> Therefore, the training data should have the same technical shortcomings as what will be used in production.
I think training and the application of the trained system are separable. Nothing is accomplished by training with data sets that lack data or detail. Inference or classification is impossible or deeply impaired by the lack of data.
As a hypothesis, the solution is likely to involve training one network with good data and a separate network to be the interface between the first network and the imperfect perceptual data in real-world applications.
At the end of the day AI/ML need to leave the world of classification behind and move on to the concept of understanding. This is not an easy task, yet it is necessary in order for these amazing technologies to truly become generally useful.
We don't show a human child ten thousand images of a thousand different cups in ten different orientations in order for that child to recognize and understand cups. The reason for this is that our brains evolved to give us the ability to understand, not simply classify. This means we need far fewer samples in order to have effective interactions with our physical world.
The focus on using massive data sets to train classification engines is a neat parlor trick, yet it will never result in understanding and is unlikely to develop into useful general artificial intelligence. The problem quickly becomes exponential and low quality data becomes noise. We need to develop paradigms for encoding understanding rather than massive classification networks that can't even match the performance of a dog in some applications. As I said before, this is a very difficult problem. I don't think we know how to do this yet. Not even sure we have any idea how to do it. I certainly don't.
I think training and the application of the trained system are separable. Nothing is accomplished by training with data sets that lack data or detail. Inference or classification is impossible or deeply impaired by the lack of data.
As a hypothesis, the solution is likely to involve training one network with good data and a separate network to be the interface between the first network and the imperfect perceptual data in real-world applications.
At the end of the day AI/ML need to leave the world of classification behind and move on to the concept of understanding. This is not an easy task, yet it is necessary in order for these amazing technologies to truly become generally useful.
We don't show a human child ten thousand images of a thousand different cups in ten different orientations in order for that child to recognize and understand cups. The reason for this is that our brains evolved to give us the ability to understand, not simply classify. This means we need far fewer samples in order to have effective interactions with our physical world.
The focus on using massive data sets to train classification engines is a neat parlor trick, yet it will never result in understanding and is unlikely to develop into useful general artificial intelligence. The problem quickly becomes exponential and low quality data becomes noise. We need to develop paradigms for encoding understanding rather than massive classification networks that can't even match the performance of a dog in some applications. As I said before, this is a very difficult problem. I don't think we know how to do this yet. Not even sure we have any idea how to do it. I certainly don't.