Right now, no one signs up for any of these filters because filters are primarily installed by platforms. The 200 average friends of your average Facebook member already post such a torrent of updates that Facebook feels it must cut, edit, clip, and filter your news to a more manageable stream. You do not see all the posts your friends make. Which ones have been filtered out? By what criteria? Only Facebook knows, and it considers the formulas trade secrets. What it is optimizing for is not even communicated. The company talks about increasing the satisfaction of members, but a fair guess is that it is filtering your news stream to optimize the amount of time you spend on Facebook—a much easier thing to measure than your happiness. But that may not be what you want to optimize Facebook for.
Amazon uses filters to optimize for maximum sales, and that includes filtering the content on the pages you see. Not just what items are recommended, but the other material that appears on the page, including bargains, offers, messages, and suggestions. Like Facebook, Amazon performs thousands of experiments a day, altering their filters to test A over B, trying to personalize the content in response to actual use by millions of customers. They fine-tune the small things, but at such a scale (a hundred thousand subjects at a time) that their results are extremely useful. As a customer I keep returning to Amazon because it is trying to maximize the same thing I am: cheap access to things I will like. That alignment is not always present, but when it is, we return.
Google is the foremost filterer in the world, making all kinds of sophisticated judgments about what search results you see. In addition to filtering the web, it processes 35 billion emails a day, filtering out spam very effectively, assigning labels and priorities. Google is the world’s largest collaborative filter, with thousands of interdependent dynamic sieves. If you opt in, it personalizes search results for you and will customize them for your exact location at the time you ask. It uses the now proven principles of collaborative filtering: People who found this answer valuable also found this next one good too (although they don’t label it that way). Google filters the content of 60 trillion pages about 2 million times every minute, but we don’t often question how it recommends. When I ask it a query, should it show me the most popular, or the most trusted, or the most unique, or the options most likely to please me? I don’t know. I say to myself I’d probably like to have the choice to rank results each of those four different ways, but Google knows that all I’d do is look at the first few results and then click. So they say, “Here’s the top few we think are the best based on our deep experience in answering 3 billion questions a day.” So I click. Google is trying to optimize the chance I’ll return to ask it again.
As they mature, filtering systems will be extended to other decentralized systems beyond media, to services like Uber and Airbnb. Your personal preferences in hotel style, status, and service can easily be ported to another system in order to increase your satisfaction when you are matched to a room in Venice. Heavily cognified, incredibly smart filters can be applied to any realm with a lot of choices—which will be more and more realms. Anywhere we want personalization, filtering will follow.
Twenty years ago many pundits anticipated the immediate arrival of large-scale personalization. A 1992 book called Mass Customization by Joseph Pine laid out the plan. It seemed reasonable that custom-made work—which was once the purview of the rich—could be widened to the middle class with the right technology. For instance, an ingenious system of digital scans and robotic flexible manufacturing could provide personally tailored shirts for the middle class, instead of just bespoke shirts for the gentry. A few startups tried to execute “mass customization” for jeans, shirts, and baby dolls in the late 1990s, but they failed to catch on. The main hurdle was that, except in trivial ways (choosing a color or length), it was very difficult to capture or produce significant uniqueness without raising prices to the luxury level. The vision was too far ahead of the technology. But now the technology is catching up. The latest generation of robots are capable of agile manufacturing, and advanced 3-D printers can rapidly produce units of one. Ubiquitous tracking, interacting, and filtering means that we can cheaply assemble a multidimensional profile of ourselves, which can guide any custom services we desire.
Here is a picture of where this force is taking us. My day in the near future will entail routines like this: I have a pill-making machine in my kitchen, a bit smaller than a toaster. It stores dozens of tiny bottles inside, each containing a prescribed medicine or supplement in powdered form. Every day the machine mixes the right doses of all the powders and stuffs them all into a single personalized pill (or two), which I take. During the day my biological vitals are tracked with wearable sensors so that the effect of the medicine is measured hourly and then sent to the cloud for analysis. The next day the dosage of the medicines is adjusted based on the past 24-hour results and a new personalized pill produced. Repeat every day thereafter. This appliance, manufactured in the millions, produces mass personalized medicine.
My personal avatar is stored online, accessible to any retailer. It holds the exact measurements of every part and curve of my body. Even if I go to a physical retail store, I still try on each item in a virtual dressing room before I go because stores carry only the most basic colors and designs. With the virtual mirror I get a surprisingly realistic preview of what the clothes will look like on me; in fact, because I can spin my simulated dressed self around, it is more revealing than a real mirror in a dressing room. (It could be better in predicting how comfortable the new clothes feel, though.) My clothing is custom fit based on the specifications (tweaked over time) from my avatar. My clothing service generates new variations of styles based on what I’ve worn in the past, or on what I spend the most time wishfully gazing at, or on what my closest friends have worn. It is filtering styles. Over years I have trained an in-depth profile of my behavior, which I can apply to anything I desire.
My profile, like my avatar, is managed by Universal You. It knows that I like to book inexpensive hostels when I travel on vacation, but with a private bath, maximum bandwidth, and always in the oldest part of the town, except if it is near a bus station. It works with an AI to match, schedule, and reserve the best rates. It is more than a mere stored profile; rather it is an ongoing filter that is constantly adapting to wherever I have already gone, what kind of snapshots and tweets I made about past visits, and it weighs my new interests in reading and movies since books and movies are often a source for travel desires. It pays a lot of attention to the travels of my best friends and their friends, and from that large pool of data often suggests specific restaurants and hostels to visit. I generally am delighted by its recommendations.
Because my friends let Universal You track their shopping, eating out, club attendance, movie streaming, news screening, exercise routines, and weekend excursions, it can make very detailed recommendations for me—with minimal effort on their part. When I wake in the morning, Universal filters through my update stream to deliver the most vital news of the type I like in the morning. It filters based on the kinds of things I usually forward to others, or bookmark, or reply to. In my cupboard I find a new kind of cereal with saturated nutrition that my friends are trying this week, so Universal ordered it for me yesterday. It’s not bad. My car service notices where the traffic jams are this morning, so it schedules my car later than normal and it will try an unconventional route to the place I’ll work today, based on several colleagues’ commutes earlier. I never know for sure where my office will be since our startup meets in whatever coworking space is available that day. My personal device turns the space’s screens into my screen. My work during the day entails tweaking several AIs that match doctoring and health styles with clients. My job is to help the AIs understand some of the outlier cases (such as folks with faith-healing tendencies) in order to increase the effectiveness of the AIs’ diagnoses and recommendations.