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Jarvis was built using the suite of tools that facebook developers are constantly updating. The answer to this question is that there's no simple answer; it has a lot of moving parts. Take for example natural language processing. There are a number of sub-topics that are each considered "big" problems, such as part-of-speech recognition, ...


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James Ryan has done a lot of 'archaeological' work on this; you can find references to his work on his website. Story generation has been a dream for a long time (in computing terms), and various genres have been explored, with not that much success. There have been episodes of a Western written by a computer (and actually filmed and acted out by human ...


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The VR and computer speech aspects are entirely corollary. Adding them in would be relatively trivial in comparison to creating an algorithm that can dynamically generate stories of interest to humans. Essentially, aesthetic components not related to story structure (images, sounds, speech) are "window dressings". A story generation algorithm would have ...


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In short, repetition with feedback. You are correct that machine learning (ML) models such as neural networks work with fixed dimensions for input and output. There are a few different ways to work around this when desired input and output is more variable. The most common approaches are: Padding: Give the ML model capacity to cope with the largest expected ...


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The state of the art in text generation is the GPT model. GPT-3, which was just released in summer of 2020, has been used to generate many very impressive articles, and is widely considered the best text generation model. This article and this one should give you an example of how powerful it is at text generation. GPT is a transformer based architecture, ...


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