【可能有一些地方出现单词错误或翻译不准确，请自行斟酌】
【如果本文有帮助到您，请给我点个赞或者是收藏吧】
Unit 5
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 neuro science, 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.
[自从20世纪50年代计算机被发明以来，人们就觉得它“像大脑一样”。在早期，由于它们处理信息的能力，它们被广泛地称为“电子大脑”。但计算机和大脑之间的相似之处并不仅仅是表面上的;在最基本的层面上，计算机和大脑以相似的方式处理数据。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以二进制传输信息，即动作电位。这种基本的相似性是新兴的“计算神经科学”领域的基础，该领域希望了解神经网络如何产生记忆和面部识别等过程，以便在智能机器中进行复制]
But artificial intelligence has progressed slower than many had initially hope. Yes, Al 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.
[但人工智能的发展速度比许多人最初希望的要慢。是的，人工智能可能已经解决了跳棋游戏，但这与能够模拟意识还相距甚远。核心问题仍然存在:我们对大脑如何产生思维、神经元和动作电位如何创造意识没有真正的理解。]
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 brain's 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，几个大型项目不再试图从头开始制造会思考的机器，而是转向了一种新的方法:通过逆向工程复制虚拟大脑。通过研究大脑中的神经网络，科学家们构建了基于计算机的模型，这些模型反映了大脑复杂的生物网络。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（亨利·马克拉姆）is the South African neuroscientist who heads the Blue Brain Project at the Ecole Polytechnique Federal de Lausanne in Switzerland. For 15 years, Markram（马克拉姆）and his team collected data from the neocortex 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从老鼠大脑的新皮层收集数据，希望将其整合到一个3D模型中。如果他们能够准确地重现生物大脑的行为和结构，那么他们的计算机模拟将有助于揭示正常认知和抑郁症、精神分裂症等疾病。在试验阶段，该项目成功地重建了一只两周大的老鼠的单个新皮质柱，其中包含大约10,000个神经元。当然，与人类大脑中的1000亿个神经元相比，这个样本是无穷小的。但这个项目只是规模的问题。“从技术上讲，就计算机和获取数据的技术而言，在10年内建立一个人类大脑模型是可能的，”马克拉姆去年告诉《发现》杂志。]
But will this full-scale model teach us how to re-create consciousness, or perhaps even become conscious itself? “It's really difficult to say how much detail is needed for consciousness to emerge” said Markram（马克拉姆）. “I do believe that consciousness is an emergent phenomenon. It's like a shift from a liquid to a gas ... It's like a machine that has to run fast enough and suddenly it's flying. In other words, they can't know for sure until the model is finished.
[但是，这个全尺寸模型会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吗?“很难说意识的形成需要多少细节，”马克拉姆说。“我确实相信意识是一种涌现的现象。就像从液体到气体的转变…它就像一台机器，必须跑得足够快，然后突然就飞起来了。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. "Free association is a kind of thinking also. My mind doesn't shut off, but I'm certainly not solving problems; I'm wandering around."
[即使模型可以学习和推理，也不能保证它是一个真正的智能生物。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，耶鲁大学计算机科学家大卫·格勒恩特说。为了证明这一点，他指出了白日梦和自由联想。自由联想也是一种思维。我的思维没有停止，但我肯定不是在解决问题;我在四处游荡。]
"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 Geclernter（格伦特）."It tends to say, what is thinking? It’s 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!
[格伦特说:“人工智能领域过去只研究了光谱的最高端，现在也倾向于只研究最高端。”它倾向于说，什么是思考?这是一种高度专注、清醒、警觉、解决问题的精神状态。但这不仅不是故事的全部，而且问题一直困扰着心灵哲学、认知心理学和人工智能的最大的未解决问题——是创造力!]
The general consensus is that creativity is the ability to invent new analogies, 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 for one thought to another in a sort of free-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（垄断） attention by standing at the front.
[人们普遍认为，创造力是发明新类比、将两个不明显相关的事物联系起来的能力。 盖伦特说，这种类比的发明并不依赖于分析解决问题的思维，而是依赖于让你的思维在一种自由联想的状态下从一个想法转移到另一个想法。 “当你注意力高度集中时，创造力就不会发挥作用，”盖伦特在《Edge》的一篇文章中写道，“只有当你的思想开始漂移时，创造力才有可能。当问题在我们脑海中徘徊时，我们就会找到创造性的解决方案， 不是当它站在前面垄断注意力时。]
So how can computers create new analogies? The answer probably has something to do with emotion, says Gelernter. 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 t 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 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 it's not going to snow anymore and there's a certain springiness in the air, what do you feel? Gelernter asks. "It's not that you feel happy exactly. There are million kinds of happiness. It's 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.
[那么计算机如何创建新的类比呢？ 盖伦特说，答案可能与情绪有关。 是什么让我们能够将两种看似截然不同的想法或想法联系在一起，因为情感是一种极其微妙的代码，附着在一个非常复杂的场景上。我们倾向于用离散的术语来思考情绪，比如快乐、悲伤和愤怒，但它们实际上比这要微妙得多“，如果我说，四月或三月的第一个真正温暖的一天，当你出去并且 你不需要穿外套，你可以闻到花开的味道，可能还有雪的残留，但你知道不会再下雪了，空气中有一定的春天，你感觉如何？ 盖伦特问道。 “这并不是说你确实感到快乐。快乐有数百万种。它是一种特殊的情绪。”虽然可能没有一个确切的词来描述这种微妙的情绪，但大脑可以识别它，并可以将两个截然不同的场景联系起来 可能激发了同样的情感。]
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 feels 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.
[情感的另一个困难——也是计算机永远无法像人类那样体验情感的原因——是它们是由大脑和身体相互作用产生的。 盖伦特解释说：“当你感到快乐时，你的身体会有某种感觉，你的大脑也会注意到，身体和大脑之间的共鸣会产生一种情绪。”在计算机能够模拟这种体验之前，它们永远不会真正具有智能。]
思考题：After an introduction of one major project to replicate virtual brains, the article inspires the readers to ponder on a few deeper questions: Will this model or the future Al become. conscious itself? Is it possible for Al to learn how to create new analogies and how to experience emotions the way, we humans do? Summarize the article and give your point.
【文章在介绍了一个复制虚拟大脑的重大项目后，引发读者思考一些更深层次的问题：这个模型或未来的人工智能会变成什么样子。 有意识本身吗？ 人工智能是否有可能像我们人类一样学习如何创造新的类比以及如何体验情感？ 总结这篇文章并提出你的观点。】
About 10 years ago, most people more tend to believe that AI is beneficial at some kinds of work like cheap labor, but now by the development of AI and the network, we wit the creativity on machine such as Stable Diffusion, that is a program of AI that can create impressive paint or picture, these kinds of picture full of creativity and it will take a long day if you ask a people to draw the same one, instead of few second on machine. I believe AI will show more potential in feature.
【大约10年前，大多数人更倾向于相信人工智能对某些工作有好处，比如廉价劳动力，但现在通过人工智能和网络的发展，我们在机器上有了创造力，比如稳定扩散，这是一个程序 人工智能可以创造出令人印象深刻的绘画或图片，这些充满创造力的图片，如果你让一个人画出同样的一张图片，那将需要一整天的时间，而不是在机器上画几秒钟。 我相信AI会在功能上展现出更多的潜力。】