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什么是人工智能?

1955年9月,达特茅斯学院年轻的数学助理教授约翰·麦卡锡(John McCarthy)大胆提出“原则上可以精确地描述学习的各个方面或智能的任何其他特征,从而可以制造出机器来对其进行仿真。”

麦卡锡称这个新的研究领域为“人工智能”,并建议由10名科学家组成的小组进行为期两个月的努力,可以在开发可以“使用语言,形成抽象和概念,解决目前保留的各种问题的机器”方面取得重大进展。为人类,并改善自己。”

当时,科学家乐观地认为,我们很快就会想到机器可以完成人类可以完成的任何工作。 六十年后的今天,计算机科学和机器人技术的进步帮助我们使许多以前需要人类进行体力劳动和认知劳动的任务实现了自动化。

但是,正如麦卡锡(McCarthy)所构想的那样,真正的人工智能继续使我们迷惑。

AI到底是什么?

人工智能面临的一大挑战是它是一个广义术语,并且对其定义尚无明确共识。

如前所述,麦卡锡提出的人工智能将以人类的方式解决问题:“最终的努力是制造能够解决问题并实现人类与人类目标的计算机程序,”麦卡锡说。

卡内基·梅隆大学计算机科学系主任安德鲁·摩尔在2017年接受《福布斯》采访时提供了一个更现代的术语定义:“人工智能是使计算机以直到人们最近才认为需要人类操作的方式进行的科学和工程设计。情报。”

不要期望AI像人一样玩 But our understanding of "human intelligence" and our expectations of technology are constantly evolving. Zachary Lipton, the editor of Approximately Correct, describes the term AI as "aspirational, a moving target based on those capabilities that humans possess but which machines do not." In other words, the things we ask of AI change over time.

For instance, In the 1950s, scientists viewed chess and checkers as great challenges for artificial intelligence. But today, very few would consider chess-playing machines to be AI. Computers are already tackling much more complicated problems, including detecting cancer, driving cars, and processing voice commands.

Narrow AI vs. General AI

The first generation of AI scientists and visionaries believed we would eventually be able to create human-level intelligence.

But several decades of AI research have shown that replicating the complex problem-solving and abstract thinking of the human brain is supremely difficult. For one thing, we humans are very good at generalizing knowledge and applying concepts we learn in one field to another. We can also make relatively reliable decisions based on intuition and with little information. Over the years, human-level AI has become known as artificial general intelligence (AGI) or strong AI.

The initial hype and excitement surrounding AI drew interest and funding from government agencies and large companies. But it soon became evident that contrary to early perceptions, human-level intelligence was not right around the corner, and scientists were hard-pressed to reproduce the most basic functionalities of the human mind. In the 1970s, unfulfilled promises and expectations eventually led to the "AI winter, " a long period during which public interest and funding in AI dampened.

It took many years of innovation and a revolution in deep-learning technology to revive interest in AI. But even now, despite enormous advances in artificial intelligence, none of the current approaches to AI can solve problems in the same way the human mind does, and most experts believe AGI is at least decades away.

' Today's Mathletes - AI Math Equations' The flipside, narrow or weak AI doesn' t aim to reproduce the functionality of the human brain, and instead focuses on optimizing a single task. Narrow AI has>

许多科学家认为,我们最终将创建AGI,但有些科学家对思维机时代有反乌托邦式的看法。 2014年,著名的英国物理学家史蒂芬·霍金(Stephen Hawking)将AI描述为对人类的生存威胁,并警告说“充分的人工智能可能意味着人类的终结”。

2015年,Y Combinator总裁Sam Altman和Tesla首席执行官Elon Musk(另两位AGI信徒)共同创立了OpenAI,这是一家非营利性研究实验室,旨在以造福全人类的方式创建人工智能。 (马斯克自此出发。)

其他人则认为,人工智能是没有意义的目标。 “我们不需要复制人。这就是为什么我专注于拥有帮助我们的工具,而不是复制我们已经知道如何做的事情。我们希望人与机器合作并做他们自己无法做的事情,” Google研究总监Peter Norvig说。

诺维格(Norvig)等科学家认为,狭窄的AI可以帮助执行重复繁琐的任务并使人类变得更有生产力。 例如,医生可以使用AI算法高速检查X射线扫描,使他们可以看到更多的患者。 狭窄的AI的另一个例子是与网​​络威胁作斗争:安全分析师可以使用AI在通过公司网络传输的千兆字节数据中查找数据泄露的信号。

基于规则的AI与机器学习

早期的AI创建工作专注于将人类的知识和情报转变为静态规则。 程序员必须为定义AI行为的每条规则精心编写代码(if-then语句)。 基于规则的AI(后来被称为“良好的老式人工智能”(GOFAI))的优势在于,人类可以完全控制自己开发的系统的设计和行为。

在规则明确的领域中,基于规则的AI仍然非常流行。 一个例子是视频游戏,其中开发人员希望AI提供可预测的用户体验。

GOFAI的问题在于,与麦卡锡的最初假设相反,我们无法以可以转化为计算机规则的方式精确地描述学习和行为的各个方面。 例如,定义用于识别语音和图像的逻辑规则(人类本能地完成的一项复杂任务)是经典AI历来苦苦挣扎的领域。

人工智能的未来 An alternative approach to creating artificial intelligence is machine learning. Instead of developing rules for AI manually, machine-learning engineers "train" their models by providing them with a massive amount of samples. The machine-learning algorithm analyzes and finds patterns in the training data, then develops its own behavior. For instance, a machine-learning model can train on large volumes of historical sales data for a company and then make sales forecasts.

Deep learning, a subset of machine learning, has become very popular in the past few years. It' s especially good at processing unstructured data such as images, video, audio, and text documents. For instance, you can create a deep-learning image classifier and train it on millions>

深度学习模型的挑战之一是,它们基于训练数据来开发自己的行为,这使它们变得复杂且不透明。 通常,即使是深度学习专家也很难解释他们创建的AI模型的决策和内部工作原理。

人工智能的例子有哪些?

以下是AI在不同领域带来巨大变化的一些方式。

自动驾驶汽车:人工智能的发展使我们非常接近实现数十年的自动驾驶梦想。 人工智能算法是使自动驾驶汽车能够感知周围环境的主要组件之一,它可以从安装在车辆周围的摄像头中获取信息,并检测道路,交通标志,其他汽车和人员等物体。

数字助理和智能扬声器: Siri,Alexa,Cortana和Google Assistant使用人工智能将口语转换为文本并将文本映射为特定命令。 人工智能可以帮助数字助理理解口语中的细微差别,并合成类似人的声音。

如何使用Google翻译应用 The Future of AI

In our quest to crack the code of AI and create thinking machines, we' ve learned a lot about the meaning of intelligence and reasoning. And thanks to advances in AI, we are accomplishing tasks alongside our computers that were once considered the>

AI所涉足的新兴领域包括音乐和艺术,其中AI算法正在彰显其独特的创造力。 人们也希望人工智能能够帮助应对气候变化,照顾老年人,并最终创造一个人类根本不需要工作的乌托邦式的未来。

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还有人担心,人工智能将导致大量失业,破坏经济平衡,引发另一场世界大战,并最终将人类带入奴隶制。

我们仍然不知道AI会朝哪个方向发展。 但是随着人工智能科学技术的持续稳定发展,我们对AI的期望和定义将发生变化,我们今天所认为的AI可能会成为未来计算机的普通功能。