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welcome back everyone to this second video of gato where were going to talk more about the details of how we encode information so in the last video we went over the structure of gato and we looked at for example all of these different tasks that were able to perform quite well on with the architecture and one of the questions that came up was how do we actually encode information from a variety of sources to make them compatible with the model so for example were using uh text parsing here so we have a task for gato to maintain a dialogue and we need some way of interpreting that data but similarly we also have for example Performance on Atari games up here where you need to look at an image and from that image come up with some suggested actions so you have very very informative data and you need some way of interpreting this we also looked at the overall structure of gato and the way we encode data which you can see down here in a sequence of both observations and actions then we