文章来源: https://backchannel.com/the-ai-takeover-is-coming-lets-embrace-it-d764d61f83a
On Tuesday, the White House released a chilling report on AI and the economy. It began by positing that “it is to be expected that machines will continue to reach and exceed human performance on more and more tasks,” and it warned of massive job losses.
周二，白宫发布了关于人工智能和经济的令人不寒而栗的报告。 它首先提出“人们预计机器将在越来越多的任务中继续达到并超过人类的表现”，并且它警告说会造成大量失业。
Yet to counter this threat, the government makes a recommendation that may sound absurd: we have to increase investment in AI. The risk to productivity and the US’s competitive advantage is too high to do anything but double down on it.
然而，为了应对这种威胁，政府提出的建议可能听起来很荒谬：我们必须增加对人工智能的投资。 生产力风险和美国的竞争优势太高，无法做任何事情，只能加倍努力。
This approach not only makes sense, but also is the only approach that makes sense. It’s easy — and justified — to worry about the millions of individual careers that something like self-driving cars and trucks will retool, but we also have chasms of need that machine learning could help fill. Our medical system is deeply flawed; intelligent agents could spread affordable, high-quality healthcare to more people in more places. Our education infrastructure is not adequately preparing students for the looming economic upheaval; here, too, AI systems could chip in where teachers are spread too thin. We might gain energy independence by developing much smarter infrastructure, as Google subsidiary DeepMind did for its parent company’s power usage. The opportunities are too great to ignore.
这种方法不仅有意义，而且是唯一有意义的方法。 很容易 - 并且有理由 - 担心数百万个人的职业生涯会像自动驾驶汽车和卡车一样重新装备，但我们也有机会学习可以帮助填补的需要。 我们的医疗系统存在严重缺陷; 智能代理可以为更多地方的更多人提供经济实惠的高质量医疗服务。 我们的教育基础设施不足以让学生为迫在眉睫的经济动荡做好准备; 在这里，人工智能系统也可以在教师分散的地方进行筹码。 我们可能通过开发更智能的基础设施来获得能源独立性，因为谷歌子公司DeepMind为其母公司的电力使用做了这些。 机会太大了，不容忽视。
More important, we have to think beyond narrow classes of threatened jobs, because today’s AI leaders—at Google and elsewhere—are already laying the groundwork for an even more ambitious vision, the former pipe dream that is general artificial intelligence.
更重要的是，我们必须超越狭隘的受威胁工作类别，因为今天的人工智能领导者 - 谷歌和其他地方 - 已经为更加雄心勃勃的愿景奠定了基础，这个前梦想是普通的人工智能。
To visit the front lines of the great AI takeover is to observe machine learning systems routinely drubbing humans in narrow, circumscribed domains. This year, many of the most visible contestants in AI’s face-off with humanity have emerged from Google. In March, the world’s top Go player weathered a humbling defeat against DeepMind’s AlphaGo. Researchers at DeepMind also produced a system that can lip-read videos with an accuracy that leaves humans in the dust. A few weeks ago, Google computer scientists working with medical researchers reported an algorithm that can detect diabetic retinopathy in images of the eye as well as an ophthalmologist can. It’s an early step toward a goal many companies are now chasing: to assist doctors by automating the analysis of medical scans.
访问伟大人工智能接管的前线是观察机器学习系统经常在狭窄的，受限制的领域中摧毁人类。 今年，人工智能面对人性化的许多最明显的参赛者都来自谷歌。 三月份，这位世界顶级的Go玩家经历了对DeepMind的AlphaGo的惨败。 DeepMind的研究人员还制作了一个系统，可以精确地读取视频，使人类处于灰尘中。 几个星期前，与医学研究人员合作的谷歌计算机科学家报告了一种算法，可以检测眼睛图像中的糖尿病视网膜病变以及眼科医生。 这是许多公司正在追求的目标的早期步骤：通过自动分析医疗扫描来协助医生。
Also this fall, Microsoft unveiled a system that can transcribe human speech with greater accuracy than professional stenographers. Speech recognition is the basis of systems like Cortana, Alexa, and Siri, and matching human performance in this task has been a goal for decades. For Microsoft chief speech scientist XD Huang, “It’s personally almost like a dream come true after 30 years.”
同样在今年秋天，微软公布了一种能够比专业速记员更准确地转录人类语音的系统。 语音识别是Cortana，Alexa和Siri等系统的基础，在此任务中匹配人类表现已成为数十年的目标。 对于微软首席演讲科学家XD Huang来说，“30年后，它的个人生活几乎就像一个梦想成真。”
But AI’s 2016 victories over humans are just the beginning. Emerging research suggests we will soon move from these slim slivers of intelligence to something richer and more complex. Though a true general intelligence is at least decades away, society will still see massive change as these systems acquire an ever-widening circle of mastery. That’s why the White House (well, at least while Obama’s still in office) isn’t shrinking from it. We are in the midst of developing a powerful force that will transform everything we do.
但人工智能2016年对人类的胜利只是一个开始。 新兴研究表明，我们很快就会从这些纤细的智慧中转向更丰富，更复杂的东西。 虽然真正的一般情报至少需要几十年的时间，但随着这些系统获得越来越广泛的掌控，社会仍将看到巨大的变化。 这就是为什么白宫（好吧，至少在奥巴马执政期间）并没有萎缩。 我们正在发展一股强大的力量，将改变我们所做的一切。
To ignore this trend — to not plunge headlong into understanding it, shaping it, monitoring it — might well be the biggest mistake a country could make.
忽视这种趋势 - 不要急于理解它，塑造它，监控它 - 可能是一个国家可能犯下的最大错误。
Training one system to do many things is exactly what it takes to develop a general intelligence, and juicing up that process is now a core focus of AI boosters. Earlier this month OpenAI, the research consortium dreamed up by Elon Musk and Sam Altman, unveiled Universe, an environment for training systems that are not just accomplished at a single task, but that can hop around and become adept at various activities.
培养一个系统来做很多事情正是开发一般智能所需要的，而榨汁过程现在是AI助推器的核心焦点。 本月早些时候，由艾伦·马斯克和山姆·奥特曼共同设想的研究财团推出了Universe，这是一个训练系统的环境，不仅可以在一项任务中完成，而且可以在各种活动中蹦蹦跳跳。
As cofounder Sustkever puts it, “If you try to look forward and see what it is exactly we mean by “intelligence,” it definitely involves not just solving one problem, but a large number of problems. But what does it mean for a general agent to be good, to be intelligent? These are not completely obvious questions.”
正如联合创始人Sustkever所说的那样，“如果你试图向前看，看看我们所说的”智能“究竟是什么意思，它肯定不仅涉及解决一个问题，还涉及大量问题。 但是，对于一般的经纪人来说，做好事，聪明是什么意思呢？ 这些都不是完全明显的问题。“
So he and his team designed Universe as a way to help others measure the general problem-solving abilities of AI agents. It includes about a thousand Atari games, Flash games, and browser tasks. If you were to enter whatever AI you’re building into the training ring that is Universe, it would be equipped with the same tools a human uses to manipulate a computer: a screen on which to observe the action, and a virtual keyboard and mouse.
因此，他和他的团队将Universe设计为一种帮助他人衡量AI代理人解决问题能力的方法。 它包括大约一千个Atari游戏，Flash游戏和浏览器任务。 如果您要将您正在构建的任何AI输入到Universe的训练环中，它将配备人类用来操作计算机的相同工具：用于观察动作的屏幕，以及虚拟键盘和鼠标。
The intent is for an AI to learn how to navigate one Universe environment, such as Wing Commander III, then apply that experience to quickly get up to speed in the next environment, which could be another game, such as World of Goo, or something as different as Wolfram Mathematica. A successful AI agent would display some transfer learning, with a degree of agility and reasoning.
目的是让AI学习如何导航一个Universe环境，例如Wing Commander III，然后应用这种体验在下一个环境中快速加速，这可能是另一个游戏，例如World of Goo，或者其他什么东西 与Wolfram Mathematica不同。 一个成功的AI代理会显示一些转移学习，具有一定的敏捷性和推理能力。
This approach is not without precedent. In 2013, DeepMind revealed a single deep learning-based algorithm that discovered, on its own, how to play six out of seven Atari games on which it was tested. For three of those games — Breakout, Enduro, and Pong — it outperformed a human expert player. Universe is a sort of scaled-up version of that DeepMind success story.
这种方法并非没有先例。 2013年，DeepMind发布了一种基于深度学习的算法，该算法独立发现了如何在测试它的七个Atari游戏中发挥六个。 对于其中三款游戏 - Breakout，Enduro和Pong--它的表现优于人类专家。 Universe是DeepMind成功故事的一种扩展版本。
As Universe grows, AI trainees can start learning innumerable useful computer-related skills. After all, it is essentially a portal into the world of any contemporary desk jockey. The diversity of Universe environments might even allow AI agents to pick up some broad world knowledge that otherwise would be tough to collect.
随着宇宙的发展，人工智能培训生可以开始学习无数有用的计算机相关技能。 毕竟，它本质上是进入任何当代桌面骑师世界的门户。 宇宙环境的多样性甚至可能允许AI代理人获得一些本来难以收集的广泛的世界知识。
It’s a bit of a leap from a Flash-and-Atari champion to an agent that improves the quality of healthcare, but that’s because our intelligent systems are still in kindergarten. For many years, AI hadn’t made it even this far. Now it is on the path to first grade, middle school, and eventually, advanced degrees.
这是一个从Flash-and-Atari冠军到提高医疗质量的代理商的一次飞跃，但那是因为我们的智能系统仍然在幼儿园。 多年来，人工智能甚至没有做到这一点。 现在它正在通往一年级，初中，最终获得高级学位。
Yes, the outcome is uncertain. Yes, it’s totally scary. But we have a choice now. We can try to shut down this murky future that we can neither fully control nor predict, and run the risk that the technology seeps out unbidden, potentially triggering massive displacement. Or we can actively try to guide it to the paths of greatest social gain, and encourage the future we want to see.
是的，结果不确定。 是的，这完全是可怕的。 但我们现在有一个选择。 我们可以试图关闭这个我们既无法完全控制也无法预测的模糊未来，并冒着技术渗出不受约束，可能引发大规模流离失所的风险。 或者我们可以积极地尝试引导它走上最大的社会收益之路，并鼓励我们希望看到的未来。
I’m with the White House on this one. A deep learning-powered world is coming, and we might as well rush right into it.
我和白宫在这一个。 一个深度学习动力的世界即将到来，我们也可能会紧锣密鼓地进入它。