Quantum Supremacy

Earlier this week, Google researchers announced a major computing breakthrough in the journal Nature:

Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy for this specific computational task, heralding a much-anticipated computing paradigm.

Quantum supremacy heralds an era of not merely faster computing, but one in which computers can solve new types of problems. There was a time when I’d expect such breakthroughs from “information technology” companies such as IBM. But Google’s tech is ultimately in service to another business: advertising.

TAOI: Facebook Hiding Likes

The Architecture of Information:

Likes are one of the most important concepts of the Facebook experience. Giving users the ability to cast their approval (or disapproval) on a post or comment — and to see how others have “voted” — is one of the most engaging aspects of the system, both for users and content authors. Facebook even uses the Like icon as a symbol of the company as a whole:

The sign outside the main entrance to Facebook headquarters. (Photo: Facebook.)

However, according to a report in the NY Times, Facebook is experimenting with hiding post measurements:

On [September 26], the social network said it was starting a test in Australia, where people’s Likes, video view counts and other measurements of posts would become private to other users. It is the first time the company has announced plans to hide the numbers on its platform.

Why would they do this? Because seeing these metrics may have an impact on users’ self-esteem. According to a Facebook spokesperson quoted in the article, the company will be testing the change to see if it helps improve people’s experiences. A noble pursuit. But, I wonder: How would this impact user engagement? If it benefits users but hurts advertising revenue, will Facebook discontinue the experiment?

Facebook Tests Hiding ‘Likes’ on Social Media Posts

Collaborating by Default

Writing in his blog, Benedict Evans highlights the new wave of startups focused on personal productivity, “dozens of companies that remix some combination of lists, tables, charts, tasks, notes, light-weight databases, forms, and some kind of collaboration, chat or information-sharing.”

The cycle of bundling and unbundling functionality isn’t new:

There’s an old joke that every Unix function became an internet company – now every Craigslist section, or LinkedIn category, or Excel template, becomes a company as well. Depending on the problem, that might be a new collaboration canvas, or a new networked app, or a new network or marketplace, and you might switch from one form to the other. Github is a developer tool that also became a network – it became LinkedIn for developers.

What is new is the social nature of the experience. Old-school computing was lonely: the user interacted with his/her computer alone. Even if the system included communications software, such as email, interactions with other people were limited to that software alone. Today, we expect web-based applications to be collaborative by default.

We experience software differently when we assume other people will be sharing the place with us. As I’ve written before, we may ultimately discover that the purpose of social media was to teach us how to collaborate with people in information environments.

New Productivity

Understanding Customer Mental Models

How well do you understand your customers? Do you know how they make decisions? How they see your business’s domain? What makes them tick?

Everyone understands things a bit differently. Nobody has a perfect, complete understanding of the whole of reality. A neurosurgeon may understand the human nervous system but be unable to successfully configure the security settings of her smartphone. Knowledge of one domain doesn’t necessarily translate to another.

You carry around in your mind internal representations of how things work. These representations are called mental models. Wikipedia has a “good enough” definition:

A mental model is an explanation of someone’s thought process about how something works in the real world. It is a representation of the surrounding world, the relationships between its various parts and a person’s intuitive perception about his or her own acts and their consequences.

The more accurately these representations mirror the way things really are, the more skillfully you can act. If you understand the distinctions between the components that define your phone’s security and how they relate to each other, you’ll be able to make good predictions about the consequences of your decisions.

Forming good mental models for complex domains isn’t easy. Modeling calls for thinking in abstract terms. You may be tempted to apply a model from one domain you understand well to another you don’t. (E.g., “I bet this works just like x.”) We aren’t formally trained to model the world. Instead, we form mental representations ad hoc, filling out the broader picture as we go along. Thus, we have imperfect models of much of reality.

Ideally, you want your customers to have good mental models of your business’ domain. This is easier to do in well established domains than in new ones. For example, more people are likely to have good mental models of the process of renting a car than securing their smartphone.

It’s important that you understand your customers’ mental models for your domain. This isn’t something you can ask them about in an interview. We don’t express our mental models overtly. Instead, they manifest indirectly in our actions. What to do?

One way to go about it is to observe them interacting with prototypes and making note of how they interpret its major concepts and their relations to each other. Another is to engage customers in co-creation sessions to design solutions for the domain.

In this second approach, we don’t expect the solutions that emerge to lead directly to products or features. Instead, the artifact functions as a MacGuffin that allows us to map the customers’ mental models of the domain. This approach is especially useful in early stages of the design process, when we don’t yet have a prototype to test.

With a better understanding of how customers see the domain, we can design solutions that allow them to make more skillful decisions. This may call for producing means for them to adjust their mental models to more closely align to reality. Or it may require that we adjust the system we’re designing to better match the models users bring with them.

In either case, we’re not starting from a blank slate: we must meet people’s understanding of the domain. This requires that we understand their mental models.

From Monolithic to Distributed Architectures

Amazon CTO Werner Vogels on how the company transitioned from a monolithic application architecture to a distributed one:

We created a blueprint for change with our “Distributed Computing Manifesto.” This was an internal document describing a new architecture. With this manifesto, we began restructuring our application into smaller pieces called “services” that enabled us to scale Amazon dramatically.

But changing our application architecture was only half the story. Back in 1998, every Amazon development team worked on the same application, and every release of that application had to be coordinated across every team.

To support this new approach to architecture, we broke down our functional hierarchies and restructured our organization into small, autonomous teams, small enough that we could feed each team with only two pizzas. We focused each of these “two-pizza teams” on a specific product, service, or feature set, giving them more authority over a specific portion of the app. This turned our developers into product owners who could quickly make decisions that affected their individual products.

Breaking down our organization and application structures was a bold idea, but it worked. We were able to innovate for our customers at a much faster rate, and we’ve gone from deploying dozens of feature deployments each year to millions, as Amazon has grown. Perhaps more dramatically, our success in building highly scalable infrastructure ultimately led to the development of new core competencies and resulted in the founding of AWS in 2006.

Technological change requires new ways of working — especially when the change is happening at the structural level. Decentralizing the implementation at the technical level isn’t enough; decision-making must be decentralized as well. I read Amazon’s transition to two-pizza teams as a push towards bottom-up systemic interventions.

This strikes me as a more appropriate response to today’s complex challenges than the top-down hierarchies of the past. Alas, many designers and product managers are still operating within organizational structures that emerged during the industrial revolution, and which don’t easily accommodate bottom-up decision-making.

Modern applications at AWS – All Things Distributed

TAOI: Personalized Yelp Results

The architecture of information:

Per TechCrunch, Yelp announced earlier this week that it will allow users to personalize search results:

Once you’ve made your selections, those preferences will start affecting the search results you see. The personalization should be obvious because the results will be identified as having “many vegetarian options” or “because you like Chinese food.” The homepage will also start highlighting locations that it thinks you would like.

Seems like an obvious feature, especially for a system like Yelp that aims to connect users with places they will like. A short video explains how it works:

A baseline 21st Century tech literacy skill: Training the algorithms that personalize your search results. (For designers: Watch for emerging user interface standards for such training mechanisms. I was intrigued by Yelp’s use of the heart icon to signify personalization.)

Yelp will let users personalize their homepage and search results

Touchscreens and the Loss of Nuanced Control

From a report in The Verge about the aftermath of the collision of the USS John S. McCain, which killed ten sailors and injured many more:

The US Navy will replace the touchscreen throttle and helm controls currently installed in its destroyers with mechanical ones starting in 2020, says USNI News. The move comes after the National Transportation Safety Board released an accident report from a 2017 collision, which cites the design of the ship’s controls as a factor in the accident.

Not the only factor, to be sure; fatigue and lack of training also played a role. Still:

Specifically, the board points to the touchscreens on the bridge, noting that mechanical throttles are generally preferred because “they provide both immediate and tactile feedback to the operator.” The report notes that had mechanical controls been present, the helmsmen would have likely been alerted that there was an issue early on, and recommends that the Navy better adhere to better design standards.

There are systems in which humans are expected to play key control roles. A destroyer is one such system; as far as I know, fully autonomous vessels of this size don’t yet exist. User interfaces aren’t only means for users to send commands to systems; UIs also provide feedback to users. Some of this feedback is visual and auditory. But humans are creatures with more than two senses, and traditional mechanical controls can provide richer interactions than touchscreens, most of which rely primarily on sight.

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Artificial Intransigence

Me: Ooh, X looks interesting. I wonder if I can find a short video about X. [Finds a video on X and watches to the end.]

Recommendation algorithm: Oh, s/he watched X! I know what s/he likes. X! Like? Nay! X is the bread on his/her table, the air s/he breathes, his/her raison d’être. S/he has a visible X tattoo on his/her body. His/her firstborn will be/is named after X. X in continuous rotation, 24 x 7! More X! More X! MORE X!

Me: Whoa, whoa! [Looks around for a way to say “no more X.” Finds a link to hide video about X. Clicks it. The video disappears from the recommendations feed.]


Me: [Idly visits video site.]

Recommendation algorithm: New X video! Oh, and here are three others you may have missed. And these two are kinda like X.

Me: Hmmm. I thought I said no more X. How does this thing work? [Clicks on hide links for three other videos about X. Reloads page.]

Recommendation algorithm: New X video! Oh, and here are three others you may have missed. And these two are kinda like X. Oh, and here are some about Y and Z, just in case.

Me: Really?! [Clicks on hide link for another X video. Reloads page.]

Recommendation algorithm: New X video! Oh, and here are three others you may have missed. And these two are kinda like X. Oh, and here are some Ys and Zs, just in case.

Me: Sigh. [Clicks on video about Z. Watches to the end.]

Recommendation algorithm: Oh, s/he watched Z! I know what s/he likes. Z! Like? Nay! Z is the bread on his/her table, the air s/he breathes, his/her raison d’être. S/he has a visible Z tattoo on his/her body. His/her firstborn will be/is named after Z. Z in continuous rotation, 24 x 7. More Z! More Z! MORE Z!

TAOI: Disneyland App

The architecture of information:

Digital experiences are changing our understanding of physical environments. Google Maps gives you the ability to walk around a new city as though you’d known it for a long time. And should you develop a sudden hankering for ice cream, Yelp allows you to locate the nearest gelateria. The most noticeable change comes from layering information on the environment. For example, when trying to decide between two neighboring restaurants you’re no longer constrained to judging them solely by their appearance; you can also peruse their reviews in Yelp. Restaurant A has four-and-a-half stars, whereas restaurant B has three — A it is!

The number of stars is information about the place. You won’t find it in the physical place itself, but in its representation in an information environment which you access through your magical pocket-sized slab of glass. We’ve grown used to these augmented interactions with physical space, and mostly take them for granted. But recently I had one such interaction with an app I hadn’t used before, and which stood out to me for 1) its clarity of purpose and 2) the degree to which that purpose changed the experience of the place. I’m referring to the Disneyland app.

My family and I visited Disneyland a few weeks ago. We hadn’t been in five years, and the Disneyland app was one of the novelties since our last visit. The app presents a map of the Disney theme parks. As such it mostly replaced the parks’ old (and sometimes beautiful) paper-based maps. Thanks to the phone’s sensors, the Disneyland app makes it easy to figure out where you are, where to go next, and how to get there. But the app adds an additional key piece of information to the experience that can’t be had with paper-based maps: attraction wait times. Over every representation of an attraction in the park, you see a little callout that indicates how long you’ll have to wait in line to experience that ride or show:

Disneyland app

This piece of information is always available at all levels of zoom in the map. It’s the definitive element of the experience: in these maps, attraction wait times have the highest visual priority. As a result, wait times become the defining factor in sequencing the exploration of the park. The apps preferred answer to the question “What should we do next?” is always “Whatever is closest that has the shortest lines.”

This is an interesting choice that recalls the park’s old ticket levels. A long time ago, each Disneyland attraction required a separate ticket. Not all attractions used the same tickets; there were several levels ranging from A to E. “E-tickets,” such as the Haunted Mansion, were the most popular and desirable. These were considered the park’s premium attractions; their tickets were worth more than the others. This economic scheme influenced how visitors experienced the park. Ticket “coupon books” only included a limited number of E-tickets as compared to the lower denominations. Guests could buy more tickets inside the park, but having a limited number of the various level tickets affected choices. (I remember visiting Walt Disney World when it had a similar scheme, and hearing things like, “let’s visit this ride next, we have to use up our C-tickets.”)

The Disneyland app creates a similar economy by making attraction wait times the key informational element of the experience. When you’re trying to decide between two rides, knowing you’ll have to wait 65 minutes in line in one versus 15 minutes in another could be the key factor in your choice. (It was for my wife and me. Children get very cranky after waiting in long lines all day!) Our choosing to go on the ride with the lower wait times would contribute to slightly increasing that ride’s wait times and lowering the wait times for the more popular rides. I don’t have data, but my expectation is that this would help even out wait times throughout the park.

That is, of course, if all other things are equal — which they aren’t. The Haunted Mansion is a much more elaborate and compelling experience than Dumbo the Flying Elephant. Also, some rides have higher throughput than others. So the choice of riding one rather than the other doesn’t come down solely to which has the shortest waits.

That said, for someone like myself, who knows Disneyland very well, having this extra bit of information made the experience of visiting the park much better. In our two days at Disneyland, my family and I experienced more of the park than we’d ever been able to before. We also had more fun, since we spent a lower percentage of our time there in queues. But I wonder about the effect on folks who are less familiar with the parks. Will the emphasis on wait times drive them to prioritize less popular attractions over the park’s highlights? Adding feedback mechanisms to a system influences the way the system works. In what unexpected ways does this app change the experience of visiting Disneyland?