原文链接：[AlphaGo Zero: Learning from scratch](https://deepmind.com/blog/alphago-zero-learning-scratch/?from=hackcv&hmsr=hackcv.com&utm_medium=hackcv.com&utm_source=hackcv.com)
Artificial intelligence research has made rapid progress in a wide variety of domains from speech recognition and image classification to genomics and drug discovery. In many cases, these are specialist systems that leverage enormous amounts of human expertise and data.**
However, for some problems this human knowledge may be too expensive, too unreliable or simply unavailable. As a result, a long-standing ambition of AI research is to bypass this step, creating algorithms that achieve superhuman performance in the most challenging domains with no human input. In our most recent [paper](http://nature.com/articles/doi:10.1038/nature24270), published in the [journal Nature](https://www.nature.com/), we demonstrate a significant step towards this goal.
@@ -14,6 +12,8 @@ However, for some problems this human knowledge may be too expensive, too unreli
The paper introduces AlphaGo Zero, the latest evolution of [AlphaGo](https://deepmind.com/research/alphago/), the first computer program to defeat a world champion at the ancient Chinese game of Go. Zero is even more powerful and is arguably the strongest Go player in history.
Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the [previously published](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html?foxtrotcallback=true) champion-defeating version of AlphaGo by 100 games to 0.
原文链接：[Perceptually uniform color spaces](https://programmingdesignsystems.com/color/perceptually-uniform-color-spaces/?from=hackcv&hmsr=hackcv.com&utm_medium=hackcv.com&utm_source=hackcv.com)
> “In visual perception a color is almost never seen as it really is - as it physically is. This fact makes color the most relative medium in art.”
If you rounded up a group of graphic designers and asked them to define the concept of perceptually uniform color spaces, there is a good chance that none of them would know what to say. On the surface, perceptual uniformity is somewhat easy to explain: These color spaces are human-friendly alternatives to color spaces such as sRGB, and they are incredibly helpful for designers working in code. Despite of this, they can feel daunting to use in programmatic designs. Perceptually uniform color spaces have roots in scientific color theory, and this community does little to make them accessible to a larger audience. In this chapter, we will look at the concept of perceptually uniform color spaces, and answer some common questions related to them: What are they? Why do we need them? How can we use them in code?
如果你围捕了一组图形设计师并要求他们定义感知统一色彩空间的概念,t那么很可能他们都不知道该说些什么。 On the surface, perceptual uniformity is somewhat easy to explain: These color spaces are human-friendly alternatives to color spaces such as sRGB, and they are incredibly helpful for designers working in code. Despite of this, they can feel daunting to use in programmatic designs. Perceptually uniform color spaces have roots in scientific color theory, and this community does little to make them accessible to a larger audience. In this chapter, we will look at the concept of perceptually uniform color spaces, and answer some common questions related to them: What are they? Why do we need them? How can we use them in code?
