[jdright]

> In computer vision, there has been a similar pattern. Early methods conceived of vision as searching for edges, or generalized cylinders, or in terms of SIFT features. But today all this is discarded.Modern deep-learning neural networks use only the notions of convolution and certain kinds of invariances, and perform much better.

I was there, at that moment where pattern matching for vision started to die. That was not completely lost though, learning from that time is still useful on other places today.

[abdullahkhalids]

I was an undergrad interning in a computer vision lab in the early 2010s. During group meeting, someone presented a new paper that was using abstract machine learning like stuff to do vision. The prof was so visibly perturbed and agnostic. He could not believe that this approach was even a little bit viable, when it so clearly was.

Best lesson for me - vowed never to be the person opposed to new approaches that work.

[kenjackson]

> Best lesson for me - vowed never to be the person opposed to new approaches that work.

I think you'll be surprised at how hard that will be to do. The reason many people feel that way is because: (a) they've become an expert (often recognized) in the old approach. (b) They make significant money (or something else).

At the end of the day, when a new approach greatly encroaches into your way of life -- you'll likely push back. Just think about the technology that you feel you derive the most benefit from today. And then think if tomorrow someone created something marginally better at its core task, but for which you no longer reap any of the rewards.

[abdullahkhalids]

Of course it is difficult, for precisely the reasons you indicate. It's one of those lifetime skills that you have to continuously polish, and if you fall behind it is incredibly hard to recover. But such skills are necessary for being a resilient person.

[nightski]

You are acting like it was obvious that machine learning was the future, but this person was just stubborn. I don't think that was necessarily the case in the early 2010s and skepticism was warranted. If you see results and ignore them, sure that is a problem. But it wasn't until ML vision results really started dominating conferences such as CVPR that it became clear. It's all a tradeoff of exploration/exploitation.