11–20 of 107 posts
With the definition of curiosity from the article, it’s not that surprising? A dynamic “screen” is always more interesting than the static map. Definition: The definition that OpenAI team used for artificial curiosity was relatively simple: The algorithm would try to predict what its environment would look like one frame into the future. When that next frame happened, the algorithm would be rewarded by how wrong it w…
With the definition of curiosity from the article, it’s not that surprising? A dynamic “screen” is always more interesting than the static map. Definition: The definition that OpenAI team used for artificial curiosity was relatively simple: The algorithm would try to predict what its environment would look like one frame into the future. When that next frame happened, the algorithm would be rewarded by how wrong it w…
> OpenAI researcher Harri Edwards tells Quartz that the idea for letting the AI agent flip through channels came from a thought experiment called the noisy-TV problem. The static on a TV is immensely random, so a curious AI agent could never truly predict what would happen next, and get drawn into watching the TV forever. In the real world, you could think of it as something completely random, like the way light shimmers off a waterfall.
The headline is really just inappropriate anthropomorphization.
With the definition of curiosity from the article, it’s not that surprising? A dynamic “screen” is always more interesting than the static map. Definition: The definition that OpenAI team used for artificial curiosity was relatively simple: The algorithm would try to predict what its environment would look like one frame into the future. When that next frame happened, the algorithm would be rewarded by how wrong it w…
/s
Using prediction error as the definition of curiosity rings hollow for me. Curiosity in my mind is more about mapping out an unexplored thing and not about being surprised.
Literally getting dopamine rewards for seeing something new is what keeps people glued to tik tok feeds and twitter.
I tend to get bored halfway through a book if it is predictable.
With the definition of curiosity from the article, it’s not that surprising? A dynamic “screen” is always more interesting than the static map. Definition: The definition that OpenAI team used for artificial curiosity was relatively simple: The algorithm would try to predict what its environment would look like one frame into the future. When that next frame happened, the algorithm would be rewarded by how wrong it w…
I don't know the details, but probably you would want to seek unpredictability in a higher level representation of the observed state. White noise is highly unpredictable per pixel, but will get a very predictable representation after a layer or two of featurization if the features are trained/designed for real world observations.
With the definition of curiosity from the article, it’s not that surprising? A dynamic “screen” is always more interesting than the static map. Definition: The definition that OpenAI team used for artificial curiosity was relatively simple: The algorithm would try to predict what its environment would look like one frame into the future. When that next frame happened, the algorithm would be rewarded by how wrong it w…
With the definition of curiosity from the article, it’s not that surprising? A dynamic “screen” is always more interesting than the static map. Definition: The definition that OpenAI team used for artificial curiosity was relatively simple: The algorithm would try to predict what its environment would look like one frame into the future. When that next frame happened, the algorithm would be rewarded by how wrong it w…
It is a fun thought experiment - how do our brains systems manage to reward seeking new information without getting trapped by simplistic pseudo-RNG patterns in nature
Link to the actual paper. https://pathak22.github.io/large-scale-curiosity/ I was curious how they define or reward curiosity, it says it right here: Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intr…