML", is at least 20 years old. Other data science techniques? Even older. The big spurt seen recently is mainly the result of a) faster hardware and b) larger datasets [1]. At some point the "low-hanging fruit" achievable by scaling up and crunching down MOAR DATA will be all taken and then soemone will need to do all the hard work of figuring out new techniques, or at least building bigger computers. Who's going to do that? All the people who are "passionate about machine learning" and are experts in Tensorflow and PyTorch? I wouldn't bet my money on that. Thirty years from now the people leading the field will be people with thirty years of experience in thinking real hard about real hard problems. As they usually are [2]. __________________ [1] My standard reference for this is a guy called Geoff Hinton. Some people in machine learning may have heard the name. Quoting: AMT: Ok, so you have been working on neural networks for decades but it has only exploded in its application potential in the last few years, why is that? Geoffrey Hinton: I think it’s mainly because of the amount of computation and the amount of data now around but it’s also partly because there have been some technical improvements in the algorithms. Particularly in the algorithms for doing unsupervised learning where you’re not told what the right answer is but the main thing is the computation and the amount of data. http://techjaw.com/2015/06/07/geoffrey-hinton-deep-learning-in-baby-steps-and-the-future-of-google-and-ai/ http://techjaw.com/2015/06/07/geoffrey-hinton-deep-learning-... [2] Check out the birth dates of the current generation of deep learning luminaries. Geoff Hinton: 1947. Yoshua Bengio: 1964. Yann LeCun: 1960. Jurgen Schmidhuber: 1963. It goes on. Go ahead and tell those guys they're greybeards who don't get what the kids today are doing.