Supersmart algorithms won’t take all the jobs, but they are learning faster than ever, doing everything from medical diagnostics to serving up ads.
绝顶聪明的算法不会拿走所有的工作，但它们在做从医疗诊断到广告推送等一切事情时，学习得比以往更快。
By TOM SIMONITE
[an Ideas contributor for WIRED. He is a senior partner at Flagship Pioneering, a firm in Boston that creates, builds, and funds companies that solve problems in health, food, and sustainability. From 2004 to 2017 he was the editor in chief and publisher of MIT Technology Review. Before that he was the editor of Red Herring magazine, a business magazine that was popular during the dot-com boom.]
Feb 01, 2018
ARTIFICIAL INTELLIGENCE IS overhyped—there, we said it. It’s also incredibly important.
我们曾说人工智能是言过其实的。这也是非常重要的。
Superintelligent algorithms aren’t about to take all the jobs or wipe out humanity. But software has gotten significantly smarter of late. It’s why you can talk to your friends as an animated poop on the iPhone X using Apple’s Animoji, or ask your smart speaker to order more paper towels.
超级智能的算法并不是要拿走所有的工作或消灭人类。但是近来软件变得更聪明了。这就是为什么你可以使用苹果的Animoji表情像iPhone X上的动画便便与你的朋友交谈，或者要求你的智能音箱订购更多的纸巾。
Tech companies’ heavy investments in AI are already changing our lives and gadgets, and laying the groundwork for a more AI-centric future.
科技公司对人工智能的大量投资已经改变了我们的生活和工具，并为更加以智能为中心的未来打下了基础。
The current boom in all things AI was catalyzed by breakthroughs in an area known as machine learning. It involves “training” computers to perform tasks based on examples, rather than by relying on programming by a human. A technique called deep learning has made this approach much more powerful. Just ask Lee Sedol, holder of 18 international titles at the complex game of Go. He got creamed by software called AlphaGo in 2016.
目前，所有人工智能的繁荣都是通过机器学习领域的突破实现的。它包括“训练”计算机来执行基于示例的任务，而不是依靠人类编程。一种叫做“深度学习”的技术使这种方法更加强大。问问在复杂的围棋比赛中获得18个国际冠军的李世石。他在2016年被称为AlphaGo的软件彻底打败了。
For most of us, the most obvious results of the improved powers of AI are neat new gadgets and experiences such as smart speakers, or being able to unlock your iPhone with your face. But AI is also poised to reinvent other areas of life. One is health care. Hospitals in India are testing software that checks images of a person’s retina for signs of diabetic retinopathy, a condition frequently diagnosed too late to prevent vision loss. Machine learning is vital to projects in autonomous driving, where it allows a vehicle to make sense of its surroundings.
对我们大多数人来说，人工智能增强最明显的结果是整洁的新设备，以及智能扬声器，或者能用你的脸解锁你的iPhone等这些体验。但人工智能也准备重塑生活的其他领域。一个是医疗保健。印度的医院正在测试一种软件，该软件检查一个人的视网膜的图像，以寻找糖尿病视网膜病变的征兆，这种情况常常被诊断得太迟以致于无法防止视力丧失。机器学习对于自主驾驶的项目至关重要，因为它可以让车辆了解周围环境。
There’s evidence that AI can make us happier and healthier. But there’s also reason for caution. Incidents in which algorithms picked up or amplified societal biases around race or gender show that an AI-enhanced future won’t automatically be a better one.
有证据表明人工智能可以使我们更幸福更健康。但也有谨慎的理由。算法在种族或性别上发现或放大了社会偏见的事件表明，人工智能增强的未来不会自动成为一个更好的未来。
Artificial intelligence as we know it began as a vacation project. Dartmouth professor John McCarthy coined the term in the summer of 1956, when he invited a small group to spend a few weeks musing on how to make machines do things like use language. He had high hopes of a breakthrough toward human-level machines. “We think that a significant advance can be made,” he wrote with his co-organizers, “if a carefully selected group of scientists work on it together for a summer.”
正如我们所知，人工智能开始作为一个度假项目。达特茅斯教授约翰·麦卡锡在1956年的夏天创造了这个词，当时他邀请一小组人花几个星期沉思如何让机器做像使用语言这样的事情。他对人类级机器的突破寄予厚望。他与他的共同组织者写道，“我们认为如果一组精心挑选的科学家一起研究一个夏天的话，可以取得重大进展。”
Those hopes were not met, and McCarthy later conceded that he had been overly optimistic. But the workshop helped researchers dreaming of intelligent machines coalesce into a proper academic field.
这些希望并没有得到满足，麦卡锡后来承认他过于乐观了。但是这个研讨会帮助研究者们梦想将智能机器融合到一个适当的学术领域。
MOMENTS THAT SHAPED AI
塑造人工智能的时刻
1956
The Dartmouth Summer Research Project on Artificial Intelligence coins the name of a new field concerned with making software smart like humans.
关于人工智能的达特茅斯夏季研究项目命名一个使软件像人类一样聪明的新领域的名称。
1965
Joseph Weizenbaum at MIT creates Eliza, the first chatbot, which poses as a psychotherapist.
麻省理工学院的Joseph Weizenbaum创造了第一个聊天机器人Eliza，它充当一个心理治疗师。
1975
Meta-Dendral, a program developed at Stanford to interpret chemical analyses, makes the first discoveries by a computer to be published in a refereed journal.
由斯坦福开发用于解释化学分析的程序Meta-Dendral，完成了首次由计算机作出的发现并发表在杂志。
1987
A Mercedes van fitted with two cameras and a bunch of computers drives itself 20 kilometers along a German highway at more than 55 mph, in an academic project led by engineer Ernst Dickmanns.
在一个由工程师Ernst Dickmanns领导的学术项目中，一辆装有两台相机和一堆电脑驱动器的梅赛德斯货车自己沿着德国公路以超过55英里的速度行驶了20公里。
1997
IBM’s computer Deep Blue defeats chess world champion Garry Kasparov.
IBM计算机深蓝击败了国际象棋世界冠军Garry Kasparov。
2004
The Pentagon stages the Darpa Grand Challenge, a race for robot cars in the Mojave Desert that catalyzes the autonomous-car industry.
五角大楼上演了在Mojave沙漠进行的机器人骑车比赛的这样一个DARPA大挑战，它催生了自动汽车工业。
2012
Researchers in a niche field called deep learning spur new corporate interest in AI by showing their ideas can make speech and image recognition much more accurate.
一个名为“深度学习”领域中的研究人员通过展示他们的想法能够使语音和图像识别更加准确，激发了新公司对人工智能的兴趣。
2016
AlphaGo, created by Google unit DeepMind, defeats a world champion player of the board game Go.
由谷歌部门DeepMind创建的AlphaGo，击败围棋世界冠军。
Early work often focused on solving fairly abstract problems in math and logic. But it wasn’t long before AI started to show promising results on more human tasks. In the late 1950s Arthur Samuel created programs that learned to play checkers. In 1962 one scored a win over a master at the game. In 1967 a program called Dendral showed it could replicate the way chemists interpreted mass-spectrometry data on the makeup of chemical samples.
早期的工作往往集中在解决相当抽象的数学和逻辑问题。但是不久后，人工智能就开始在更多的人类任务上显示出有希望的结果。在上世纪50年代后期，Arthur Samuel创造了学习玩跳棋的程序。在1962年的比赛中，在与大师的对决中赢得了胜利。在1967年，一个被称为Dendral的程序显示它可以复制化学家解释由化学样品组成的质谱数据的方式。
As the field of AI developed, so did different strategies for making smarter machines. Some researchers tried to distill human knowledge into code or come up with rules for tasks like understanding language. Others were inspired by the importance of learning to human and animal intelligence. They built systems that could get better at a task over time, perhaps by simulating evolution or by learning from example data. The field hit milestone after milestone, as computers mastered more tasks that could previously be done only by people.
随着人工智能领域的发展，制造智能机器的不同策略也随之发展起来。一些研究人员试图将人类知识提炼成代码，或为理解语言等任务制定规则。其他人从学习人类和动物智力的重要性中得到启发。他们建立了一个可能通过模拟进化或从示例数据中学习就可以在一段时间内更好地完成任务的系统。这个领域不断突破，因为计算机掌握了更多以前只能由人完成的任务。
Deep learning, the rocket fuel of the current AI boom, is a revival of one of the oldest ideas in AI. The technique involves passing data through webs of math loosely inspired by how brain cells work, known as artificial neural networks. As a network processes training data, connections between the parts of the network adjust, building up an ability to interpret future data.
当前被视为AI热潮的火箭燃料的深度学习，是AI最古老的思想之一的复兴。这项技术包括通过受脑细胞如何工作的启发的数学网，即人工神经网络，来传递数据。当网络处理训练数据时，网络各部分之间的连接进行调整，从而建立了解释未来数据的能力。
Artificial neural networks became an established idea in AI not long after the Dartmouth workshop. The room-filling Perceptron Mark 1 from 1958, for example, learned to distinguish different geometric shapes, and got written up in The New York Times as the “Embryo of Computer Designed to Read and Grow Wiser.” But neural networks tumbled from favor after an influential 1969 book co-authored by MIT’s Marvin Minsky suggested they couldn’t be very powerful.
人工神经网络在达特茅斯研讨会之后不久就成为了AI中的知名理念。例如，1958年充满整间屋子的感知机Mark 1，学会了区分不同的几何形状，并在纽约时报上被报道成“设计来阅读并变得更智慧的计算机雏形”。但是在1969年麻省理工学院的Marvin Minsky合著的极具影响力的书中认为它们并不是很强大后神经网络不再受青睐。
Not everyone was convinced, and some researchers kept the technique alive over the decades. They were vindicated in 2012, when a series of experiments showed that neural networks fueled with large piles of data and powerful computer chips could give machines new powers of perception.
并非每个人都信服，一些研究人员在几十年里保持着这种技术的生命力。它们在2012年被证明是正确的，当时一系列实验表明，以大量数据和强大的计算机芯片为动力的神经网络，可以给机器带来新的感知能力。
In one notable result, researchers at the University of Toronto trounced rivals in an annual competition where software is tasked with categorizing images. In another, researchers from IBM, Microsoft, and Google teamed up to publish results showing deep learning could also deliver a significant jump in the accuracy of speech recognition. Tech companies began frantically hiring all the deep-learning experts they could find.
一个值得注意的结果，多伦多大学的研究人员在一年一度的竞赛中击败了对手，该比赛中软件的任务是分类图像。另一方面，来自IBM、微软和谷歌的研究人员联手发布结果，表明深度学习也能显著提高语音识别的准确性。科技公司开始疯狂地雇佣他们能找到的所有深度学习专家。
Even if progress on making artificial intelligence smarter stops tomorrow, don’t expect to stop hearing about how it’s changing the world.
即使明天使人工智能更聪明的进展停止了，也别指望听不到它是如何改变世界的。
Big tech companies such as Google, Microsoft, and Amazon have amassed strong rosters of AI talent and impressive arrays of computers to bolster their core businesses of targeting ads or anticipating your next purchase.
谷歌，微软和亚马逊这样的大公司已经积聚了AI人才和令人印象深刻的计算机阵列等强大的阵容来支持他们的广告定位或预期下次购买等核心业务。
They’ve also begun trying to make money by inviting others to run AI projects on their networks, which will help propel advances in areas such as health care or national security. Improvements to AI hardware, growth in training courses in machine learning, and open source machine-learning projects will also accelerate the spread of AI into other industries.
他们也开始尝试通过邀请其他人在他们的网络上运行人工智能项目来赚钱，这将有助于推动诸如卫生保健或国家安全等领域的进步。对人工智能硬件的改进、机器学习培训课程以及开源机器学习项目的增加也将加速人工智能在其他行业的传播。
Meanwhile, consumers can expect to be pitched more gadgets and services with AI-powered features. Google and Amazon in particular are betting that improvements in machine learning will make their virtual assistants and smart speakers more powerful. Amazon, for example, has devices with cameras to look at their owners and the world around them.
同时，消费者可以期待更多的带有人工智能特征的小工具和服务。特别地，谷歌和亚马逊认为，改善机器学习将使他们的虚拟助手和智能音箱更加强大。例如，亚马逊有带有摄像头的设备来观察它们的主人和它们周围的世界。
YOUR AI DECODER RING
你的AI解码器环
Artificial intelligence
人工智能
The development of computers capable of tasks that typically require human intelligence.
能够完成通常需要人类智慧才能完成任务的计算机的发展。
Machine learning
机器学习
Using example data or experience to refine how computers make predictions or perform a task.
使用示例数据或经验来改进计算机进行预测或执行任务的方式。
Deep learning
深度学习
A machine learning technique in which data is filtered through self-adjusting networks of math loosely inspired by neurons in the brain.
一种通过受脑神经元启发产生的自我调节的数学网络来过滤数据的机器学习技术。
Supervised learning
监督学习
Showing software labeled example data, such as photographs, to teach a computer what to do.
向软件展示照片等标记的示例数据，教计算机做什么。
Unsupervised learning
无监督学习
Learning without annotated examples, just from experience of data or the world—trivial for humans but not generally practical for machines. Yet.
只从数据的经验或对人类来说微不足道但对机器来说一般不切实可行的事情，学习没有注释的例子。
Reinforcement learning
强化学习
Software that experiments with different actions to figure out how to maximize a virtual reward, such as scoring points in a game.
用不同动作的实验来找出如何最大化如游戏中的得分等虚拟奖励的软件。
Artificial general intelligence
通用人工智能
As yet nonexistent software that displays a humanlike ability to adapt to different environments and tasks, and transfer knowledge between them.
显示一个适应不同的环境和任务，以及它们之间的知识转移等类人能力的还不存在的软件。
The commercial possibilities make this a great time to be an AI researcher. Labs investigating how to make smarter machines are more numerous and better-funded than ever. And there’s plenty to work on: Despite the flurry of recent progress in AI and wild prognostications about its near future, there are still many things that machines can’t do, such as understanding the nuances of language, common-sense reasoning, and learning a new skill from just one or two examples. AI software will need to master tasks like these if it is to get close to the multifaceted, adaptable, and creative intelligence of humans. One deep-learning pioneer, Google’s Geoff Hinton, argues that making progress on that grand challenge will require rethinking some of the foundations of the field.
商业上的可能性使这成为一个人工智能研究人员的好时机。研究如何制造更聪明的机器的实验室比以往任何时候都多，资金也更充足。还有很多工作要做：尽管有一系列AI的最近进展和关于它不久的将来的一些轻率预言，但仍然有许多机器不能做的事情，如理解语言的细微差别，常识推理，以及从一两个例子中学习新技能等。如果人工智能软件接近人类的多面性、适应性和创造性的智能，那它将需要掌握这样的任务。深度学习先锋，谷歌的Geoff Hinton，认为在大挑战中取得进展需要重新思考领域中的一些基础。
As AI systems grow more powerful, they will rightly invite more scrutiny. Government use of software in areas such as criminal justice is often flawed or secretive, and corporations like Facebook have begun confronting the downsides of their own life-shaping algorithms. More powerful AI has the potential to create worse problems, for example by perpetuating historical biases and stereotypes against women or black people. Civil-society groups and even the tech industry itself are now exploring rules and guidelines on the safety and ethics of AI. For us to truly reap the benefits of machines getting smarter, we’ll need to get smarter about machines.
随着AI系统变得越来越强大，它们将理所当然地招致更多的审查。刑事司法等领域软件的政府使用往往是有缺陷的或隐秘的，脸书等公司已经开始面对自己的塑造生活算法的缺点。更强大的人工智能有可能产生更坏的问题，例如，对妇女或黑人永久性的历史偏见和成见。民间社会团体，甚至科技产业本身，正在探索有关人工智能安全和伦理的规则和准则。为了让我们真正获得机器变得更聪明的好处，我们需要使机器变得更聪明。
What The AI Behind AlphaGo Can Teach Us About Being Human
AlphaGo背后的AI可以使我们明白什么关于人类的信息
Drama, emotion, server racks, and existential questions. Find them all in our on-the-scene account from the triumph of Google’s Go-playing bot over top player Lee Sedol in South Korea.
戏剧、情感、服务器以及与人类存在有关的问题。都能在谷歌围棋机器人对韩国顶级选手李世石的伟大胜利中找到。
John McCarthy, Father Of AI And Lisp, Dies At 84
人工智能和Lisp之父约翰·麦卡锡，享年84岁。
WIRED’s 2011 obituary of the man who coined the term artificial intelligence gives a sense of the origins of the field. McCarthy’s lasting, and unfulfilled, dream of making machines as smart as humans still entrances many people working on AI today.
WIRED在2011年的一份讣告，他创造了人工智能这个术语，这被视为该领域的起源。麦卡锡的使机器像人类一样聪明这样一个久未实现的梦想，今天仍然吸引了许多从事人工智能的人们。
Are We Ready for Intimacy With Androids?
我们准备好与机器人亲密了吗？
People have always put themselves into their technological creations—but what happens when those artificial creations look and act just like people? Hiroshi Ishiguro builds androids on a quest to reverse engineer how humans form relationships. His progress may provide a preview of issues we’ll encounter as AI and robotics evolve.
人们总是把自己放在他们的技术作品中，但是当这些人造物看起来并且动作上都像人一样时会发生什么呢？Hiroshi Ishiguro构造了机器人以寻求逆向工程出人类是如何形成关系的。他的进展可能会为我们在人工智能和机器人技术发展过程中遇到的问题提供一个预览。
When it Comes to Gorillas, Google Photos Remains Blind
说到大猩猩，谷歌Photos仍然不能识别。
The limitations of AI systems can be as important as their capabilities. Despite improvements in image recognition over recent years, WIRED found Google still doesn’t trust its algorithms not to mix up apes and black people.
人工智能系统的局限性与它们的能力一样重要。尽管近年来图像识别技术有所改进，但WIRED发现谷歌仍然不相信它的算法不会把猿和黑人混在一起。
Why Artificial Intelligence is Not Like Your Brain—Yet
为什么人工智能不像你的大脑？
You might hear companies, marketers, or drinking companions say AI algorithms work like the brain. They’re wrong, and here’s why.
你可能会听到公司、营销人员或正在喝酒的朋友说人工智能算法像大脑一样工作。他们错了，这就是为什么。
Artificial Intelligence Seeks an Ethical Conscience
人工智能寻求伦理良知
As companies and governments rush to embrace ever-more powerful AI, researchers have begun to ponder ethical and moral questions about the systems they build, and how they’re put to use.
随着公司和政府争相拥抱越来越强大的人工智能，研究人员已经开始思考关于他们构建的系统的伦理和道德问题以及如何使用它们。
A ‘Neurographer’ Puts The Art In Artificial Intelligence
Neurographer将艺术置于人工智能中
Some artists are repurposing the AI techniques tech companies use to process images into a new creative tool. Mario Klingemann’s haunting images, for example, have been compared to the paintings of Francis Bacon.
一些艺术家再利用科技公司使用的人工智能技术，来将图像处理成一个新的创意工具。例如，Mario Klingemann令人难忘的图像，已经可以与弗朗西斯·培根的画作相提并论了。
Written on February 1st , 2018 by 李军