Artificial Intelligence

Our narratives of Man versus Machine focus on Machine becoming Man, then surpassing Him.

Man is something that shall be overcome. Man is a rope, tied between beast and overman-a rope over an abyss. What is great in man is that he is a bridge and not an end. (Zarathustra, thus imaginarily reported by Friedrich Nietzche) 

The AlphaGo documentary is about Man qua Man[1], or, more precisely, about one man by the name of Lee Sedol, who has a soft, high-pitched voice, a wife, and a daughter. In March of 2016, Sedol went from being well known to Go fans to being well known to everyone after losing 4 out of 5 games to AlphaGo, a computer built by machine learning engineers at Deepmind.

Here is what the film beckoned me to see, feel, and infer.

1. Fear eats Man’s mind

And thus the native hue of resolution/ Is sicklied o’er with the pale cast of thought (Hamlet, Act III, Scene I) [2]

Sedol is a champion. He has cultivated excellence, put in his 10,000 hours of practice. Played game after game after game to get where he is today, working humbly and patiently with his coach. Playing Go the way he plays is an act of respect towards his elders, his nation, his family.

For Sedol, therefore, the match against AlphaGo was much more than a match. It was the appointed time to exhibit elegance, grace, and creativity above and beyond standard play. The moment when he left the hallowed halls of practice to squint into the harsh lights of the stage. When they applauded. When he bowed, and, lifting his as slowly as possible to protract time into the infinite dilation of Cantor’s continuity, pupils dilating into eye drop blurs, seconds half-lived to infinitesimals, further, until he couldn’t stop it anymore, until, as raised his head back up, he noticed his sense of self had changed, he observed himself being observed, knowing everyone was watching, rid himself of the caterpillar cloak called Lee Sedol to stretch his powdery wings as Man. He had become an allegory of human intelligence pitted against the machine.

No biggie. You got this. Just a little blip in history. Just a game. Underwear. Chickens in underwear with scraggly little legs hobbling under the weight of tubby guts bloated with donuts and Budweiser. Just like yesterday when no one was watching.

What a horrible place to be.

And yet, we honor it. We honor the resilience of the golfer who keeps his cool after a dud shot hooks way too far left. We honor the focus of the concert violinist who can make her way through the Mephistophelian haze of a Paganini caprice. We honor the ease excellent TED Talk speakers find when they share an idea they believe in. We honor it because we know how hard it is. Because we recognize that the difference between good and excellent is the fortitude of practice and the gumption to keep the mind in check, to settle its sabotage, to focus.

We are all Hamlet. Some of us more than others.

Sedol is also Hamlet. The documentary does a marvelous job eliciting our empathy as we watch him doubt, furrow, fear, apologize, strategize, wrestle with the pastiche reflection of what he could have done, who he could have been, how the narrative could have gone if only he had done this move instead of that move. We never hear the voices in his head but we can infer their clamor: “calm down, stay here, focus.” Sedol plays the game in context. He knows the stakes of the match and has no choice but to devote a portion of his brain to the everything else that is not the local task. It’s plausible that only 30% of his brain power could be devoted to the actual game.

AlphaGo has no voices in its head. It has no runaway probabilities. The only probabilities it calculates span the trees it searches to find the next move and win.

2. Man is a social animal who relies on nonverbal communication

Man is by nature a social animal; an individual who is unsocial naturally and not accidentally is either beneath our notice or more than human. Society is something that precedes the individual. Anyone who either cannot lead the common life or is so self-sufficient as not to need to, and therefore does not partake of society, is either a beast or a god. (Aristotle, Politics) [3]

AlphaGo has no hands. It has no face. Unless Deepmind decides to embody future versions in a robot somersaulting down the uncanny valley, it will never feel the silky lamination of a Go stone, never calm its nerves by methodically circling the stone between the pads of its right thumb and index finger as it contemplates its next move.

Like the infamous Godot in Samuel Beckett’s play, in the documentary, AlphaGo feels more like a prop than a character. It’s undoubtedly there, ubiquitous, but somehow also absent. Sedol engages with AlphaGo through a ventriloquist named Aja Huang, a Taiwanese computer scientist on the DeepMind team who is also an amateur 6-dan Go player. Sedol never engages with AlphaGo directly: only with its diplomat, its emissary.

Huang is no throw-away character. The ventriloquist could have been anyone: his task was to look at the digital display indicating AlphaGo’s move and translate this to the physical board by placing the stone in the right place. He could have carried out this task with zero knowledge of what it meant. Brawn without brains. Pure, robotic execution.

The positions of the stones mean something to Huang. He bridges two ways of seeing the game, like a computer scientist charting probabilities and like a Go player strategizing moves.

And this means that his face could have relayed emotional content back to Sedol, allowing the champion to plunder the emotional cues that are such an integral part of the game. In the first match, Sedol felt alienated because when he looked up at Huang to gather information from his temples, eyebrows, forehead, pupils, cheeks, lips, chin, elbows, freckles, arm hairs, face hairs, eyes, sweat beads, breath, aura, the signals were absent. Huang didn’t exhibit the weight of concentration or even the active restraint of a bluff. It was almost worse that he wasn’t just a robot man because he had enough knowledge to lead Sedol to anticipate emotional cues but fell short because his ego wasn’t engaged. He was, in the end, only an observer. The stage shifted to a theater of deliberate alienation, as in the movie The Lobster.

This inverted uncanny valley tells us something about how we communicate. It’s cliché to underscore the importance of nonverbal communication, but it was quite powerful to see how much Sedol typically relies upon emotional cues as animal, as mammal, when he plays against a normal opponent, and how the absence of those cues threw him off. I suspect some of the reticence we feel around trust and explainability stems from our brains processing the world as animals. We don’t actually require explanations from people to trust them and obey them. Power and persuasion seep through different seams.

3. Computer scientists and subject matter experts see the same thing different ways

While Huang speaks neural network and speaks Go, most of the DeepMind scientists lack the same bilingual subject matter expertise (I may be incorrect, but I’m pretty sure not everyone who worked in AlphaGo knows the game). Indeed, one fascinating aspect of contemporary machine learning is that the systems can learn what aspects of the data are relevant for a prediction or classification task rather than having a person apply their knowledge to hand pick which aspects will be most relevant. This is not universally the case, and it’s not to denigrate the value of subject matter expertise: on the contrary, there is excellent research afoot to make it easier for people with subject matter expertise in some domain-be that cancer diagnostics or fashion taste or 50-years of experience tweaking knobs to offset the quirks of an office building in lower Manhattan-to represent their knowledge as distributions and parameters without needing to be a scientist do to so. But a characteristic of the deep learning moment is that a crafty scientist can consider a problem abstractly, move away from the particular details we observe as the problem’s phenotype (e.g., a move in the game of Go) and focus on the mathematical underpinnings of the problem (e.g., the number of hidden layers or some other architectural choices to make in a neural network). Add to this that what makes a machine learning problem a machine learning problem is that there is too much variance for us to deterministically write out all the rules: instead, we provide primers that enable the system to iterate fast (that’s where we need all the computational power) to map inputs to outputs until the mapping works well most of the time. It’s like selecting the yeast that will yield the best bread.

Programs for playing games often fill the role in artificial intelligence research that the fruit fly Drosophila plays in genetics. Drosophilae are convenient for genetics because they breed fast and are cheap to keep, and games are convenient for artificial intelligence because it is easy to compare a computer’s performance on games with that of a person. (John McCarthy and Ed Feigenbaum, Tribute to Arthur Samuel) 

The AlghaGo documentary did a wonderful job juxtaposing how computer scientists tracked the game’s progress and how Sedol and the Go commentators tracked the game’s progress. The scientists viewed the game mathematically, as a series of abstract scores and probabilities. The players viewed the game phenotypically, as a series of moves on the board. It was two fundamentally different ways of viewing the same problem, illustrating the silos of communication companies that quickly emerge like tectonic plates shooting mountain sprouts in any enterprise. The endgame for the opponents was also quite different. The AlphaGo team was fundamentally interested in using Go as a testing ground for computational possibility, the particular use case required to explore the larger problem of building a system that can act intelligently. Sedol was fundamentally interested in playing perfect Go, and potentially abstracting lessons from play to other aspects of his life. These conflicting endgames are often at work in the dialectic of innovation, yin and yang dancing drunk through the discrete step changes of technological progress.

I do wonder if we could rewrite the narrative of Man versus Machine as one of two different ways of creating, encapsulating, and sharing knowledge. The documentary made this about Demis Hassabis and the AlphaGo Team versus Sedol, West versus East, traditional culture versus computer science, two ways of representing knowledge and viewing the world. It’s ultimately a more grounded narrative. In our HBR Ideacast episode, I suggested to host Sarah Green Carmichael that it’s helpful to reframe a supervised learning system as “one human judgment versus the statistical average of thousands of human judgments,” and then ask which one you’d rather rely on. Granted, the new AlphaGo Zero system is one of self play, not one that mines past human judgment. But the yeast primer is still coded and crafted by human minds with a particular way of framing problems as engineered mathematical models.

4. Algorithms change how Man makes sense of the world

In a 2011 TED Talk, Kevin Slavin explained how trading algorithms have reshaped the physical landscape (we build structures to transmit the fastest signal possible so our algos can outcompete one another by fractions of a second). In a 2018 phone conversation, my partner John Frankel at ffVC helped me crystallize my understanding that task-specific machine learning algorithms are poised to reshape-if not already actively reshaping-our cognitive landscape.[4]

Much of the language used to describe AlphaGo betokens alienness and strangeness, facets of thought that are not only not human but antihuman. From a 2017 Atlantic article:

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.

AlphaGo makes a few moves in the match versus Sedol that flummox him. As a non Go player, I couldn’t make sense of the moves myself, but relied upon the commentary and interpretation offered by the film. What I took away was the sense that AlphaGo did not rely upon the same leading indicator heuristics that are the common tropes of seasoned Go players. If we think about it, it shouldn’t come as a surprise that the search space of 10¹⁷² positions (according to 11th-century Chinese scholar Shen Kuo) contains brilliance that has to date evaded master Go players. But what’s even more interesting is the cultural significance of knowledge transfer from generation to generation. If Go is staging ground for life, then mastery can and should be measured by analogical transferability and applicability. It’s like a pedagogical philosophy that values critical thinking: teach them Shakespeare, teach them whatever nouns you want, but focus on enabling them to transfer the verbs so they can shape shift to solve problems as they arise.

What comes off as alien is a system that is optimized for one task, regardless of analogy and transfer. And, one only win Go by one point, not many. The move with the highest likelihood of winning by the narrowest of margins will look different than the move that betokens potentially less likelihood of success, but a larger cushion. Map this to making big choices in life: most people study the safe subject to keep options open rather than following the risky path of studying what they love. The tradeoffs are different. Their optimizing for different types of outcomes and using a different calculus.

So, following John Frankel, I’d like to propose that our heuristics will change as our minds increasingly engage with tools that optimize ruthlessly against one task. Machine Go is different than Man Go because it’s not designed as a pedagogical tool to teach life lessons. It’s designed to win, designed to exploit the logic of one search space and one game and one set of rules. But that need not be all that bad. There’s something lovely in coming to terms with the fact that success only requires one point, that we need not rely upon the greedy heuristics that are familiar as we navigate the world. What’s deemed as alien is a means of coming to terms with our own predilections to generalize, when it may not always (or often) be the best bellwether of success. It’s the inverse interpretation of Bostrom’s paper clip optimization monster. An invitation for us to ponder our values and ethical stance as we increasingly interact with algorithms geared to optimize without questioning if that’s ultimately what we want and need.

The Pythagorean Cup is at once practical joke, physics lesson, and moral chastiser. If you are greedy and put too much wine into the cup, a siphon effect kicks in and all the liquid drains out. This kind of analogical triple meaning is the opposite of algorithmic thinking in its current form.

Conclusion

The AlphaGo documentary left me feeling empathy and admiration for Lee Sedol. Not as a Go champion, not as an allegory of Man’s Intelligence, but as a man. His humility was beautiful. His striving was admirable. His kindness towards his daughter was noticeable. His Korean duty was evident. He was many features cobbled into a being, with feelings and a heartbeat, and a mind. He learned something from the matches and lost gracefully, shaking Hassabis’ hand as he left the press conference, cameras flashing in his wake.

[1] Gender warriors, please do forgive me. There’s implicit critique about who controls the AI narrative in keeping the reference to Man, and I find the capitalization lends a curious aura of allegory to this post, which is riddled with references to male heroes.

[2] Rainer Werner Fassbinder (whose name I always mistake for Rainer Maria until I remember that’s the other Rainer, the Rilke Rainer) has a marvelous film entitled Angst Essen Seele Auf, translated as Ali: Fear Eats the Soul (which I should be translated as Ali: Fear Eat the Soul to better capture the grammatical error Ali, one of the film’s protagonists, makes when he speaks broken German without conjugating verbs) about an “almost accidental romance kindled between a German woman in her mid-sixties and a Moroccan migrant worker around twenty-five years younger.” While released in 1973, the lessons are all the more relevant today. The other eating metaphor on my mind is Andreessen’s software eating the world, and now Steven Cohen and Matthew Granade saying that models will run the world. What Cohen and Granade get right in their article is that AI systems are about much more than just Jupyter notebooks with models. You have to put models into production, use them as hypothesis to build closed-loop systems that get better as they engage with the world. So, so, so, so, so, so, so many companies still seem to miss this part. It’s hard, and requires work that isn’t deemed sexy by the cognoscenti and the rockstars (how awesome is the word cognoscenti?).

[3] I’ve been thinking a lot about the social value of work and the workplace and have the early, what-my-mind-does-walking-and-running-level intuitions of a blog post about why work is an opportunity to experience positive, Aristotelian freedom (where self-actualization occurs through participation in a common, social goal) versus negative freedom (how we normally conceptualize freedom as the absence of constraint for the individual) and what that means for team and meaning and also the intrinsic value of work (for the leisure promised by some UBI pundits rubs me the wrong way; not all UBI pundits believe self-actualization is an individual project, and the most sober ones think it’s a bump needed to become more socially connected (including Charles Murray, which is interesting…). Stay tuned.

[4] John has an uncanny ability to understand and represent the heart of the matter in emerging technologies. It’s a privilege to learn from him. I’ve mentioned this before on the blog, but John also has the world’s best out-of-office emails, which have inspired my own (mine are far less sardonic and far more earnest, not by choice but by the ineluctable traps of my style).

The featured image is from an article the newspaper Korea Portal posted March 15, 2016. In the article, Sedol says: “I wanted to end the tournament with good results, but feel sad that I couldn’t do it. As I said before, this is not a loss for man, but a loss for me. This tournament really showed what my shortcomings are.” As in the documentary, Sedol interprets his loss as a personal failure. He doesn’t view himself as the representative of mankind. This isn’t man versus machine. It is one match. One man versus his opponent. But because the opponent doesn’t feel like Sedol does, doesn’t care if it wins, it becomes one man versus himself. 

I recently published an article about explainability in machine learning systems for the Harvard Business Review. The article argues that many businesses get stuck applying machine learning because they worry about black boxes; that they should think about what matters for a given use case, as sometimes other governance and assessment metrics are more relevant than an explanation (e.g., precision and recall for information retrieval); and that a close reading of recital 71 in the EU GDPR suggests that an individual’s right to an explanation applies to the procedures used to build and govern the entire system, not to which input features, with which weights, lead to which outputs.[1]

The article’s goal is to help businesses innovate. It seeks to empower people by helping them ask the right questions. The battle cry is: There’s no silver bullet. You have to think critically. Compliance and business teams should align with data scientists early in the machine learning system-development process to align on constraints required for a given use case. Businesses should be as clear as possible on what algorithms actually optimize for, as ethical pitfalls arise between what we can and can’t measure, what our data do and do not index about the world.[3]

The day it was published, I received two comically opposite responses from data scientists working in executive positions in technology companies. The first complimented me, mentioning that they were pleasantly surprised to see someone with my educational background writing so cogently about machine learning. The second condemned me, mentioning that someone with my educational background had no right to write about machine learning and that I was peddling dangerous hype.

I didn’t learn much from the compliment. My Mr. Peanut Butter labrador ego enjoyed being stroked.

I learned a few things from the critique. It helped clarify some of my own tacit assumptions, my ideology, my ethics, the grey matter between the words, the stuff that makes it hard to write because it feels vulnerable and exposed, the implicit stuff that signals community acceptance and alignment and that we rarely sit back, unpack, analyze, and articulate.

Here are some of the lessons.

1. Precision always matters

As with (close to) all tricky situations, I wonder if I did the right thing at the time. Upon being attacked, I chose to diffuse rather than ignite. I thanked the person for voicing their critique and disengaged. The only thing I mentioned was that it appeared that they drew their conclusion from the title of my article rather than its content. They stated that scientists have long had ways to interpret the output of neural networks and that I was peddling hype to write an article entitled “When is it important for an algorithm to explain itself?” I was surprised to see the narrow focus on algorithmic interpretability, as I felt the work my article did was to expand the analytical framework of explainability to systems and procedures, not just algorithm. So, underneath the amygdala’s attack response, my mind said “Did they even read it?”

It took me a few minutes to put (what I assume are) the pieces together. I don’t write the titles for my HBR articles and hadn’t taken the time to internalize how the title could be interpreted. When my editor suggested it, I quickly approved. Had I felt it was important that the title precisely reflect the content, I would have recommended that we say “an algorithmic system,” not an “algorithm” and say “when is it important for businesses to consider explainability in machine learning systems” versus implying that algorithms have agency. (Although it is a thought-provoking and crucial task to think about how we can and should design system front-ends to translate math-speak into people-speak, be that to communicate and quantify uncertainty or to indicate other performance metrics in a way that is meaningful and useful to developers and users.)

Let me be clear on the lesson here. I try to take as much responsibility as possible for outcomes, especially negative ones. I approved this title very quickly because I was excited to see the piece go live. Next time, I’ll think more deeply. I’m not blaming the HBR editorial team. They thought about the title and considered a few different options. I love working with my editor. He’s a wonderful partner. We bounce topics by one another. He’ll push back on stuff he’s not excited about; I’ll do the same for him. What I enjoy most is giving him feedback on questions unrelated to my writing. I like giving back, as he has done much to help me build my reputation as a writer.

The reason I wonder if I did the right thing is that I wonder if it is my duty to other writers, to other professionals, to have stood up for myself as opposed to stepping back and disengaging. But attacks beget strong emotions. For all of us. I needed time to think and let the lessons sink in. This is, obviously, my response.

2. Read things before sharing them and commenting on them

I’m guilty of having shared things without reading them, or having only skimmed an abstract. Mostly because I’m busy and can be impulsive. This is a good lesson about why that’s always a bad idea. I’d feel ashamed if people suspected I hadn’t read an article before critiquing it. There’s a lot at stake here, like democracy. It’s meaningful to engage deeply and charitably with another person’s ideas. To take the time to understand what they are trying to communicate, to find an opportunity to refine an idea, challenge an idea, improve the structure or flow. To teach one another.

3. Don’t judge someone based on their resume

The person who critiqued me seemed to draw conclusions about what I could and could not know based on my LinkedIn profile. That doesn’t reveal that much. You don’t see that much of my college education was funded by Siemens because I was one of 2 female students in New England awarded for having the highest scores on our math and science AP tests. You don’t see that I was a math major at U Chicago who always got straight As in math and struggled much more in humanities, but was ultimately more interested in literature so decided to pursue that path. You don’t see how much analysis, complex analysis, number theory, linear algebra, and group theory I studied. You don’t see how I came to understand how important that training would be once I ended up working in machine learning. You don’t see that I focused on history of philosophy and math in the 17th and 18th century in graduate school, and, while the specific math of that period is no outdated, have thought deeply about the philosophical questions associated with statistics, empirical science, and the diffusion of knowledge in the wake of new scientific discovery. You don’t see how my fellow literature graduate students told me reading my writing was like being in a prison because I was always trying to prove things, as in a math proof.

You might see that I’m not interested in competing with machine learning researchers in their own field. I want to drink in as much of their thinking as possible, want to learn everything they can teach me, want to understand why and what it means for the field, want to experience the immense joy of recognizing structural similarities between two disciplines or applications that can be the seat of innovation, that place where you realize that a mathematical technique originally explored for problem A comes into its own in the world in problem B.

You might see that the role I’ve come to accept is that of the translator, the generalist curious enough to dive deeply into whatever subject matter I’m working on, but will never be the disciplined expert. There will always be questions and gaps. Always more to learn and explore. Always people who can go deeper and narrower. Like Sheldon Levy, I viciously and vibrantly admire those whose creative minds will discover things, will reframe problems to uncover solutions stuck for centuries. They are the heroes. All we do is to sing their song, and help others hear its beauty.

4. Allow people to learn

This is the most important lesson. The one I care about. The one that puts fire into my heart and makes my fingers type quickly.

We must allow people to learn from experiences after school. We must not accept a world where the priests alone are allowed to understand, where the experts alone have the authority to write about, talk about, and share ideas about a subject. Technologies like artificial intelligence are already impacting us all. Work will change. Jobs will change. New jobs and new opportunities will arise. If people are not given the space to change, to learn, if the only people we deem qualified to do this work, to write about this work, are those who come with a certain PhD, a certain educational certificate, a certain type of social rubric of authority, we are fucked. We must trust that people can learn new things and find ways to give them opportunities. We must engage with one another so as to promote openness, to probe and push without the searing pain of judgment, to provide people with the confidence required to ask the simple questions needed to get to the heart of the matter, to give people the breathing room to embrace the initial anxiety of change so they can come to do something new.

No, I did not do my PhD in machine learning. It’s not impossible that I won’t go back and do a second PhD in reinforcement learning, as I find the epistemological questions associated with that subfield incredibly rich, incredibly akin to the perennial questions I have loved in the Greeks, in the early moderns, and again today. Time will tell. I have, however, worked in the field over the past few years. I was fortunate enough to have been granted the opportunity to learn a lot at Fast Forward Labs. I will forever be grateful to Hilary Mason for giving me a chance to help her build her business, and for believing that I could learn. I learned. I am still learning. I write about what I’ve come to learn, and accept criticism, feedback, refinements, all the stuff other people can share with me to expand my understanding and help us all grow. I’m decent at recognizing what I know with precision and where my knowledge starts to falter into fuzziness, and I tell people that. I have made mistakes, thought about them deeply, and try my best not to make them a second time (which I don’t always succeed it). I love enabling others to build sound intuitions about mathematical concepts and technology. To feel empowered, feel like they get it in ways they hadn’t before. And not because it’s dumbed down. Not because we resort to the Platonic blindfolds for the masses. Because we can all do it. It’s just that we have to break down the power walls, break down the barriers, break down our egos, and do our best to make something meaningful.

I will fight for it. There are too many people who hold themselves back because they are excluded from circles protecting themselves within elitism. Everyone deserves a voice. Everyone deserves a chance to understand.

5. Everyone should be allowed to write about technology

It’s not all going to be good. There is a lot of hype. I’m not sure hype is all bad, as it has the power to mobilize large groups of people who wouldn’t otherwise be interested. Nothing like the fine print of precise qualifications to dampen the mood of disruptive innovation. There is damage when the hype breeds unnecessary fear, rather than unbounded excitement. And there’s certainly lots of work to be done to help businesses bring expectations down to earth to capitalize on what’s possible. But there’s wonderful satisfaction that emerges when businesses start to get traction with a narrowly-focused, real-world application. And it takes different people with different viewpoints from different teams to make that happen, in particularly in established enterprises with their processes and people and quirks and habits and culture.

We may not all want to write, but we all have a part to play. And I’ll always subscribe to the Ratatouille philosophy: it’s not that everyone can cook, but that the greatest chef in the world may not necessarily have a resume our priors deem likely to succeed.

[1] Peter Sweeney has written many great articles about epistemology and AI, and argued that we should conceptualize the outputs of machine learning algorithms as observations, not explanations. He was responding to David Weinberger, who has argued that we should focus governance efforts on optimization, not explanation. I’m partial to that, but again think it depends on the use case. Nick Frosst, who wrote the capsule network paper with Sara Sabour[2] and Geoff Hinton, thinks that interpretability (I must admit that I use the words interpretability and explainability interchangeably, and should take the time to parse the two, both philosophically and technically) is important because those creating systems and those impacted by systems should have the right to intervene to change their behaviour or change the system to change outcomes. So, for example, if a system denies an individual a mortgage because they missed their last 3 credit card payments, then that gives the individual meaningful recourse to act differently to meet the requisite rules in the future. It does indeed get dicey if there are so many dimensions that end up correlated to some output that impacts big deal opportunities for real people living real lives. I analyzed a couple of examples in this podcast.

[2] I’m 99% confident that Sara wasn’t able to attend NIPS in Los Angeles last year because she is Iranian. Knowledge, and credit for new knowledge, is cosmopolitan.

[3] My company recently published a framework to help consumer enterprises develop responsible machine learning systems. It’s practical and breaks down the different privacy, security, governance, and ethics questions cross-functional teams should ask and address at different points in the machine learning system-development process. We worked hard on it. I’m proud of it.

The featured image is of Remy the rat chef. He has a heightened sense of taste and smell but is naturally overlooked as an awesome chef because he’s a rat. He ends up making a ratatouille that softens the curmudgeonly critique because it brings him back to his childhood like Proust’s madeleine. So worth watching over and over again. 

Sandstone blushed pink, washed gold drips spires like kids plodding sand clods, layer upon layer tapering into antique vases and Victorian crowns, cobweb queens crooning nocturnal arias of desert winds in desert pines, ghosts within Native American ghosts, burnt sage bushes carmelizing wellbeing and peace as they caress their canyons, their friends, leaving them be, pruning caves where they might dwell and carve and paint and eat, ghosts long silenced by Manifest Destiny blasting His metallic, electric, self-driving cries to Mars, for yes, the future is already here, just not evenly distributed, moguls gluttonously rich as our anorexic middle class, addicted to their heroine gaze in selfie sticks and Facebook Likes, vanishes from photos like Trotsky, a Mexican ice pick nailing his moustache to the cross, forsaken by his father as parched tears transubstantiate into blood droplets fixed in sacred time together with the ocean hoodoos, voodoo rocks moving, flowing, crooning their ocean hymns with the wind queens till ice cracks the foundations and the avalanche falls.

But, somehow, these same rosy blushes and gold lashes appear in Barcelona, on the recently restored façade of Gaudí’s unfinished Sagrada Familia.

I behold the Barcelonan stone blushed pink, washed gold and time somersaults in my lonely chest. Bryce Canyon and the Sagrada Familia stand, silent, 5,586 miles apart. They have co-existed for 136 years. Neither is complete; neither ever will be. The truth is that their pink gold stones likely aren’t very similar to the scientific eye. But to me they are. Similar enough to tear apart the fabric of time as a lover tears silk to expose milk skin, Harem beauty, breasts blanched only by moon rays. So similar that tears pierce their possibility. I don’t hold them back even if others are watching. The others are busy being with loved ones anyway. No one watches. No one except every sometimes the perfect meeting eye to eye, not the groping kind, the seeking kind, the kind astonished to have encountered a self, a soul, curious to see deep inside for an instant, to cradle the shock of what must be beauty, observant enough to recognize unique meeting unique before the footsteps go too far and a we vanishes, stillborn.

I stand alone in Barcelona, walking streets watching others walk streets with others. My very loneliness grants me passage back to the silent sandcastles of Bryce Canyon. Inside this crevice of similarity, I recognize two separate constructions that have come to be one through the patient ravishing of wear and time. In Barcelona, by the grime of the city, exhaust from bankers’ lungs twitching stock exchange profit, orange precipitating scallops doused in chorizo oil; in Bryce, by the violence of the desert, ice tearing limestone hymen with glacier patience, tourist footsteps gently tweezing out the old ocean soul in camera flashes and plastic baggies. The difference is that in Barcelona this pristine blush of pink and gold only juts out when juxtaposed to the tarnished, uncleaned façade, whereas in Bryce it cannot but swallow everyone in its magnificence. It’s likely I wouldn’t have perceived the shocking similarity had I visited Barcelona a few months later, presuming the restoration work will have advanced to no longer leave the striking difference between clean and dirty façades. And I wouldn’t have been primed to see the similarity had I not visited Bryce Canyon just a few weeks before. I, then,-and by I, I mean the set of experiences collected into this unifier we call memory and consciousness, where analogies forge similarity in blacksmith strokes—am the condition of the similarity. It took me moving between continents to notice this unique and beautiful elision. And, it’s likely that it took me being alone to feel it deeply enough to make it matter. Had I wandered the world with a companion, I probably would have noticed the similarities, but they probably wouldn’t have penetrated deeply into the place where the beauty breathes so pure it hurts. Hurts because it carries with it the basic fact of my existence, inviting me to have a seat. To feast upon my life.

Why yes, the hues of pink and gold in the muted limestone of Bryce Canyon and Barcelona are so beautiful because the perception of their similarity is the trace of my existence. The heightening of what is to what is meaningful. It’s a nostalgic and slightly mourning meaning, as walking the streets of Barcelona I think about García Llorca’s Yerma*, a play about a woman who never bears a child. I often face moments of sadness at not being married, not having children, not being cushioned by normativity’s blessings. But my jealousy and covetousness for others’ lives have eased over time. This is evident in how my relationship with my mother has changed. I’ve done the emotional prep work of still being without child at 40 or 45, empathizing with a future self in a future state and thereby also growing more compassionate to others, today. I’ve experienced many places and opened my heart to many people. It’s an existence worth a second act.

This took place yesterday morning. Saturday. Friday afternoon I recovered a different past. It’s likely Friday’s experience primed my mind and my emotions to notice Saturday’s sandstone similarities.

For Friday I walked into the Picasso museum in Barcelona’s Gothic quarter. The air was damp but the rain held off, at least then (later on I waited out the raindrops with strangers under a group of trees near the waterfront, watching a mother spoon yogurt to some little mouth covered by stroller canvas; the little one seemed to eat well, the yogurt went fast). I’d wandered to the Cathedral, saw the foreboding chiaro-oscuro of the heraldic escutcheons, black and shadowed and tall into cracked gothic arches. I wandered through narrow streets weaved with balconies, some square, others round like Gaudí hobbit holes.

The Picasso museum is housed in a medieval cloister. The entrance is asymmetric, with matte greens and greys and a staircase up the right-hand side. Standing in line for tickets, I encountered my sixteen-year-old self. She was waiting for me; she had never left; she lived in the matte green of the entrance hall. I relived the mild disgust noticing our Spanish teacher’s fanny pack hang like a limp holster under the taught piqué cotton of his mint green polo shirt, I saw the moles on his hairy legs and the forced kindness in his smile. He stood there waiting for all 17 or 18 or 20 of us to gather in the museum. Watching him, sixteen, I relived my projection of myself in the eyes of the boys on the trip, they were juniors, I was a sophomore, I had a crush on Lyle, my experience of Gaudí’s balconies and Picasso’s cubism and Velázquez’s portraits and Franco’s phallic monument and the Roman aqueducts in Segovia were filtered through this prism of insecurity and adolescent desire, my personality still so much in flux, my introversion still so marked. I brought my violin to Spain and played every day. I brought a suitcase that was much too large, as I had yet to pride myself on my practicality, how easily I could move about the world. At the time, I was absorbed by the pulse of my feelings, by the inklings of the self I wanted to project. I was so governed by how I thought others perceived me; still am, but more so then. Painfully so then, my superego cruel and chastising. I jumped forward a few years, into my mid twenties, where I regretted my stupid crushes and insecurity and self-absorption, as I didn’t have strong memories of the objects and monuments and art I was supposed to have learned about. I was so focused on Lyle, so focused on how I projected Lyle saw me, that I missed out on Gaudí, Barcelona, Spain.

But now, years later, I love the distortions. I love how walking into the Picasso museum on a damp Friday afternoon, I recover not just the memory of the place, but the feelings and vulnerability and sensations of the past observer. I love how I’m still there in my sensitivity, still there shaping observations based on who I am and what I’ve lived and where I project I might find future happiness. And that that snapshot of a self in development is still available to inhabit, to re-inhabit once more. That we can become again. That just as the pink and gold hues collapse space into the pulse of a single mind, so too do the matte greens collapse time, the identity of place revealing a self growing in time. Eighteen years of experience elapsed under a staircase.

I walked upstairs and, migrating at my own pace from room to room, understood how, like David Bowie, Picasso didn’t have one style, but iterated upon a given style in a given creative period until it was exhausted, then moved on. Impressionism cedes to the blue period cedes to the Russian ballets cedes to cubism cedes to the bombastic primitivism cedes to recreating las Meninas cedes to ad infinitum adoration of his wife cedes to the black and white still lives of old age. It was his recreating Las Meninas that caught my attention. Picasso takes this work, this exemplar of Spanish Golden Age style, where Velázquez enacts the Christ-like elision of creator and spectator, the Baroque practice of inverting the artwork—as representation of reality—to fuse the moment of creation with the moment of observation, perfected through the gaze of the painter himself as of the man escaping from the back lit door, well, Picasso takes this work and exploits the conceit of the artist and, with algorithmic insistence, repaints and repaints and recreates, distilling the essence of the form in the variations of style and look and feel. And the variations themselves eclipse his own journey as a painter, never tied to one style, always free to pivot and redefine himself anew. Picasso’s Meninas telescoping my own experience recovering my younger self, the privilege of my loneliness opening me raw and whole to meet him there, to imagine I might be with him and the pigeons while he painted. Another meeting eye to eye, the seeking kind, inside the artist, back to Velázquez. Complete in a way that can only be described as human.

*I picked up a book in the airport in Barcelona, nearly finished with Galloway’s diatribe against the Four. Niebla en Tánger, by Cristina López Barrio. Funny I’d just mentioned Yerma as I wrote this post on the plane, for I came across this sentence from a similarly childless protagonist: “Está vacío, como el de Yerma, piensa, hueco por esperar vida del hombre equivocado.”

The featured image dates from my recent trip to Bryce Canyon. It’s like a big field of deep dream art, dripping in its delicate phantasy. It was my favourite of the Utah canyons.