中国科学院大学 研究生学术英语读写教程Unit5 TextA翻译（DeepL翻译+人工校对）

Can Computers Be Conscious?

计算机会有意识吗？

Computers have seemed "mind-like" to people since they were invented in 1950s. In the early days they were widely called "electronic brains" for their ability to process information. But the similarity between computers and brains isn't just superficial: At their most fundamental levels, computers and brains process data in a similar binary fashion. Whereas computers use zeros and ones to store and manipulate data, the neurons in our brains transmit information in binary, on/off spikes known as action potentials. This basic similarity is what underlies the burgeoning field of computational neuroscience, which hopes to understand how neuronal networks give rise to processes like memory and facial recognition so that they might be replicated in intelligent machines.

计算机自 20 世纪 50 年代发明以来，一直被人们视为“类似于大脑”。但是，计算机与大脑之间的相似之处并不只是表面上的：在最基本的层面上，计算机和大脑以类似的二进制方式处理数据。计算机使用“0”和“1”来存储和处理数据，而我们大脑中的神经元则以二进制的“开/关”尖峰（即动作电位）来传输信息。这种基本相似性正是计算神经科学这一新兴领域的基础，该领域希望了解神经元网络是如何产生记忆和面部识别等过程的，以便在智能机器中复制这些过程。

But artificial intelligence has progressed slower than many had initially hoped. Yes, AI may have solved the game of checkers, but this is a far cry from being able to simulate consciousness. The central problem remains: We have no real understanding of how the brain gives rise to the mind, of how neurons and action potentials create consciousness.

是的，人工智能可能已经解决了跳棋游戏，但这与模拟意识的能力相去甚远。核心问题依然存在： 我们并不真正了解大脑如何产生思维，神经元和动作电位如何产生意识。

Instead of trying to build thinking machines from the ground up, several major projects have recently turned to a new approach: replicating virtual brains through reverse-engineering. By studying the neural networks in the brain, scientists have constructed computer-based models that mirror the brains complex biological networks. In turn, they can then run experiments on these brain-like computers in order to learn about how the brain thinks.

最近，几个重大项目转而采用了一种新方法，即通过逆向工程复制虚拟大脑，而不是从头开始制造思维机器。通过研究大脑中的神经网络，科学家们构建出了能够反映大脑复杂生物网络的计算机模型。反过来，他们可以在这些类似大脑的计算机上进行实验，以了解大脑是如何思考的。

Henry Markram is the South African neuroscientist who heads the Blue Brain Project at the Ecole Polytechnique Federale de Lausanne in Switzerland. For 15years, Markram and his team collected data from the neocortexes of rats brains with the hopes of integrating it into a 3D model. If they could accurately recreate the behaviors and structures of a biological brain, their computer simulation should shed light on both normal cognition and disorders like depression and schizophrenia. In its trial stages the project successfully recreated a single neocortical column of a two-week-old rat, which contains about 10,000 neurons. Of course, this sample is infinitesimally small compared to the 100 billion neurons in a human brain. But this project is all a matter of scaling. "Technologically, in terms of computers and techniques to acquire data, it will be possible to build a model of the human brain within 10 years," Markram told Discover magazine last year.

亨利-马克拉姆（Henry Markram）是南非神经科学家，他是瑞士洛桑联邦理工学院蓝脑项目的负责人。15 年来，马克拉姆和他的团队一直在收集老鼠大脑新皮质的数据，希望能将这些数据整合到三维模型中。如果他们能准确再现生物大脑的行为和结构，他们的计算机模拟就能揭示正常认知以及抑郁症和精神分裂症等疾病的真相。在试验阶段，该项目成功地再现了一只两周大的老鼠的单个新皮质柱，其中包含约10,000个神经元。当然，与人脑中的 1000 亿个神经元相比，这个样本实在是微不足道。但这个项目的关键在于规模。马克拉姆去年对《发现》杂志说：“从技术上讲，就计算机和获取数据的技术而言，在10年内建立一个人脑模型是可能的。”

But will this full-scale model teach us how to re-create consciousness, or perhaps even become conscious itself? "Its really difficult to say how much detail is needed for consciousness to emerge," said Markram. "I do believe that consciousness is an emergent phenomenon. Its like a shift from a liquid to a gas ... Its like a machine that has to run fast enough and suddenly its flying." In other words, they cant know for sure until the model is finished.

但是，这个完整的模型是否能教会我们如何重新创造意识，或者甚至让意识本身变得有意识呢？马克拉姆说：“很难说意识的产生需要多少细节，我相信意识是一种突现现象。它就像从液体到气体的转变...... 就像机器必须跑得足够快，然后突然飞起来一样。”换句话说，在模型完成之前，他们无法确定。

Even if the model can learn and reason, that doesn't guarantee that it will be a truly intelligent being. Many people studying AI have equated problem-solving with thinking, but thinking is different from reasoning, says Yale computer scientist David Gelernter. To demonstrate this, he points to daydreaming and free association. "Freeassociation is a kind of thinking also. My mind doesnt shut off, but Im certainly not solving problems; I'm wandering around.”

即使模型能够学习和推理，也不能保证它将成为真正的智能生物。耶鲁大学计算机科学家戴维-格伦特（David Gelernter）说，许多研究人工智能的人把解决问题等同于思考，但思考不同于推理。为了证明这一点，他提到了白日梦和自由联想。“我的大脑并没有关闭，但我肯定不是在解决问题，而是在四处游荡。”

"The field of Artificial intelligence had studied only the very top end of the spectrum and still tends to study only the very top end," says Gelernter. "It tends to say, what is thinking? Its this highly focused, wide awake, alert, problem-solving state of mind. But not only is that not the whole story. but the problem - the biggest unsolved problem that has tended to haunt philosophy of mind, cognitive psychology, and AI - is creativity.”

盖勒恩特说：“人工智能领域只研究了最顶端的东西，而且仍然倾向于只研究最顶端的东西。它倾向于说，什么是思维？它是一种高度集中、清醒、警觉、解决问题的思维状态。但这不仅不是故事的全部，而且问题--困扰思维哲学、认知心理学和人工智能的最大难题--是创造力。”

The general consensus is that creativity is the ability to invent new analogies, to connect two things that are not obviously related. And this invention of analogy relies not on analytic problem-solving thought but on letting your mind drift from one thought to another in a sort of fee-associative state, says Gelernter. “Creativity doesn't operate when your focus is high," Gelernter writes in an essay for Edge. "Only when your thoughts have started to drift is creativity possible. We find creative solutions to a problem when it lingers at the back of our minds, not when it monopolizes attention by standing at the front."

人们普遍认为，创造力是一种发明新类比的能力，是将两个没有明显关联的事物联系起来的能力。盖勒恩特说，这种类比的发明并不依赖于分析问题的思维，而是让你的思维在一种费联想的状态下从一个想法漂移到另一个想法。盖勒恩特在为《边缘》撰写的一篇文章中写道：“当你的注意力高度集中时，创造力不会发挥作用。只有当你的思绪开始漂移时，创造力才有可能产生。当一个问题在我们的脑海中徘徊时，我们会找到创造性的解决方案，而不是当它站在最前面垄断了我们的注意力时。”

So how can computers create new analogies? The answer probably has something to do with emotion, says Gelernter. “Emotion is what allows us to take two thoughts or ideas that seem very different and connect them together, because emotion is a tremendously subtle kind of code or tag that can be attached to a very complicated scene.” We tend to think of emotions in discrete terms, like happy, sad, and angry, but they're really much more subtle than that. “If I say, “What is your emotion on the first really warm day in April or March when you go out and you don't need a coat and you can smell the flowers blooming and there may be remnants of snow but you know its not going to snow anymore and there's a certain springiness in the air, what do you feel?” Gelernter asks. “Its not that you feel happy exactly There are a million kinds of happiness. Its a particular shade of emotion.” Though there may not be an exact word to describe this nuanced emotion, the mind can recognize it and can connect two very different scenes that may have inspired the same emotion.

那么，计算机如何创造新的类比呢？盖勒恩特说，答案可能与情感有关。“情感让我们能够把两种看似截然不同的想法或观念联系在一起，因为情感是一种非常微妙的代码或标签，可以附加在非常复杂的场景上。我们倾向于从离散的角度来看待情绪，比如快乐、悲伤和愤怒，但它们其实比这要微妙得多。”如果我说：“在四月或三月第一个非常温暖的日子里，你出门时不需要穿大衣，你能闻到花开的香味，可能还有残雪，但你知道它不会再下雪了，空气中弥漫着某种春意，你的情绪是什么？”格伦特问道。“这并不是说你感到幸福，幸福有无数种。这是一种特殊的情绪。”虽然可能没有一个确切的词来描述这种微妙的情感，但心灵可以识别它，并将可能激发了相同情感的两个截然不同的场景联系起来。

The other difficulty with emotion - and the reason why computers won't ever be able to experience emotions the way humans do - is that they are produced by an interaction between the brain and the body working together. "When you feel happy, your body feles a certain way, your mind notices, and the resonance between body and mind produces an emotion," Gelernter explains. Until computers can simulate this experience, they will never be truly intelligent.

情绪的另一个难点--也是计算机无法像人类一样体验情绪的原因--在于情绪是由大脑和身体共同作用产生的。格伦特解释说：“当你感到快乐时，你的身体会产生某种感觉，你的大脑也会注意到这一点，身体和大脑之间的共鸣就会产生情绪。在计算机能够模拟这种体验之前，它们永远无法真正实现智能化。”