by Summary:
There are obvious technological applications for GQN, but it has also caught the eye of neuroscientists, who are particularly interested in the training algorithm it uses to learn how to perform its tasks. From the presented image, GQN generates predictions about what a scene should look like — where objects should be located, how shadows should fall against surfaces, which areas should be visible or hidden based on certain perspectives — and uses the differences between those predictions and its actual observations to improve the accuracy of the predictions it will make in the future. “It was the difference between reality and the prediction that enabled the updating of the model,” said Ali Eslami, one of the project’s leaders. According to Danilo Rezende, Eslami’s co-author and
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Mike Norman considers the following as important: artificial intelligence
This could be interesting, too:
There are obvious technological applications for GQN, but it has also caught the eye of neuroscientists, who are particularly interested in the training algorithm it uses to learn how to perform its tasks. From the presented image, GQN generates predictions about what a scene should look like — where objects should be located, how shadows should fall against surfaces, which areas should be visible or hidden based on certain perspectives — and uses the differences between those predictions and its actual observations to improve the accuracy of the predictions it will make in the future. “It was the difference between reality and the prediction that enabled the updating of the model,” said Ali Eslami, one of the project’s leaders. According to Danilo Rezende, Eslami’s co-author and
Topics:
Mike Norman considers the following as important: artificial intelligence
This could be interesting, too:
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There are obvious technological applications for GQN, but it has also caught the eye of neuroscientists, who are particularly interested in the training algorithm it uses to learn how to perform its tasks. From the presented image, GQN generates predictions about what a scene should look like — where objects should be located, how shadows should fall against surfaces, which areas should be visible or hidden based on certain perspectives — and uses the differences between those predictions and its actual observations to improve the accuracy of the predictions it will make in the future. “It was the difference between reality and the prediction that enabled the updating of the model,” said Ali Eslami, one of the project’s leaders.
According to Danilo Rezende, Eslami’s co-author and DeepMind colleague, “the algorithm changes the parameters of its model in such a way that next time, when it encounters the same situation, it will be less surprised.”
Neuroscientists have long suspected that a similar mechanism drives how the brain works. (Indeed, those speculations are part of what inspired the GQN team to pursue this approach.) According to this “predictive coding” theory, at each level of a cognitive process, the brain generates models, or beliefs, about what information it should be receiving from the level below it. These beliefs get translated into predictions about what should be experienced in a given situation, providing the best explanation of what’s out there so that the experience will make sense. The predictions then get sent down as feedback to lower-level sensory regions of the brain. The brain compares its predictions with the actual sensory input it receives, “explaining away” whatever differences, or prediction errors, it can by using its internal models to determine likely causes for the discrepancies.Quanta
To Make Sense of the Present, Brains May Predict the Future
Jordana Capelewicz
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Philosophically, intellectually—in every way—human society is unprepared for the rise of artificial intelligence...I think we are already seeing signs of this as we enter the digital era. Cultural and institutions were developed in the analogy era. What will be the effect of entering the digital era? No one knows. But it also seem that peering into the future will be different for analog natives and digital natives based on differences in information acquisition and processing.
"The medium is the message." — Marshall McLuhan
The phrase was introduced in McLuhan's book Understanding Media: The Extensions of Man, published in 1964. McLuhan proposes that a medium itself, not the content it carries, should be the focus of study. He said that a medium affects the society in which it plays a role not only by the content delivered over the medium, but also by the characteristics of the medium itself.In interacting with digital natives, it seems clear to me that they are operating in a different psychological-epistemological space than those who grew up in the analogy age. This observation is only anecdotal at this point, and it would be interesting to see some studies exploring this and what it may imply individually and socially.
I don't think that Kissinger is being overly alarmist on this, but that is a view that is clearly based by people that are used to framing the world in terms of their worldview. New worldviews are in the process of developing both in reaction to changing conditions but also shaping them. History is Dynamic, non-linear, dialectical and spiraling, which would be expected in complex adaptive systems characterized by reflectivity and emergence.
Kissinger's article is worthy of consideration.
The Atlantic
How the Enlightenment Ends
Henry A. Kissinger