This originally was featured on my free blog on robotics, automation, and AI.

最初在 我 有关机器人技术，自动化和AI的 免费博客中进行了 介绍。

I figured the logical followup on my analysis of recommender systems from last week (it’s worth a read) would be a walkthrough of what I can learn about the algorithms that affect you and me, at a low level. This is a survey with some specific metrics companies use and a high-level analysis of themes I found. Mostly, else it turn into a full length research paper, this article is a distillation of articles I found into digestible bites, with the links to read more if you’re interested in a particular player.

我认为上周对推荐系统的分析具有逻辑性( 值得一读 )，这将是我可以从低层次了解影响您我的算法的演练。 这是一项针对公司使用的一些特定指标的调查，并对我发现的主题进行了高级分析。 大多数情况下，它会变成一篇完整的研究论文，这篇文章是对我发现的可消化食物的提炼，如果您对特定玩家感兴趣的话，可以链接。

Upon doing this research, there are way more points of algorithmic interaction then I expected — I mean that algorithms can touch us and interact in more ways than we expect. Someone should study the game-theory of algorithmic competition within human brains. How does TikTok’s algorithm help it hold onto users that Youtube may pull? Hopefully this is a starting point if people want to figure out how an app works.

进行这项研究后，有比我预期更多的算法交互点-我的意思是，算法可以接触我们并以比我们预期更多的方式进行交互。 有人应该研究人脑内算法竞争的博弈论 。 TikTok的算法如何帮助它抓住Youtube可能吸引的用户？ 如果人们想弄清楚应用程序的工作方式，希望这是一个起点。

If you want to get the most value out of this post, click a couple links.

如果您想从这篇文章中获得最大的价值，请单击几个链接。

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，则可以将该功能纳入我们的系统。

The general problem formulation is: how compounding interactions change how we act and can harm our well-being.

一般的问题表述是： 复合互动如何改变我们的行为方式并可能损害我们的福祉。

核心玩家 (The core players)

Facebook, Apple, Amazon, Netflix, and Google (FAANG) are often the most coveted companies to work for, and they have an outsized effect on the population of the world. This article does not go into all the geopolitical and ethical issues of the companies, it just tries to show how they work, a bit.

Facebook，Apple，Amazon，Netflix和Google(FAANG)通常是最令人垂涎​​的公司，它们对世界人口产生了巨大影响。 本文不涉及公司的所有地缘政治和道德问题，而只是试图稍微展示它们的工作方式。

脸书 (Facebook)

The algorithm is designed to show individuals things in their circles, and get them to engage with it. This considers the contents’ sources, the geographic location of the user, the social position of the user, the history of user engagements, paid advertisements, and more. The algorithm has been studied well enough to realize it has a dramatic effect, but conditioning on any one input is impossible, hence common references as a dangerous black box. A second-order effect of this is that the algorithms end up predicting which circle you are in, and guiding you to the middle of it (sometimes called polarization if your circle was a moderate, to begin with).

该算法旨在向个人展示他们圈子中的事物，并使他们参与其中。 这将考虑内容的来源，用户的地理位置，用户的社会地位，用户参与的历史，付费广告等。 已经对该算法进行了充分的研究以实现其显着效果，但是不可能对任何一个输入进行条件限制，因此通常将其称为危险的黑匣子 。 这样做的第二种效果是，算法最终会预测您所在的圆，然后将您引导到该圆的中间(首先，如果您的圆是中等的，则称为极化)。

I use Instagram off and on, and the biggest effect is likely to drive me to try and glamorize my life, but thankfully I consider the effects minor.

我不停地使用Instagram，最大的影响可能是驱使我尝试美化我的生活，但值得庆幸的是，我认为这种影响很小。

[Sources: WSJ, WSJ2, Verge, Facebook AI]

[来源： WSJ ， WSJ2 ， Verge ， Facebook AI ]

推特 (Twitter)

The algorithm is like Facebook’s, but more prone to vitality — especially for users that start with a large audience. From Twitter itself, it considers the following features:

该算法类似于Facebook的算法，但更容易发挥活力-特别是对于从大量受众开始的用户。 从Twitter本身，它考虑了以下功能：

The Tweet itself: its recency, presence of media cards (image or video), total interactions (e.g. number of Retweets or likes)

Tweet本身：其新近度，媒体卡(图像或视频)的存在，互动总数(例如Retweets或喜欢的数量)

The Tweet’s author: your past interactions with this author, the strength of your connection to them, the origin of your relationship

Tweet的作者：您过去与该作者的互动，与他们的联系紧密程度，关系的起源

You: Tweets you found engaging in the past, how often and how heavily you use Twitter

您：您发现过去参与的推文，使用Twitter的频率和频率

Honestly, this doesn’t say much. Given Twitter’s quirks, I could see them having an obscure end-to-end optimizer and not many controls.

老实说，这并不多说。 鉴于Twitter的怪癖，我可以看到它们具有晦涩的端到端优化器，并且控件不多。

I am definitely addicted to the knowledge vitality and access to other intellectuals on Twitter. These blue notification bubbles get me.

我绝对沉迷于知识的活力以及在Twitter上与其他知识分子的接触。 这些蓝色的通知气泡让我明白。

[Sources: Sprout Social, Twitter Blog, Hootsuite — a social media marketing company]

[来源： Sprout Social ， Twitter博客 ， Hootsuite-社交媒体营销公司]

的YouTube (YouTube)

A mysterious entertainment platform upon closer inspection. While governed by a public American company, there have been multiple studies showing recommendations tracking pathways to radicalization (See NYT below) and even showing children troubling material (NYT2). The wealth of content available makes series’ of recommendations incredibly depth and impactful. I would like to see more public insight, now that the company is competing with TikTok.

经过仔细检查，这是一个神秘的娱乐平台。 在一家美国上市公司的监管下，有多项研究显示了追踪激进化途径的建议(请参阅下面的《纽约时报》)，甚至还显示了儿童令人烦恼的材料(NYT2)。 可用的丰富内容使一系列建议的深度和影响力令人难以置信。 既然公司正在与TikTok竞争，我希望看到更多的公众见解。

I consume a lot of my casual content on Youtube. I think that watching Joe Rogan clips has gotten me some Trump ads (Yes dramatically more for Biden), but it makes me think about how the videos I am watching can drift.

我在YouTube上消费了很多休闲内容。 我认为观看乔·罗根(Joe Rogan)的剪辑带给我一些特朗普的广告(对于拜登来说是更多的东西)，但这使我想到了我正在观看的视频如何漂移。

[Source: NYT, NYT2, Shopify — yes they analyze the Youtube algorithm?]

[来源： NYT ， NYT2和Shopify-是的，他们分析了Youtube算法？

谷歌 (Google)

As the first widely useful search engine, everyone uses Google. Google uses a variety of tools to index webpages (see PageRank as the origin) including text, links to other pages, history of readers on the site, and more. Its hand in the dissemination of information is crucial, and I worry that it has biases that are impossible to track.

作为第一个广泛使用的搜索引擎，每个人都使用Google。 Google使用多种工具为网页建立索引(请参见PageRank作为来源)，包括文本，指向其他页面的链接，网站上读者的历史记录等。 它在传播信息方面的作用至关重要，我担心它具有无法追踪的偏见。

Their position is now more precarious in the context of anti-trust hearings and competitors. In recent years, Google shifted its results to include many more ads and self-referencing results (especially in profitable searches like travel and shopping). This self-referencing is what I see as a competitive angle against Google.

在反托拉斯听证会和竞争对手的背景下，他们的立场现在更加不稳定。 近年来，Google将搜索结果转移到了更多广告和自引用搜索结果(尤其是在旅行和购物等获利搜索中)。 我认为这种自我参照是与Google竞争的角度。

Google controls how I find other academic papers, blogs, news, and more. I don’t know how to measure the effects beyond big.

Google控制着我如何查找其他学术论文，博客，新闻等等。 我不知道如何衡量巨大的影响。

[Source: WSJ, The Mark Up, Search Engine Journal]

[来源： 《华尔街日报》 ，《 The Mark Up》 ，《 搜索引擎杂志》 ]

即将到来的球员 (The coming players)

These are areas where I see algorithms coming into play in new ways, and particularly in ways that may disadvantage some and or have deleterious effects.

在这些领域中，我看到算法以新的方式发挥作用，尤其是以可能使某些方法不利或产生有害影响的方式。

新闻 (News)

Classical media shifting online (New York Times, Wall Street Journal, etc.) and new online-only publications (Medium, etc.) will corral our political and global world views. Clickbait has shifted into reading time, but that changes from a clickbait title to a clickbait title + a readbait introductory paragraph. I saw the effects of this in my own Medium articles — my writing was being tuned to the algorithm (and it worked — tutorials, code examples, and lists get more views than in-depth analysis). The reading population of the world is re-emerging on Substack (and other platforms — such as context is recent events).

在线传播的经典媒体(《纽约时报》，《华尔街日报》等)和仅在线发布的新出版物(《 Medium》等)将巩固我们的政治和全球视野。 Clickbait已转变为阅读时间，但从clickbait标题变为clickbait标题+ readbait简介段落。 我在自己的中型文章中看到了这种效果-我的写作被调到了算法上(并且有效-教程，代码示例和列表比深入分析有更多的观点)。 在Substack(和其他平台上，例如上下文是最近发生的事件 )，世界上的阅读人群正在重新出现。

Beyond clickbait, the New York times is beginning to use research engagement algorithms on the news platform(specifically, something called contextual bandits — context in research). To be blunt, when I read the news I want to read the events with minor added opinions. I do not want control over how I receive national and global news, but it is an expected development.

除了点击诱饵之外，《纽约时报》开始在新闻平台上使用研究参与算法(特别是所谓的情境匪徒- 研究中的情境 )。 直言不讳，当我阅读新闻时，我想阅读带有些许附加意见的事件。 我不想控制我如何接收国家和全球新闻，但这是预期的发展。

Medium drove me away from clickbait to Substack (here). I am subscribing to more newsletters and could see myself unsubscribing from NYT and WSJ if trends continue too far. The picture of America being ruined solely by Trump is too narrow-minded from me and not giving me a broad enough education — as an avid Trump critic.

中号驱使我从clickbait转到Substack(此处)。 我订阅了更多新闻通讯，如果趋势继续发展太快，我可能会取消订阅NYT和WSJ。 作为一个狂热的特朗普评论家，美国完全被特朗普毁掉的图片对我太狭narrow了，没有给我足够广泛的教育。

[Sources: NYT]

[来源： 纽约时报 ]

购物 (Shopping)

Amazon wants to be the go-to search engine for shopping. They are expanding into many different areas of shopping (e.g. groceries, retail, and more soon) to get a bigger picture of individuals needs. When you ask Alexa to remind you of something, that could be recorded too. These form a multi-modal recommender system for ads and sales.

亚马逊希望成为购物的首选搜索引擎。 他们正在扩展到许多不同的购物领域(例如，杂货店，零售店，以及不久之后)，以全面了解个人需求。 当您要求Alexa提醒您某些事情时，也可以将其记录下来。 这些形成了用于广告和销售的多模式推荐系统。

Facebook created a new marketplace to try and fight Amazon’s rule. Facebook wants to be a middle man with Shopify (Shopify isn’t an Amazon competitor without other companies) and vendors to create a platform where people can buy anything. With differing incentives (financials), it will be interesting to see how this plays out.

Facebook创造了一个新的市场来尝试对抗亚马逊的统治。 Facebook希望成为Shopify的中间人(Shopify在没有其他公司的情况下不是Amazon的竞争对手)和供应商，以创建人们可以购买任何东西的平台。 有了不同的激励措施(财务措施)，看看如何发挥作用将会很有趣。

I have taken steps to use Amazon much less. Its recommendations are weird, is filled with duplicate content, but is still incredibly convenient. The prospect of the Facebook marketplace makes me think of a more automated customer-vendor connection.

我已采取措施减少使用Amazon。 它的建议很奇怪，充满了重复的内容，但是仍然非常方便。 Facebook市场的前景使我想到了更加自动化的客户-供应商连接。

[Sources: Recipe Express, Axios, Sellics]

[来源： Recipe Express ， Axios ， Sellics ]

服务 (Services)

New applications are coming to offer value across many areas: including financial market access, customer analysis, ride-sharing, and more. These services will have less public oversight, to begin with, but time will show that multiple of them will have extremely harmful algorithmic effects. I include services in the coming players because the timescale from adoption to problem will be very short (apps can explode in usership, see the example of Zoom). The negative metrics usually lag behind adoption, except in the example of the least thought through service maybe ever.

新的应用程序正在许多领域提供价值：包括金融市场准入，客户分析，乘车共享等。 首先，这些服务将受到较少的公共监督，但是时间将证明其中的多个将对算法产生极其有害的影响。 我将在即将到来的播放器中包含服务，因为从采用到出现问题的时间范围非常短(应用程序可能会以用户数量激增， 请参见Zoom的示例 )。 负面指标通常滞后于采用率，除非有可能是通过服务进行最少思考的例子。

[Sources: UC Berkeley on Fintech, Genderfying AI Fail, GIG Economies]

[来源： 加州大学伯克利分校的 金融科技 ， 性别不平等的AI失败 ， GIG经济体 ]

二阶效应 (Second order effects)

These are current applications that are undiscussed and deserve to be questioned on possible side effects (of initially positive applications).

这些是目前尚未讨论的应用，因此(在最初的积极应用中)可能产生的副作用值得质疑。

消费类设备 (Consumer devices)

Apple watches attempting to detect atrial fibrillation for their users opens a huge problem of false-positives (see StatNews below). Technology companies will without a doubt attempt to add more features to their devices, and with a market penetration of millions of users, the inevitable false positive rate will have potential medical and financial implications. The compounding effect of these devices and misleading datapoints is left to be observed in the 2020s — with the uninformed most at risk.

苹果手表试图为其用户检测房颤会带来一个巨大的假阳性问题(请参见下面的StatNews)。 毫无疑问，技术公司将尝试在其设备中添加更多功能，并且随着数百万用户的市场渗透，不可避免的误报率将对医疗和财务产生潜在影响。 这些设备和误导性数据点的复合效应将在2020年代观察到-知情人士处于最大风险。

[Sources: Apple, StatNews, Oura]

[来源： Apple ， StatNews ， Oura ]

公共医疗/保健工具 (Public medical / health tools)

There are many new companies in the health space, such as Strive.ai that’ll analyze workouts and Bloodsmart.ai that’ll analyze blood tests to give you a health score (based on normalized, age-adjusted risk of death). This is preliminary, but gamifying one’s health is coming. Only a few steps after Apple’s “fill the rings” watch campaign is health-dashboards telling users how their habits may be numerically shortening their life.

在健康领域，有许多新公司，例如将分析锻炼的Strive.ai和将分析血液测试以为您提供健康评分的Bloodsmart.ai(基于经过年龄校正的正常死亡风险)。 这是初步的，但是要健康化。 在苹果“填满戒指”手表运动之后的仅几步之内，就是健康仪表板，它告诉用户他们的习惯可能在数字上缩短他们的寿命。

I don’t use that many trackers now, but simply having a readout of estimated calories on my watch caused me to be self-conscious of eating too much when not working out in a day. This problem would be compounded with noisy readings on blood-markers or emotional well-being.

我现在不使用那么多追踪器，但是只是在手表上读出估计的卡路里，这使我在一天不锻炼的时候就意识到进食过多。 这个问题会与嘈杂的血液标记读数或情感幸福感相关联。

[Sources: Strive, Bloodsmart]

[来源： 努力 ， Bloodsmart ]

这不是机器人博客吗？ (Isn’t this a robotics blog?)

Everything we have said in the last two iterations of Democratizing Automation regarding recommenders and algorithms will apply to robotic systems in the next decade. I forecast a dramatic increase in the penetration of robotic-human interaction with the incentives of the robots governed (for the most part) by big technology companies (informed by some research).

我们在民主化自动化的最后两次迭代中所说的关于推荐器和算法的所有内容都将在未来十年应用于机器人系统。 我预测，在大型技术公司(根据一些研究 )控制的机器人的激励下(大部分情况下)，人机交互的渗透将急剧增加。

The few smaller companies making personal robots may seem more exciting (e.g. Hello Robotics), but that market share will be relatively small to start with (high-cost) while many companies can afford to replace the human cashier and salesperson with an embodied autonomous agent.

少数制造个人机器人的小型公司似乎更令人兴奋(例如， Hello Robotics )，但从(高成本)开始，该市场份额将相对较小，而许多公司则可以负担得起，用有经验的自主代理人取代收银员和销售员。 。

I made a resource tracking when these algorithms go wrong and some philosophical background.

当这些算法出错时， 我进行了资源跟踪，并提供了一些哲学背景。

Like this? Please subscribe to my direct newsletter on robotics, automation, and AI at democraticrobots.com.

像这样？ 请订阅我的有关机器人技术，自动化和AI的直接通讯，网址为民主机器人网站。