Dr. Lance Eliot, AI Insider

AI Insider博士Lance Eliot博士

[Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/ and his podcast: http://ai-selfdriving-cars.libsyn.com/website

[编辑 注意：如果读者对Eliot博士正在进行的有关自动驾驶汽车问世的业务分析感兴趣，请参阅他的在线《福布斯》专栏： https ： //forbes.com/sites/lanceeliot/ 和他的播客： http : //ai-selfdriving-cars.libsyn.com/website

Time to set the record straight about Strong AI versus Weak AI.

是时候就强AI与弱AI打破记录了。

Let’s consider what is being incorrectly stated and then make our way to the correct understanding about the matter.

让我们考虑一下错误陈述的内容，然后对问题进行正确的理解。

Some speak of weak AI as though it is AI that is wimpy and not up to the same capabilities as strong AI, including that weak AI is decidedly slower, or much less optimized, or otherwise inevitably and unarguably feebler in its AI capacities. That’s not it.

有人说弱AI好像是弱小的AI，没有达到与强AI相同的功能，包括弱AI的速度肯定慢得多，或者优化程度差得多，或者在AI能力方面不可避免且毫无争议地显得脆弱。 不是这个。

Another form of distortion is to use “narrow” AI, which generally refers to AI that will only work in a narrowly-defined domain such as in a specific medical use or in a particular financial analysis use, and equate it with weak AI, while presumably strong AI is broader and more all-encompassing. That’s not it either.

失真的另一种形式是使用“狭窄”的AI，通常是指仅在狭窄的领域(例如在特定医疗用途或特定财务分析用途中)工作的AI，并将其等同于弱AI，而大概强大的AI会更广泛，更包罗万象。 也不是。

To understand what Strong AI and Weak AI are all about, we need to briefly turn back the clock to when the debate about such aspects was at a feverish pitch. Hark back to an earlier era of AI, around the late 1970s and early 1980s, a period of time that was characterized as the first era of AI flourishing, which you might know as a time when Knowledge-Based Systems (KBS) and Expert Systems (ES) were popular.

要了解强AI和弱AI的含义，我们需要简短地回顾一下关于此类方面的争论时。 回溯到1970年代末和1980年代初的AI早期时代，这段时期被描述为AI蓬勃发展的第一个时代，您可能将其称为基于知识的系统(KBS)和Expert Systems的时代。 (ES)很受欢迎。

The latest era, today, which some consider the second era of AI flourishing, seems to have become known as the time of Machine Learning (ML) and Deep Learning (DL).

今天的最新时代，被某些人认为是AI蓬勃发展的第二个时代，似乎已经被称为机器学习(ML)和深度学习(DL)的时代。

Using a season-oriented metaphor, the current era is depicted as the AI Spring, while the period between the first era and this now existent second era has been called the AI Winter (doing so to suggest that things were either dormant or slowed-down like how a winter season can clamp down via snow and other dampening weather conditions).

使用以季节为导向的隐喻，当前时代被描述为AI Spring，而第一个时代到现在已经存在的第二个时代之间的时期被称为AI Winter(这样做是为了表明事情要么处于Hibernate状态要么被放慢了速度)例如冬季如何通过降雪和其他潮湿天气来缓解天气)。

The first era consisted of quite a bit of hand wringing about whether AI was going to become sentient and if so, how would we get there.

第一个时代包括相当多的人为AI是否会变得有情而绞尽脑汁，如果是的话，我们将如何实现。

Even during this second era, there are still similar discussions and debates taking place now, though the first era really seemed to fully take the matter in-hand and slews of philosophers joined onto the AI bandwagon as to what the future might hold and how AI could be or might not become truly intelligent.

即使在第二个时代，尽管第一个时代似乎真的已经完全掌握了这个问题，而且哲学家的许多人加入了AI潮流，但关于未来的前景以及AI的方式如何，现在仍在进行类似的讨论和辩论。可能会也可能不会真正变得聪明。

Into that fray came the birth of the monikers of weak AI and strong AI.

弱小的AI和强大的AI的绰号诞生了。

Most would agree that the verbiage originated or at least was solidified in a paper by philosopher John Searle entitled “Minds, Brains, And Programs” (see link: http://cogprints.org/7150/1/10.1.1.83.5248.pdf).

大多数人都同意，这种动词起源于或至少在哲学家约翰·塞尔(John Searle)的一篇题为《心灵，大脑和程序》的论文中得到了巩固(参见链接： http : //cogprints.org/7150/1/10.1.1.83.5248。 pdf )。

What was the weak AI and what was the strong AI?

弱AI是什么，强AI是什么？

They are philosophical differences about how AI might ultimately be achieved, assuming that you agree as to what it means to achieve AI (more on this in a moment).

它们是关于如何最终实现AI的哲学差异，假设您同意实现AI的含义(稍后对此进行更多介绍)。

Let’s see what Searle said about defining the terminology of weak AI: “According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion.”

让我们来看看Searle关于定义弱AI的术语的看法：“根据弱AI，计算机在研究思维方面的主要价值在于它为我们提供了非常强大的工具。 例如，它使我们能够以更加严格和精确的方式来制定和检验假设。”

And, furthermore, he indicated this about strong AI: “But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states.”

而且，他还谈到了关于强大的AI的这一观点：“但是，根据强大的AI来说，计算机不仅是研究思维的工具，而且还不仅仅是计算机。 确切地说，经过适当编程的计算机确实是一个头脑，从某种意义上说，只要给了正确的程序，就可以说计算机可以理解并具有其他认知状态。”

With this added clarification: “In strong AI, because the programmed computer has cognitive states, the programs are not mere tools that enable us to test psychological explanations; rather, the programs are themselves the explanations.”

补充说明：“在强大的AI中，因为编程的计算机具有认知状态，所以这些程序不仅仅是使我们能够测试心理解释的工具； 相反，程序本身就是解释。”

The rest of his famous (now infamous) paper then proceeds to indicate that he has “no objection to the claims of weak AI,” and thus he doesn’t tackle particularly the weak AI side of things, and instead his focus goes mainly toward the portent of strong AI.

然后，他的其他著名论文(现在臭名昭著)继续表明他“不反对弱AI的主张”，因此他没有特别针对弱AI方面，而是他的重点主要放在强大AI的预兆。

In short, he doesn’t have much faith or belief that strong AI is anything worth writing home about either. He says this: “On the argument advanced here only a machine could think, and only very special kinds of machines, namely brains and machines with internal causal powers equivalent to those of brains. And that is why strong AI has little to tell us about thinking, since it is not about machines but about programs, and no program by itself is sufficient for thinking.”

简而言之，他没有太多的信念或信念，认为强大的AI值得一提。 他说：“在这里提出的论点中，只有机器可以思考，只有非常特殊的一种机器，即大脑和具有与大脑相同的内在因果力的机器。 这就是为什么强大的AI几乎没有告诉我们思考的原因，因为它与机器无关，而与程序有关，没有任何程序本身足以思考。”

Here’s what that signifies, at least as has been interpreted by some.

至少在某些人的解释中，这就是这个意思。

Conventional AI is presumably doomed in trying to reach true AI if you stick with using “computer programs” since those programs aren’t ever going to cut it, and lack the needed capabilities to embody those things we associate with thinking and sentience.

如果您坚持使用“计算机程序”，那么传统的AI大概注定要失败，因为这些程序永远不会削减它，并且缺乏体现我们与思维和情感相关联的东西所需的能力。

Humans and animals have a kind of intentionality, somehow arising from the use of our brains, and for those that believe true AI requires that intentionality, you are barking up the wrong tree via the pursuit of “computer programs” (they are the wrong stuff and can’t go that high up the intelligence ladder).

人和动物具有某种意图，某种程度上是由于我们大脑的使用而引起的，对于那些认为真正的AI要求这种意图的人，您通过追求“计算机程序”在错误的树上吠叫(它们是错误的东西)。并且不能在智能阶梯上走得那么高)。

All of this presupposes two key assumptions or propositions that Seale lays out:

所有这些都以Seale提出的两个关键假设或命题为先决条件：

1. “Intentionality in human beings (and animals) is a product of causal features of the brain…”“人类(和动物)的故意性是大脑因果特征的产物……”
2. “Instantiating a computer program is never by itself a sufficient condition of intentionality.”“实例化计算机程序本身绝不是故意的充分条件。”

If your goal then is to devise a computer program that can think, you are on a fool’s errand and won’t ever get there, though, it isn’t completely foolish because you might well learn a lot along the way and could have some cool results and insights, but it isn’t going to be a thinker.

如果您的目标是设计一个可以思考的计算机程序，那您就傻子了，永远也不会到那里，但这并不是完全愚蠢的，因为您可能会在此过程中学到很多东西，并且可能会有一些很酷的结果和见解，但不会成为思想家。

I believe it is self-evident that this is a deeply intriguing philosophical consideration, one worthy of scholars and others pontificating about.

我认为，这是一种令人深感兴趣的哲学考虑，这一点值得学者和其他学者赞扬。

Does this make a difference for everyday AI work that those making AI-based systems such as Alexa or Siri or robots that function on a manufacturing line are going to be worrying about and losing sleep over?

这对于每天进行AI工作是否有所不同，因为那些制造基于AI的系统(例如Alexa或Siri)或在生产线上运行的机器人会担心并失去睡眠的人吗？

No.

没有。

To clarify, we are a long, long, long, long way from crafting AI systems that can exhibit human-level intelligence in any genuine meaning of the range, scope, and depth of human intelligence.

需要澄清的是，我们距离制造可以展现人类水平智能的人工智能系统还有很长的路要走，而人工智能系统可以展现人类智能的范围，范围和深度的任何真实含义。

That’s a shocker to some that keep hearing about AI systems that are as adept as humans.

对于那些不断听到像人类一样熟练的AI系统的人来说，这真是令人震惊。

Take a slow and measured breath and keep reading herein.

缓慢并测量呼吸，并在此处继续阅读。

Achieving True AI Is The Hearty Question

实现真正的AI是最重要的问题

I had earlier mentioned narrow AI.

我之前提到过狭窄的AI。

Some AI applications do seemingly well in narrow domains, though maybe they should have a Surgeon General type small print that identifies the numerous caveats and limitations about what that AI can do.

一些AI应用程序似乎在狭窄的领域中表现不错，尽管也许它们应该具有Surgeon General类型的小字体，以标识出有关AI可以做什么的众多警告和限制。

AI systems today cannot undertake or showcase common-sense reasoning, which I believe we all agree that humans generally have (for those snickering about humans having or not having common-sense reasoning, yes, there are people that we know that seems to at times lack common-sense, but that’s not the same as what overall is considered common-sense reasoning and don’t conflate those two into meaninglessness).

当今的AI系统无法进行或展示常识性推理，我相信我们都同意人类普遍拥有(对于那些嘲笑人类是否具有常识性推理的人，是的，有些人我们有时似乎知道缺乏常识，但这与总体上被认为是常识的推理并不相同，并且不要将二者混为一谈。

To insiders of AI, today’s AI applications are narrow AI, and not yet AGI (Artificial General Intelligence) systems, which is yet another term that is being used to get around the fact that “AI” has been watered down as terminology and used for anything that people want to say is AI, meanwhile, others are striving mightily to get to the purists’ version of AI, which would be AGI.

对于AI内部人士而言，当今的AI应用程序是狭窄的AI，而不是AGI(人工通用情报)系统，这是又一个术语，用于避开“ AI”已被简化为术语并用于人们想说的任何东西都是AI，与此同时，其他人也在大力争取达到纯粹主义者的AI版本，即AGI。

The debate about weak AI and strong AI is aimed at those that wonder whether we will be able to someday achieve true AI.

关于弱AI和强AI的争论是针对那些想知道我们是否有一天能够实现真正AI的人。

True AI is a loaded term that needs some clarification.

真正的AI是一个加载术语，需要澄清。

One version of true AI is an AI system that can pass the Turing Test, a simple yet telling kind of test that involves asking an AI system questions and asking a human being questions. They are essentially two distinct players in a game of wielding intelligence, of sorts, and if you cannot tell which is which, presumably the AI is the “equivalent” of human intelligence since it was indistinguishable from a human exhibiting intelligence.

真正的AI的一种版本是可以通过Turing测试的AI系统，该图灵测试是一种简单而有效的测试，涉及询问AI系统问题和询问人类问题。 在使用智力的游戏中，它们本质上是两个截然不同的参与者，如果您不能分辨出哪个是人工智能，那么推测AI就是人类智力的“等效”，因为它与人类展示智力是无法区分的。

Though the Turing Test is handy, and a frequently invoked tool for judging AI’s efforts to become true AI, it does have its downsides and problematic considerations. Anyway, how can we craft AI to succeed at the Turing Test, and have AI be ostensibly indistinguishable from human intelligence?

尽管图灵测试很方便，并且是判断AI努力成为真正AI的常用工具，但它确实有其缺点和问题。 无论如何，我们如何才能使AI在图灵测试中取得成功，并在表面上将AI与人类智能区分开？

One belief is that we’ll need to embody into the AI system the same kind of intentionality, casualty, thinking, and essence of sentience that exists in humans (and to some extent, in animals).

一种信念是，我们需要在人工智能系统中体现与人类(某种程度上在动物中)相同的意图，伤亡，思维和情感本质。

As a side note, the day that we reach AI sentience is often referred to as the singularity, and some believe that it will inevitably be reached and we’ll have then the equivalent of human intelligence, whilst others believe that the AI will exceed human intelligence, and we will arrive at a form of AI super-intelligence.

附带说明一下，我们达到AI情感的那一天通常被称为奇点，有些人认为它将不可避免地达到，因此我们将拥有与人类智力相当的水平，而另一些人则认为AI将超越人类智能，我们将得出一种AI超级智能形式。

Keep in mind that not everyone agrees with the precondition of needing to discover and re-invent artificial intentionality, asserting that we can nonetheless arrive at AI that exhibits human intelligence yet do so without tossing into the cart this squishy stuff referred to as intentionality and its variants.

请记住，并非每个人都同意需要发现和重新发明人为意图的前提，并声称尽管如此，我们仍可以到达展示人类智能的AI，而不必将这种被称为意图性的肮脏物品扔进购物车。变体。

Anyway, setting aside that last aspect, the other big question is whether “computer programs” will be the appropriate tool to get us there (whatever the there might be).

无论如何，撇开最后一个方面，另一个大问题是“计算机程序”是否将是使我们到达那里的合适工具(不管那里有什么可能)。

This brings up another definitional consideration. What do you mean by computer programs?

这带来了另一个定义上的考虑。 您所说的计算机程序是什么意思？

At the time when this debate first flourished, computer programs generally meant hand-crafted coding using both conventional and somewhat unconventional programming languages, exemplified by programs such as ELIZA by Weizenbaum and SHRDLU by Winograd.

在这场辩论开始盛行之时，计算机程序通常意味着使用传统的编程语言和某种程度上非传统的编程语言进行手工编码，例如Weizenbaum的ELIZA和Winograd的SHRDLU等程序。

Today, we are using Machine Learning and Deep Learning, so the obvious question on the minds of those that are still mulling over weak AI and strong AI would be whether the use of ML/DL constitutes “computer programs” or not.

如今，我们正在使用机器学习和深度学习，因此仍然在思考弱AI和强AI的人们的心中一个显而易见的问题是，使用ML / DL是否构成“计算机程序”。

Have we progressed past the old-time computer programs and advanced into whatever ML/DL is, such that we no longer seemingly have this albatross around our neck that computer programs aren’t the rocket ship that can get us to this desired moon?

我们是否已经超越了旧的计算机程序而前进到了任何ML / DL模式，以至于我们似乎再也找不到像计算机程序那样可以使我们登上理想月球的飞船了呢？

Well, that opens another can of worms, though it is pretty much the case that most would agree that ML/DL is still a “computer program” in the meaning of even the 1980s expression, so, if you buy into the argument that any use of or a variant of computer programs is insufficient to arrive at thinking AI, we are still in the doom-and-gloom state of affairs.

好吧，这打开了另一种蠕虫的罐头，尽管大多数情况下大多数人都同意ML / DL仍然是“计算机程序”，即使在1980年代的表达中也是如此，因此，如果您接受这样的论点，使用计算机程序或计算机程序的变体不足以引起人们对AI的思考，但我们仍处在厄运与沮丧的状态。

Searle though does cover the ML/DL topic to some degree since he mentions that a man-made machine could think if it:

Searle确实在某种程度上涵盖了ML / DL主题，因为他提到一台人造机器可以考虑是否：

“Assuming it is possible to produce artificially a machine with a nervous system, neurons with axons and dendrites, and all the rest of it, sufficiently like ours, again the answer to the question seems to be obvious, yes. If you can exactly duplicate the causes, you could duplicate the effects. And indeed it might be possible to produce consciousness, intentionality, and all the rest of it using some other sorts of chemical principles than those that human beings use.”

“假设有可能人工制造具有神经系统的机器，带有轴突和树突的神经元，而其余所有机器，就像我们的机器一样，同样，这个问题的答案似乎显而易见，是的。 如果可以准确地复制原因，则可以复制结果。 实际上，有可能使用除人类使用的化学原理以外的其他化学原理来产生意识，意向性及其所有其他部分。”

Please be aware that today’s ML/DL is a far cry from being the same as human neurons and a human brain.

请注意，当今的ML / DL与人类神经元和人类大脑完全不同。

At best, it is a crude and extremely simplified simulation, usually deploying Artificial Neural Networks (ANNs), way below anything approaching a human biological equivalent. We might someday get closer and indeed some believe we will achieve the equivalent but don’t be holding your breath for now.

充其量，这是一个粗略且极其简化的模拟，通常会部署人工神经网络(ANN)，远低于接近人类生物等效性的任何事物。 我们也许有一天会变得更近一些，的确有些人相信我们会实现与之相当的成就，但是暂时不要屏住呼吸。

Bringing us home to the argument about weak and strong AI, no matter what you do in either the case of weak AI or strong AI, here’s where you’ll land as per Searle: “But could something think, understand, and so on solely in virtue of being a computer with the right sort of program? Could instantiating a program, the right program of course, by itself be a sufficient condition of understanding?”

不管您在弱人工智能还是强人工智能的情况下做什么，这都会带给我们关于弱人工智能和强人工智能的论点，按照塞尔的说法，您将在这里着陆：“但是，某些东西可以完全地思考，理解等等。成为一台拥有正确程序的计算机？ 实例化一个程序，当然是正确的程序，本身就足以成为理解的条件吗？”

And his clear-cut answer is: “This I think is the right question to ask, though it is usually confused with one or more of the earlier questions, and the answer to it is no.”

他明确的答案是：“我认为这是个正确的问题，尽管它通常与一个或多个先前的问题相混淆，但答案是否定的。”

Ouch! That smarts.

哎哟! 太聪明了

There is nonetheless a glimmer of hope for strong AI, as it could be potentially turned into something that could achieve the thinking brand of AI (says Searle): “Any attempt literally to create intentionality artificially (strong AI) could not succeed just by designing programs but would have to duplicate the causal powers of the human brain.”

尽管如此，对于强大的AI仍有一线希望，因为它有可能转化为可以实现AI思维品牌的东西(Searle说)：“任何从字面上人工创造意图(强AI)的尝试都不能仅仅通过设计来获得成功。程序，但必须复制人脑的因果能力。”

Practical Significance For Today

对当今的现实意义

I hope it is obvious that the original meaning associated with weak and strong AI is far afield of what the popular press tends to use those catchy phrases for today. When trying to point out to people that their use of weak AI and strong AI is not aligned with the original meanings, they usually get huffy and tell you to not be such a stickler. Or, they tell you to knock the cobwebs out of your mind and become hipper with the present age.

我希望很明显，与弱而强的AI相关的原始含义已经远远超出了大众媒体在今天倾向于使用这些醒目的短语的含义。 当试图向人们指出，他们对弱AI和强AI的使用与原始含义不符时，他们通常会变得很笨拙，并告诉您不要那么固执。 或者，他们告诉您将蜘蛛网从您的脑海中扑灭，并随着当今的时代变得更加时髦。

Fine, I suppose, you can change up the meaning if you want, just please be aware that it is not the same as the original.

很好，我想，您可以根据需要更改含义，只是请注意它与原始含义不同。

This comes up in numerous applied uses of AI. For example, consider the emergence of AI-based true self-driving cars. True self-driving cars are ones where the AI drives the car entirely on its own and there isn’t any human assistance during the driving task.

这在AI的众多应用用途中都有体现。 例如，考虑基于AI的真正自动驾驶汽车的出现。 真正的自动驾驶汽车是指AI完全自行驾驶汽车，并且在驾驶过程中没有任何人工协助。

These driverless vehicles are considered a Level 4 and Level 5, while a car that requires a human driver to co-share the driving effort is usually considered at a Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-on’s that are referred to as ADAS (Advanced Driver-Assistance Systems).

这些无人驾驶汽车被认为是4级和5级，而要求驾驶员共同分担驾驶努力的汽车通常被认为是2级或3级。共同分担驾驶任务的汽车被描述为：是半自治的，通常包含称为ADAS(高级驾驶员辅助系统)的各种自动化附件。

There is not yet a true self-driving car at Level 5, which we don’t yet even know if this will be possible to achieve, and nor how long it will take to get there.

5级还没有真正的无人驾驶汽车，我们甚至都不知道是否有可能实现这一目标，以及达到目标需要多长时间。

Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se (we are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some point out).

同时，尽管是否应允许进行这种测试本身存在争议(我们在实验中都是生死攸关的豚鼠)，但4级研究人员正在通过非常狭窄和选择性的公共道路试验逐渐尝试吸引一些关注指出在我们的高速公路和小路上发生)。

Some media describe the semi-autonomous ADAS as weak AI, while the autonomous AI to be strong AI. Well, that’s not aligned with the original definitions of weak AI and strong AI. You have to be willing to put to the side the original definitions if you are seeking to use those terms in that manner.

一些媒体将半自治ADAS描述为弱AI，而将自治AI称为强AI。 好吧，这与弱AI和强AI的原始定义不一致。 如果您试图以这种方式使用这些术语，则必须愿意将原始定义放在一边。

Personally, I don’t like it. Similarly, I don’t like it when the weak AI and strong AI are used to characterize the difference between autonomous AI.

就个人而言，我不喜欢它。 同样，当弱AI和强AI用来表征自主AI之间的差异时，我也不喜欢它。

For example, some say that Level 4 is weak AI, while Level 5 is strong AI, but this once again is nonsensical in the nature of what those terms were intended to signify.

例如，有人说第4级是弱AI，而第5级是强AI，但这再次在这些术语意图表示的本质上是荒谬的。

If you genuinely want to try and apply the argument to true self-driving cars, there is an ongoing dispute as to whether driverless cars will need to exhibit “intentionality” to be sufficiently safe for our public roadways.

如果您真的想尝试将该论点应用于真正的无人驾驶汽车，那么对于无人驾驶汽车是否需要表现出“意图”以对我们的公共道路足够安全而言，一直存在争议。

In other words, can we craft AI without any seeming embodiment of intentionality and yet nonetheless have that AI be good enough to trust AI-based self-driving cars cruising around on our highways, byways, and everyday streets?

换句话说，我们是否可以在没有任何明显的故意表现的情况下制造AI，但是AI是否足以信任公路，小路和日常街道上行驶的基于AI的自动驾驶汽车？

It’s a complex debate and no one yet knows whether the driving domain can be considered limited enough in scope that such intentionality is not a necessity, plus, the question within a question is what might be rated as safe or safe enough for society to accept self-driving cars as fellow drivers.

这是一场复杂的辩论，尚无人知道驾驶领域是否可以在范围上受到足够的限制，以至于没有必要有这样的意图，此外，问题中的一个问题是，什么可以被认为是足够安全或足以使社会接受自我的安全？驾驶汽车作为同伴。

Conclusion

结论

For those of you wanting to get further into the weeds on this topic, you’ll also want to get introduced to the Chinese Room Argument (CRA), a foil used in Searle’s argument and something that has become a storied punching bag in the halls of AI and philosophy.

对于那些想要进一步了解这一主题的人，您还希望了解“中国房间争论”(CRA)，这是塞尔先生的争论中所用的一种箔纸，并且已经成为大厅中的一个传奇的出气筒。 AI和哲学。

That’s a story for another day.

那是另一天的故事。

Practitioners of AI might see this whole discussion about weak AI and strong AI as academic and much ado about nothing.

AI的从业者可能会把关于弱AI和强AI的整个讨论视为学术上的事，而毫无争议。

Use those phrases whatever way you want, some say.

有人说，可以使用任何方式使用这些短语。

Hold your horses.

稍安毋躁。

Perhaps we ought to heed the words of William Shakespeare: “Words without thoughts never to heaven go.”

也许我们应该留心威廉·莎士比亚的话：“没有思想的话就永远不会上天堂。”

The words we use do matter, and especially in the high stakes aims and outcomes of AI.

我们使用的词语确实很重要，尤其是在AI的高风险目标和成果中。

For free podcast of this story, visit: http://ai-selfdriving-cars.libsyn.com/website

有关此故事的免费播客，请访问： http : //ai-selfdriving-cars.libsyn.com/website

The podcasts are also available on Spotify, iTunes, iHeartRadio, etc.

播客还可以在Spotify，iTunes，iHeartRadio等上获得。

More info about AI self-driving cars, see: www.ai-selfdriving-cars.guru

有关AI自动驾驶汽车的更多信息，请参见： www.ai-selfdriving-cars.guru

To follow Lance Eliot on Twitter: https://twitter.com/@LanceEliot

要在Twitter上关注Lance Eliot： https ： //twitter.com/@LanceEliot

For his Forbes.com blog, see: https://forbes.com/sites/lanceeliot/

有关他的Forbes.com博客，请访问： https ： //forbes.com/sites/lanceeliot/

For his AI Trends blog, see: www.aitrends.com/ai-insider/

有关他的AI趋势博客，请访问： www.aitrends.com/ai-insider/

For his Medium blog, see: https://medium.com/@lance.eliot

有关其Medium博客，请访问： https : //medium.com/@lance.eliot

For Dr. Eliot’s books, see: https://www.amazon.com/author/lanceeliot

有关艾略特博士的书，请参见： https : //www.amazon.com/author/lanceeliot

Copyright © 2020 Dr. Lance B. Eliot

版权所有©2020 Lance B.Eliot博士