Chasm of comprehension

Last year, Google's AI AlphaGo beat Korean Lee Sedol in Go, a game many expected humans to continue to dominate for years, if not decades, to come.

With the 37th move in the match’s second game, AlphaGo landed a surprise on the right-hand side of the 19-by-19 board that flummoxed even the world’s best Go players, including Lee Sedol. “That’s a very strange move,” said one commentator, himself a nine dan Go player, the highest rank there is. “I thought it was a mistake,” said the other. Lee Sedol, after leaving the match room, took nearly fifteen minutes to formulate a response. Fan Gui—the three-time European Go champion who played AlphaGo during a closed-door match in October, losing five games to none—reacted with incredulity. But then, drawing on his experience with AlphaGo—he has played the machine time and again in the five months since October—Fan Hui saw the beauty in this rather unusual move. 
 

Indeed, the move turned the course of the game. AlphaGo went on to win Game Two, and at the post-game press conference, Lee Sedol was in shock. “Yesterday, I was surprised,” he said through an interpreter, referring to his loss in Game One. “But today I am speechless. If you look at the way the game was played, I admit, it was a very clear loss on my part. From the very beginning of the game, there was not a moment in time when I felt that I was leading.”
 

The first time Gary Kasparov sensed deep intelligence in Deep Blue, he described the computer's move as a very human one. 

I GOT MY FIRST GLIMPSE OF ARTIFICIAL INTELLIGENCE ON Feb. 10, 1996, at 4:45 p.m. EST, when in the first game of my match with Deep Blue, the computer nudged a pawn forward to a square where it could easily be captured. It was a wonderful and extremely human move. If I had been playing White, I might have offered this pawn sacrifice. It fractured Black's pawn structure and opened up the board. Although there did not appear to be a forced line of play that would allow recovery of the pawn, my instincts told me that with so many "loose" Black pawns and a somewhat exposed Black king, White could probably recover the material, with a better overall position to boot.
 

But a computer, I thought, would never make such a move. A computer can't "see" the long-term consequences of structural changes in the position or understand how changes in pawn formations may be good or bad.
 

Humans do this sort of thing all the time. But computers generally calculate each line of play so far as possible within the time allotted. Because chess is a game of virtually limitless possibilities, even a beast like Deep Blue, which can look at more than 100 million positions a second, can go only so deep. When computers reach that point, they evaluate the various resulting positions and select the move leading to the best one. And because computers' primary way of evaluating chess positions is by measuring material superiority, they are notoriously materialistic. If they "understood" the game, they might act differently, but they don't understand.
 

So I was stunned by this pawn sacrifice. What could it mean? I had played a lot of computers but had never experienced anything like this. I could feel--I could smell--a new kind of intelligence across the table. While I played through the rest of the game as best I could, I was lost; it played beautiful, flawless chess the rest of the way and won easily.
 

Later, in the Kasparov-Deep Blue rematch that IBM's computer won, again a move in the 2nd game was pivotal. There is debate or whether the move was a mistake or intentional on the part of the computer, but it flummoxed Kasparov (italics mine):

'I was not in the mood of playing at all,'' he said, adding that after Game 5 on Saturday, he had become so dispirited that he felt the match was already over. Asked why, he said: ''I'm a human being. When I see something that is well beyond my understanding, I'm afraid.''
 

...
 

At the news conference after the game, a dark-eyed and brooding champion said that his problems began after the second game, won by Deep Blue after Mr. Kasparov had resigned what was eventually shown to be a drawn position. Mr. Kasparov said he had missed the draw because the computer had played so brilliantly that he thought it would have obviated the possibility of the draw known as perpetual check.

''I do not understand how the most powerful chess machine in the world could not see simple perpetual check,'' he said. He added he was frustrated by I.B.M.'s resistance to allowing him to see the printouts of the computer's thought processes so he could understand how it made its decisions, and implied again that there was some untoward behavior by the Deep Blue team.

Asked if he was accusing I.B.M. of cheating, he said: ''I have no idea what's happening behind the curtain. Maybe it was an outstanding accomplishment by the computer. But I don't think this machine is unbeatable.''

Mr. Kasparov, who defeated a predecessor of Deep Blue a year ago, won the first game of this year's match, but it was his last triumph, a signal that the computer's pattern of thought had eluded him. He couldn't figure out what its weaknesses were, or if he did, how to exploit them.

Legend has it that a move in Game One and another in Game Two were actually just programming glitches that caused Deep Blue to make random moves that threw Kasparov off, but regardless, the theme is the same: at some point he no longer understood what the program was doing. He no longer had a working mental model, like material advantage, for his computer opponent.

This year, a new version of AlphaGo was unleashed on the world: AlphaGo Zero.

As many will remember, AlphaGo—a program that used machine learning to master Go—decimated world champion Ke Jie earlier this year. Then, the program’s creators at Google’s DeepMind let the program continue to train by playing millions of games against itself. In a paper published in Nature earlier this week, DeepMind revealed that a new version of AlphaGo (which they christened AlphaGo Zero) picked up Go from scratch, without studying any human games at all. AlphaGo Zero took a mere three days to reach the point where it was pitted against an older version of itself and won 100 games to zero.
 

(source)

That AlphaGo Zero had nothing to learn from playing the world's best humans, and that it trounced its artificial parent 100-0, is evolutionary velocity of a majesty not seen since the ectomorphs in the Alien movie franchise. It is also, in its arrogance, terrifying.

DeepMind released 55 games that a previous version of AlphaGo played against itself for Go players around the world to analyze.

Since May, experts have been painstakingly analyzing the 55 machine-versus-machine games. And their descriptions of AlphaGo’s moves often seem to keep circling back to the same several words: Amazing. Strange. Alien.
 

“They’re how I imagine games from far in the future,” Shi Yue, a top Go player from China, has told the press. A Go enthusiast named Jonathan Hop who’s been reviewing the games on YouTube calls the AlphaGo-versus-AlphaGo face-offs “Go from an alternate dimension.” From all accounts, one gets the sense that an alien civilization has dropped a cryptic guidebook in our midst: a manual that’s brilliant—or at least, the parts of it we can understand.
 

[...]
 

Some moves AlphaGo likes to make against its clone are downright incomprehensible, even to the world’s best players. (These tend to happen early on in the games—probably because that phase is already mysterious, being farthest away from any final game outcome.) One opening move in Game One has many players stumped. Says Redmond, “I think a natural reaction (and the reaction I’m mostly seeing) is that they just sort of give up, and sort of throw their hands up in the opening. Because it’s so hard to try to attach a story about what AlphaGo is doing. You have to be ready to deny a lot of the things that we’ve believed and that have worked for us.”

Like others, Redmond notes that the games somehow feel “alien.” “There’s some inhuman element in the way AlphaGo plays,” he says, “which makes it very difficult for us to just even sort of get into the game.”
 

Ke Jie, the Chinese Go master who was defeated by AlphaGo earlier this year, said:

“Last year, it was still quite humanlike when it played. But this year, it became like a god of Go.”
 

After his defeat, Ke posted what might be the most poetic and bracing quote of 2017 on Weibo (I first saw it in the WSJ):

“I would go as far as to say not a single human has touched the edge of the truth of Go.”
 

***

When Josh Brown died in his Tesla after driving under a semi, it kicked off a months long investigation into who was at fault. Ultimately, the NHTSA absolved Autopilot of blame. The driver was said to have had 7 seconds to see the semi and apply the brakes but was suspected of watching a movie while the car was in Autopilot.

In this instance, it appeared enough evidence could be gathered to make such a determination. In the future, diagnosing why Autopilot or other self-driving algorithms made certain choices will likely only become more and more challenging as the algorithms rise in complexity.

At times, when I have my Tesla in Autopilot mode, the car will do something bizarre and I'll take over. For example, if I drive to work out of San Francisco, I have to exit left and merge onto the 101 using a ramp that arcs to the left almost 90 degrees. There are two lanes on that ramp, but even if I start in the far left lane and am following a car in front of me my car always seems to try to slide over to the right lane.

Why does it do that? My only mental model is the one I know, which is my own method for driving. I look at the road, look for lane markings and other cars, and turn a steering wheel to stay in a safe zone in my lane. But thinking that my car drives using that exact process says more about my limited imagination than anything else because Autopilot doesn't drive the way humans do. This becomes evident when you look at videos showing how a self-driving car "sees" the road.

When I worked at Flipboard, we moved to a home feed that tried to select articles for users based on machine learning. That algorithm continued to be to tweaked and evolved over time, trying to optimize for engagement. Some of that tweaking was done by humans, but a lot of it was done by ML.

At times, people would ask why a certain article had been selected for them? Was it because they had once read a piece on astronomy? Dwelled for a few seconds on a headline about NASA? By that point, the algorithm was so complex it was impossible to really offer an explanation that made intuitive sense to a human, there were so many features and interactions in play.

As more of the world comes to rely on artificial intelligence, and as AI makes great advances, we will walk to the edge of a chasm of comprehension. We've long thought that artificial intelligence might surpass us eventually by thinking like us, but better. But the more likely scenario, as recent developments have shown us, is that the most powerful AI may not think like us at all, and we, with our human brains, may never understand how they think. Like an ant that cannot understand a bit about what the human towering above them is thinking, we will gaze into our AI in blank incomprehension. We will gaze into the void. The limit to our ability to comprehend another intelligence is our ability to describe its workings, and that asymptote is drawn by the limits of our brain, which largely analogizes all forms of intelligence to itself in a form of unwitting intellectual narcissism.

This is part of the general trend of increasing abstraction that marks modern life, but it is different than not knowing how a laptop is made, or how to sew a shirt for oneself. We take solace in knowing that someone out there can. To admit that it's not clear to any human alive how an AI made a particular decision feels less like a ¯\_(ツ)_/¯ and more like the end of some innocence.

I suspect we'll continue to tolerate that level of abstraction when technology functions as we want it to, but we'll bang our heads in frustration when it doesn't. Like the annoyance we feel when we reach the limits of our ability to answer a young child who keeps asking us "Why?" in recursive succession, this frustration will cut deep because it will be indistinguishable from humiliation.

Evaluating mobile map designs

I saw a few links to this recent comparison by Justin O'Beirne of the designs of Apple Maps vs. Google Maps. In it was a link to previous comparisons he made about a year ago. If you're into maps and design, it's a fairly quick read with a lot of useful time series screenshots from both applications to serve as reference points for those who don't open both apps regularly.

However, the entire evaluation seems to come from a perspective at odds with how the apps are actually used. O'Beirne's focus is on evaluating these applications from a cartographic standpoint, almost as if they're successors to old wall-hanging maps or giant road atlases like the ones my dad used to plot out our family road trips when we weren't wealthy enough to fly around the U.S. 

The entire analysis is of how the maps look when the user hasn't entered any destination to navigate to (what I'll just refer to as the default map mode). Since most people use these apps as real-time navigation aids, especially while driving, the views O'Beirne dissects feel like edge cases (that's my hypothesis, of course; if someone out there who has actual data on % of time these apps are used for navigation versus not, I'd love to hear it, even if it's just directional to help frame the magnitude).

For example, much of O'Beirne's ink is spent on each application's road labels, often at really zoomed out levels of the map. I can't remember the last time I looked at any mobile mapping app at the eighth level of zoom, I've probably only spent a few minutes of my life in total in all of these apps at that level of the geographic hierarchy, and only to answer a trivia question or when visiting some region of the world on vacation.

What would be of greater utility to me, and what I've yet to find, is a design comparison of all the major mapping apps as navigation aids, a dissection of the UX in what I'll call their navigation modes. Such an analysis would be even more useful if it included Waze, which doesn't have the market share of Apple or Google Maps but which is popular among a certain set of drivers for its unique approach to evaluating traffic, among other things.

Such a comparison should analyze the visual comprehensibility of each app in navigation mode, which is very different from their default map views. How are roads depicted, what landmarks are shown, how clear is the selected path when seen only in the occasional sidelong glance while driving, which is about as much visual engagement as a user can offer if operating a 3,500 pound vehicle. How does the app balance textual information with the visualization of the roads ahead, and what other POI's or real world objects are shown? Waze, for example, shows me other Waze users in different forms depending on how many miles they've driven in the app and which visual avatars they've chosen.

Of course, the quality of the actual route would be paramount. It's difficult for a single driver to do A/B comparisons, but I still hope that someday someone will start running regular tests in which different cars, equipped with multiple phones, each logged into different apps, try to navigate to the same destination simultaneously. Over time, at some level of scale, such comparison data would be more instructive than the small sample size of the occasional self-reported anecdote.

[In the future, when we have large fleets of self-driving cars, they may produce insights that only large sample sizes can validate, like UPS's "our drivers save time by never turning left." I'd love if Google Maps, Apple Maps, or Waze published some of what they've learned about driving given their massive data sets, a la OKCupid, but most of what they've published publicly leans towards marketing drivel.]

Any analysis of navigation apps should also consider the voice prompts: how often does the map speak to you, how far in advance of the next turn are you notified, how clear are the instructions? What's the signal to noise? What are the default wording choices? Syntax? What voice options are offered? Both male and female voices? What accents?

Ultimately, what matters is getting to your destination in the safest, most efficient manner, but understanding how the applications' interfaces, underlying data, and algorithms influence them would be of value to so many people who now rely on these apps every single day to get from point A to B. I'm looking for a Wirecutter-like battle of the navigation apps, may the best system win.

The other explicit choice O'Beirne makes is noted in a footnote:

We’re only looking at the default maps. (No personalization.)
 

It is, of course, difficult to evaluate personalization of a mapping app since you can generally only see how each map is personalized for yourself. However, much of the value of Google Maps lies in its personalization, or what I suspect is personalization. Given where we are in the evolution of many products and services, analyzing them in their non-personalized states is to disregard their chief modality.

When I use Google Maps in Manhattan, for example, I notice that that the only points of interest (POI's) the map shows me at various levels of zoom seem to be places I've searched for most frequently (this is in the logged in state, which is how I always use the app). Given Google's reputation for being a world leader in crunching large data sets, it would be surprising if they weren't selecting POI labels, even for non-personalized versions of their maps, based on what people tend to search for most frequently.

In the old days, if you were making a map to be hung on the wall, or for a paper map or road atlas, what you chose as POI's would be fixed until the next edition of that map. You'd probably choose what felt like the most significant POI's based on reputation, ones that likely wouldn't be gone before the next update. Eiffel Tower? Sure. Some local coffee shop? Might be a Starbucks in three months, best leave that label off.

Now, maps can be updated dynamically. There will always be those who find any level of personalization creepy, and some are, but I also find the lack of personalization to be immensely frustrating in some services. That I search for reservations in SF on Open Table and receive several hundred hits every time, sorted in who knows what order, instead of results that cluster my favorite or most frequently booked restaurants at the top, drives me batty.

When driving, personalization is even more valuable because it's often inconvenient or impossible to type or interact with the device for safety reasons. It's a great time saver to have Waze guess where I'm headed automatically ("Are you driving to work?" it asks me every weekday morning), and someday I just want to be able to say "give me directions to my sister's" and have it know where I'm headed.

My quick first person assessment, despite the small sample size caveats noted earlier:

• I know that Apple Maps, as the default on iOS, has the market share lead on iPhone by a healthy margin. Still, I'll never get past the time the app took me off to a dead end while I was on the way to a wedding, and I've not used it since except to glance at the design. It may have the most visually pleasing navigation mode aesthetic, but I don't trust their directions at the tails. Some products are judged not on their mean outcome but their handling of the tails. For me, navigation is one of those.
• It's not clear if Apple Maps should have a data edge over Google Maps and Waze (Google bought Waze but has kept the app separate). Most drivers use it on the iPhone because it's the default, but Google got a headstart in this space and also has a fleet of vehicles on the road taking Google street photos. Eventually, Google may augment that fleet with self-driving cars.
• I trust Google Maps directions more than those of Apple Maps. However, I miss the usability of the first version of Google Maps, which came out on iOS way back with the first iPhone. I'd heard rumors Apple built that app for Google, but I'm not sure if that's true. The current flat design of Google Maps often strands me in a state in which I have no idea how to initiate navigation. I'd like to believe I'm a fairly sophisticated user and yet I sometimes sit there swiping and tapping in Google Maps like an idiot, trying to get it to start reading turn by turn directions. Drives me batty.

I use Waze the most when driving in the Bay Area or wherever I trust that there are enough other drivers using Waze that it will offer the quickest route to my destination. That seems true in most major metropolitans. I can tell a lot of users in San Francisco use Waze because sometimes, when I have to drive home to the city from the Peninsula, I find myself in a line of cars exiting the highway and navigating through some random neighborhood side street, one that no one would visit unless guided by an algorithmic deity. 

I use Waze with my phone mounted to one of those phone clamps that holds the phone at eye level above my dashboard because the default Tesla navigation map is still on Google Maps and is notoriously oblivious to traffic when selecting a route and estimating an arrival time. Since I use Waze more than any other navigation app, I have more specific critiques.

• One reason I use Waze is that it seems the quickest to respond to temporary buildups of traffic. I suspect it's because the UI has a dedicated, always visible button for reporting such traffic. Since I'm almost always the driver, I have no idea how people are able to do such reporting, but either a lot of passengers are doing the work or lots of drivers able to do so while their car is stuck in gridlock. The other alternative, that drivers are filing such reports while their cars are in motion, is frightening.
• I don't understand the other social networking aspects of Waze. They're an utter distraction. I'm not immune to the intrinsic rewards of gamification, but in the driving context, where I can't really do much more than glance at my phone, it's all just noise. I don't feel a connection to the other random Waze drivers I see from time to time in the app, all of which are depicted as various pastel-hued cartoon sperm. In wider views of the map, all the various car avatars just add a lot of visual noise.
• I wish I could turn off some of the extraneous voice alerts, like "Car stopped on the side of the road ahead." I'm almost always listening to a podcast in the background when driving, and the constant interruptions annoy me. There's nothing I can do about a car on the side of the road, I wish I could customize which alerts I had to hear.
• The ads that drop down and cover almost half the screen are not just annoying but dangerous as I have to glance over and then swipe them off the screen. That, in and of itself, is disqualifying. But beyond that, even while respecting the need for companies to make money, I can't imagine these ads generate a lot of revenue. I've never looked at one. If the ads are annoying, the occasional survey asking me which ads/brands I've seen on Waze are doubly so. With Google's deep pockets behind Waze, there must be a way to limit ads to those moments where they're safe or clearly requested, for example when a user is researching where to get gas or a bit to eat. When a driver has hands on the wheel and is guiding a giant mass of metal at high velocity, no cognitive resources should be diverted to remembering what brands you recall seeing on the app.
• Waze still doesn't understand how to penalize unprotected left turns, which are almost completely unusable in Los Angeles at any volume of traffic. At rush hour it's a fatal failure, like being ambushed by a video game foe that can kill you with one shot with no advance warning. As long as it remains unfixed, I use Google Maps when in LA. I can understand why knowledge sharing between the two companies may be limited by geographic separation despite being part of the same umbrella company, but that the apps don't borrow more basic lessons from other seems a shame.
• I use Bluetooth to listen to podcasts on Overcast when driving, and since I downloaded iOS 11, that connection has been very flaky. Also, if I don't have the podcast on and Waze gives me an voice cue, the podcast starts playing. I've tried quitting Overcast, and the podcast still starts playing every time Waze speaks to me. I had reached a good place in that Overcast would pause while Waze spoke so they wouldn't overlap, but since iOS 11 even that works inconsistently. This is just one of the bugs that iOS 11 has unleashed upon my phone, I really regret upgrading.

Things I learned from The Defiant Ones

Despite believing myself fairly in tune with the pop culture scene, I missed a lot of promotion for The Defiant Ones until I started seeing recommendations on social media from folks who'd watched it. I finally blitzed through the four episodes recently, and it's kind of a banger.

I typically don't love documentaries which comprise so many talking head interviews because it feels like the default Powerpoint template of documentary filmmaking. But Iovine and Dre and all the other musicians are such compelling, scene-filling personalities that it's a treat, and often a lark, to see them play to the camera. Allen and Albert Hughes interview all the principals individually, but as with all oral histories, they ask all of them about the same events so they can use shots from one interviews as a reaction shot to a shot from someone else's interview. Or as a reaction shot to historical footage, like Puff Daddy recalling his reaction to Suge Knight's acceptance speech at the 1995 Source Music Awards.

In part, I was an easy mark because so much of that is the music of my youth. I was an intern at Procter and Gamble, living with a bunch of the other interns in a corporate apartment, the summer The Chronic came out. My roommates and I listened to that album just about every day, on loop, for no other reason than to mainline its hooks.

The Defiant Ones is also fascinating as a case study of two immensely successful people, Jimmy Iovine and Dr. Dre. It is dangerous to draw too many conclusions from a documentary like this. Survivor and selection bias influence the narrative, and two people does not a large sample make. The mere act of narrative construction is a con game, and always will be, even when it isn't hagiography, which first person narratives like this always veer towards. So take the following with a Himalayan salt block, because I do.

And yet...

If I lump the stories in this documentary with what I know of other successful people, a few things stood out to me. Call this a Malcolm Gladwellian attempt at teasing out a few lessons from anecdotal evidence.

The first is that people who are really good at what they do stand out from others by not only recognizing when something is exceptional immediately but articulating why it is so, especially when no one else believes it is. Designers experience this when they show a design to someone else, maybe a peer, maybe an executive, and that audience member immediately notices something the creator is particularly proud of. Stories of Steve Jobs moments like this abound, which is why everyone who has met Jobs even once seems to speak of it as some mystical experience.

Filmmakers all have stories of screening cuts for others and having the sharpest among the notice a particular bit of directorial intent, maybe something in the choreography of the actors, or the camera's movement, or even something in the sound design, that no one else picks up on.

Whether that pattern recognition is innate or trained over many years, and likely both, we see it again and again in The Defiant Ones. It's Jimmy Iovine cribbing "Stop Draggin' My Heart Around" from Tom Petty for Stevie Nicks. It's Iovine hearing Trent Reznor and fighting tooth and nail to grab Nine Inch Nails for Interscope from TVT Records. Or Iovine meeting Gwen Stefani and telling her she'd be a star in six years, and having No Doubt release Tragic Kingdom exactly six years after that conversation.

[Remember my caveats up front? Steve Gottlieb of TVT records disputes the way the Nine Inch Nails story is framed in the documentary. I certainly don't think Iovine and Dre are the only ones in the music industry who possess this skill, but this documentary is their story so I'll roll with these examples for those who have or will watch the documentary.]

The most memorable aha moment in the documentary, for me, is when Dre hears one bit from a demo tape from among hundreds of demo tapes stacked in Iovine's garage. 

"Back in those days, I didn't have an artist to work with. I'd go to Jimmy's house, and we'd have listening sessions. He was trying to help me figure out where I was going to go with my music. He'd take me down to his garage. There was cassette tapes everywhere. And I remember him picking up this cassette tape. He pops it in. I was like 'What the fuck, and who the fuck is that?!"
 

Who he was was an unknown white rapper from Detroit. In the documentary, in the recreation of that seminal moment, the label on the cassette tape reads Slim Shady. I'm not sure if that's actually true to history, but it's remarkable both ways. In one, it's a wonderful bit of historical trivia, in another, it's a laughably on the nose historical recreation.

Again, we have this pattern, the flash of recognition, picking out this tape from all the demo tapes, and hearing what no one else heard. With things like music, or even food, the articulation of excellence isn't as critical as the recognition. As in the excerpt above from Dre's memory of that moment, it was probably just a series of expletives, perhaps a literal WTF as he recalls.

The moment where Dre recognizes the kid's talent isn't online, but this clip from Eminem and Dre's first meeting is, and it's amazing because it contains footage of the end from their first session in the studio. The tail end of this clip reveals what happened when Dre started playing a few beats he was working on for Eminem, and it's gobsmacking because so rarely is the moment of creative conception captured on video. See for yourself.

Eminem and Dr. Dre tell the story of when they first met and went into the studio together. Stream all episodes of The Defiant Ones now. Follow The Defiant

"Like yo. Stop. Shit's hot. That's what happened our first day, in the first few minutes of us being in the studio," remembers Dre.

Because Eminem was a scrawny white rapper from Detroit, many resisted. He didn't look the part. That brings up the second lesson.

"My gut told me Eminem was the artist that I'm supposed to be working with right now," Dre recalls. "But, I didn't know how many racists I had around me."

"Everybody around me, the so-called execs and what have you, were all against it. The records I had done at the time, they didn't work, they wanted me out the building. And I come up with Eminem, this white boy."

As in many moments in their long collaboration, Iovine and Dre persisted and profited yet again by arbitraging the biases of the herd.

"We weren't looking for a white, controversial rapper," Iovine says. "We were looking for great."
 

"Great can come from anywhere."
 

He means it.

"Lady Gaga walked into my office, Italian girl with brown hair, started telling me about Andy Warhol, and dance music, but yet industrial, and paintings. I don't know, she confused me so much that I signed her."
 

None of the other Interscope execs thought Gaga had breakout appeal. Iovine did.

"I was at a club with Timbaland, and I saw the room move. It felt like pop music. It felt like it could break through."
 

Perhaps not a snap judgment, but no one would confuse Lady Gaga for Eminem. When Iovine says great can come from anywhere, his diverse roster of artists backs him up.

How do you find alpha in an otherwise efficient market? Iovine and Dre arbitraged the biases of the market, of which one is rampant pattern recognition.

Much of this makes it sound as if identifying hit music is Iovine and Dre's talent. But plenty of evidence exists that much of cultural taste is socially constructed and is subject to path dependence.

The truth, as always, is somewhere in the middle. However, most people underestimate how much It is possible to socially hack popularity since some of popularity is a social construction and has nothing to do with any inherent quality of the goods being sold. This is a third lesson which the documentary reinforces.

Derek Thompson's Hit Makers, which I will write up soon, and Michael Mauboussin's The Success Equation, both of which I loved, both cite Duncan Watts and Matthew Salganik's MusicLab experiments. The key finding of that study was that people rank some things higher simply because they were given randomly generated hints that those things were already popular with other people.

This Kevin Simler post offering an alternative explanation for how ads work is actually how many people who work in advertising understand ads to work, at least in part. Simler theorizes that ads create common knowledge, and much as Watts and Salganik's experiment reveals, so much of human behavior is socially constructed. In the case of the MusicLab study, it's popularity. In Simler's examples, ads cue consumers on which products are likely to be the most effective signals in a world which status is socially constructed in large part through such consumer product totems.

Iovine understands this, and nowhere is it more evident than in the latter part of the final episode of Defiant Ones, the Beats Headphones saga. 

Dre spends some lots of time engineering Beats headphones for a particular sound. More on that later. I'm a moderate headphone geek; enough so that I own more than four pairs of over ear headphones (I prefer the sound of some for specific types of music) and two headphone amps, so I appreciate what Dre understands, which is that the personality of headphones can be a matter of personal taste.

Iovine cuts to what's far more important in the headphone decision. Most people don't give a hoot what the response curves of a headphone are measured at, what they sound like. People wear them as fashion accessories, and people want to be cool.

Iovine and Dre set up a day where they test all the leading headphones on the market. They're not impressed.

"We realized that all headphones sound boring and looked like medical equipment. We wanted more bass in these headphones to exaggerate all of it. We wanted to put it on steroids," Iovine said.
 

Producer Jon Landau recalls: "The Bose headphones, they were advertising noise canceling, total quiet. Jimmy says, 'Noise canceling?! Yeah, they're the headphones if you want to go to sleep on a plane. Our headphones are the where's the party headphones.'"
 

The distribution and marketing leverage was to be found through Iovine's celebrity friendships, so he starts smiling and dialing, or perhaps more appropriately in Iovine's case, dialing and cajoling. He gives those headphones away to all his artists and asks them to wear them in their music videos, in public, anywhere a camera or a human eye is present. Anyone famous walking in Iovine's office has to don a pair of the headphones and submit to a photo. The design of the Beats headphones, like the iconic white headphones for the iPods, is brilliant. The iconic b imprinted on each colorful molded plastic ear cup is like a walking billboard.

After artists, Iovine moves onto athletes, and soon it's rare to see Lebron benching in any of his workout videos on Instagram without his Beats by Dre headphones. I almost can't picture Ronda Rousey walking into the ring or out of the ring without picturing her with her Beats headphones draped around her neck. Who can forget Michael Phelps staring down Chad Le Clos in the 2016 Olympics, his Beats headphones blasting what must surely be some angry heavy metal that would ripple the surface of the Olympic pool.

All PR isn't good PR, but when the sports leagues like FIFA and the NFL and the Olympics issue bans on the Beats headphones, it's a dream come true for a product seeking renegade cachet.

It works. Any self-respecting audiophile considers Beats to be an absolute scam from a sound quality perspective and yet Beats dominates the premium headphone ($99 or greater) market.

Not every product market sees market share driven by socially constructed popularity, but headphones are perhaps the perfect fashion accessory and cultural signal in the age where everyone can listen to music through their smartphone at any time.

Iovine pushes the headphones so much that Eminem admits it annoyed him.

"There would be times where we would be shooting a video until like six in the morning, and we had to do one more take with me or somebody in the video wearing some goddamn [Beats] headphones. Are you fucking kidding me?!"
 

Iovine is a great producer, but he's also a consummate marketer.

"The only person that does it better than him is me," says Puff Daddy.
 

There may be a line which is shameful to cross when it comes to marketing, but who knows where that line is if you have no shame.

"He's got good instinct, and he's shameless," says Trent Reznor about Iovine.
 

In fairness to the documentary, Dre does talk a lot about tuning the sounds of the Beats headphones, so why do audiophiles dislike the sound? Beats are notoriously bass heavy. Dre grew up listening to music in cars in LA, with subwoofers so heavy that people outside the car can feel their organs being jostled.

Music, especially for young people, is raw emotion and energy. Not that audiophiles don't also love to turn up their music, but the bass-heavy sound Dre and Iovine amplifies the primal elements of the music, something that non-audiophiles can feel. In a revealing scene, Dre demos the mix of an album by taking Iovine to a garage to listen to the album in a tricked out van. Dre knows that the music of the street is often heard, literally, on the streets, coming through some car stereo, bass pumping, car rocking. Dre isn't above understanding the social transmission of music, it's just that he understands a particular form of that virality, when it comes through the original social network, the streets of the neighborhood. If it weren't likely to render its listeners deaf, Dre would probably want his headphones to sound like those cars which wake the neighborhood, the bass so powerful that the subwoofers seem to shake windows and cause a car to bounce up and down.

The last bit, which is a meta point, and one I've been thinking about a lot recently, is how many more entrepreneurs The Defiant Ones will reach and teach than any single book on entrepreneurship. Video may be a lossy medium in terms of how much it leaves out in service of the narrative structure, but its inherent visual and "autoplay" quality are proven to be much lower friction as an educational medium than text. We need more like this and less like the typical MOOC video which replicates all the excitement of your median classroom lecture.

When you come to the 2^100 forks in the road...

In some simple games, it is easy to spot Nash equilibria. For example, if I prefer Chinese food and you prefer Italian, but our strongest preference is to dine together, two obvious equilibria are for both of us to go to the Chinese restaurant or both of us to go to the Italian restaurant. Even if we start out knowing only our own preferences and we can’t communicate our strategies before the game, it won’t take too many rounds of missed connections and solitary dinners before we thoroughly understand each other’s preferences and, hopefully, find our way to one or the other equilibrium.
 

But imagine if the dinner plans involved 100 people, each of whom has decided preferences about which others he would like to dine with, and none of whom knows anyone else’s preferences. Nash proved in 1950 that even large, complicated games like this one do always have an equilibrium (at least, if the concept of a strategy is broadened to allow random choices, such as you choosing the Chinese restaurant with 60 percent probability). But Nash — who died in a car crash in 2015 — gave no recipe for how to calculate such an equilibrium.
 

By diving into the nitty-gritty of Nash’s proof, Babichenko and Rubinstein were able to show that in general, there’s no guaranteed method for players to find even an approximate Nash equilibrium unless they tell each other virtually everything about their respective preferences. And as the number of players in a game grows, the amount of time required for all this communication quickly becomes prohibitive.
 

For example, in the 100-player restaurant game, there are 2&100 ways the game could play out, and hence 2^100 preferences each player has to share. By comparison, the number of seconds that have elapsed since the Big Bang is only about 2^59.
 

Interesting summary of a paper published last year that finds that for many games, there is not clear path to even an approximate Nash equilibrium. I don't know whether this is depressing or appropriate to the state of the world right now, it's probably both. Also, it's great to have mathematical confirmation of the impossibility of choosing where to eat when with a large group.

Regret is a fascinating emotion. Jeff Bezos' story of leaving D.E. Shaw to start Amazon based on a regret minimization framework is now an iconic entrepreneurial myth, and in most contexts people frame regret the same way, as something to be minimized. That is, regret as a negative.

In the Bezos example, regret was a valuable constant to help him come to an optimal decision at a critical fork in his life. Is this its primary evolutionary purpose? Is regret only valuable when we feel its suffocating grip on the human heart so we avoid it in the future? As a decision-making feedback mechanism?

I commonly hear that people regret the things they didn't do more than the things they do. Is that true? Even in this day and age where one indiscretion can ruin a person for life?

In storytelling, regret serves two common narrative functions. One is as the corrosive element which reduces a character, over a lifetime of exposure, to an embittered, cynical drag on those around them. The second is as the catalyst for the protagonist to make a critical life change, of which the Bezos decision is an instance of the win-win variety.

I've seen regret in both guises, and while we valorize regret as life-changing, I suspect the volume of regret that chips away at people's souls outweighs the instances where it changes their lives for the better, even as I have no way of quantifying that. Regardless, I have no contrarian take on minimizing regret for those who suffer from it.

In that sense, this finding on the near impossibility of achieving a Nash equilibrium in complex scenarios offers some comfort. What is life or, perhaps more accurately, how we perceive our own lives but as a series of decisions, compounded across time.

We do a great job of coming up with analogies for how complex and varied the decision tree is ahead of us. The number of permutations of how a game of chess or Go might be played is greater than the number of atoms in the universe, we tell people. But we should do a better job of turning that same analogy backwards in time. If you then factor in the impact of other people in all those forks in the road, across a lifetime, what we see is just as dense a decision tree behind us ahead of us. At any point in time, we are at a node on a tree with so many branches behind it that it exceeds our mind's grasp. Not so many of those branches are so thick as to deserve the heavy burden of regret.

One last tidbit from the piece which I wanted to highlight.

But the two fields have very different mindsets, which can hamper interdisciplinary communication: Economists tend to look for simple models that capture the essence of a complex interaction, while theoretical computer scientists are often more interested in understanding what happens as the models grow increasingly complex. “I wish my colleagues in economics were more aware, more interested in what computer science is doing,” McLennan said.

Selfies as a second language

Of the people I follow on Snapchat, about half are old people (lots of middle-aged white male VC's, maybe trying to make sense of what it is), the other half are young and what I'd consider Snapchat natives. As a product person, it's fascinating to observe stark divides in consumer behavior. Often these are generational divides, less discussed than technological shifts like those fueling platform shifts or industrial revolutions, but no less fascinating. Often, these behavioral changes happen because of technology shifts, as humans evolve with their new tools and altered environment. In Snapchat is one of the cleanest, most universal of these behavioral fault lines.

When I send a Snap to any of the people in my address book, the oldies respond, inevitably, with some text message, maybe an emoji if they're somewhat hip. If I send a Snap to a young'un, inevitably I'll receive a selfie in response.

Since I noticed this a few years back, I've tracked it across the years, and it's still the case, to an astonishingly consistent degree. I'm talking nearly 100%, and I can't remember any exceptions.

My theory on this is that older folks did not grow up with front facing cameras on smartphones and thus experience an uncomfortable body alienation from seeing themselves in photographs akin to how most people hate hearing their own voices the way other people hear them. We perceive our own voice differently than others because we hear our own voices reflected back from the world mixed in with feedback from the machinery we use to generate that sound. Other people only hear the former.

For my generation, we grew up mostly seeing ourselves in mirrors, and thus that's the way we visualize our face and body. When we see ourselves in photos, we see a flip of what we're used to seeing in the mirror, and it's discomfiting.

Cameras can introduce additional distortion depending on the focal length of the lens. Almost all smartphone cameras have wide-angle lenses. The iPhone camera is something like 28mm (in 35mm camera terms); I'm not sure what the front-facing camera is, but it's wide. A 50mm focal length is typically considered a neutral lens in 35mm cameras, and any focal length shorter than that is usually considered wide. 

That old cliche about how the camera adds ten pounds? It refers to the distorting effect of wide angle lenses which are very common in television and film, especially for a lot of closeups and medium shots. If you ever see an actor or model in person they look surprisingly thin. People who look normal on camera look thin in person, and models, who look thin on camera, look malnourished in person.

What is unpleasant for faces can be flattering for spaces. Almost all housing interiors for sites like Airbnb or any real estate listing are shot with wide angle lenses. Often it's the only way to capture an entire room from a photo shot within the room, but it has the pleasant effect of expanding the space. This is why, if you ever go to a live taping of a TV show like Saturday Night Live or The Daily Show, it's shocking how tiny the studio actually is compared to how it appears on TV, and why that seemingly spacious apartment you rented on Airbnb feels like a bathroom stall when you arrive in person, roller bag in tow.

If the wide angle smartphone camera lens renders people's faces larger, the effect is exaggerated when the camera is held at mere arm's length. It makes people look heavier than they're used to seeing themselves in the mirror, and that's not pleasant for all except those with tail distribution positive body image. There's a reason a portrait lens is usually longer than neutral, often starting at 85mm or longer, and why fashion shoots often use telephoto lenses that require a photographer to stand really far from their subjects, sometimes so far they have to shout directions to the model through a megaphone. The longer the lens, the shallower the focus, the more flattering the portrait.

However, this generation of kids who've grown up with a smartphone pointed at their faces from the time they were infants have seen themselves hundreds if not thousands of times through the funhouse mirror eye that is the smartphone camera. So much so, I speculate, that they experience a much lower degree of photographic body alienation than my generation. I may see myself in a bathroom mirror a few times a day, once in the morning, a few times at work, and once at night. Kids of this generation, armed with smartphones from an early age, often see themselves in photographs, on a screen, dozens of times a day.

Furthermore, they've internalized this disparity in impact between their photographic and real world representation the way celebrities and models do. It's just math. Ubiquitous smartphones and social media allow exponentially more people to see their photographic self than their real world body. It's entirely rational to consider their virtual self to be more important in the accumulation of social capital than their physical selves. Time spent mastering the selfie is time spent on the largest audience, and while it may be horrifying to see young people shooting dozens of photographs of themselves before posting to social media, then subsequently A/B testing which photos garner the most positive social media feedback, it's behavior one would predict for homo socialis.

They say a picture is worth a thousand words. While the exact ratio may be something we can actually calculate with a cleverly designed experiment, even in the absence of such a test it's clear that the multiplier is significant. We are, all of us, straining against the more narrow emotional register of text, especially when we often face character limits, both imposed and self-inflicted (due to the inconvenience of typing on smartphone keyboards). As email and text messaging replaced more expressive mediums like phone calls and handwritten letters, we find ourselves apologizing, quite often, for coming across other than we intended.

It's no surprise that many writers resorted to adding emotional modifiers like =) to emails. Even prior to those early emoji, the use of exclamation points in online communication was noticeably higher than in regular writing, lest we come off as bored, or even worse, cold (increasingly, it becomes a contrarian power move to eschew exclamation points entirely; the irony of Donald Trump ending every tweet with an exclamation point is how silly it is for the leader of the free world to resort to such linguistic chest puffery).

After early emoticons came the age of emoji, and now the GIF has shouldered its way into the conversation. Each successive communications trend brings a more efficient carrier of emotion, per byte, than text, and compression matters in this clipped conversational age.

In most cases, I much prefer receiving a selfie in reply to a message I send than a text response, because the human face is a miraculous instrument, almost incapable of the abstraction of raw text. Still, I can't bring myself to send selfies as responses.

Perhaps if I looked like Ryan Gosling or Gal Gadot, I'd spend hours admiring myself in a mirror, snapping selfies at all different angles just to see if it was even possible to make myself look anything less than gorgeous. Is it even possible for Denzel Washington to cringe at the sound of his own voice played back to him? If I were Denzel I'd just talk to myself all day, just to marvel at how I could make anything sound like the word of God.

But I suspect it's more than that. I've happily embraced emoji and the fetishistic allusiveness of the GIF. When it comes to the selfie, however, I'm a not-yet adopter. I am of that generation for whom selfies are not second nature but instead a second language.