"...we’ve seen that modern Asians, like the ancient Chinese, view the world in holistic terms: They see a great deal of the field, especially background events; they are skilled in observing relationships between events; they regard the world as complex and highly changeable and its components as interrelated; they see events as moving in cycles between extremes; and they feel that control over events requires coordination with others. Modern Westerners, like the ancient Greeks, see the world in analytic, atomistic terms; they see objects as discrete and separate from their environments; they see events as moving in linear fashion when they move at all; and they feel themselves to be personally in control of events even when they are not. Not only are worldviews different in a conceptual way, but also the world is literally viewed in different ways. Asians see the big picture and they see objects in relation to their environments—so much so that it can be difficult for them to visually separate objects from their environments. Westerners focus on objects while slighting the field and they literally see fewer objects and relationships in the environment than do Asians. "
I won't say it's totally about cultural difference. Many asians are reductionists, while many westerners are holistic thinking people.
First, I want to programmatically control what I see on some websites. For example, facebook, linkedIn, or more websites are too distracting, so I'd like to directly fetch some information from them with my signed-in accounts.
It seems that the discriminator tests if the output of the generator obeys the pattern of a language. Isn't it still possible that after training the generator outputs the text wrong for the input? On the other words, the output is understandable but actually not what the sound indicate?
Actually supervised learning is "learning missing data dimension" by parameter tuning via associative learning rules.
Gradient descents are a special case of associative learning rules assuming all data points the same importance.
A type of associative learning rules is Hebbian learning rule.
In the very fundamental we only need associative learning. Of course, for practical application we need diverse tools with different conceptual frameworks to choose for commercial: human resource or cost performance.
There would be other issues like how much we can change our sleeping hours and circadian. The article mentioned it's crazy to say everybody has to be the same height, but nutrition and exercise can influence how tall a children is going to be. Besides our heights, from weight to intellectual performance it's always interaction between nature and nurture.
Similarly, we shouldn't consider gene as an excuse against trying to be productive given fewer sleep hours. On the other hand, it could be better if we don't have to force ourselves or anyone to sleep fewer even if someday there would be a scientific way to do so.
Any single word recommendation for "UI component that can have some of the multimodality?"
Thanks.