That's still remarkably grandiose.
AI solves problems that are vastly too hard for humans to figure out.
"A tenth of the accuracy" of a 100% accurate model is 10%. So you're basically claiming that nobody had achieved anything with machine vision before NNs (you probably mean AlexNet in particular), which is patently absurd and completely unhistorical.
Why is it that this subject attracts the attention of so many people who know so little of what they're talking about?
Additionally very few machine vision systems are completely hand designed. They all use some form of machine learning and optimization. Just not necessarily NNs.
Edit: Btw I just had a look to confirm and what you're saying about "1000 equally balanced classes" is inexact, to put it midly. ImageNet has 100,000 SynSets (as in WordNet) and aims to have about 1000 images illustrating each. Individual images may illustrate several synsets- you don't get one image per class, say.
Also: AlexNet was not a cat recogniser and having unbalanced classes is a problem for machine learning, but not a dealbreaker, per se.
Classification is extremely representative of the challenge of machine vision. Good classifiers can often be adapted to do localization and other tasks, e.g. by scanning the image to see what parts contain an object.
The Imagenet Large Scale Visual Recognition Challenge has 1000 classes which you can browse here: http://image-net.org/challenges/LSVRC/2015/browse-synsets I forgot that Imagenet itself has more classes than that. But it's best known for this specific competition of 1000 classes, and everyone uses that as a benchmark.
>freaking AlexNet was not a fraking cat recogniser.
I never said it was... It's actually a dog recognizer mostly : )
False. Plenty of work has been done to show what exactly is going on inside NNs including some very impressive visualizations.
> Hand designed programs to do machine vision don't even get a tenth of the accuracy of NNs.
And that's false too. Hand designed programs to do machine vision can get significant fractions of the accuracy of NNs and sometimes even perform better and/or faster depending on the domain.
It is a lot more work to write that code though and a NN might clue into features that you weren't aware even existed.
>And that's false too. Hand designed programs to do machine vision can get significant fractions of the accuracy of NNs and sometimes even perform better and/or faster depending on the domain.
Source? A few years ago people thought it was amazing an algorithm could recognize cats 12% of the time. And even the best non-neural approaches use some form of machine learning.
>a NN might clue into features that you weren't aware even existed.
That's the whole point...
It's just that NNs are rapidly moving ahead and maybe some time in the future both your statements will be true but today they are not.
Recognizing cats is not the sum total of machine vision problems and those 'pretty visualizations' go a lot further than what you give them credit for.
I've been doing nothing but reading up on machine learning and working through piles of example code and models for the last couple of months and I think based on what I've seen so far that we are definitely headed in the direction that you suggest. At the same time we aren't there yet and such hype does not help.
Keep in mind that the domain of machine vision is extremely broad and much wider than Kaggle competitions and that saying 'we have no idea' when clearly we do tries to sketch NNs as magic and they're not.
If anything the present generation is best characterized as an optimization of what you could have done for many years, the only reason they are the way they are is because we'd like to have our results in a reasonable time. That makes applying NNs to large datasets possible and that in turn is what helps to solve some of these problems.
I'd love a source for this. The last time I checked I didn't find much. Sure we know the first few layers do stuff like edge detection. But the higher layers are much harder. And even if you know images are activating them, writing code that can accomplish the same task is much harder.
>It's just that NNs are rapidly moving ahead and maybe some time in the future both your statements will be true but today they are not.
The best NNs have already surpassed human accuracy at imagenet. That's insane! No one expected that result a few years ago. Yes object recognition isn't the only task in machine vision, but it's pretty representative.
I will do some digging for you tomorrow, it's 3:30 am and I really should get some sleep.
> Sure we know the first few layers do stuff like edge detection.
Yes, basic primitives.
> But the higher layers are much harder.
Well, those higher layers are built up out of the primitives the lower layers contain. So you get things that are conceptually easier to understand rather than harder.
> And even if you know images are activating them, writing code that can accomplish the same task is much harder.
That I completely agree with, I've basically gone that route in the last couple of months.
> The best NNs have already surpassed human accuracy at imagenet.
Yep. It's extremely impressive.
It's a watershed moment in the history of computing and half of us are still asleep or in denial. Which is fine by me. But please don't hype. It isn't magic and it isn't perfect - yet.
edit: ok, found two of the things I've looked at:
https://stats.stackexchange.com/questions/114385/what-is-the...
https://stats.stackexchange.com/questions/146413/why-convolu...
And this is interesting code:
https://github.com/yosinski/deep-visualization-toolbox
If anything I think that NNs as applied to vision problems are somewhat easier to make sense of than doing the same for NLP or audio.
Though it definitely feels like cheating compared to actually programming a solution.
An alternative name for 'deep learning' would be 'automatic multi-level feature engineering', but that wouldn't be nearly as sexy.