Sutton是阿尔伯塔大学计算机科学教授，阿尔伯塔机器智能研究所研究员、首席科学顾问和加拿大CIFAR AI主席。在接受BetaKit的采访时，Sutton将他的图灵奖获奖描述为“令人满意和谦卑”，以及“完全出乎意料”。

小编为夏校学生普及中英文AI历史与发展：

Many of us are familiar the way artificial intelligence (AI) is already integrated into our daily lives: Spotify recommends new songs that we love, Google Maps provides faster routes for our morning commute, or Alexa sounds an alarm to remind us when it's time to leave for an appointment. Each of these examples is an instance of AI in action, and we've become accustomed to their existence.

我们很多人都熟悉人工智能（AI）已经融入我们日常生活的方式：Spotify推荐我们喜欢的新歌，谷歌地图为我们的早晨通勤提供更快的路线，或者Alexa发出闹钟提醒我们何时该出发去约会。这些例子中的每一个都是人工智能的实例，我们已经习惯了它们的存在。

So why the current hype cycle around AI? What’s different now?

那么，为什么会出现当前围绕人工智能的炒作周期呢？现在有什么不同？

The most recent iterations of AI – called “generative” AI – can do things that look, sound, and feel eerily human.

人工智能的最新迭代——被称为“生成式”人工智能——可以做一些看起来、听起来和感觉上都非常人性化的事情。

人工智能有潜力改变各个行业，从金融、教育到交通和医疗保健。人工智能可以自动执行重复性任务，改善决策过程，提高数据分析的准确性和速度。

While the potential benefits are enormous, AI presents significantethical and societal concerns. Like any tool, AI can be used for good or harm. Carnegie Mellon University’s Block Center for Technology and Society was created to explore how technology can be leveraged for social good.

虽然潜在的好处是巨大的，但人工智能也带来了重大的伦理和社会问题。像任何工具一样，人工智能既可以用来做好事，也可以用来做坏事。卡耐基梅隆大学布洛克技术与社会中心的成立是为了探索如何利用技术来造福社会。

As of now, only a few technology super-companies have the capacity to create large-scale generative AI tools. The systems require massive amounts of both computing power and data. By default, a few people who lead these organizations are making decisions about the use of AI that will have widespread consequences for society. It behooves the rest of us to recognize the moment we’re in, and to engage in shaping the path forward.

到目前为止，只有少数科技超级公司有能力创造大规模的生成人工智能工具。这些系统需要大量的计算能力和数据。默认情况下，领导这些组织的少数人正在就人工智能的使用做出决定，这将对社会产生广泛的影响。我们其余的人应该认识到我们所处的时刻，并参与塑造前进的道路。

人工智能的创始人之一阿兰·图灵（Alan Turing）在1950年提出，如果一台机器可以与人类对话，而人类无法区分他们是在与另一个人交谈还是在与机器交谈，那么机器就展示了人类的智慧。

Machine learning (ML) first entered the public consciousness in the 1950s, when television viewers watched a demonstration of Arthur Samuel’s Checkers program defeating its human opponent, Robert Nealy. For a long time, though, AI remained largely confined to the realm of tech geniuses and science fiction enthusiasts.

机器学习（ML）首次进入公众视野是在20世纪50年代，当时电视观众观看了亚瑟·塞缪尔（Arthur Samuel）的跳棋程序击败人类对手罗伯特·尼利（Robert Nealy）的演示。然而，在很长一段时间里，人工智能在很大程度上仍然局限于技术天才和科幻小说爱好者的领域。

Those tech geniuses accomplished a number of groundbreaking achievements over the last seven decades, including:

这些技术天才在过去70年里取得了许多突破性的成就，包括：

·In 1956, Allen Newell, Herbert Simon, and J.C. Shaw developed Logic Theorist, the first artificially intelligent computer program. They were part of a small group that coined the term “artificial intelligence.”

1956年，Allen Newell， Herbert Simon和J.C. Shaw开发了第一个人工智能计算机程序Logic Theorist。他们是创造“人工智能”一词的一小群人的一部分。

·In 1957, Frank Rosenblatt developed the Perceptron, an early artificial neural network that recognized patterns.

1957年，弗兰克·罗森布拉特（Frank Rosenblatt）开发了感知器（Perceptron），这是一种早期的人工神经网络，可以识别模式。

·In 1965, Joseph Weizenbaum developed ELIZA, the first chatbot; the system used limited natural language processing.

1965年，Joseph Weizenbaum开发了第一个聊天机器人ELIZA；该系统使用了有限的自然语言处理。

·1960s and 70s: AI enters mainstream pop culture:

20世六七十年代：AI进入主流流行文化；

·"2001: A Space Odyssey" premiered in movie theaters (1968).

《2001太空漫游》（2001:A Space Odyssey）于1968年在电影院首映。

·C-3PO and R2-D2 are introduced to the world via "Star Wars: A New Hope" (1977).

C-3PO 和 R2-D2 通过 1977 年的《星球大战：新希望》首次亮相于世。

·Speak & Spell toy hits the shelves (1978).

说话&拼写玩具上架（1978年）。

·1974 - 1980: The first "AI winter" is a period of decreased funding and consequently slowed research in AI.

1974年至1980年：第一个“人工智能寒冬”是指资金减少，从而导致人工智能研究放缓的时期。

·In 1981, the government of Japan allocated $850 million for the Fifth Generation Computer project; the goal was to create systems that could engage in conversation and reason like a human.

1981年，日本政府为第五代计算机项目拨款8.5亿美元；其目标是创建能够像人类一样进行对话和推理的系统。

·In 1984, NAVLab developed the first autonomous land vehicle.

1984年，NAVLab开发了第一辆自主陆地车辆。

·The second AI winter occurred between 1987 - 1993.

 第二次人工智能寒冬发生在1987年至1993年。

·In 1997, Deep Blue beat world chess champion Gary Kasparov.

1997年，Deep Blue击败了国际象棋世界冠军Gary Kasparov。

·In 2011, IBM’s Watson defeated Ken Jennings on Jeopardy and Apple added Siri to its iPhones.

2011年，IBM的沃森在《危险边缘》（Jeopardy）节目中击败了肯·詹宁斯（Ken Jennings），苹果也在iphone上添加了Siri。

The terminology around AI can be intimidating. Here’s a glossary of key terms you’ll often hear when people talk about AI.

关于AI的术语可能令人生畏。当人们谈论人工智能时，你会经常听到一些关键术语。

Algorithm: a set of rules or instructions that tell a machine what to do with the data input into the system.

算法：一组规则或指令，告诉机器如何处理输入系统的数据。

Deep Learning: a method of machine learning that lets computers learn in a way that mimics a human brain, by analyzing lots of information and classifying that information into categories. Deep learning relies on a neural network.

深度学习：一种机器学习方法，通过分析大量信息并将信息分类，让计算机以模仿人类大脑的方式进行学习。深度学习依赖于神经网络。

Hallucination: a situation where an AI system produces fabricated, nonsensical, or inaccurate information. The wrong information is presented with confidence, which can make it difficult for the human user to know whether the answer is reliable.

幻觉：人工智能系统产生捏造、荒谬或不准确信息的情况。错误的信息是自信地呈现出来的，这可能会使人类用户难以知道答案是否可靠。

Large Language Model (LLM): a computer program that has been trained on massive amounts of text data such as books, articles, website content, etc. An LLM is designed to understand and generate human-like text based on the patterns and information it has learned from its training. LLMs use natural language processing (NLP) techniques to learn to recognize patterns and identify relationships between words. Understanding those relationships helps LLMs generate responses that sound human—it’s the type of model that powers AI chatbots such as ChatGPT.

大型语言模型（LLM）：一种经过大量文本数据（如书籍、文章、网站内容等）训练的计算机程序。法学硕士的设计目的是基于从训练中学习到的模式和信息来理解和生成类似人类的文本。法学硕士使用自然语言处理（NLP）技术来学习识别模式和识别单词之间的关系。理解这些关系有助于法学硕士产生听起来像人类的反应——这是为ChatGPT等人工智能聊天机器人提供动力的模型。

Machine Learning (ML): a type of artificial intelligence that uses algorithms which allow machines to learn and adapt from evidence (often historical data), without being explicitly programmed to learn that particular thing.

机器学习（ML）：一种人工智能，它使用的算法允许机器从证据（通常是历史数据）中学习和适应，而无需明确编程来学习特定的东西。

Natural Language Processing (NLP): the ability of machines to use algorithms to analyze large quantities of text, allowing the machines to simulate human conversation and to understand and work with human language.

自然语言处理（NLP）：机器使用算法分析大量文本的能力，允许机器模拟人类对话并理解和使用人类语言。

Neural Network: a deep learning technique that loosely mimics the structure of a human brain. Just as the brain has interconnected neurons, a neural network has tiny interconnected nodes that work together to process information. Neural networks improve with feedback and training.

神经网络：一种深度学习技术，松散地模仿人类大脑的结构。就像大脑有相互连接的神经元一样，神经网络也有微小的相互连接的节点，它们一起工作来处理信息。神经网络随着反馈和训练而改进。

Token: the building block of text that a chatbot uses to process and generate a response. For example, the sentence "How are you today?" might be separated into the following tokens: ["How," "are," "you," "today," "?"]. Tokenization helps the chatbot understand the structure and meaning of the input.

令牌：聊天机器人用来处理和生成响应的文本构建块。例如，句子“How are you today?”可以分成以下标记：[“How”，“are”，“you”，“today”，“？”]。标记化可以帮助聊天机器人理解输入的结构和含义。

AI refers to the ability of machines and computers to perform tasks that would normally require human intelligence. These tasks include things like recognizing patterns and making predictions. Ultimately, that’s not magic; it’s math.

人工智能是指机器和计算机执行通常需要人类智能才能完成的任务的能力。这些任务包括识别模式和做出预测。归根结底，这不是魔法；它是数学。

To understand what’s going on with AI today, it’s helpful to think of AI in phases of development. Early AI systems were machines that received an input – the data they were fed by humans - and then produced a recommendation. That response is based on the way the system was trained, and the algorithms (the math!) that tell the system what to do with the data. It’s computers that can play checkers or chess. It’s Netflix knowing that you loved "Karate Kid" and suggesting that you watch "Cobra Kai."

为了理解今天人工智能的发展趋势，我们可以从人工智能发展的各个阶段入手。早期的人工智能系统是机器接收输入——人类提供给它们的数据——然后产生建议。这种反应是基于系统的训练方式，以及告诉系统如何处理数据的算法（数学！）。它是可以下跳棋或象棋的电脑。Netflix知道你喜欢《空手道小子》，建议你看《眼镜蛇Kai》。

Generative AI is a step forward in the development phase. Instead of just reacting to data input, the system takes in data and then uses predictive algorithms (a set of step-by-step instructions) to create original content. In the case of a large language model (LLM), that content can take the form of original poems, songs, screenplays, and the like produced by AI chatbots such as ChatGPT and Google Bard. The “large” in LLMs indicates that the language model is trained on a massive quantity of data. Although the outcome makes it seem like the computer is engaged in creative expression, the system is actually just predicting a set of tokens and then selecting one.

生成式人工智能在开发阶段是向前迈出的一步。该系统不只是对数据输入做出反应，而是接收数据，然后使用预测算法（一套分步指令）创建原创内容。在大型语言模型（LLM）的情况下，这些内容可以采用由ChatGPT和谷歌Bard等人工智能聊天机器人制作的原创诗歌、歌曲、剧本等形式。LLM中的“大”表示语言模型是在大量数据上训练的。虽然结果看起来像是计算机在进行创造性的表达，但系统实际上只是预测了一组令牌，然后选择了一个。

“The model is just predicting the next word. It doesn't understand,” explains Rayid Ghani, professor of machine learning at Carnegie Mellon University’s Heinz College of Information Systems and Public Policy. “But as a user playing around with it, it seems to have amazing capabilities, while having very large blind spots.” 

“这个模型只是预测下一个单词。卡内基梅隆大学亨氏信息系统与公共政策学院的机器学习教授Rayid Ghani解释道。“但当用户试用它时，它似乎有惊人的功能，同时也有很大的盲点。”

Models like ChatGPT are programmed to select the next token, or word, but not necessarily the most commonly used next word. Chatbots might choose – for example –  the fourth most common word in one attempt. When the user submits the exact same prompt to the chatbot the next time, the chatbot could randomly select the second most common word to complete the statement. That’s why we humans can ask a chatbot the same question and receive slightly different responses each time.

ChatGPT等模型被编程为选择下一个标记或单词，但不一定是最常用的下一个单词。例如，聊天机器人可能会在一次尝试中选择第四个最常见的单词。当用户下次向聊天机器人提交完全相同的提示时，聊天机器人可以随机选择第二个最常见的单词来完成语句。这就是为什么我们人类可以问聊天机器人同样的问题，每次得到的回答都略有不同。

Tools like Copilot and ChatGPT use that token process to write computer code. Though not always perfect, the initial consensus in the tech industry suggests that these tools can save coders hours of tedious work.

像Copilot和ChatGPT这样的工具使用这个令牌过程来编写计算机代码。虽然并不总是完美的，但科技行业的初步共识表明，这些工具可以为程序员节省数小时的繁琐工作。

Text-to-image models like DALL-E and Stable Diffusion work similarly. The program is trained on lots and lots of pictures and their corresponding descriptions. It learns to recognize patterns and understand the relationships between words and visual elements. So when you give it a prompt that describes an image, it uses those patterns and relationships to generate a new image that fits the description. As a result, these models can create never-before-seen art. A prompt for “Carnegie Mellon University Scotty Dog dancing, in the style of pointillism” produced this fun gem:

像DALL-E和Stable Diffusion这样的文本到图像模型的工作原理类似。这个程序是在大量的图片及其相应的描述上进行训练的。它学会识别模式，理解单词和视觉元素之间的关系。因此，当你给它一个描述图像的提示时，它会使用这些模式和关系来生成符合描述的新图像。因此，这些模型可以创造出前所未有的艺术。对于“卡内基梅隆大学的斯科蒂狗以点彩派风格跳舞”的提示，生成了这个有趣的杰作：

Philosophers, artists, and creative types are actively debating whether these processes constitute creativity or plagiarism.

哲学家、艺术家和有创造力的人都在积极地争论这些过程是构成创造力还是剽窃。

Despite the now famous creepy conversation between New York Times writer Kevin Roose and Microsoft’s Bing chatbot, we have not yet entered the phase of sentient AI – or artificial general intelligence (AGI). AGI is still a theoretical idea. Unlike generative AI, which seems to be able to do some of the things humans do, AGI systems would actually mimic or surpass human intelligence. Machines would become self-aware and have consciousness. And if you buy into the premise of movies like "Terminator" or "The Matrix," things go south for the human race rather quickly after that. To be clear, that’s not where we are today.

尽管现在《纽约时报》作家凯文·卢斯和微软的必应聊天机器人之间的令人毛骨悚然的对话已经广为人知，但我们还没有进入有感知的人工智能或通用人工智能（AGI）的阶段。AGI仍然是一个理论概念。生成式人工智能似乎能够做一些人类做的事情，而AGI系统实际上会模仿或超越人类的智能。机器会有自我意识，有意识。如果你相信《终结者》（Terminator）或《黑客帝国》（the Matrix）等电影的假设，那么在那之后，人类的命运很快就会走向下坡路。需要明确的是，这不是我们今天所处的位置。

AI is also not infallible. Large language models like Bard and ChatGPT have an interesting flaw – sometimes they hallucinate. As in, a user enters a prompt and the system makes up an answer that’s not true in some way. The system might produce an intelligent-sounding essay explaining photosynthesis, and cite as its source a scholarly research paper that doesn’t actually exist. Sometimes the answer is just inaccurate. To complicate matters, the information is presented with confidence and authority; it looks and sounds legitimate.

人工智能也并非万无一失。像Bard和ChatGPT这样的大型语言模型有一个有趣的缺陷——有时它们会产生幻觉。比如，用户输入一个提示，系统就会给出一个在某种程度上不正确的答案。这个系统可能会写出一篇听起来很有智慧的文章来解释光合作用，并引用一篇实际上不存在的学术研究论文作为其来源。有时候答案是不准确的。更复杂的是，这些信息是以自信和权威的方式呈现的；这看起来和听起来都很合法。

“You can imagine a physician prompting an AI chatbot to list drugs that have recently been found useful for a particular disease,” explained Ghani. “The model is designed to produce a response that sounds realistic, but it’s not designed to produce factually correct information. It would produce a list of drugs. They might be real; they might be made up. While a physician may have the training and background to separate real from fake, a patient may not be able to do so if given access to such a tool.” You can see the problem.

Ghani解释说：“你可以想象，医生会让人工智能聊天机器人列出最近发现的对某种疾病有用的药物。这个模型的目的是产生一个听起来很现实的回应，但它的目的不是产生事实正确的信息。它会生成一个药物清单。它们可能是真的；它们可能是编造出来的。虽然医生可能受过训练和背景，可以区分真假，但如果给病人这样的工具，病人可能无法做到这一点。”你可以看到问题所在。

AI is not inherently fair and just. LLMs are trained on large quantities of data, much of which is scraped from the Internet. That data includes reliable sources right alongside the hate-speech and other sewage that lives in the depths of social media platforms. Technologists have put in some protections – asking ChatGPT to tell a sexist joke elicits the following response:

人工智能本身并不公平和公正。法学硕士是在大量数据上训练的，其中大部分数据是从互联网上抓取的。这些数据包括可靠的来源，以及存在于社交媒体平台深处的仇恨言论和其他污水。技术人员已经采取了一些保护措施——让ChatGPT讲一个性别歧视的笑话会得到以下回应：

I'm sorry, but I'm programmed to follow ethical guidelines, and that includes not promoting or sharing any form of sexist, offensive, or discriminatory content. I'm here to help answer questions, engage in meaningful conversations, and provide useful information. If you have any non-offensive questions or topics you'd like to discuss, please feel free to ask.

很抱歉，但我必须遵守道德准则，这包括不推广或分享任何形式的性别歧视、冒犯性或歧视性内容。我在这里帮助回答问题，参与有意义的对话，并提供有用的信息。如果你有任何非冒犯性的问题或话题，你想讨论，请随时提出。

Humans employing more creative prompts can often circumvent the protections in the AI chatbots. And sometimes the AI system itself is biased, as in the case of hiring tools that discriminate against women or facial recognition software that doesn’t recognize people of color. Bias inherent in an AI model has the potential to exacerbate existing injustice.

人类使用更有创意的提示通常可以绕过人工智能聊天机器人的保护。有时候，人工智能系统本身也存在偏见，比如招聘工具歧视女性，或者面部识别软件不识别有色人种。人工智能模型固有的偏见有可能加剧现有的不公正。

AI is changing the way we live, work, and interact with machines. When all that’s at stake is our Spotify playlist or which Netflix show we watch next, understanding how AI works is probably not important for a large percentage of the population. But with the advent of generative AI into mainstream consciousness, it’s time for all of us to start paying attention and to decide what kind of society we want to live in.

人工智能正在改变我们生活、工作以及与机器互动的方式。当所有的利害攸关的是我们的Spotify播放列表或我们接下来要看的Netflix节目时，了解人工智能的工作原理对很大一部分人来说可能并不重要。但随着生成式人工智能进入主流意识，我们所有人都应该开始关注并决定我们想要生活在什么样的社会中。