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So much in our lives is driven by things we cannot explain, why should AI be any different?
我们生活中有太多的事情是由我们无法解释的事物驱动的，为什么AI应该与众不同？
The mathematician John von Neumann is known to have said, “In mathematics you don’t understand things. You just get used to them.” If you have ever had the chance to study math you know exactly what he meant. So much of math is inexact and not fully explained. It just works. The set of imaginary numbers, the notion of infinity, the real number system, and the set of irrational numbers are a few examples of this. And of course, there is Pi.
众所周知，数学家约翰·冯·诺依曼(John von Neumann)说过：“在数学中，您不了解事物。 您只是习惯了。” 如果您曾经有过学习数学的机会，那么您将确切地了解他的意思。 太多的数学是不精确的，不能完全解释。 它只是工作。 虚数集，无穷大概念，实数系统和无理数集就是其中的一些示例。 当然有Pi。
Wikipedia describes Pi as a “mathematical constant” and we have all come to except that fact. But it is a constant that has no repeating pattern and never ends. Does anyone besides myself find that a little disconcerting? Pi is to the set of irrational numbers what the Beatles are to rock and roll. It is foundational. Without Pi, we would not understand angles and the relationships that exist in a circle. And if we did not understand circles and angles then we would have no comprehension of trigonometry. Without trigonometry, there is no calculus. Without calculus, we lose everything from our understanding of physics to Artificial Intelligence.
Wikipedia将Pi描述为“数学常数”，除此以外，我们都来了。 但这是一个没有重复模式且永无止境的常数。 除了我自己之外，还有人感到有些不安吗？ Pi是披头士乐队摇滚的非理性数字。 这是基础。 没有Pi，我们将无法理解圆中存在的角度和关系。 如果我们不理解圆和角，那么我们将不会理解三角学。 没有三角函数，就不会有演算。 没有微积分，我们将失去对物理的理解以及对人工智能的理解。
It could be said that our entire understanding of math is built upon a set of concepts that we have an incomplete understanding of. We have an approximation of things and that seems to be good enough.
可以说，我们对数学的全部理解是建立在一组我们不完全理解的概念的基础上的。 我们对事物有一个近似的估计，这似乎足够好。
This phenomenon is not reserved for math. There are many incomplete theories in our lives that we accept as the unshakable truth.
这种现象不是数学专用的。 我们生活中有许多不完整的理论被我们视为不可动摇的真理。
To start with there is the science of Quantum Mechanics. It is the foundation of modern physics. But have you ever seen an atom or a quark or a boson? Spoiler alert — you haven’t and nobody else has either.
首先是量子力学的科学。 它是现代物理学的基础。 但是您见过原子，夸克或玻色子吗？ 剧透警报-您没有，也没有其他人也有。
Then of course there is Evolution. Evolution is about as settled as any theory can be as far as I can tell. It gives me pause to even bring it up for fear of being cancelled as a science denier. The problem with Evolution is, it is settled until it comes to explaining things like, humans or DNA. Big gaps in my view.
当然，还有进化。 据我所知，进化论已定下来。 它使我有些停顿，甚至挂起它，以免担心被取消为科学否认者。 进化的问题是，直到解决人类或DNA之类的问题时，它才得以解决。 我认为差距很大。
How about gravity? I certainly believe in it, but does anyone have any idea what it is?
重力怎么样？ 我当然相信它，但是有人知道它是什么吗？
Would you believe me if I told you that there is no consensus on what makes planes fly?
如果我告诉您，关于使飞机飞行的原因尚未达成共识，您会相信我吗？
And of course, you know what I think about time.
当然，您知道我对时间的看法。
My question to you is, in a world full of unexplainable phenomenon that we have simply come to accept without question, why are people so adamant that Artificial Intelligence algorithms be explainable?
我对您的问题是，在一个充满无法解释的现象的世界中，我们已经毫无疑问地接受了这个问题，为什么人们如此固执，以至于人工智能算法是可以解释的？
规则，规则，规则 (Rules, Rules, Rules)
“Know the rules well, so you can break them effectively.”
“很好地了解规则，因此您可以有效地打破它们。”
-Dalai Lama
-达赖喇嘛
Humans like rules. They do not like to be ruled, but having a set of rules they can follow makes humans happy in general. Even people who “don’t live by the rules” are living by a rule.
人类喜欢规则。 他们不喜欢被统治，但是拥有一套可以遵循的规则可以使人们普遍感到高兴。 即使是“不遵守规则”的人也遵守规则。
For most of its history, computer science has been driven by programs that encode rules into computers to perform some task or function. Structures such as logical operators, if-then statements, loops, and arrays have become the masters of programming logic and enforce the rules that run the world. Programmers look to create neatly structured data operated on by neatly structured code.
在其大部分历史中，计算机科学一直由将规则编码到计算机中以执行某些任务或功能的程序所驱动。 诸如逻辑运算符，if-then语句，循环和数组之类的结构已成为编程逻辑的主体，并执行运行世界的规则。 程序员希望创建由整齐的代码操作的整齐的数据。
Things are exact. Nothing is approximated. Everything is accounted for in the code or it throws an exception. And that seems natural. It feels right.
事情是准确的。 没有什么是近似的。 一切都在代码中说明，否则会引发异常。 这似乎很自然。 感觉不错。
In reality, it is unnatural.
实际上，这是不自然的。
人工智能更符合自然 (AI Is More in Line with Nature)
As I described above, the natural world is full of theories that are just approximations of what is happening. Our model of the world fits the world we observe and that is satisfies us. In the same way, Artificial Intelligence is built upon the idea of making approximations from data. As a result, the AI approach is a lot more robust than the rules based approach.
如上所述，自然世界充满了各种理论，这些理论只是对正在发生的事情的近似。 我们的世界模型适合我们所观察到的世界，这使我们满意。 同样，人工智能也基于从数据进行近似的想法。 结果，AI方法比基于规则的方法更加健壮。
However, as it turns out, this same approach is the reason why we cannot explain why the AI algorithm reached its conclusion. An algorithm based upon rules will always follow the same path to the answer (or return an error) and you can tell by looking at the code what that path will be. An Artificial Intelligence algorithm will follow a random path to the answer and there is no easy way of knowing what that path was. With an AI algorithm, we will not know the answer until the model is trained and we give it some input. A rules-based approach attempts to program all possible answers into the code ahead of time.
但是，事实证明，这种方法也是我们无法解释AI算法得出其结论的原因。 基于规则的算法将始终遵循相同的答案路径(或返回错误)，您可以通过查看代码来确定该路径是什么。 人工智能算法将遵循随机的答案路径，没有简单的方法可以知道答案是什么。 使用AI算法，直到训练模型并提供一些输入后，我们才能知道答案。 基于规则的方法试图将所有可能的答案提前编程到代码中。
To understand this better let us consider the following example. Suppose we wanted to write a computer program to determine the appropriate price for a house in a particular neighborhood. We could collect data on prices for different houses with variables that describe the size, style, and condition of each house. We could then write a computer program that used rules to determine the appropriate price of a new house on the market based upon those features. In simple terms a rule might look like the sentence below.
为了更好地理解这一点，让我们考虑以下示例。 假设我们想编写一个计算机程序来确定特定邻里房屋的合适价格。 我们可以使用描述每个房屋的大小，样式和状况的变量来收集不同房屋的价格数据。 然后，我们可以编写一个计算机程序，该程序使用规则根据这些功能确定市场上新房子的合适价格。 简单来说，规则可能看起来像下面的句子。
if style = “colonial” and size = “medium” and condition = “fair” then price = 450,000
如果样式=“殖民地”，尺寸=“中等”，条件=“中等”，则价格= 450,000
This is somewhat simplistic but it helps illustrates the point. The problem with this approach is the code will never keep up with the data. As the data gets more complex, the rules must change to keep up with it. The computer program will grow longer and more complex. And that is exactly what we have spent the last 50 years doing — creating longer and more complex programs that are impossible to maintain and nobody understands.
这有些简单化，但有助于说明这一点。 这种方法的问题是代码将永远无法跟上数据。 随着数据变得越来越复杂，必须更改规则以跟上它。 计算机程序将变得更长，更复杂。 而这正是我们过去50年以来一直在做的事情–创建更长且更复杂的程序，这些程序无法维护并且没人能理解。
AI takes the complete opposite approach. The code does not grow as the problem grows. Rather than try to create a function that can fit the view of the world based upon the data, AI uses the data to discover a function that describes the world that already exists. To change the function, you do not change the code, you simply give the algorithm more data.
人工智能采取完全相反的方法。 随着问题的增长，代码不会增长。 AI不会尝试根据数据创建适合世界视图的功能，而是使用数据来发现描述已经存在的世界的功能。 要更改功能，您无需更改代码，只需为算法提供更多数据即可。
As a result, AI can understand relationships that are infinitely more complex than a structured program could ever hope to and it will adapt to changes without changing the algorithm or the code.
结果，AI可以理解比结构化程序所希望的关系更加复杂的关系，并且它可以适应变化而无需更改算法或代码。
Which approach seems more natural now?
现在哪种方法看起来更自然？
人工智能是民主的 (AI Is Democratic)
Let me leave you on a hopeful note. The algorithms that we use in Artificial Intelligence are not closed or hidden or created by some nameless entity. It is true that people have novel and proprietary implementations of algorithms. However, anyone can implement the math behind them, and that math has been around for hundreds if not thousands of years in some cases. Furthermore, much of the innovation in Artificial Intelligence comes out of research institutions and universities and are published works we can all use. The bottom line is AI is democratic.
让我留下希望的音符。 我们在人工智能中使用的算法不是由某个无名实体关闭，隐藏或创建的。 人们确实拥有新颖而专有的算法实现。 但是，任何人都可以实施其背后的数学，在某些情况下，这种数学已经存在了数百年甚至数千年。 此外，人工智能的许多创新都来自研究机构和大学，并且都是我们都可以使用的已发表作品。 最重要的是AI是民主的。
So, sit back, relax, and know that all will be well.
所以，坐下来，放松一下，知道一切都会好起来的。
翻译自: https://medium.com/swlh/ai-takes-you-out-of-your-comfort-zone-deal-with-it-ebf9cad71b41
ai中画板脱离绘图区域