Part 2: The dawn of a new species

第2部分：新物种的黎明

介绍(Introduction)

Technology was at first only for specialists. As it evolved, it is now used universally by anyone.

首先，技术只是针对专家。 随着它的发展，现在任何人都普遍使用它。

Scientists on the other hand started as generalists and ended up as specialists. These opposite directions is because human and artificial intelligence are dealing differently with complexity. In this blog we explore how AI systems already are good as specialist experts, but may become generalists as well.

另一方面，科学家起初是通才，后来又变成了专家。 这些相反的方向是因为人类和人工智能处理复杂性的方式有所不同。 在此博客中，我们探讨了人工智能系统如何既可以作为专家，又可以成为通才。

通用人 (Homo universalis)

The renaissance age brought some of the brightest scientists of all time. The so-called renaissance men developed his abilities in all kinds of areas like art, philosophy, religion and science. Consider the following thinkers:

文艺复兴时期带来了一些有史以来最聪明的科学家。 所谓的文艺复兴时期的人在艺术，哲学，宗教和科学等各个领域发展了自己的能力。 考虑以下思想家：

• Copernicus哥白尼
• Erasmus伊拉斯mus
• Leonardo da Vinci达芬奇(Leonardo da Vinci)

Copernicus is especially known as astronomer who placed the sun rather than the earth in the center of the universe. However he was also educated at canonical church law, worked on economics principles (such as the quantity of money) and worked as governor and diplomat for the Polish king.

C opernicus被称为天文学家，他将太阳而不是地球置于宇宙的中心。 但是，他还曾受过教会经典法的教育，从事经济学原理(例如货币数量)的工作，并曾担任波兰国王的州长和外交官。

Erasmus also known as the “Prince of the Humanists” worked as translator (from Greek to Latin), on philosophic topics (like Free Will), on religious topics (Protestantism), but also wrote about pedagogy (On Civility in Children).

伊拉斯mus斯( E rasmus)也被称为“人文主义者的王子”，曾在哲学主题(如自由意志)，宗教主题(新教)方面担任过翻译(从希腊语到拉丁语)，但也撰写了关于教育学的文章(《儿童文明》 )。

Leonardo da Vinci wasn’t just a painter. He did research on anatomy and physiology and as inventor draw all kinds of technical drawings.

大号eonardo达·芬奇不仅是一名画家。 他从事解剖学和生理学研究，并作为发明家绘制了各种技术图纸。

These renaissance men are today more seen as thinkers than as rigorous scientists. From the Renaissance to the 21st century more and more knowledge was necessary to made any progress in science. So generalists became specialists. It’s impossible now to excel as astronomer while also work as an economists and a painter.

今天，这些文艺复兴时期的人们被视为思想家，而不是严格的科学家。 从文艺复兴时期到21世纪，越来越多的知识对于科学的任何进步都是必需的。 因此通才成为专家。 现在不可能以天文学家的身份出类拔萃，同时又要担任经济学家和画家。

在AI上 (On AI)

One of these modern experts specializes in the artificial intelligence field. These experts are creating specialized algorithms who excel in a specific domain. Examples are:

这些现代专家之一专门研究人工智能领域。 这些专家正在创建在特定领域出类拔萃的专业算法。 例如：

• Translation翻译
• Autonomous Driving自动驾驶
• Contextual Code Completion上下文代码完成
• Fraud detection欺诈识别
• Stock market patterns recognition股市模式识别
• Object recognition物体识别
• Analysis of medical images医学图像分析

These are all real world usages of AI where AI assists human intelligence in a specialized field. Are these AI not experts themselves? This a debate among AI scientists and philosophers. Are these narrow-interest AI more tools or is it a form of intelligence?

这些都是AI在现实世界中的用法，其中AI在特定领域辅助人类智能。 这些AI本身不是专家吗？ 这是AI科学家和哲学家之间的辩论。 这些狭narrow的AI是更多工具还是智能形式？

In the examples on AI every use case uses a different approach. This may lead to expert knowledge, but not to wisdom. AI delivers already results that surpass human ability. Much like a car drives faster than we can run and a calculator can solve a math problem faster than we can calculate. This is not something we would call general intelligence.

在关于AI的示例中，每个用例都使用不同的方法。 这可能会导致专家知识，但不会导致智慧。 人工智能已经提供了超越人类能力的结果。 就像汽车的行驶速度快于我们的运行速度，而计算器可以解决的数学问题比我们的计算速度快。 这不是我们所说的一般情报。

Maybe we should not attach intelligence in human sense too much to AI. It’s not general intelligence, but an intelligent generalist we seek. To become a generalist AI needs to calculate, but also translate a sentence to Chinese, play a game of Chess, drive a car and write this blog.

也许我们不应该过多地将人类的智慧附加到AI上。 这不是一般的情报，而是我们寻求的聪明的综合主义者。 要成为多才多艺的AI，需要进行计算，还要将句子翻译成中文，玩国际象棋，开车，并撰写此博客。

To reach this level a combination of different AI approaches is needed. But before we explore such a combination of advanced algorithms we go back to the most basic software: command line programs.

为了达到这一水平，需要结合使用不同的AI方法。 但是，在探索这种高级算法的组合之前，我们先回到最基本的软件：命令行程序。

输入输出 (Input/Output)

In the history of computing, the first operating systems tasks were performed by command line tools with text input and text output, for example the program: “cp”. A program to copy files or directories. You can use it on the command line like this: “cp a b” (copy files from a to b).

在计算历史上，第一个操作系统任务是由带有文本输入和文本输出的命令行工具执行的，例如程序：“ cp”。 复制文件或目录的程序。 您可以在命令行上使用它，如下所示：“ cp ab”(将文件从a复制到b)。

There are a lot of possible use cases. For example a destination already exist or only a subset of all files needs to be copied. This can be controlled by parameters which make a simple program more versatile:

有很多可能的用例。 例如，目的地已经存在，或者仅需要复制所有文件的子集。 这可以通过使简单程序更通用的参数来控制：

An operation system has of course a lot more tasks than copying files. Think of deleting, creating, searching files or all kinds of other tasks. For this the GNU command line toolset was created. This consist of more than 100 utilities with dozens of parameters.

操作系统当然比复制文件具有更多的任务。 考虑删除，创建，搜索文件或其他各种任务。 为此，创建了GNU命令行工具集。 它由100多个具有数十个参数的实用程序组成。

Of course there are thousands of other programs for specialized tasks. Old-skool system engineers know a lot of programs and its parameters by heart. They created scripts and cheat sheets to control them. When new to the command line the number of tasks and how to perform them can be daunting.

当然，还有成千上万的其他程序可以执行专门的任务。 老派的系统工程师非常了解很多程序及其参数。 他们创建了脚本和备忘单来控制它们。 如果不是命令行新手，那么任务的数量及其执行方式可能会令人望而生畏。

IBM recently brought AI to command line. With their program users can explore the man pages. These are manuals with explanations of all the parameters. This is kind of an über program which can make use of hundreds of programs and thousand of parameters. Still this is quite domain specific. If we want to do more tasks with flexible input we need to fill the AI with lots more data than manuals. This data is often labeled so that the program can use millions of parameters.

IBM最近将AI引入了命令行。 用户可以使用他们的程序浏览手册页。 这些是手册，其中包含所有参数的说明。 这是一个über程序，可以使用数百个程序和数千个参数。 但这仍然是特定领域的。 如果我们想通过灵活的输入执行更多任务，我们需要给AI填充比手册更多的数据。 该数据通常带有标签，以便程序可以使用数百万个参数。

震惊世界 (Shocking the world)

Currently, there is program which is fed with an enormous amount of unlabeled and unstructured data from websites and books. The program: GPT-3. This AI uses several machine learning techniques to create all parameters itself. The result is a program with 175 Billion parameters. To use it, you don’t need to follow a manual, but just ask it to perform a task. This AI shocked the IT world.

当前，有一种程序可以从网站和书籍中获取大量的未标记和非结构化数据。 程序：GPT-3。 该AI使用多种机器学习技术自行创建所有参数。 结果是一个带有1750亿个参数的程序。 要使用它，您不需要遵循手册，而只是要求它执行任务。 这种AI震惊了IT世界。

演示地址

已经震惊了(Already shocked)

Let’s consider another program which shocked the world, AlphaGo by Deepmind (Google). This is not a program about text input and text output, but one that plays the game Go. Because the almost infinite number of moves a player can make in this game, it was impossible to compute all the moves. AlphaGo developed a strategy from a finite number of steps to defeat every human player.

让我们考虑一下另一个使世界震惊的程序， Deepmind的AlphaGo(Google)。 这不是一个有关文本输入和文本输出的程序，而是一个玩游戏Go的程序。 由于玩家可以在此游戏中进行的动作几乎是无限的，因此无法计算所有动作。 AlphaGo通过有限的步骤制定了战胜每个人类玩家的战略。

The AI researchers developed AlphaGo further in its successor AlphaZero which was more powerful and could play Chess and Shogi as well. This approach is fundamentally different from that of GPT-3 in that is starts from scratch and with the rules of the game uses reinforcement learning to become expert level. The AI plays millions of times against itself, before competing with a human.

AI研究人员在其继任者AlphaZero中进一步开发了AlphaGo，后者更强大，并且可以玩国际象棋和将棋。 这种方法与GPT-3的方法从根本上不同，因为它是从头开始的，并且随着游戏规则的运用，强化学习已成为专家级别。 在与人类竞争之前，AI对自己进行了数百万次的对抗。

演示地址

Deepmind (AlphaZero) and OpenAI (GPT-3) have thus fundamental different ways of approaching AI. Whereas Alpha Zero can learn by reinforcement learning (playing against itself) with only from the rules and goal of game as start, GPT-3 based on the input of millions of texts which labels itself to produce all kinds of output texts.

因此，Deepmind(AlphaZero)和OpenAI(GPT-3)具有与AI根本不同的方式。 Alpha Zero只能通过以游戏的规则和目标作为开始进行强化学习(对战)来学习，而GPT-3则基于数以百万计的文本输入来进行标记，以产生各种输出文本。

Both approaches have no real world knowledge so they don’t have a kind of common sense. This is troublesome to move these approaches forward.

两种方法都没有现实世界的知识，因此它们没有常识。 将这些方法向前推进很麻烦。

Now there is yet another approach which is called COMET. This is a meta model approach which allow a common sense way of reasoning as explained in the following article

现在还有另一种方法，称为COMET 。 这是一种元模型方法，允许使用常识推理方法，如以下文章中所述

结合AI方法(Combining AI approaches)

All approaches are still more fundamental research endeavors than the real world examples we earlier gave. Still they are key for making long-term progress. AlphaZero, GPT-3 and COMET all are parts of the puzzle for general artificial intelligence. The combination of these approaches would create a much stronger AI. A combination of task, goal and reasoning orientated ways of coping with the world.

与我们之前给出的实际示例相比，所有方法仍是更基础的研究工作。 它们仍然是取得长期进展的关键。 AlphaZero，GPT-3和COMET都是通用人工智能难题的一部分。 这些方法的组合将创建一个更强大的AI。 以任务，目标和推理为导向的应对方式相结合。

So instead of experts in their field, combined AI functions more like a network of experts. Much like human scientists cooperate through universities.

因此，合并的AI功能代替了自己领域的专家，更像是专家网络。 就像人类科学家通过大学合作一样。

Homo universalis became a specialist which created AI experts that becoming AI universalis. This means we may get advice in a way Coopernicus, Erasmus or Leanardo da Vinci would give us. As a specialist it’s namely hard to find cross-links between your own subject area and other disciplines. These links are extremely valuable. It takes mostly a certain of brilliancy to connect the dots in new ways. So we may have a lot of knowledge and insight on a particular subject, but this doesn’t mean we can find links to other fields, as for the simple reason that we don’t have deep knowledge in these fields.

通用智人成为专家，创造了成为通用人工智能的AI专家。 这意味着我们可能会以哥白尼，伊拉斯mus或达·芬奇的方式给我们提供建议。 作为专家，很难在您自己的学科领域和其他学科之间找到交叉链接。 这些链接非常有价值。 以新的方式将点连接起来通常需要一定的才能。 因此，我们可能对特定主题有很多知识和洞察力，但这并不意味着我们可以找到其他领域的链接，原因很简单，因为我们对这些领域没有深入的了解。

AI is good in taking objects, ideas, methods from totally different fields or unrelated subjects and applies this to all other kinds of use cases and situations. MIT used AI for example to discover links between works of art.

人工智能擅长从完全不同的领域或无关主题中获取对象，思想，方法，并将其应用于所有其他类型的用例和情况。 例如，麻省理工学院使用AI来发现艺术品之间的联系。

Here is one of the results:

这是结果之一：

In the current state AI is not capable to give meaning to such links, but it can work as a new kind of recommendation system. Like YouTube or NetFlix recommend a video, these AI systems can recommend a scientific link. Researcher can investigate unexpected links leading to whole new discoveries. In the future scientists and AI working together can be both specialist as generalist.

在当前状态下，AI无法赋予此类链接以含义，但是它可以作为一种新型的推荐系统。 就像YouTube或NetFlix推荐视频一样，这些AI系统也可以推荐科学链接。 研究人员可以调查导致整个新发现的意外链接。 将来，科学家和AI可以一起作为通才专家。