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.

通過讓自主代理與世界互動來學習如何學習。 為什么像Facebook和Google這樣的大型科技公司聘請機器人專家來充實自己多余的會議室。

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

這最初發布在我的免費新聞稿中，有關機器人技術與自動化， 民主自動化 。

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.

您正在兄弟的另一個技術辦公室中徘徊，您會看到一個中等大小的會議室，里面裝滿了機器人。 當您拜訪朋友在其廣告技術初創公司的辦公室中嘗試新的微型咖啡館時，這不是您期望的(嗯，當允許訪問者時)，但是它正逐漸成為現實。

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資金( 最大的公司每年10到200億美元 )，在下雨天還要拿更多的錢(蘋果，微軟，谷歌，亞馬遜和Facebook共同擁有5500億美元的現金) )。 就數十億美元而言，招聘幾個機器人研究人員簡直是天花亂墜。 它還收集了更多的頂尖人才，但我認為這比我考慮的要短。

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,

在接下來的十年中，機器人技術作為一個行業的前景將產生近乎指數級的影響(或更早地，如我所詳細描述的那樣，大流行將繼續)。 技術公司希望捕獲的部分是那些在您的家庭中體現其算法的機器人。 正如我在《 推薦是游戲和自動化》中所寫，

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.

推薦系統和這些平臺的 一般主題 是：如果我們可以預測用戶想要做什么，那么我們可以作出這樣的功能到我們的系統。

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.

將此問題擴展到占用您的生活和家中空間的代理，問題就會加劇：關閉數據或關閉應用程序( 如果可以啟動并詢問您的工作狀況)不會導致設備逃脫。

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.

我是這些技術的大力支持者，但是，大公司為什么要在該領域進行大量投資(一個重要原因)的框架很重要。 自治機構可以在房屋中獲得很多很多好處，包括為高危人群提供安全的生活，幫助他們處理家務，與您的數字生活(電子郵件)進行對話等。 我們需要確保建立正確的權利。

體現的AI (Embodied AI)

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

使自治代理具有與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.

我認為，這不僅僅是在人工智能和機器學習中翻譯最先進的方法，而是更細微的話題-通過物理交互進行學習是我們體驗到的第一類學習。 孩子們通過觸摸它們來了解什么是新物體，以及它們如何與新生命互動。

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.

嘗試在硬件中學習的任務具有不同的約束條件：樣本更有價值(我們不能永遠運行硬件)，結果在視覺上可以解釋(與人類的操作方式良好連接)，并且更多的證據表明學習是可能存在的。 我們正在嘗試模仿自然，因為它可以起作用，但是沒有任何理由使體現機器的智能不能遠遠超過人類的學習方式。

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.

構建專為硬件設計的AI系統可以調整目標和方法，為我們每天與之交互的更多數字AI方法(例如計算機視覺和自然語言處理)創建反饋回路。 我將繼續打開此框 ，說明學習機器人將如何與傳統機器學習研究協同作用。

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，我想擴展體現的AI的定義： 通過讓自治主體具有與世界進行交互的新方式 來學習如何學習 。

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).

總結一下- 了解Facebook的機器人技術 ， Facebook的內部探索推動力(棲息地) ，微軟的公共 研究網站，蘋果的職位發布或Google Brain的機器人團隊 。 亞馬遜在此方面的目標更為直接(物流，交付，接管更多行業等)。

I am working to figure out how to safely have quadrotors (above) learn to fly in people’s homes.我正在研究如何安全地使四旋翼飛行器(上圖)學會在人們的家中飛行。

放置家用機器人的土地 (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.

這就是具體化AI的位置，時間和方式-我們需要進一步考慮。

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?

家用機器人的神奇世界 ：我們需要解決為什么如此瘋狂以至于有些機器人公司給他們的玩具擺上了人臉。 另外，請問：為什么支持機器人針對最脆弱的目標(兒童和老人)？

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

不要將您的機器人局限于您的世界觀 ：我們如何才能改變我們訓練和建造機器人的方式，使它們更廣泛地發揮作用？

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.

給算法提供密鑰 ：是什么限制了Alexa等。 還因為它非常有用，并且過度安排了商品，進行日常購買等等？ 不僅僅是隱私問題。

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

當個人機器人不吸吮時 ：將所有零件放在一起將是一個挑戰。

Source-Author.來源作者。 https://democraticrobots.substack.com/https://democraticrobots.substack.com/

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像這樣？ 請訂閱我的有關機器人技術，自動化和AI的直接通訊，網址為民主機器人網站。

翻譯自: https://towardsdatascience.com/why-digital-based-technology-companies-do-robotics-research-ff3ef3025d98

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