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> The author suggests that the model "understands" colour because it drafts decks of specific colours. Well, its dataset consists of decks with cards of specifi

by tveita 8y ago

> The author suggests that the model "understands" colour because it drafts decks of specific colours. Well, its dataset consists of decks with cards of specific colours. It learned to reproduce those decks. It didn't learn anything about why those decks pick particular cards, or what particular cards are. All it has is a list of numbers that it has to learn to put together in specific ways. > This is as far from "understanding the concept of colour", or anything, as can be. It is very arguably bad feature engineering - if you have the information readily available, don't make the network infer it - but I think the description is fair. Word2vec uses a similar model. It starts out knowing nothing about each word except an arbitrary numeric index, and learns everything else by predicting words that appear next to each other. By the end of the training it clearly has internal representations of concepts like "color", "verb", "gender", etc. The same concept should apply here - by observing what cards are used in similar decks, with enough training data it should eventually associate concepts like card type, color and mana costs to each card. In this case there isn't enough training data for that kind of resolution, but it has learned that blue cards go with blue cards, and red cards with red cards, and there's no hard lines from there to the concept of color. Sure this isn't going to "solve" MtG, and I don't think it is a particularly good approach for the problem statement, but I think the idea is workable, and the network could already contain a proto-concept of "color" that would be refined with more training.

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YeGoblynQueenne 8y ago
>> In this case there isn't enough training data for that kind of resolution, but it has learned that blue cards go with blue cards, and red cards with red cards, and there's no hard lines from there to the concept of color. A card is blue (resp. red, etc) because it has a blue mana symbol in its casting cost. Not because it is found in the company of other blue cards. That is the concept of colour that a model must represent before you can say with any conviction that it "understands" the concept of colour. In terms of "hard lines"- that's the hard line you must cross. The kind of model you're talking about then would be a classifier able to label individual cards with their colours, or an end-to-end model with an internal representation of cards' charactersitics. That is not what was shown here.
- tveita 8y ago
  By observing what cards people tend to pick together, you can infer that certain cards have certain properties, even if you never get see the card face. A blue card is found in the company of other blue cards, because humans picked them, because of the blue mana symbol in its casting cost. With proper training, you end up with exactly the "end-to-end model with an internal representation of cards' charactersitics" Since it can't see the cards, it can't say anything useful about a card it hasn't seen during training, but if you added some new cards and started training again, a pre-trained net might learn the new cards faster than one you train from scratch. That would be evidence that the network has learnt a meaningful embedding. There is no proof that this network has done so, but I think word2vec shows that it's a feasible approach.
  - YeGoblynQueenne 8y ago
    >> By observing what cards people tend to pick together, you can infer that certain cards have certain properties, even if you never get see the card face. You're assuming way too much capability that is not present. Just because a human can make this inference, it doesn't mean that a neural net can. Neural networks are notoriously incapable of inference, or anything that requires reasoning. >> There is no proof that this network has done so, but I think word2vec shows that it's a feasible approach. Word2vec (word embeddings in general) are actually a good example why this kind of thing doesn't work the way you think it does. A word embedding model represents information about the context in which tokens (words, sentences, etc) are found but it does not, in and of itself, represent the meaning of words. The only reason why we know that words it places in the general vicinity of each other have similar meaning is because we already understand meaning and we can interpret the results. But the model itself does not have anything like "understanding". It only models collocations. Same thing here. You seem pretty certain that with more data (perhaps with a deeper model) you can represent something that the model doesn't have an internal representation for. But just because the behaviour of the model partially matches the behaviour of a system that does have an internal representation for such a thing, in other words, a human, that doesn't mean that the model also behaves the way it behaves because it models the world in the same way that the human does. And you can see that very clearly if you try to use a model like the one in the article, or one trained on all the magic drafts ever, to draft a set of cards it hasn't seen before. It should be obvious that such a model would be entirely incapable of doing so. That's because it doesn't represent anything about the characteristics of cards it hasn't seen and so can't handle new cards. A human understands what the cards' characteristics means and so can just pick up and play a new card with little trouble. As to what I mean by "internal representation"; machine learning models that are trained end-to-end and that are claimed to learn constituent concepts in the process of learning a target concept actually have concrete representations of those constituent concepts as part of their structure. For example, CNNs have internal representations of each layer of features they learn in the process of classifying an image. Without such an internal reprsentation all you have is some observed behaviour and some vague claims about understanding this or learning that, at which point you can claim anything you like.
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by tveita 8y ago

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YeGoblynQueenne 8y ago

>> In this case there isn't enough training data for that kind of resolution, but it has learned that blue cards go with blue cards, and red cards with red cards, and there's no hard lines from there to the concept of color. A card is blue (resp. red, etc) because it has a blue mana symbol in its casting cost. Not because it is found in the company of other blue cards. That is the concept of colour that a model must represent before you can say with any conviction that it "understands" the concept of colour. In terms of "hard lines"- that's the hard line you must cross. The kind of model you're talking about then would be a classifier able to label individual cards with their colours, or an end-to-end model with an internal representation of cards' charactersitics. That is not what was shown here.

tveita 8y ago
By observing what cards people tend to pick together, you can infer that certain cards have certain properties, even if you never get see the card face. A blue card is found in the company of other blue cards, because humans picked them, because of the blue mana symbol in its casting cost. With proper training, you end up with exactly the "end-to-end model with an internal representation of cards' charactersitics" Since it can't see the cards, it can't say anything useful about a card it hasn't seen during training, but if you added some new cards and started training again, a pre-trained net might learn the new cards faster than one you train from scratch. That would be evidence that the network has learnt a meaningful embedding. There is no proof that this network has done so, but I think word2vec shows that it's a feasible approach.
- YeGoblynQueenne 8y ago
  >> By observing what cards people tend to pick together, you can infer that certain cards have certain properties, even if you never get see the card face. You're assuming way too much capability that is not present. Just because a human can make this inference, it doesn't mean that a neural net can. Neural networks are notoriously incapable of inference, or anything that requires reasoning. >> There is no proof that this network has done so, but I think word2vec shows that it's a feasible approach. Word2vec (word embeddings in general) are actually a good example why this kind of thing doesn't work the way you think it does. A word embedding model represents information about the context in which tokens (words, sentences, etc) are found but it does not, in and of itself, represent the meaning of words. The only reason why we know that words it places in the general vicinity of each other have similar meaning is because we already understand meaning and we can interpret the results. But the model itself does not have anything like "understanding". It only models collocations. Same thing here. You seem pretty certain that with more data (perhaps with a deeper model) you can represent something that the model doesn't have an internal representation for. But just because the behaviour of the model partially matches the behaviour of a system that does have an internal representation for such a thing, in other words, a human, that doesn't mean that the model also behaves the way it behaves because it models the world in the same way that the human does. And you can see that very clearly if you try to use a model like the one in the article, or one trained on all the magic drafts ever, to draft a set of cards it hasn't seen before. It should be obvious that such a model would be entirely incapable of doing so. That's because it doesn't represent anything about the characteristics of cards it hasn't seen and so can't handle new cards. A human understands what the cards' characteristics means and so can just pick up and play a new card with little trouble. As to what I mean by "internal representation"; machine learning models that are trained end-to-end and that are claimed to learn constituent concepts in the process of learning a target concept actually have concrete representations of those constituent concepts as part of their structure. For example, CNNs have internal representations of each layer of features they learn in the process of classifying an image. Without such an internal reprsentation all you have is some observed behaviour and some vague claims about understanding this or learning that, at which point you can claim anything you like.

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