The race to Artificial Intelligence is a grueling, sweaty marathon that human beings have been running for over 80 years. We’ve faced steep climb after steep climb, the gradients cranking higher every time we run into a new funding gap or technological roadblock. Recent advances in deep learning and neural networks are leading some to wonder if we might, finally, be approaching the finishing line.

人工智能竞赛是人类已经运行了80多年的艰苦而汗流mar背的马拉松比赛。 我们经历了一次又一次的陡峭攀爬，每次遇到新的资金缺口或技术障碍时，坡度都会不断攀升。 深度学习和神经网络的最新进展使一些人想知道我们是否最终会接近终点线。

We were all impressed when Google’s DeepMind computer beat a human at the game of Go. This was the first breakthrough of artificial neural networks that captured popular imagination. Despite its simple rules, the popular Chinese board game is mind-bogglingly complex, and possesses more possibilities than the total number of atoms in the visible universe. Mastery of Go is supposed to be the ultimate expression of human intuition — so when world champion Lee Sedol was beaten by a computer program, Google’s groundbreaking AI made headlines worldwide.

当Google的DeepMind计算机在Go游戏中击败人类时，我们都印象深刻。 这是人工神经网络的第一个突破，引起了人们的普遍想象。 尽管其规则简单，但广受欢迎的中国棋盘游戏却令人难以置信地复杂，并且比可见宇宙中的原子总数具有更多的可能性。 精通Go被认为是人类直觉的最终体现-因此，当世界冠军Lee Sedol被计算机程序击败时，谷歌开创性的AI成为全球头条新闻。

And from predicting the outcome of the US election to rushing an incapacitated driver with a blood clot to hospital, the achievements of artificial intelligence continue to excite.

从预测美国大选的结果到将无血量的血块赶到医院 ，人工智能的成就继续令人兴奋。

But really, if our finishing-line goal is a general artificial intelligence that can solve humankind’s biggest problems (climate change, inequality, resource depletion) then current techniques are running with their legs tied. Neural networks are inspired by the human brain but in no way think like a human brain. In truth these are largely deterministic systems, which means that the same sequence of inputs will result in the exact same output, each and every time. Their edge over regular, rules-based software is that they can be made to learn the rules using inputs with known outputs. This evolution of software is truly powerful, but is it enough?

但是，实际上，如果我们的终点线目标是能够解决人类最大问题(气候变化，不平等，资源枯竭)的通用人工智能，那么当前的技术将束手无策。 神经网络受到人脑的启发，但丝毫不像人脑那样思考。 实际上，这些在很大程度上是确定性系统，这意味着相同的输入序列将每次产生完全相同的输出。 与常规的基于规则的软件相比，它们的优势在于，可以使它们使用具有已知输出的输入来学习规则。 软件的这种发展确实强大，但这足够了吗？

We are right to begin dreaming about the possibilities such feats arouse and it is true we are living in exciting times. But if we are to make the first steps to a machine brain, we must be wary not to commit the first sin of AI research: the mistaking of complex behavior for intelligence.

我们开始梦想这种壮举的可能性是正确的，我们生活在一个激动人心的时代。 但是，如果我们要迈向机器大脑的第一步，那么我们一定要警惕不要犯AI研究的第一个罪过：将复杂行为误认为智能。

Humor, Creativity and Wisdom

幽默，创造力和智慧

Consider the female digger wasp. Before she brings food into her burrow, she drops it off at the entrance and goes inside to check for intruders. Only once satisfied her nest is safe will she bring in the food. If, while the wasp is busy securing her burrow, a meddling human moves the food a couple of inches, the wasp will move the food back to its original drop-off point and repeat her burrow-check all over again. Because the wasp is not capable of remembering she just checked the nest, she can be made to repeat this cycle of behavior more than 40 times. Complex and premeditated it may be — but her behavior is not intelligent.

考虑一下女性挖掘机的WaSP。 在将食物带入洞穴之前，她将其放在入口处，然后走进去检查入侵者。 只有满足了她的巢是安全的，她才会带进食物。 如果在胡蜂忙于挖洞的时候，一个有情调的人将食物移了几英寸，那么WaSP会将食物移回原来的落点，并再次进行挖洞检查。 由于WaSP无法记住她刚刚检查过的巢穴，因此可以使她重复此行为循环40次以上。 可能是复杂而有预谋的-但她的行为并不聪明。

Learning from large-but-limited datasets, most commercial applications of artificial intelligence exhibit cognitive shortcomings similar to the wasp. You might ask why this is important: Is it not enough that we have the complex behavior? If a computer can automate a human task, does it really matter if that computer doesn’t exhibit real human intelligence?

从大型但有限的数据集中学习，大多数人工智能的商业应用都表现出类似于WaSP的认知缺陷。 您可能会问为什么这很重要：我们拥有复杂的行为还不够吗？ 如果计算机可以自动执行人工任务，那台计算机是否没有真正的人类智力真的很重要吗？

Well, yes. Eminent AI thinker Jack Copeland contrasts the behavior of the digger wasp — and by extension most 21st century AI — with that produced by human intelligence. In the same situation, a human would probably wonder who has been messing about with their food — but they would not repeat the ritual of checking their home was free from invaders. We know we don’t need to do this again because our intelligence has naturally evolved to enable not only experiential learning but also the extrapolating of those experiences to new situations. It is obvious the burrow is still safe — without a moment’s thought, we would adapt our behavior.

嗯，是。 杰出的AI思想家杰克·科普兰德(Jack Copeland)将挖掘者WaSP的行为(并延伸到大多数21世纪的AI)与人类智慧产生的行为进行了对比。 在相同的情况下，一个人可能会想知道谁一直在弄弄他们的食物-但他们不会重复检查自己的房屋免受侵略者侵扰的仪式。 我们知道我们不需要再做一次，因为我们的智力已经自然发展，不仅可以进行体验式学习，而且可以将这些经验推算到新的情况下。 显然，洞穴仍然很安全-片刻之后，我们会适应我们的行为。

Copeland literally defines intelligence as the ability to adapt one’s behavior to fit new circumstances, and this is what made AlphaGo so special; through deep learning, it could extrapolate its experience of playing Go to new in-game situations. Ask it to attend to any other task outside of the game, even one as simple as booking the flights for your next holiday, and it would not be able to do so. It must be equipped with tools, connected with the appropriate interfaces, trained again from scratch and possibly rewritten in large parts to do anything in the non-Go world. Google did announce another software to do some of these things, but that, too, is very specialized.

谷轮在字面上将智力定义为适应个人行为以适应新情况的能力，这就是AlphaGo如此特别的原因。 通过深度学习，它可以推断出在新的游戏场景中玩Go的经验。 让它参加游戏之外的任何其他任务，甚至只是为下一个假期预订航班一样简单，而它将无法做到。 它必须配备有与适当的接口连接的工具，必须重新进行培训，并且可能需要大篇幅地重写才能在非Go语言环境中执行任何操作。 Google确实宣布了另一种软件来执行其中的某些功能，但是它也非常专业。

Robert Epstein, a research psychologist points out that the human brain does not store facts or data, like a computer does; it continuously edits itself and relives experiences, like an artist painting a landscape. Our unique combination of experiential learning, ability to abstract concepts and deft extrapolation leads to seemingly-incomprehensible phenomena like humor, creativity and wisdom.

研究心理学家罗伯特·爱泼斯坦(Robert Epstein)指出，人脑不像计算机那样存储事实或数据 。 它不断地自我编辑并重现经验，就像画家画风景一样。 我们将体验式学习，抽象概念的能力和灵巧外推的独特结合，导致了诸如幽默感，创造力和智慧之类难以理解的现象。

If we are to build a truly intelligent artificial intelligence, we need to build something like a machine brain, and to build a machine brain we need to start being more ambitious.

如果我们要构建真正的智能人工智能，就需要构建类似于机器大脑的东西，而要构建机器大脑，我们需要开始变得更加雄心勃勃。

The Incredible Wisdom of Human Thought Process

人类思维过程的不可思议的智慧

Trying to imitate human thought process is intoxicating.

试图模仿人类的思维过程令人陶醉。

When we’re building artificially intelligence natural language systems, a key Coseer design principle is to encapsulate every point of data in the form of an idea — concepts, rather than keywords. This is still nothing close to the processing power of a human brain, but even a relatively simple emulation leads to incredible results: Accuracy shoots up. Latencies and training times collapse.

当我们构建人工智能自然语言系统时，关键的Coseer设计原则是以概念(而不是关键字)的形式封装每个数据点。 这仍然远不能与人脑的处理能力相提并论，但是即使是相对简单的仿真也可以产生令人难以置信的结果：准确性不断提高。 延迟和培训时间崩溃了。

All this with a relatively simple emulation of human thought process. Word2Vec, ELMo, BERT, GPT — all popular natural language processing models — adopt the same philosophy to translate each word into an abstract representation of ideas.

所有这些都相对简单地模拟了人类的思维过程。 所有流行的自然语言处理模型Word2Vec，ELMo，BERT，GPT都采用相同的哲学，将每个单词转换为思想的抽象表示。

Designing algorithms to mimic human thought processes makes business sense for real world problems even today: finding actionable stock market insights from three million documents everyday, assisting doctors through oncological pathways, seamless knowledge management are just some examples. AI is already helping largest and most innovative organizations across sectors. I know this first-hand.

设计模拟人类思维过程的算法甚至对于当今的现实世界来说仍具有商业意义：每天从300万份文档中寻找可行的股市见解，通过肿瘤学途径协助医生，实现无缝知识管理只是其中的一些例子。 人工智能已经在帮助跨部门的最大和最具创新性的组织。 我是第一手知道的。

First Steps to a Machine Brain

机器大脑的第一步

To continue in our journey towards an artificial general intelligence, let’s ask two simple questions:

为了继续朝着人工智能迈进，请问两个简单的问题：

• First, what happens when all our applications can learn not only from the data they see, but from the experiences of other applications, and also from the data each application sees?首先，当我们所有的应用程序不仅可以从他们看到的数据中学习到其他应用程序的经验时，还可以从每个应用程序看到的数据中学习时，会发生什么？
• Second, what happens when each application (and all of them collectively) can learn from the repository of collective human knowledge, available in the form the Internet?其次，当每个应用程序(以及所有这些应用程序)可以从以互联网形式提供的集体人类知识库中学习时，会发生什么？

To achieve any success on these two questions, a language must emerge that can translate insights from every context to a common set of binary code. Current approach to neural networks is woefully short on this. Each model must cater to a specific problem only, and with very limited data sets.

为了在这两个问题上取得成功，必须出现一种语言，该语言可以将见解从每个上下文转换为一组通用的二进制代码。 目前神经网络的方法在这一方面简直是可悲的。 每种模型都只能满足特定问题，并且数据集非常有限。

Sure, humans also have specialists. Doctors and military generals are trained differently. However, the differences are at a level too abstract for a neural network to ever achieve. Both may have the same political views, sense of duty, familial commitment or investment acumen. Even at basic level, both may be excellent drivers, adept conversationalists, and masters at Go. One would need a different neural network to even scratch the surface on each of these.

当然，人类也有专家。 医生和军事将领受到不同的训练。 但是，这些差异在某种程度上太抽象了，以至于神经网络都无法实现。 两者可能具有相同的政治观点，责任感，家庭承诺或投资敏锐度。 即使在基本的水平上，两者都可能是Go的杰出司机，熟练的对话家和大师。 一个人需要一个不同的神经网络来均匀刮擦每个表面。

The long-term goal is bold: such a development will set technology free to pursue true intelligence. It will let AI apps benefit from the knowledge that already exists. Applications will ponder on their own time to bring more accurate decisions that the applications are more confident about. And each time that happens, every other application will become smarter.

长期目标是大胆的：这样的发展将使技术自由地追求真正的智慧。 它将使AI应用程序受益于已经存在的知识。 应用程序将考虑自己的时间，以做出更准确的决定，这些决定使应用程序更加有信心。 每次发生这种情况时，其他所有应用程序都会变得更加智能。

And that would just be a baby step towards a true machine brain.

这只是迈向真正的机器大脑的第一步。