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That makes no sense. If you can already infer the labels beforehand, then you already have a working model, right? The job of the model is to infer those label
by codesushi42 7y ago
That makes no sense. If you can already infer the labels beforehand, then you already have a working model, right?
The job of the model is to infer those labels. The model needs to be trained to accurately predict those labels by using examples that were labeled beforehand.
- m_mueller 7y agoBy model of the world I mean something much more than what's called an ML model today. I mean the cognitive ability to predict the past and future of some part of the real world based on observation. Most simple example: Seeing two video frames of a massive object moving in front of a reference background and predicting the curve it's going to do, and where it came from. Only having such an understanding of the world (physical laws, human interactions with it etc.) allows to program in much more general cost functions that can get the system to explore on its own, not just within a given rule set, but the rule set itself.
- codesushi42 7y agoAgain, what you're proposing has no basis in today's reality of how ML is implemented. Especially not for classifying image data. What you're proposing is some kind of machine intelligence that can only exist right now in a William Gibson novel. Reinforcement learning is something similar, along with unsupervised learning. But you still need to define some kind of objective function and rules to play by. The AI cannot come up with this on its own. And the real world is far too complex to model all of these physical rules you've mentioned. Reinforcement learning can work for Go because the rules of the game are relatively simple. It is the number of possible moves/outcomes that make it enormously complex.