Learning how to learn, by letting autonomous agents interact with the world. Why big tech companies like Facebook and Google hire roboticists to bring life to their excess meeting rooms.

通过让自主代理与世界互动来学习如何学习。 为什么像Facebook和Google这样的大型科技公司聘请机器人专家来充实自己多余的会议室。

This was originally posted on my free newsletter on robotics & automation, Democratizing Automation.

这最初发布在我的免费新闻稿中，有关机器人技术与自动化， 民主自动化 。

You’re wandering through the brother’s other tech office, and you see a moderately sized meeting room filled with robots. This is not what you expect when you visit a friend to try out the new micro-cafe in the office of their ad-tech startup (well, when visitors were allowed), but it is increasingly becoming a reality.

您正在兄弟的另一个技术办公室中徘徊，您会看到一个中等大小的会议室，里面装满了机器人。 当您拜访朋友在其广告技术初创公司的办公室中尝试新的微型咖啡馆时，这不是您期望的(嗯，当允许访问者时)，但是它正逐渐成为现实。

Large technology companies invest large amounts of money in exploratory research ($10-$20billion per year among the biggest players) and hold onto even more money for a rainy day (Apple, Microsoft, Google, Amazon, and Facebook jointly have $550billion in cash). In terms of billions, hiring a couple of robotics researchers is pennies in a sea. It also collects more top talent under their roof, but I think that helps shorter term than I am considering.

大型科技公司在探索性研究中投入大量资金( 最大的公司每年10到200亿美元 )，在下雨天还要拿更多的钱(苹果，微软，谷歌，亚马逊和Facebook共同拥有5500亿美元的现金) )。 就数十亿美元而言，招聘几个机器人研究人员简直是天花乱坠。 它还收集了更多的顶尖人才，但我认为这比我考虑的要短。

The outlook for robotics as an industry is nearly-exponential impact in the next decade (or sooner, pending pandemic continuation as I wrote about in detail). The portion of this that technology companies are looking to capture is those robots embodying their algorithms in your homes. As I wrote about in Recommendations are a Game and Automated,

在接下来的十年中，机器人技术作为一个行业的前景将产生近乎指数级的影响(或更早地，如我所详细描述的那样，大流行将继续)。 技术公司希望捕获的部分是那些在您的家庭中体现其算法的机器人。 正如我在《 推荐是游戏和自动化》中所写，

A general theme of recommender systems and these platforms is: if we can predict what the user wants to do, then we can make that feature into our system.

推荐系统和这些平台的 一般主题 是：如果我们可以预测用户想要做什么，那么我们可以作出这样的功能到我们的系统。

Extend this to agents that take up space in your life and home, and the problem compounds: there is no escaping devices by turning off your data or closing your app if it can come up and ask how you are doing.

将此问题扩展到占用您的生活和家中空间的代理，问题就会加剧：关闭数据或关闭应用程序( 如果可以启动并询问您的工作状况)不会导致设备逃脱。

I am a big proponent of these technologies, but the framing of (one important reason) why the large companies are investing heavily in the area is important. There are many, many more huge gains that autonomous agents can have in homes — including safety for at-risk populations living alone, helping with chores, interfacing with your digital life (email) in conversation, and more. We need to make sure we build this right.

我是这些技术的大力支持者，但是，大公司为什么要在该领域进行大量投资(一个重要原因)的框架很重要。 自治机构可以在房屋中获得很多很多好处，包括为高危人群提供安全的生活，帮助他们处理家务，与您的数字生活(电子邮件)进行对话等。 我们需要确保建立正确的权利。

体现的AI (Embodied AI)

Giving autonomous agents the ability to interact with the physical world.

使自治代理具有与物理世界交互的能力。

I think this a more nuanced topic than only translating state-of-the-art approaches in artificial intelligence and machine learning — learning with physical interaction is the first type of learning we experience. Children learn what new objects are by touching them and how they interact with their budding lives.

我认为，这不仅仅是在人工智能和机器学习中翻译最先进的方法，而是更细微的话题-通过物理交互进行学习是我们体验到的第一类学习。 孩子们通过触摸它们来了解什么是新物体，以及它们如何与新生命互动。

The tasks of trying to learn in hardware has different constraints: samples are more valuable (we cannot run hardware forever), the results are interpretable visually (connecting well with how humans operate), and more proofs that the learning is possible to exist. We are trying to mimic nature because it works, but there is no reason that embodied-machine intelligence cannot far surpass how we humans learn.

尝试在硬件中学习的任务具有不同的约束条件：样本更有价值(我们不能永远运行硬件)，结果在视觉上可以解释(与人类的操作方式良好连接)，并且更多的证据表明学习是可能存在的。 我们正在尝试模仿自然，因为它可以起作用，但是没有任何理由使体现机器的智能不能远远超过人类的学习方式。

Building AI systems that are designed for hardware tunes the objectives and methods, creating a feedback loop to more digital AI methods that we interact with every day, like computer vision and natural language processing. I will continue to open up this box of how learning robots will synergize with traditional machine learning research.

构建专为硬件设计的AI系统可以调整目标和方法，为我们每天与之交互的更多数字AI方法(例如计算机视觉和自然语言处理)创建反馈回路。 我将继续打开此框 ，说明学习机器人将如何与传统机器学习研究协同作用。

So, I want to expand the definition of embodied AI: learning how to learn, by letting autonomous agents have new ways of interacting with the world.

因此，我想扩展体现的AI的定义： 通过让自治主体具有与世界进行交互的新方式 来学习如何学习 。

To wrap up — learn about Facebook’s robotics efforts, Facebook’s in-home exploration push (Habitat), Microsoft’s public research websites, Apple’s job postings, or Google Brain’s Robotics team. Amazon’s goals in the matter are much more direct (logistics, delivery, taking over more industries, etc).

总结一下- 了解Facebook的机器人技术 ， Facebook的内部探索推动力(栖息地) ，微软的公共 研究网站，苹果的职位发布或Google Brain的机器人团队 。 亚马逊在此方面的目标更为直接(物流，交付，接管更多行业等)。

放置家用机器人的土地 (Lay of the land for at-home robots)

This is the where, when, and how of embodied AIs — what we need to think about further.

这就是具体化AI的位置，时间和方式-我们需要进一步考虑。

1. The uncanny world of at-home robots: we need to address why it is so crazy that some robotics companies give their toys human faces. Also, ask: why are support robots targeted for the most vulnerable targets — children and the elderly?

   家用机器人的神奇世界 ：我们需要解决为什么如此疯狂以至于有些机器人公司给他们的玩具摆上了人脸。 另外，请问：为什么支持机器人针对最脆弱的目标(儿童和老人)？

2. Don’t restrict your robot to your worldview: How can we change how we train and build robots to make them more broadly useful?

   不要将您的机器人局限于您的世界观 ：我们如何才能改变我们训练和建造机器人的方式，使它们更广泛地发挥作用？

3. Giving the algorithm the keys: What is limiting Alexa et. al from being very useful and taking over-scheduling, routine purchasing, and more? There are more than just privacy concerns.

   给算法提供密钥 ：是什么限制了Alexa等。 还因为它非常有用，并且过度安排了商品，进行日常购买等等？ 不仅仅是隐私问题。

4. When personal robots don’t suck: Putting all the pieces together is going to be challenging.s

   当个人机器人不吸吮时 ：将所有零件放在一起将是一个挑战。

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