Alternate World Algorithm
If we had the time and patience to teach an agent grounded knowledge , knowledge from experience and historical context of concepts, we would have more success in giving it a model of language to work with. We could create a hypothetical world like our own as a simulation, and in this environment it learns how to use language as tools to achieve goals within it. E.g. there would be characters that teach the agent how to relate to objects by naming them and by the agent observing how our language and activities we do when using it correlate.
So one character would be seen to pick up a cup when another character says "please pick up that cup" then it correlates the two and realizes what those words mean because of all the correlations it observes. There are problems with this , namely that the characters training the agent are agents in their own right and need that training first also. i propose another solution.
We create a hypothetical world that is nothing like our real world and place the agent in it. Then the agent explores this world and develops discrete behavior of the sort that would cause it to label each action with a different label that it comes up with by itself , while experiencing this world. So we wait until it has developed enough concepts to do with this world. Once that is achieved we replace each of these concepts with a word from natural language like say English. Now the agent has grounded knowledge of some sort that uses English words to "describe" it. But of course since we arbitrarily assigned the English words to its concepts, a series of actions that this agent performs in its world would produce a sequence of unrelated words.
To fix this we use gradient decent to alter the hypothetical world that this agent lives in, till there is a correlation between how it operates in this world and how we use language in our world.Despite the fact that the worlds are totally different. E.g. when the agent performs actions that correlate to "the cup is red"...that phrase will have a totally different meaning to it (as a permutation of actions) but we would have arranged the happy coincident that the agent will be communicating information to us.
So one character would be seen to pick up a cup when another character says "please pick up that cup" then it correlates the two and realizes what those words mean because of all the correlations it observes. There are problems with this , namely that the characters training the agent are agents in their own right and need that training first also. i propose another solution.
We create a hypothetical world that is nothing like our real world and place the agent in it. Then the agent explores this world and develops discrete behavior of the sort that would cause it to label each action with a different label that it comes up with by itself , while experiencing this world. So we wait until it has developed enough concepts to do with this world. Once that is achieved we replace each of these concepts with a word from natural language like say English. Now the agent has grounded knowledge of some sort that uses English words to "describe" it. But of course since we arbitrarily assigned the English words to its concepts, a series of actions that this agent performs in its world would produce a sequence of unrelated words.
To fix this we use gradient decent to alter the hypothetical world that this agent lives in, till there is a correlation between how it operates in this world and how we use language in our world.Despite the fact that the worlds are totally different. E.g. when the agent performs actions that correlate to "the cup is red"...that phrase will have a totally different meaning to it (as a permutation of actions) but we would have arranged the happy coincident that the agent will be communicating information to us.
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