Yeah, it doesn't appear to be very advanced. I tried:
I have two mangoes, Jill has three mangoes. Alan has
three oranges, and I have two apples and a carrot. How
many mangoes do we have? How many fruits do we all have?
How long can we survive?
One would think it did some basic parsing, and managed to file away three oranges and two apples in a parse tree of some kind -- and if those were associated with "fruits" -- it should be able to answer?
And if it were able to answer, one might be able to have it hand out advice on diets ("Give me 30 examples of a 3000 calorie diet featuring no red meat") and a lot of other things that would be "easy" to answer based on ingesting some pretty standard databases.
I see a couple issues with handling your "30 examples of 3000 calorie diet with no red meat" query. One is the complexity of finding a subset of all the food items in the Wolfram Alpha database that add up to around 3000, you could certainly make an algorithm to do this but all queries time out after a few seconds so it is likely to fail. The other is that it would have to understand which foods constitute red meat, which it doesn't (try searching "red meat food", the only red meat it knows is a movie by that name). Even if it did, you would need to categorize all foods so the query worked with "3000 calorie diet with no vegetables" and so on. Obviously if it can solve word problems about apples but can't solve the same problems about mangoes, computation like this is far beyond what they will accomplish in many years.
Oh, yes, that might have been the "long way 'round" of doing it. I was alluding at crawling a recipe database (with calories per meal), and filtering out those with red meat, then doing a random selection. Not very hard at all.
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Yeah, it doesn't appear to be very advanced. I tried:
One would think it did some basic parsing, and managed to file away three oranges and two apples in a parse tree of some kind -- and if those were associated with "fruits" -- it should be able to answer?And if it were able to answer, one might be able to have it hand out advice on diets ("Give me 30 examples of a 3000 calorie diet featuring no red meat") and a lot of other things that would be "easy" to answer based on ingesting some pretty standard databases.
I see a couple issues with handling your "30 examples of 3000 calorie diet with no red meat" query. One is the complexity of finding a subset of all the food items in the Wolfram Alpha database that add up to around 3000, you could certainly make an algorithm to do this but all queries time out after a few seconds so it is likely to fail. The other is that it would have to understand which foods constitute red meat, which it doesn't (try searching "red meat food", the only red meat it knows is a movie by that name). Even if it did, you would need to categorize all foods so the query worked with "3000 calorie diet with no vegetables" and so on. Obviously if it can solve word problems about apples but can't solve the same problems about mangoes, computation like this is far beyond what they will accomplish in many years.
Oh, yes, that might have been the "long way 'round" of doing it. I was alluding at crawling a recipe database (with calories per meal), and filtering out those with red meat, then doing a random selection. Not very hard at all.