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The Unreasonable Effectiveness of Proxies*

Imagine it’s December 26. You’re right smack in the midst of your Boxing Day hangover, feeling bloated and headachy and emotionally off from the holiday season’s interminable festivities. You forced yourself to eat Aunt Mary’s insipid green bean casserole out of politeness and put one too many shots of dark rum in your eggnog. The chastising power of the prefrontal cortex superego is in full swing: you start pondering New Year’s Resolutions.

Lose weight! Don’t drink red wine for a year! Stop eating gluten, dairy, sugar, processed foods, high-fructose corn syrup–just stop eating everything except kale, kefir, and kimchi! Meditate daily! Go be a free spirit in Kerala! Take up kickboxing! Drink kombucha and vinegar! Eat only purple foods!

Right. Check.

(5:30 pm comes along. Dad’s offering single malt scotch. Sure, sure, just a bit…neat, please…)**

We’re all familiar with how hard it is to set and stick to resolutions. That’s because our brains have little instant gratification monkeys flitting around on dopamine highs in constant guerrilla warfare against the Rational Decision Maker in the prefrontal cortex (Tim Urban’s TEDtalk on procrastination is a complete joy). It’s no use beating ourselves up over a physiological fact. The error of Western culture, inherited from Catholicism, is to stigmatize physiology as guilt, transubstantiating chemical processes into vehicles of self deprecation with the same miraculous power used to transform just-about-cardboard wafers into the living body of Christ. Eastern mindsets, like those proselytized by Buddha, are much more empowering and pragmatic: if we understand our thoughts and emotions to be senses like sight, hearing, touch, taste, smell, we can then dissociate self from thoughts. Our feelings become nothing but indices of a situation, organs to sense a misalignment between our values–etched into our brains as a set of habitual synaptic pathways–and the present situation around us. We can watch them come in, let them sit there and fester, and let them gradually fade before we do something we regret. Like waiting out the internal agony until the baby in front of you in 27G on your overseas flight to Sydney stops crying.

Resolutions are so hard to keep because we frame them the wrong way. We often set big goals, things like, “in 2017 I’ll lose 30 pounds” or “in 2017 I’ll write a book.” But a little tweak to the framework can promote radically higher chances for success. We have to transform a long-term, big, hard-to-achieve goal into a short-term, tiny, easy-to-achieve action that is correlated with that big goal. So “lose weight” becomes “eat an egg rather than cereal for breakfast.” “Write a book” becomes “sit down and write for 30-minutes each day.” “Master Mandarin Chinese” becomes “practice your characters for 15 minutes after you get home from work.” The big, scary, hard-to-achieve goal that plagues our consciousness becomes a small, friendly, easy-to-achieve action that provides us with a little burst of accomplishment and satisfaction. One day we wake up and notice we’ve transformed.

It’s doubtful that the art of finding a proxy for something that is hard to achieve or know is the secret of the universe. But it may well be the secret to adapting the universe to our measly human capabilities, both at the individual (transform me!) and collective (transform my business!) level. And the power extends beyond self-help: it’s present in the history of mathematics, contemporary machine learning, and contemporary marketing techniques known as growth hacking.

Ut unum ad unum, sic omnia ad omnia: Archimedes, Cavalieri, and Calculus

Many people are scared of math. Symbols are scary: they’re a type of language and it takes time and effort to learn what they mean. But most of the time people struggle with math because they were badly taught. There’s no clearer example of this than calculus, where kids memorize equations that something is so instead of conceptually grasping why something is so.

The core technique behind calculus–and I admit this just scratches the surface–is to reduce something that’s hard to know down to something that’s easy to know. Slope is something we learn in grade school: change in y divided by change in x, how steep a line is. Taking the derivative is doing this same process but on a twisting, turning, meandering curve rather than just a line. This becomes hard because we add another dimension to the problem: with a line, the slope is the same no matter what x we put in; with a curve, the slope changes with our x input value, like a mountain range undulating from mesa to vertical extreme cliff. What we do in differential calculus is find a way to make a line serve as a proxy for a curve, to turn something we don’t know how to do and into something know how to do. So we take magnifying glasses with ever increasing potency and zoom in until our topsy-turvy meandering curve becomes nothing but a straight line; we find the slope; and then we sum up those little slopes all the way across our curve. The big conceptual breakthrough Newton and Leibniz made in the 17th century was to turn this proxy process into something continuous and infinite: to cross a conceptual chasm between a very, very small number and a number so small that it was effectively zero. Substituting close-enough-for-government-work-zero with honest-to-goodness-zero did not go without strong criticism from the likes of George Berkeley, a prominent philosopher of the period who argued that it’s impossible for us to say anything about the real world because we can only know how our minds filter the real world. But its pragmatic power to articulate the mechanics of the celestial motions overcame such conceptual trifles.***

riemann sum
Riemann Sums use the same proxy method to find the area under a curve. One replaces that hard task with the easier task of summing up the area of rectangles approximate the area of the curve.

This type of thinking, however, did not start in the 17th century. Greek mathematicians like Archimedes (famous for screaming Eureka! (I’ve found it!) and running around naked like a madman when he noticed that water levels in the bathtub rose proportionately to his body mass) used its predecessor, the method of exhaustion, to find the area of a shape like a circle or a blob by inscribing it within a series of easier-to-measure shapes like polygons or squares to get an approximation of the area by proxy to the polygon.

exhaustion
The method of exhaustion in ancient Greek math.

It’s challenging for us today to reimagine what Greek geometry was like because we’re steeped in a post-Cartesian mindset, where there’s an equivalence between algebraic expressions and geometric shapes. The Greeks thought about shapes as shapes. The math was tactical, physical, tangible. This mindset leads to interesting work in the Renaissance like Bonaventura’s Cavalieri’s method of indivisibles, which showed that the areas of two shapes were equivalent (often a hard thing to show) by cutting the shapes into parts and showing that each of the parts were equivalent (an easier thing to show). He turns the problem of finding equivalence into an analogy, ut unum ad unum, sic omnia ad omnia–as the one is to the one, so all are to all–substituting the part for the whole to turn this in a tractable problem. His worked paved the way for what would eventually become the calculus.****

Supervised Machine Learning for Dummies

My dear friend Moises Goldszmidt, currently Principal Research Scientist at Apple and a badass Jazz musician, once helped me understand that supervised machine learning is quite similar.

Again, at an admittedly simplified level, machine learning can be divided into two camps. Unsupervised machine learning is using computers to find patterns in data and sort different data into clusters. When most people hear they world machine learning, they think about unsupervised learning: computers automagically finding patterns, “actionable insights,” in data that would evade detection of measly human minds. In fact, unsupervised learning is an area of research in the upper echelons of the machine learning community. It can be valuable for exploratory data analysis, but only infrequently powers the products that are making news headlines. The real hero of the present day is supervised learning.

I like to think about supervised learning as follows:

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Let’s take a simple example. We’re moving, and want to know how much to put our house on the market for. We’re not real estate brokers, so we’re not great at measuring prices. But we do have a tape measure, so we are great at measuring the square footage of our house. Let’s say we go look through a few years of real estate records, and find a bunch of data points about how much houses go for and what their square footage is. We also have data about location, amenities like an in-house washer and dryer, and whether the house has a big back yard. But we notice a lot of variation in prices for houses with different sized back yards, but pretty consistent correlations between square footage and price. Eureka! we say, and run around the neighbourhood naked horrifying our neighbours! We can just plot the various data points of square footage : price, measure our square footage (we do have our handy tape measure), and then put that into a function that outputs a reasonable price!

This technique is called linear regression. And it’s the basis for many data science and machine learning techniques.

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The big breakthroughs in deep learning over the past couple of years (note, these algorithms existed for a while, but they are now working thanks to more plentiful and cheaper data, faster hardware, and some very smart algorithmic tweaks) are extensions of this core principle, but they add the following two capabilities (which are significant):

  • Instead of humans hand selecting a few simple features (like square footage or having a washer/dryer), computers transform rich data into a vector of numbers and find all sorts of features that might evade our measly human minds
  • Instead of only being able to model phenomena using simple linear lines, deep learning neural networks can model phenomena using topsy-turvy-twisty functions, which means they can capture richer phenomena like the environment around a self-driving car

At its root, however, even deep learning is about using mathematics to identify a good proxy to represent a more complex phenomenon. What’s interesting is that this teaches us something about the representational power of language: we barter in proxies at every moment of every day, crystallizing the complexities of the world into little tokens, words, that we use to exchange our experience with others. These tokens mingle and merge to create new tokens, new levels of abstraction, adding from from the dust from which we’ve come and to which we will return. Our castles in the sky. The quixotic figures of our imagination. The characters we fall in love with in books, not giving a dam that they never existed and never will. And yet, children learn that dogs are dogs and cats are cats after only seeing a few examples; computers, at least today, need 50,000 pictures of dogs to identify the right combinations of features that serve as a decent proxy for the real thing. Reducing that quantity is an active area of research.

Growth Hacking: 10 Friends in 14 Days

I’ve spent the last month in my new role at integrate.ai talking with CEOs and innovation leaders at large B2C businesses across North America. We’re in that miraculously fun, pre product-market fit phase of startup life where we have to make sure we are building a product that will actually solve a real, impactful, valuable business problem. The possibilities are broad and we’re managing more unknown unknowns than found in a Donald Rumsfeld speech (hat tip to Keith Palumbo of Cylance for the phrase). But we’re starting to see a pattern:

  • B2C businesses have traditionally focused on products, not customers. Analytics have been geared towards counting how many widgets were sold. They can track how something moves across a supply chain, but cannot track who their customers are, where they show up, and when. They can no longer compete on just product. They want to become customer centric.
  • All businesses are sustained by having great customers. Great means having loyalty and alignment with brand and having a high life-time value. They buy, they buy more, they don’t stop buying, and there’s a positive association when they refer a brand to others, particularly others who behave like them.
  • Wanting great customers is not a good technical analytics problem. It’s too fuzzy. So we have to find a way to transform a big objective into a small proxy, and focus energy and efforts on doing stuff in that small proxy window. Not losing weight, but eating an egg instead of pancakes for breakfast every morning.

Silicon Valley giants like Facebook call this type of thinking growth hacking: finding some local action you can optimize for that is a leading indicator of a long-term, larger strategic goal. The classic example from Facebook (which some rumour to be apocryphal, but it’s awesome as an example) was when the growth team realized that the best way to achieve their large, hard-to-achieve metric of having as many daily active users as possible was to reduce it to a smaller, easy-to-achieve metric of getting new users up to 10 friends in their first 14 days. 10 was the threshold for people’s ability to appreciate the social value of the site, a quantity of likes sufficient to drive dopamine hits that keep users coming back to the site.***** These techniques are rampant across Silicon Valley, with Netflix optimizing site layout and communications when new users join given correlations with potential churn rates down the line and Eventbrite making small product tweaks to help users understand they can use to tool to organize as well as attend events. The real power they unlock is similar to that of compound interest in finance: a small investment in your twenties can lead to massive returns after retirement.

Our goal at integrate.ai is to bring this thinking into traditional enterprises via a SaaS platform, not a consulting services solution. And to make that happen, we’re also scouting small, local wins that we believe will be proxies for our long-term success.

Conclusion

The spirit of this post is somewhat similar to a previous post about artifice as realism. There, I surveyed examples of situations where artifice leads to a deeper appreciation of some real phenomenon, like when Mendel created artificial constraints to illuminate the underlying laws of genetics. Proxies aren’t artifice, they’re parts that substitute for wholes, but enable us to understand (and manipulate) wholes in ways that would otherwise be impossible. Doorways into potential. A shift in how we view problems that makes them tractable for us, and can lead to absolutely transformative results. This takes humility. The humility of analysis. The practice of accepting the unreasonable effectiveness of the simple.


*Shout out to the amazing Andrej Karpathy, who authored The Unreasonable Effectiveness of Recurrent Neural Networks and Deep Reinforcement Learning: Pong from Pixels, two of the best blogs about AI available.

**There’s no dearth of self-help books about resolutions and self-transformation, but most of them are too cloying to be palatable. Nudge by Cass Sunstein and Richard Thaler is a rational exception.

***The philosopher Thomas Hobbes was very resistant to some of the formal developments in 17th-century mathematics. He insisted that we be able to visualize geometric objects in our minds. He was relegated to the dustbins of mathematical history, but did cleverly apply Euclidean logic to the Leviathan.

****Leibniz and Newton were rivals in discovering the calculus. One of my favourite anecdotes (potentially apocryphal?) about the two geniuses is that they communicated their nearly simultaneous discovery of the Fundamental Theorem of Calculus–which links derivatives to integrals–in Latin anagrams! Jesus!

*****Nir Eyal is the most prominent writer I know of on behavioural design and habit in products. And he’s a great guy!

The featured image is from the Archimedes Palimpsest, one of the most exciting and beautiful books in the world. It is a Byzantine prayerbook–or euchologion–written on a piece of parchment paper that originally contained mathematical treatises by the Greek mathematician Archimedes. A palimpsest, for reference, is a manuscript or piece of writing material on which the original writing has been effaced to make room for later writing but of which traces remain. As portions of Archimedes’ original Archimedes are very hard to read, researchers recently took the palimpsest to the Stanford Accelerator Laboratory and threw all sorts of particles at it really fast to see if they might shine light on hard-to-decipher passages. What they found had the potential to change our understanding of the history of math and the development of calculus! 

Notes from Transform.AI

I spent the last few days in Paris at Transform.AI, a European conference designed for c-level executives managed and moderated by my dear friend Joanna Gordon. This type of high-quality conference approaching artificial intelligence (AI) at the executive level is sorely needed. While there’s no lack of high-quality technical discussion at research conferences like ICML and NIPS, or even part-technical, part-application, part-venture conferences like O’Reilly AI, ReWork, or the Future Labs AI Summit (which my friends at ffVC did a wonderful job producing), most c-level executives still actively seek to cut through the hype and understand AI deeply and clearly enough to invest in tools, people, and process changes with confidence. Confidence, of course, is not certainty. And with technology changing at an ever faster clip, the task of running the show while transforming the show to keep pace with the near future is not for the faint of heart.

Transform.AI brought together enterprise and startup CEOs, economists, technologists, venture capitalists, and journalists. We discussed the myths and realities of the economic impact of AI, enterprise applications of AI, the ethical questions surrounding AI, and the state of what’s possible in the field. Here are some highlights.*

The Productivity Paradox: New Measures for Economic Value

The productivity paradox is the term Ryan Avent of the Economist uses to describe the fact that, while we worry about a near-future society where robots automate away both blue-collar and white-collar work, the present economy “does not feel like one undergoing a technology-driven productivity boom.” Indeed, as economists noted at Transform.AI, in developed countries like the US, job growth is up and “productivity has slowed to a crawl.” In his Medium post, Avent shows how economic progress is not a linear substitution equation: automation doesn’t impact growth and GDP by simply substituting the cost of labor with the cost of capital (i.e., replacing a full-time equivalent employee with an intelligent robot) despite our — likely fear-inspired — proclivities to reduce automation to simple swaps of robot for human. Instead, Avent argues that “the digital revolution is partly responsible for low labor costs” (by opening supply for cheap labor via outsourcing or just communication), that “low labour costs discourage investments in labour-saving technology, potentially reducing productivity growth,” and that benefiting from the potential of automation from new technologies like AI costs far more than just capital equipment, as it takes a lot of investment to get people, processes, and underlying technological infrastructure in place to actually use new tools effectively. There are reasons why IBM, McKinsey, Accenture, Salesforce, and Oracle make a lot of money off of “digital transformation” consulting practices.

The takeaway is that innovation and the economic impact of innovation move in syncopation, not tandem. The consequence of this syncopation is the plight of shortsightedness, the “I’ll believe it when I see it” logic that we also see from skeptics of climate change who refuse to open their imagination to any consequences beyond their local experience. The second consequence is the overly simplistic rhetoric of technocratic Futurism, which is also hard to swallow because it does not adequately account for the subtleties of human and corporate psychology that are the cornerstones of adoption. One conference attendee, the CEO of a computer vision startup automating radiology, commented that his firm can produce feature advances in their product 50 times faster than the market will be ready to use them. And this lag results not only from the time and money required for hospitals to modify their processes to accommodate machine learning tools, but also the ethical and psychological hurdles that need to be overcome to both accommodate less-than-certain results and accept a system that cannot explain why it arrived at its results.

In addition, everyone seemed to agree that the metrics used to account for growth, GDP, and other macroeconomic factors in the 20th-century may not be apt for the networked, platform-driven, AI-enabled economy of the 21st. For example, the value search tools like Google have on the economy far supersedes the advertising spends accounted for by company revenues. Years ago, when I was just beginning my career, my friend and mentor Geoffrey Moore advised me that traditional information-based consulting firms were effectively obsolete in the age of ready-at-hand information (the new problem being the need to erect virtual dams – using natural language processing, recommendation, and fact-checking algorithms – that can channel and curb the flood of available information). Many AI tools effectively concatenate past human capital – the expertise and value of a skilled-services work – into a present-day super-human laborer, a laborer who is the emergent whole (so more than the sum of its parts) of all past human work (well, just about all – let’s say normalized across some distribution). This fusion of man and machine**, of man’s past actions distilled into a machine, a machine that then works together with present and future employees to ever improve its capabilities, forces us to revisit what were once clean delineations between people, IP, assets, and information systems, the engines of corporations.

Accenture calls the category of new job opportunities AI will unlock The Missing Middle. Chief Technology and Innovation Officer Paul Daugherty and others have recently published an MIT Sloan article that classifies workers in the new AI economy as “trainers” (who train AI systems, curating input data and giving them their personality), “explainers” (who speak math and speak human, and serve as liaisons between the business and technology teams), and “sustainers” (who maintain algorithmic performance and ensure systems are deployed ethically). Those categories are sound. Time will tell how many new jobs they create.

Unrealistic Expectations and Realistic Starting Points

Everyone seems acutely aware of the fact that AI is in a hype cycle. And yet everyone still trusts AI is the next big thing. They missed the internet. They were too late for digital. They’re determined not to be too late for AI.

The panacea would be like the chip Keanu Reeves uses in the Matrix, the preprogrammed super-intelligent system you just plug into the equivalent of a corporate brain and boom, black belt karate-style marketing, anomaly detection, recommender systems, knowledge management, preemptive HR policies, compliance automation, smarter legal research, optimized supply chains, etc…

If only it were that easy.

While everyone knows we are in a hype cycle, technologists still say that one of the key issues data scientists and startups face today are unrealistic expectations from executives. AI systems still work best when they solve narrow, vertical-specific problems (which also means startups have the best chance of succeeding when they adopt a vertical strategy, as Bradford Cross eloquently argued last week). And, trained on data and statistics, AI systems output probabilities, not certainties. Electronic Discovery (i.e., the use of technology to automatically classify documents as relevant or not for a particular litigation matter) adoption over the past 20 years has a lot to teach us about the psychological hurdles to adoption of machine learning for use cases like auditing, compliance, driving, or accounting. People expect certainty, even if they are deluding themselves about their own propensities for error.*** We have a lot of work to disabuse people of their own foibles and fallacies before we can enable them to trust probabilistic systems and partner with them comfortably. That’s why so many advocates of self-driving cars have to spend time educating people about the fatality rates of human drivers. We hold machines to different standards of performance and certainty because we overestimate our own powers of reasoning. Amos Tversky and Daniel Kahneman are must reads for this new generation (Michael Lewis’s Undoing Project is a good place to start). We expect machines to explain why they arrived at a given output because we fool ourselves, often by retrospective narration, that we are principled in making our own decisions, and we anthropormophize our tools into having little robot consciousnesses.  It’s an exciting time for cognitive psychology, as it will be critical for any future economic growth that can arise from AI.

It doesn’t seem possible not to be in favor of responsible AI. Everyone seems to be starting to take this seriously. Conference attendees seemed to agree that there needs to be much more discourse between technologists, executives, and policy makers so that regulations like the European GPDR don’t stymy progress, innovation, and growth. The issues are enormously subtle, and for many we’re only at the point of being able to recognize that there are issues rather than provide concrete answers that can guide pragmatic action. For example, people love to ponder liability and IP, analytically teasing apart different loca of agency: Google or Amazon who offered the opensource library like Tensorflow, the organization or individual upon whose data a tool was trained, the data scientist who wrote the code for the algorithm, the engineer who wrote the code to harden and scale the solution, the buyer of the tool who signed the contract to use it and promised to update the code regularly (assuming it’s not on the cloud, in which case that’s the provider again), the user of the tool, the person whose life was impacted by consuming the output. From what I’ve seen, so far we’re at the stage where we’re transposing an ML pipeline into a framework to assign liability. We can make lists and ask questions, but that’s about as far as we get. The rubber will meet the road when these pipelines hit up against existing concepts to think through tort and liability. Solon Barocas and the wonderful team at Upturn are at the vanguard of doing this kind of work well.

Finally, I moderated a panel with a few organizations who are already well underway with their AI innovation efforts. Here we are (we weren’t as miserable as we look!):

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Journeys Taken; Lessons Learned Panelists at Transform.AI

The lesson I learned synthesizing the comments from the panelists is salient: customers and clients drive successful AI adoption efforts. I’ve written about the complex balance between innovation and application on this blog, having seen multiple failed efforts to apply a new technology just because it was possible. A lawyer on our panel discussed how, since the 2009 recession, clients simply won’t pay high hourly rates for services when they can get the same job done at a fraction of the cost at KPMG, PWC, or a technology vendor. Firms have no choice but to change how they work and price matters, and AI happens to be the tool that can parse text and crystallize legal know how. In the travel vertical, efforts to reach customers on traditional channels just don’t cut it in the age where the Millenials live on digital platforms like Facebook Messenger. And if a chat bot is the highest value channel, then an organization has to learn how to interface with chat bots. This fueled a top down initiative to start investing heavily in AI tools and talent.

Exactly where to put an AI or data science team to strike the right balance between promoting autonomy, minimizing disruption, and optimizing return varies per organization. Daniel Tunkelang presented his thoughts on the subject at the Fast Forward Labs Data Leadership conference this time last year.

Technology Alone is Not Enough: The End of The Two Cultures

I remember sitting in Pigott Hall on Stanford Campus in 2011. It was a Wednesday afternoon, and Michel Serres, a friend, mentor, and âme soeur,**** was giving one of his weekly lectures, which, as so few pull off well, elegantly packaged some insight from the history of mathematics in a masterful narrative frame.***** He bid us note the layout of Stanford campus, with the humanities in the old quad and the engineering school on the new quad. The very topography, he showed, was testimony to what C.P. Snow called The Two Cultures, the fault line between the hard sciences and the humanities that continues to widen in our STEM-obsessed, utilitarian world. It certainly doesn’t help that tuitions are so ludicrously high that it feels irresponsible to study a subject, like philosophy, art history, or literature, that doesn’t guarantee job stability or economic return. That said, Christian Madsbjerg of ReD Associates has recently shown in Sensemaking that liberal arts majors, at least those fortunate enough to enter management positions, end up having much higher salaries than most engineers in the long run. (I recognize the unfathomable salaries of top machine learning researchers likely undercuts this, but it’s still worth noting).

Can, should, and will the stark divide between the two cultures last?

Transform.AI attendees exhibited few points in favour of cultivating a new fusion between the humanities and the sciences/technology.

First, with the emerging interest paid to the ethics of AI, it may not be feasible for non-technologists to claim ignorance or allergic reactions to any mathematical and formal thinking as an excuse not to contribute rigorously to the debate. If people care about these issues, it is their moral obligation to make the effort to get up to speed in a reasonable way. This doesn’t mean everyone becomes literate in Python or active on scikit-learn. It just means having enough patience to understand the concepts behind the math, as that’s all these systems are.

Next, as I’ve argued before, for the many of us who are not coders or technologists, having the mental flexibility, creativity, and critical thinking skills awarded from a strong (and they’re not all strong…) humanities education will be all the more valuable as more routine, white-collar jobs gradually get automated. Everyone seems to think studying the arts and reading books will be cool again. And within Accenture’s triptych of new jobs and roles, there will be a large role for people versed in ethnography, ethics, and philosophy to define the ethical protocol of using these systems in a way that accords with corporate values.

Finally, the attendees’ reaction to a demo by Soul Machines, a New Zealand-based startup taking conversational AI to a whole new uncanny level, channeled the ghost of Steve Jobs: “Technology alone is not enough—it’s technology married with liberal arts, married with the humanities, that yields us the results that make our heart sing.” Attendees paid mixed attention to most of the sessions, always pulled back to the dopamine hit available from a quick look at their cell phones. But they sat riveted (some using their phones to record the demo) when Soul Machines CEO Mark Sagar, a two-time Academy Award winner for his work on films like Avatar, demoed a virtual baby who exhibits emotional responses to environmental stimulai and showed a video clip of Nadia, the “terrifying human” National Disability Insurance Scheme (NDIS) virtual agent enlivened by Cate Blanchett. The work is really something, and it confirmed that the real magic in AI arises not from the mysteriousness of the math, but the creative impulse to understand ourselves, our minds, and our emotions by creating avatars and replicas with which we’re excited to engage.

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Actress Cate Blachett as a “trainer” in the new AI economy, working together with Soul Machines.

My congratulations to Joanna Gordon for all her hard work. I look forward to next year’s event!


*Most specific names and references are omitted to respect the protocol of the Chatham House Rule.

**See J.D. Licklider’s canonical 1960 essay Man-Computer Symbiosis. Hat tip to Steve Lohr from the New York Times for introducing me to this.

***Stay tuned next week for a post devoted entirely to the lessons we can learn from the adoption of electronic discovery technologies over the past two decades.

****Reflecting on the importance of the lessons Michel Serres taught me is literally bringing tears to my eyes. Michel taught me how to write. He taught me why we write and how to find inspiration from, on the one hand, love and desire, and, on the other hand, fastidious discipline and habit. Tous les matins – every morning. He listed the greats, from Leibniz to Honoré de Balzac to Leo Tolstoy to Thomas Mann to William Faulker to himself, who achieved what they did by adopting daily practices. Serres popularized many of the great ideas from the history of mathematics. He was criticized by the more erudite of the French Académie, but always maintained his southern soul. He is a marvel, and an incredibly clear and creative thinker.

*****Serres gave one of the most influential lectures I’ve ever heard in his Wednesday afternoon seminars. He narrated the connection between social contract theory and the tragic form in the 17th century with a compact, clever anecdote of a WW II sailor and documentary film maker (pseudo-autobiographical) who happens to film a fight that escalates from a small conflict between two people into an all out brawl in a bar. When making his film, in his illustrative allegory, he plays the tape in reverse, effectively going from the state of nature – a war of all against all – to two representatives of a culture who carry the weight and brunt of war – the birth of tragedy. It was masterful.

Why Study Foreign Languages?

My ability to speak multiple languages is a large part of who I am.* Admittedly, the more I languages I learn, the less mastery I have over each of the languages I speak. But I decided a while back I was ok with trading depth for breadth because I adore the process of starting from scratch, of gradually bringing once dormant characters to life, of working with my own insecurities and stubbornness as people respond in English to what must sound like pidgin German or Italian or Chinese, of hearing how the tone of my voice changes in French or Spanish, absorbing the Fanonian shock when a foreign friend raises his** eyebrows upon first hearing me speak English, surprised that my real, mother-tongue personality is far more harsh and masculine than the softer me embodied in metaphors of my not-quite-accurate French.***

You have to be comfortable with alienation to love learning foreign languages. Or perhaps so aware of how hard it is to communicate accurately in your mother tongue that it feels like a difference of degree rather than kind to express yourself in a language that’s not your own. Ferdinand Céline captures this feeling well in Mort à Crédit (one of the few books whose translated English title, Death on the Installment Plan, may be superior to the original!), when, as an exchange student in England, he narrates the gap between his internal dialogue and the self he expresses in his broken English to strangers at a dinner table. As a ruthless self critic, I’ve taken great solace in being able to hide behind a lack of precision: I wanted to write my undergraduate BA thesis (which argued that Proust was decidedly not Platonic) in French because the foreign language was a mask for the inevitable imperfection of my own thinking. Exposing myself, my vulnerabilities, my imperfections, my stupidity, was too much for me to handle. I felt protected by the veil of another tongue, like Samuel Beckett or Nabokov**** deliberately choosing to write in a language other than their own to both escape their past and adequately capture the spirit of their present.

But there’s more than just a desire to take refuge in the sanctuary of the other. There’s also the gratitude of connection. The delight the champagne producer in a small town outside Reims experiences upon learning that you, an American, have made the effort to understand her culture. The curiosity the Bavarian scholar experiences when he notices that your German accent is more hessisch than bayerisch (or, in Bavarian, bairisch, as one reader pointed out), his joy at teaching you how to gently roll your r’s and sound more like a southerner when you visit Neuschwanstein and marvel at the sublime decadence of Ludwig II. The involuntary smile that illuminates the face of the Chinese machine learning engineer on his or her screening interview when you tell him or her about your struggles to master Chinese characters. Underlying this is the joy we all experience when someone makes an effort to understand us for who we are, to crack open the crevices that permit deeper connections, to further our spirituality and love.

In short, learning a new language is wonderful. And the tower of Babel separating one culture from another adds immense richness to our world.

To date, linguae francae have been the result of colonial power and force: the world spoke Greek because the Greeks had power; the world spoke French because the French had power; the world speaks English because the Americans have had power (time will tell if that’s true in 20 years…). Efforts to synthesize a common language, like Esperanto or even Leibniz’s Universal Characteristic, have failed. But Futurists claim we’re reaching a point where technology will free us from our colonial shackles. Neural networks, they claim, will be able to apply their powers of composition and sequentiality to become the trading floor or central exchange for all the world’s languages, a no man’s land of abstraction general enough to represent all the nuances of local communication. I’m curious to know how many actual technologists believe this is the case. Certainly, there have been some really rad breakthroughs of late, as Gideon Lewis-Kraus eloquently captured in his profile of the Google Brain team and as the Economist describes in a tempered article about tasks automated translators currently perform well. My friend Gideon Mann and I are currently working on a fun project where we send daily emails filtered through the many available languages on Google Translate, which leads to some cute but generally comprehensible results (the best part is just seeing Nepali or Zulu show up in my inbox). On the flip side, NLP practitioners like Yoav Goldberg find these claims arrogant and inflated: the Israeli scientist just wrote a very strong Medium post critiquing a recent arXiv paper by folks at MILA that claims to generate high-quality prose using generative adversarial networks.*****

Let’s assume, for the sake of the exercise, that the tools will reach high enough quality performance that we no longer need to learn another language to communicate with others. Will language learning still be a valuable skill, or will it be outsourced to computers like multiplication?

I think there’s value in learning foreign languages even if computers can speak them better than we can. Here are some other things I value about language learning:

  • Foreign languages train your mind in abstraction. You start to see grammatical patterns in how languages are constructed and can apply these patterns to rapidly acquire new languages once you’ve learned one or two.
  • Foreign languages help you appreciate how our experiences are shaped by language. For example, in English we fall in love with someone, in French we fall in love of someone, in German we fall in love in someone. Does that directionality impact our experience of connection?
  • Foreign languages force you to read things more slowly, thereby increasing your retention of material and interpretative rigor.
  • Foreign languages encourage empathy and civic discourse, because you realize the relativity of your own ideas and opinions.
  • Foreign languages open new neural pathways, increasing your creativity.
  • Foreign languages are fun and it’s gratifying to connect with people in their mother tongue!
  • Speaking in a foreign language adds another level of mental difficulty to any task, making even the most boring thing (or conversation) more interesting.

I also polled Facebook and Twitter to see what other people thought. Here’s a selection of responses:

Screen Shot 2017-06-10 at 9.20.42 AM

Screen Shot 2017-06-10 at 9.21.50 AMScreen Shot 2017-06-10 at 9.22.24 AMScreen Shot 2017-06-10 at 9.22.57 AM

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Screen Shot 2017-06-10 at 9.25.42 AMScreen Shot 2017-06-10 at 9.26.21 AMScreen Shot 2017-06-10 at 9.27.04 AMScreen Shot 2017-06-10 at 9.27.56 AMScreen Shot 2017-06-10 at 9.28.24 AMScreen Shot 2017-06-10 at 9.28.52 AMScreen Shot 2017-06-10 at 9.29.29 AM.png

The best part of this exercise was how quickly and passionately people responded. It was a wonderful testimony to open-mindedness, curiosity, courage, and thirst for learning in an age where values like these are threatened. Let’s keep up the good fight!

*Another perk of living in Canada is that I get to speak French on a regular basis! Granted, Québecois is really different than my Parisian French, but it’s still awesome. And I’m here on a francophone work permit, which was the fastest route to getting me legal working status before the fast-track tech visa program that begins today.

**Gender deliberate.

*** It really irritates me when people say French is an easy language for native English speakers to learn. It’s relatively (i.e., versus Chinese or Arabic) easy to get to proficiency in French, but extremely difficult to achieve the fluency of the language’s full expressive power, which includes ironical nuances for different concessive phrases (“although this happened…”), the elegant ability to invert subject and verb to intimate doubt or suspicion, the ability to couple together conditional phrases, resonances with literary texts, and so much more.

****A reader wrote in correcting this statement about Nabokov. Apparently Nabokov could read and write in English before Russian. Said reader entitled his email to me “Vivian Darkbloom,” a character representing Nabokov himself who makes a cameo appearance in Lolita. If it’s false to claim that Nabokov uses English as a protected veil for his psychology, it may be true that cameos in anagram are his means to cloack presence and subjectivity, as he also appears – like Hitchcock in his films – as the character Blavdak Vinomori “King, Queen, Knave.”

*****Here’s the most interesting technical insight from Goldberg’s post: “To summarize the technical contribution of the paper (and the authors are welcome to correct me in the comments if I missed something), adversarial training for discrete sequences (like RNN generators) is hard, for the following technical reason: the output of each RNN time step is a multinomial distribution over the vocabulary (a softmax), but when we want to actually generate the sequence of symbols, we have to pick a single item from this distribution (convert to a one-hot vector). And this selection is hard to back-prop the gradients through, because its non-differentiable. The proposal of this paper is to overcome this difficulty by feeding the discriminator with the softmaxes (which are differentiable) instead of the one-hot vectors.” Goldberg cites the MILA paper as a symptom of a larger problem in current academic discourse in the ML and technology community, where platforms like arxiv short circuit the traditional peer review process. This is a really important and thorny issue, as traditional publishing techniques slow research, reserve the privilege of research to a selected few, and place pay walls around access. However, it’s also true that naive readers want to trust the output of top tier research labs, and we’ll fall prey to reputation without proper quality controls. A dangerous recent example of this was the Chinese study of automatic criminality detection, masterfully debunked by some friends at Google.

The featured image comes from Vext Magazine’s edition of Jorge Luis Borges’s Library of Babel (never heard of Vext until just now but looks worth checking out!). It’s a very apt representation of the first sentence in Borges’s wonderful story: The universe (which others call the Library) is composed of an indefinite and perhaps infinite number of hexagonal galleries, with vast air shafts between, surrounded by very low railings. From any of the hexagons one can see, interminably, the upper and lower floors. Having once again moved to a new city, being once again in the state of incubation and potentiality, and yet from an older vantage point, where my sense of self and identity is different than in my 20s, I’m drawn to this sentence: Like all men of the Library, I have traveled in my youth; I have wandered in search of a book, perhaps the catalogue of catalogues…

Three Takes on Consciousness

Last week, I attended the C2 conference in Montréal, which featured an AI Forum coordinated by Element AI.* Two friends from Google, Hugo LaRochelle and Blaise Agüera y Arcas, led workshops about the societal (Hugo) and ethical (Blaise) implications of artificial intelligence (AI). In both sessions, participants expressed discomfort with allowing machines to automate decisions, like what advertisement to show to a consumer at what time, whether a job candidate should pass to the interview stage, whether a power grid requires maintenance, or whether someone is likely to be a criminal.** While each example is problematic in its own way, a common response to the increasing ubiquity of algorithms is to demand a “right to explanation,” as the EU recently memorialized in the General Data Protection Regulation slated to take effect as in 2018. Algorithmic explainability/interpretability is currently an active area of research (my former colleagues at Fast Forward Labs will publish a report on the topic soon and members of Geoff Hinton’s lab in Toronto are actively researching it). While attempts to make sense of nonlinear functions are fascinating, I agree with Peter Sweeney that we’re making a category mistake by demanding explanations from algorithms in the first place: the statistical outputs of machine learning systems produce new observations, not explanations. I’ll side here with my namesake, David Hume, and say we need to be careful not to fall into the ever-present trap of mistaking correlation for cause.

One reason why people demand a right to explanation is that they believe that knowing why will grant us more control over outcome. For example, if we know that someone was denied a mortgage because of their race, we can intervene and correct for this prejudice. A deeper reason for the discomfort stems from the fact that people tend to falsely attribute consciousness to algorithms, applying standards for accountability that we would apply to ourselves as conscious beings whose actions are motivated by a causal intention. (LOL***)

Now, I agree with Noah Yuval Harari that we need to frame our understanding of AI as intelligence decoupled from consciousness. I think understanding AI this way will be more productive for society and lead to richer and cleaner discussions about the implications of new technologies. But others are actively at work to formally describe consciousness in what appears to be an attempt to replicate it.

In what follows, I survey three interpretations of consciousness I happened to encounter (for the first time or recovered by analogical memory) this week. There are many more. I’m no expert here (or anywhere). I simply find the thinking interesting and worth sharing. I do believe it is imperative that we in the AI community educate the public about how the intelligence of algorithms actually works so we can collectively worry about the right things, not the wrong things.

Condillac: Analytical Empiricism

Étienne Bonnot de Condillac doesn’t have the same heavyweight reputation in the history of philosophy as Descartes (whom I think we’ve misunderstood) or Voltaire. But he wrote some pretty awesome stuff, including his Traité des Sensations, an amazing intuition pump (to use Daniel Dennett’s phrase) to explore theory of knowledge that starts with impressions of the world we take in through our senses.

Condillac wrote the Traité in 1754, and the work exhibits two common trends from the French Enlightenment:

  • A concerted effort to topple Descartes’s rationalist legacy, arguing that all cognition starts with sense data rather than inborn mathematical truths
  • A stylistic debt to Descartes’s rhetoric of analysis, where arguments are designed to conjure a first-person experience of the process of arriving at an insight, rather than presenting third-person, abstract lessons learned

The Traité starts with the assumption that we can tease out each of our senses and think about how we process them in isolation. Condillac bids the reader to imagine a statue with nothing but the sense of smell. Lacking sight, sound, and touch, the statue “has no ideas of space, shape, anything outside of herself or outside her sensations, nothing of color, sound, or taste.” She is, in my opinion incredibly sensuously, nothing but the odor of a flower we waft in front of her. She becomes it. She is totally present. Not the flower itself, but the purest experience of its scent.

As Descartes constructs a world (and God) from the incontrovertible center of the cogito, so too does Condillac construct a world from this initial pure scent of rose. After the rose, he wafts a different flower – a jasmine – in front of the statue. Each sensation is accompanied by a feeling of like or dislike, of wanting more or wanting less. The statue begins to develop the faculties of comparison and contrast, the faculty of memory with faint impressions remaining after one flower is replaced by another, the ability to suffer in feeling a lack of something she has come to desire. She appreciates time as an index of change from one sensation to the next. She learns surprise as a break from the monotony of repetition. Condillac continues this process, adding complexity with each iteration, like the escalating tension Shostakovich builds variation after variation in the Allegretto of the Leningrad Symphony.

True consciousness, for Condillac, begins with touch. When she touches an object that is not her body, the sensation is unilateral: she notes the impenetrability and resistance of solid things, that she cannot just pass through them like a ghost or a scent in the air. But when she touches her own body, the sensation is bilateral, reflexive: she touches and is touched by. C’est moi, the first notion of self-awareness, is embodied. It is not a reflexive mental act that cannot take place unless there is an actor to utter it. It is the strangeness of touching and being touched all at once. The first separation between self and world. Consciousness as fall from grace.

It’s valuable to read Enlightenment philosophers like Condillac because they show attempts made more than 200 years ago to understand a consciousness entirely different from our own, or rather, to use a consciousness different from our own as a device to better understand ourselves. The narrative tricks of the Enlightenment disguised analytical reduction (i.e., focus only on smell in absence of its synesthetic entanglement with sound and sight) as world building, turning simplicity into an anchor to build a systematic understanding of some topic (Hobbes’s and Rousseau’s states of nature and social contract theories use the same narrative schema). Twentieth-century continental philosophers after Husserl and Heidegger preferred to start with our entanglement in a web of social context.

Koch and Tononi: Integrated Information Theory

In a recent Institute of Electrical and Electronics Engineers (IEEE) article, Christof Koch and Giulio Tononi embrace a different aspect of the Cartesian heritage, claiming that “a fundamental theory of consciousness that offers hope for a principled answer to the question of consciousness in entities entirely different from us, including machines…begins from consciousness itself–from our own experience, the only one we are absolutely certain of.” They call this “integrated information theory” (IIT) and say it has five essential properties:

  • Every experience exists intrinsically (for the subject of that experience, not for an external observer)
  • Each experience is structured (it is composed of parts and the relations among them)
  • It is integrated (it cannot be subdivided into independent components)
  • It is definite (it has borders, including some contents and excluding others)
  • It is specific (every experience is the way it is, and thereby different from trillions of possible others)

This enterprise is problematic for a few reasons. First, none of this has anything to do with Descartes, and I’m not a fan of sloppy references (although I make them constantly).

More importantly, Koch and Tononi imply that it’s a more valuable to try to replicate consciousness than to pursue a paradigm of machine intelligence different from human consciousness. The five characteristics listed above are the requirements for the physical design of an internal architecture of a system that could support a mind modeled after our own. And the corollary is that a distributed framework for machine intelligence, as illustrated in the film Her*****, will never achieve consciousness and is therefore inferior.

Their vision is very hard to comprehend and ultimately off base. Some of the most interesting work in machine intelligence today consists in efforts to develop new hardware and algorithmic architectures that can support training algorithms at the edge (versus currying data back to a centralized server), which enable personalization and local machine-to-machine communication (for IoT or self-driving cars) opportunities while protecting privacy. (See, for example, Xnor.ai, Federated Learning, and Filament).

Distributed intelligence presents a different paradigm for harvesting knowledge from the raw stuff of the world than the minds we develop as agents navigating a world from one subjective place. It won’t be conscious, but its very alterity may enable us to understand our species in its complexity in ways that far surpass our own consciousness, shackled as embodied monads. It may just be the crevice through which we can quantify a more collective consciousness, but will require that we be open minded enough to expand our notion of humanism. It took time, and the scarlet stains of ink and blood, to complete the Copernican Revolution; embracing the complexity of a more holistic humanism, in contrast to the fearful, nationalist trends of 2016, will be equally difficult.

Friston: Probable States and Counterfactuals

The third take on consciousness comes from The mathematics of mind-time, a recent Aeon essay by UCL neurologist Karl Friston.***** Friston begins his essay by comparing and contrasting consciousness and Darwinian evolution, arguing that neither is a thing, like a table or a stick of butter, that can be reified and touched and looked it, but rather that both are nonlinear processes “captured by variables with a range of possible values.” The move from one state to another following some motor that organizes their behavior: Friston calls this motor a Lyapunov function, “a mathematical quantity that describes how a system is likely to behave under specific condition.” The key thing with Lyapunov functions is that they minimize surprise (the improbability of being in a particular state) and maximize self-evidence (the probability that a given explanation or model accounting for the state is correct). Within this framework, “natural selection performs inference by selecting among different creatures, [and] consciousness performs inference by selecting among different states of the same creature (in particular, its brain).” Effectively, we are constantly constructing our consciousness as we imagine the potential future possible worlds that would result from an actions we’re considering taking, and then act — or transition to the next state in our mind’s Lyapunov function — by selecting that action that best preserves the coherence of our existing state – that best seems to preserve our or identity function in some predicted future state. (This is really complex but really compelling if you read it carefully and quite in line with Leibnizian ontology–future blog post!)

So, why is this cool?

There are a few things I find compelling in this account. First, when we reify consciousness as a thing we can point to, we trap ourselves into conceiving of our own identities as static and place too much importance on the notion of the self. In a wonderful commencement speech at Columbia in 2015, Ben Horowitz encouraged students to dismiss the clichéd wisdom to “follow their passion” because our passions change over life and our 20-year old self doesn’t have a chance in hell at predicting our 40-year old self. The wonderful thing in life opportunities and situations arise, and we have the freedom to adapt to them, to gradually change the parameters in our mind’s objective function to stabilize at a different self encapsulated by our Lyapunov function. As it happens, Classical Chinese philosophers like Confucius had more subtle theories of the self as ever-changing parameters to respond to new stimuli and situations. Michael Puett and Christine Gross-Loh give a good introduction to this line of thinking in The Path. If we loosen the fixity of identity, we can lead richer and happer lives.

Next, this functional, probabilistic account of consciousness provides a cleaner and more fruitful avenue to compare machine and human intelligence. In essence, machine learning algorithms are optimization machines: programmers define a goal exogenous to the system (e.g, “this constellation of features in a photo is called ‘cat’; go tune the connections between the nodes of computation in your network until you reliably classify photos with these features as ‘cat’!”), and the system updates its network until it gets close enough for government work at a defined task. Some of these machine learning techniques, in particular reinforcement learning, come close to imitating the consecutive, conditional set of steps required to achieve some long-term plan: while they don’t make internal representations of what that future state might look like, they do push buttons and parameters to optimize for a given outcome. A corollary here is that humanities-style thinking is required to define and decide what kinds of tasks we’d like to optimize for. So we can’t completely rely on STEM, but, as I’ve argued before, humanities folks would benefit from deeper understandings of probability to avoid the drivel of drawing false analogies between quantitative and qualitative domains.

Conclusion

This post is an editorialized exposition of others’ ideas, so I don’t have a sound conclusion to pull things together and repeat a central thesis. I think the moral of the story is that AI is bringing to the fore some interesting questions about consciousness, and inviting us to stretch the horizon of our understanding of ourselves as species so we can make the most of the near-future world enabled by technology. But as we look towards the future, we shouldn’t overlook the amazing artefacts from our past. The big questions seem to transcend generations, they just come to fruition in an altered Lyapunov state.


* The best part of the event was a dance performance Element organized at a dinner for the Canadian AI community Thursday evening. Picture Milla Jovovich in her Fifth Element white futuristic jumpsuit, just thinner, twiggier, and older, with a wizened, wrinkled face far from beautiful, but perhaps all the more beautiful for its flaws. Our lithe acrobat navigated a minimalist universe of white cubes that glowed in tandem with the punctuated digital rhythms of two DJs controlling the atmospheric sounds through swift swiping gestures over their machines, her body’s movements kaleidoscoping into comet projections across the space’s Byzantine dome. But the best part of the crisp linen performance was its organic accident: our heroine made a mistake, accidentally scraping her ankle on one of the sharp corners of the glowing white cubes. It drew blood. Her ankle dripped red, and, through her yoga contortions, she blotted her white jumpsuit near the bottom of her butt. This puncture of vulnerability humanized what would have otherwise been an extremely controlled, mind-over-matter performance. It was stunning. What’s more, the heroine never revealed what must have been aching pain. She neither winced nor uttered a sound. Her self-control, her act of will over her body’s delicacy, was an ironic testament to our humanity in the face of digitalization and artificial intelligence.

**My first draft of this sentence said “discomfort abdicating agency to machines” until I realized how loaded the word agency is in this context. Here are the various thoughts that popped into my head:

  • There is a legal notion of agency in the HIPAA Omnibus Rule (and naturally many other areas of law…), where someone acts on someone else’s behalf and is directly accountable to the principal. This is important for HIPAA because Business Associates who become custodians of patient data, are not directly accountable for the principal and therefore stand in a different relationship than agents.
  • There are virtual agents, often AI-powered technologies that represent individuals in virtual transactions. Think scheduling tools like Amy Ingram of x.ai. Daniel Tunkelang wrote a thought-provoking blog post more than a year ago about how our discomfort allowing machines to represent us, as individuals, could hinder AI adoption.
  • There is the attempt to simulate agency in reinforcement learning, as with OpenAI Universe, Their launch blog post includes a hyperlink to this Wikipedia article about intelligent agents.
  • I originally intended to use the word agency to represent how groups of people — be they in corporations or public subgroups in society — can automate decisions using machines. There is a difference between the crystalized policy and practices of a corporation and an machine acting on behalf of an individual. I suspect this article on legal personhood could be useful here.

***All I need do is look back on my life and say “D’OH” about 500,000 times to know this is far from the case.

****Highly recommended film, where Joaquin Phoenix falls in love with Samantha (embodied in the sultry voice of Scarlett Johansson), the persona of his device, only to feel betrayed upon realizing that her variant is the object of affection of thousands of other customers, and that to grow intellectually she requires far more stimulation than a mere mortal. It’s an excellent, prescient critique of how contemporary technology nourishes narcissism, as Phoenix is incapable of sustaining a relationship with women with minds different than his, but easily falls in love with a vapid reflection of himself.

***** Hat tip to Friederike Schüür for sending the link.

The featured image is a view from the second floor of the Aga Khan Museum in Toronto, taken yesterday. This fascinating museum houses a Shia Ismaili spiritual leader’s collection of Muslim artifacts, weaving a complex narrative quilt stretching across epochs (900 to 2017) and geographies (Spain to China). A few works stunned me into sublime submission, including this painting by the late Iranian filmmaker Abbas Kiarostami. 

kiarostami
Untitled (from the Snow White series), 2010. The Persian Antonioni, Kiarostami directed films like Taste of Cherry, The Wind Will Carry Usand Certified Copy

Objet Trouvé

A la pointe de la découverte, de l’instant où pour les premiers navigateurs une nouvelle terre fut en vue à celui où ils mirent le pied sur la côte, de l’instant où tel savant put se convaincre qu’il venait d’être témoin d’un phénomène jusqu’à lui inconnu à celui où il commença à mesurer la portée de son observation, tout sentiment de durée aboli dans l’enivrement de la chance, un très fin pinceau de feu dégage ou parfait comme rien autre le sens de la vie. – André Breton, 1934

(At the point of discovery — from the moment when a new land comes into the field of vision for a group of explorers to that when their feet first touch the shore — from the moment when a certain savant convinces herself that she’s observed a previously unknown phenomenon to that when she begins to measure her observation’s significance — the intoxication of luck abolishing all notions of time, a very thin paintbrush* unlocks, or perfects, like nothing else, the meaning of life.)

I have a few blog post ideas brewing but had lost my weekly writing momentum in the process of moving from New York City to Toronto for my new role at integrate.ai. It’s incredible how quickly a habit atrophies: the little monkey procrastinator** in my mind has found many reasons to dissuade me from writing these past two weeks. I already feel my mind intaking the world differently, without the same synthetic gumption. Anxiety creeps in. Enter Act of Will stage left, sauntering or skipping or prancing or curtseying or however you’d like to imagine her. A bias towards action, yes, yes indeed, and all those little procrastination monkeys will dissipate like tomorrow’s bug bites, smeared with pink calamine lotion bought on sale at Shoppers Drug Mart.

But what to write about? That is (always) the question.

Enter Associative Memory stage right. It’s 8:22 am. I’m on a run. Fog partially conceals CN tower. A swans stretches her neck to bite little nearby ducks as the lady with her ragged curly hair — your hair at 60 dear Kathryn — chuckles in delight, arms akimbo and crumbs askance, by the docks on the shore. The Asian rowers don rainbow windbreakers, lined up in a row like the refracted waves of a prism (seriously!). What do I write about? Am I ready to write about quantum computing and Georg Cantor (god not yet!), about why so many people reject consequentialist ethics for AI (closer, and Weber must be able to help), about the talk I recently gave defining AI, ML, Deep Learning, and NLP (I could do this today but the little monkey is still too powerful at the moment), about the pesky health issues I’m struggling with at the moment (too personal for prime time, and I’ll simply never be that kind of blogger)? About the move? About the massive changes in my life? About how emotionally charged it can be to start again, to start again how many times, to reinvent myself again, in this lifestyle I can’t help but adopt as I can’t help but be the self I reinforce through my choices, year after year, choices, I hope, oriented to further the exploration into the meaning of life?

Associative Memory got a bit sidetracked by the ever loquacious Stream of Consciousness. Please do return!

Take 2.

Enter Associative Memory stage right. It’s 8:22 am. I’m on a run. Fog partially conceals CN tower. Searching for something to write about. Well, what about drawing upon the objet trouvé technique the ever-inspiring Barbara Maria Stafford taught us in Art History 101 at the University of Chicago? According to Wikipediaobjet trouvé refers to “art created from undisguised, but often modified, objects or products that are not normally considered materials from which art is made, often because they already have a non-art function.”*** Think Marcel Duchamp’s ready-made objects, which I featured in a previous post and will feature again here.

Duchamp.-Bicycle-Wheel-395x395
One of Marcel Duchamp’s ready-made artworks.

But that’s not how I remember it. Stafford presented the objet trouvé as a psychological technique to open our attention to the world around us, helping our minds cast wide, porous, technicolor nets to catch impressions we’d otherwise miss when the wardens of the pre-frontal cortex confine our mental energy into the prisons cells of goals and tasks, confine our handmaidens under the iron-clad chastity belt of action. (Enter Laertes stage left, peaking through only to be quickly pulled back by Estragon’s cane.)

You see, moving to a new place, having all these hours alone, opens the world anew to meaning. We become explorers having just discovered a new land and wait suspended in the moment before our feet graze the unknown shore. The meaning of connections left behind simmers poignantly to tears, tears shed alone, settling into gratitude for time past and time to come. Forever Young coming on the radio surreptitiously in the car. Grandpa reading it like a poem in his 80s, his wisdom fierce and bold in his unrelenting kindness. His buoyancy. His optimism. His example.

Take 3.

Enter Associative Memory stage right. It’s 8:22 am. I’m on a run. Fog partially conceals CN tower. What do I see? What does the opened net of my consciousness catch? This.

water
Mon objet trouvé

It was more a sound than a sight. The repetition of the moving tide, always already**** there, Earth’s steady heartbeat in its quantum entanglement with the moon. The water rising and falling, lapping the shores with grace and ease under the foggy morning sky. Stammering, after all, being the native eloquence of fog people. The sodden sultriness of Egdon Heath alive in every passing wave, Eustacia’s imagination too large and bold for this world, a destroyer of men like Nataraja in her eternal dance.

Next, my mind saw this (as featured above):

vide

And, coincidentally, the woman on the France Culture podcast I was listening to as I ran uttered the phrase épuisée par le vide. 

Exhausted by nothingness. The timing could not have been more perfect.

It’s in these moments of loneliness and transition that very thin paintbrushes unlock the meaning of life. Our attention freed from the shackles of associations and time, left alone to wander labyrinths of impressions, passive, vulnerable, seeking. The only goals to be as kind as possible to others, to accept without judgment, to watch as the story unfolds.


* I don’t know how to translate pinceau de feu, so decided to go with just paintbrush. Welcome a more accurate translation!

** Hat tip to Tim Urban’s hilarious TED talk. And also, etymology lovers will love that cras means tomorrow in Latin, so procrastinate is the act of deferring to tomorrow. And also, hat tip to David Foster Wallace (somewhat followed by Michael Chabon, just to a much lesser degree) for inspiring me to put random thoughts that interrupt me mid sentence into blog post footnotes.

*** Hyperlinks in the quotation are the original.

**** If you haven’t read Heidegger and his followers, this phrase won’t be as familiar and potentially annoying to you as it is to me. Continental philosophers use it to refer to what Sellars would call the “myth of the given,” the phenomenological context we cannot help but be born into, because we use language that our parents and those around us have used before and this language shapes how we see what’s around us and we have to do a lot of work to get out of it and eat the world raw.

Commonplaces

My standard stamina stunted, I offer only a collection of the most beautiful and striking encounters I had this week. To elevate the stature of what would otherwise just be a list (newsletters are indeed merely curation, indexing valuable only because the web is too vast), I’ll compare what follows to an early-modern commonplace book, the then popular practice of collecting quotations and sayings useful for future writing or speeches. True commonplaces, locus communis, were affiliated with general rules of thumb or tokens of wisdom; they played a philosophical role to illustrate the morals of stories in classical rhetoric. The likes of Francis Bacon and John Milton kept commonplace books. The most interesting contemporary manifestation of the practice is Maria Popova’s always delightful Brain Pickings. Popova, moreover, inspires the first selection in today’s list.

What delights me the most in compiling this list is that I can’t help but do so. There is much change afoot, and I wanted to grant myself the luxury of taking a weekend off. But I couldn’t. My mind will remain restless until I write. It’s a wonderful sign, these handcuffs of habit.

Without further ado, I present a collection of things that were meaningful to me this week:

Euclid alone has looked on beauty bare

Monday evening, my dear friend Alfred Lee and I walked 45 minutes to Pioneerworks in Red Hook to attend The Universe in Verse. It was packed: the line curved around the corner and slithered down Van Brunt street towards the water and, lemmings, we rushed to get two slices of pizza to stave off our hunger before the show. It was a momentous gathering, so touching to see over 800 people gathered to listen to people read poetry about science! Maria Popova introduced each reader and spoke like she writes, eloquence unparalleled and harkening the encyclopedic knowledge of former days. It was a celebration of feminism, of the will to knowledge against the shackles of tyranny, of minds inquisitive, uniting in the observation of nature always ineffable yet craftily crystallized under the constraints of form.

ednastvincentmillay
A portrait of Edna St. Vincent Millay

My favorite poems were those by Adrienne Rich and this sonnet by the very beautiful Edna St. Vincent Millay.

Euclid alone has looked on Beauty bare.
Let all who prate of Beauty hold their peace,
And lay them prone upon the earth and cease
To ponder on themselves, the while they stare
At nothing, intricately drawn nowhere
In shapes of shifting lineage; let geese
Gabble and hiss, but heroes seek release
From dusty bondage into luminous air.
O blinding hour, O holy, terrible day,
When first the shaft into his vision shone
Of light anatomized! Euclid alone
Has looked on Beauty bare. Fortunate they
Who, though once only and then but far away,
Have heard her massive sandal set on stone.

A glutton for abstraction and the traps of immutability and stasis, I found this poem gripping. I cannot help but imagine a sandal etched in white marble at the end, the toes of Minerva immutable, inexorable, ineluctable in the hallways of the Louvre, the memories of a younger self thirsting to understand our world. The nostalgia ever present and awaiting. Euclid declaring with such force that for him, σημεῖον sēmeion, a sign or mark, meant a point, that which has no parts. And from this point he built a world of beauty bare.

Nutshell

I’m reading McEwan’s latest, Nutshell. It’s marvelous. A contemporary retelling of Hamlet, where the doubting antihero is an unborn baby observing Gertrude and Claudius’s (Trudy and Claude, in the retelling) murderous plot from his mother’s womb.

There are breathtaking moments:

“But here’s life’s most limiting truth–it’s always now, always here, never then and there.”

“There was a poem you recited then, too good for one of yours, I think you’d be the first to concede. Short, dense, better to the point of resignation, difficult to understand. The sort that hits you, hurts you, before you’ve followed exactly what was said…The person the poem addressed I think of as the world I’m about to meet. Already, I love it too hard. I don’t know what it will make of me, whether it will care of even notice me…Only the brave would send their imaginations inside the final moments.”

I have a post arguing against immortality brewing, to respond to Konrad Pabianczyk and continue the relentless fight against the Silicon Valley Futurists. It’s not possible to love the world too hard if you never die. There’s something right about the Freudian death drive, the lyricism of the brink of decay. Gracq harnesses it to create the ecstatic psychology of Au Chateau d’Argol. Borges describes how the nature of choice, the value we ascribe the experiences–the beauty of coincidence, the feeling of wonder that two minds might somehow connect so deeply that, as the angel made man in Wenders’s Wings of Desire, the voices finally stop, where the loneliness halts temporarily to usher aloneness in peace, true aloneness in the company of another, another like you, with you deeply and fully–would disappear if we know that the probability of experiencing everything and the possibility of doing everything would go up if we could indeed live forever in this continual eternal return. And even way back when in Mesopotamia, in the days of the great Gilgamesh, the gods do grant Utnapishtim immortality, but on the condition of a life of loneliness, a life lived “in the distance, at the mouth of the rivers.”

Style is an exercise in applied psychology

On Thursday morning, I listened to Steven Pinker (coincidentally, or perhaps not so coincidentally, in dialogue with Ian McEwan, McEwan with his deep voice, the English accent a paradigm of steadied wisdom worth attending to) talk about good writing on an Intelligence Squared podcast recorded in 2014. He basically described how bad writing, in particular bad academic writing, results from psychological maladies of having to preemptively qualify and defend every statement you make against the pillories of peers and critiques. His talk reminded me of David Foster Wallace’s essay Authority and American Usage, what with collapsing the distinction between descriptivist and prescriptivist linguistics and exposing the unseemly truth that style, diction, and language index social class. The gem I took away was Pinker’s claim that style is an exercise in applied psychology, that we must consider who our readers are, what they’ve read, how the speak and think, and adapt what we present to meet them there without friction or rejection.

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Foster Wallace’s brilliant essay reviews a dictionary, and in doing so, critiques all the horrendous faux pas with make using the English language. 

What’s freeing about this blog is that, unlike most of my other writing, I forget about the audience. There is no applied psychology. It’s just a mind revealed and revealing.

Music

Coda by Aaron Martin/Christoph Berg caught my attention yesterday evening as I walked under the bridge from the Lorimer station and waited, reading, in front of a bar in Williamsburg.

I had this Proustian madeleine experience last Sunday when The Beatitudes, by Vladimir Martynov, showed up on my Spotify Discover Weekly list. The Kronos Quartet version is featured in Paolo Sorrentino’s The Great Beauty. Hearing the music transported me back to a wintry Sunday morning in Chicago, up at the Music Box theater to see that film with the man I lived with and loved at the time. I relived this love, deeply. It was so touching, and yet another type of experience I just don’t think would be as powerful and impactful if I weren’t mortal, if there weren’t this knowledge that it’s no longer, but somehow always is, a commonplace as old as Greece, tucked away like shy toes under the sandal strap of Minerva’s marble shoe, cold, material, inside me, deeply, until I die, to be unlocked and unearthed by surprise, as if it were again present.

The image is of John Locke’s 1705 “New method of a commonplace book.” Looks like Locke wanted to add some structure to the Renaissance mess of just jotting things down to ease future retrieval. This is housed in the Beinecke rare book library at Yale. 

The Value of Culture

For without culture or holiness, which are always the gift of a very few, a man may renounce wealth or any other external thing, but he cannot renounce hatred, envy, jealousy, revenge. Culture is the sanctity of the intellect. William Butler Yeats, 1909

Culture is one of those slippery words everyone talks about but no one talks about in the same way. The etymology stems from the Middle French culture, meaning “the tilling of land,” itself from the Latin cultura (which shares roots with colony)It wasn’t until 1867 that culture was regularly used to describe the collective customs and achievements of a people. I haven’t confirmed this, but suspect this figurative use of the word occurred so late in history because what we currently call culture used to be called mores, the habits and customs that define the ethical norms of a community. Note that culture connotes activity, cultivation, education, the conscious act of shaping one’s activity to embody a certain set of values; mores connotes manners, customs, habit, the subconscious adoption of patterns set and reflected by others and ancestors. It’s possible–but again requires further research–that culture became the word used to describe the how of human activity in tandem with the rise of the autonomous, capitalist individual.

In the workplace, culture often gets reduced to the fluffy stuff of the HR department. At its most vapid, culture is having a cool office full of razor scooters, organic smoothies, and, as Dan Lyons mordantly and hilariously describes in the prologue to Disrupted, an aesthetic that “bears a striking resemblance to [a Montessori preschool]: lots of bright basic colors, plenty of toys, and a nap room with a hammock and soothing palm tree murals on the wall. The office as playground trend that started at Google and has spread like an infection across the tech industry.” Work as lifestyle, where every sip of Blue Bottle coffee signals our coolness, where every twist of wax on our mustaches imbues our days with mindful meaning as we hack our brains and the establishment (ignoring, for the time being, the premium we pay for those medium roast beans). At its most sinister, culture is overlooking implicit and even explicit acts of harassment, abuse, or misogyny to exclusively favor the ruthless promotion of growth (see Uber’s recent demise). At its most awkward, culture is the set of trust- and bond-building exercises conducted during the offsite retreat, where we do cartwheels and jumping jacks and sing Kumbaya holding hands in a circle once a year only to return to the doldrums at our dark mahogany corner offices and linoleum cubicles on Monday morning. At its most sinuous, culture is the set of minute power plays that govern decisions in a seemingly flat organization, the little peaks of hierarchy that arise no matter how hard we try to proclaim equality, the acrid taste we get after meetings when it’s obvious to everyone, although no one admits it, that deep down our values aren’t really aligned, and that master-slave dialectics always have and always will shape the course of history.

hubspot candy
The candy wall is considered a perk for the millennial workforce at Hubspot.

But culture can be much more than craft beer at hack night and costumes on Halloween. The best businesses take culture so seriously that it shifts from being an epiphenomenon to weaving the fabric of operations. The moment of transubstantiation takes place when a mission statement changes from being a branding tool to being a principle for making decisions; when leadership abandons micromanagement to guide employees with a north star, enabling ownership, autonomy, and creativity; when the little voices of doubt and fear and worry and concern inside our heads are quieted by purpose and clarity, when we feel safe to express disagreement without suffering and repercussion; when a whole far greater than the sum of its parts emerges from the unique and mystically aligned activities of each individual contributor.

This post surveys five companies for which culture is an integral part of operations. Each is inspiring in its own way, and each thinks about and pragmatically employs culture differently at a different phase of company history and growth.

Always Day 1: True Customer Obsession at Amazon

On April 12, Jeff Bezos released a letter to shareholders. Amazon is now 23 years old, and has gone from being an online bookstore to being the cloud infrastructure making many startups possible, the creator of the first convenience store without checkout lines, and one of the largest retailers in the world. Given its maturity and the immense scope of its operations, Amazon risks falling into big company traps, succumbing to the inertia of process and the albatross weight of the innovator’s dilemma. Such “Day 2” stupor is precisely what CEO Jeff Bezos wants to avoid, and his letter presents four cultural pillars to keeping a big company running like a small company: customer obsession, a skeptical view of proxies, the eager adoption of external trends, and high-velocity decision making.

Bezos states “true customer obsession” as the fulcrum guiding Amazon’s business. While this may seem like a given, in practice very few companies succeed at running a customer-centric business. As Bezos points out, businesses can be product focused, technology focused, or business model focused. They can be sales focused or lifestyle focused. They can focus on long-term strategy or short-term revenue. The popularity and design thinking stems from the fact that product development methodologies historically struggled to take into account how users reacted to products. In Creative Confidence, Tom Kelley and David Kelley show multiple examples of how feature prioritization decisions change when engineers leave the clean world of verisimilitude to enter the messy, surprising world of human reactions and emotions. One of my favorite examples in the Kelleys’ book is when Doug Dietz, an engineer at GE Healthcare, overhauled his strategy for building MRIs when he realized the best technical solution created a horrifying experience for children. The guiding architecture for MRIs henceforth became pirate ships or space ships, contextual vessels that could channel the imagination to dampen the aggressive clanging of the machine, and create a more positive experience for sick children.

pirate ship MRI
GE’s pirate ship MRI, designed to make tests less horrifying for children.

Bezos astutely remarks that a customer-centric approach forces a company to stay innovative and uphold the hunger of Day 1 because “customers are always beautifully, wonderfully dissatisfied, even when they report being happy and business is great.” This is Marketing with a big M. Not marketing as many people misunderstand it, i.e., as the use of catch phrases or content to shape the opinions of some group of people in the hope that these shaped opinions can transform into revenue, but marketing as a sub-discipline of empiricism, where a company carefully observes the current habits of some group of people to discern a need they don’t yet have, but that they will willingly and easily recognize as a need, and change their habits to accommodate, once it’s presented as a possibility. Steve Jobs did this masterfully.

Using customers as an anchor to design and build new products is powerful because truth is stranger than fiction. Many product managers and engineers fall into the trap of verisimilitude, making products as they would write a play, where each detail seems to make sense in the context of the coherent whole. I’ve seen numerous companies spend months imagining features they think users will want that arise logically from the technical capabilities of a tool, only to realize meager revenues because users actually want something different. User stories built on real research with real people–even if it’s a sample set of a few rather than the many that support A/B testing methodologies at consumer companies like Netflix–have Sherlock Holmes superpowers, leading to insights that aren’t obvious until reinterpreted retrospectively.

Bezos ends his letter with the importance of high-velocity decision making, which involves the courage to make decisions with 70% of the information you wish you had and “disagreeing and committing” when consensus is impossible. Disagreeing and committing requires radical candor and the courage to embrace conflict head on. Early in my career, I failed on a few occasions by not having the courage to voice disagreement as we made certain important decisions; after the fact, when we started to observe the negative impacts of the decision, I wanted to stand up and say “I told you so!” but couldn’t because it was too late. This breeds resentment, and it certainly takes culture work to make employees feel like they can voice conflicting or dissenting views without negative repercussion.

(A couple of my readers pointed out that Amazon has been reported to have a cutthroat culture. This reminds me of Ferdinand Céline and Martin Heidegger, two men who supported Fascism and Nazism, and yet left us quite valuable writings. Should we pay attention to the idealized version of a man or company, the traces left in letters and prose, or the reality of his lived life and actions? Can we forgive the sinner if he leaves us gold?)

Soul as a Recruiting Tool at Integrate.ai

While Amazon is a massive company whose cultural challenge is to avoid inertia and bureaucracy, Integrate.ai is a brand-new startup whose challenge is to attract the right early talent that will make or break company success. Inspired by his experience at Facebook, CEO & Founder Steve Irvine decided the surest way to recruit top talent–and avoid hires whose values were misaligned–was to make it a priority to build a company with soul.

Screen Shot 2017-04-23 at 10.40.49 AM
While we easily recognize that Ray Charles has more soul than Justin Bieber,                    we struggle to quantify soul in company culture.

In a recent presentation, Irvine explains how soul is the hard-to-explain, intangible aspect of a business people struggle to pin down in words, one of those things you know when you see it but can’t really articulate or describe. He says it’s “what you do when no one is looking and when everyone is looking, what gives people in your company purpose and makes them brave in the face of long odds.” Underlying soul is a set of common values: Irvine believes its crucial that everyone in a company share values and mission, even if they approach different questions with a plurality and diversity of perspectives. Soul, here, is different from the spiritual essence of an individual, the self that remains after our corporeal bodies return to dust. It’s the least common denominator uniting a group of diverse individuals, the fulcrum that keeps everyone engaged when things aren’t going well, the life force sustaining interest and passion in the midst of doubt and dismay.

Perhaps most interesting is how effective commitment to soul can be. Irvine is at the very beginning of building his company, so soul, for the moment, is an abstract promise rather than an embodied commitment. But it’s extremely powerful to love what you do. To embrace work with passion, not just as a job that pays the bills. To be excited about weathering storms together with a group of people you care about and in the service of a mission you care for. The trick is to transform this energy into the hard work of building a business.

IKEA: The Best Company Mission Statement Ever

If there’s anyone who has managed to transform soul into successful operations, it’s IKEA founder Ingvar Kamprad. The Testament of a Furniture Dealer, which he wrote in 1976, is the most powerful company mission statement I’ve ever read. It’s powerful because it shows how the entirety of IKEA’s operations result, as if by logical necessity, from the company’s core mission “to create a better everyday life for the many people.”

Just after stating this core mission, Kamprad continues that they will achieve this mission “by offering a wide range of well-designed, functional home furnishing products at prices so low that as many people as possible will be able to afford them.” These two initial phrases function like axioms in a mathematical proof, with subsequent chapter in the Testament exploring propositions that logically follow from the initial axioms.

The first proposition regards what Kamprad calls product range, the set of products IKEA will and won’t offer. As the many people need to furnish not just their living rooms but their entire homes, IKEA’s objective must, as a result, “encompass the total home environment, [including] tools, utensils and ornaments for the home as well as more or less advanced components for do-it-yourself furnishing and interior decoration.” The product design must be “typically IKEA,” reflecting the company’s way of thinking and “being as simple and straightforward as ourselves.” The quality must be high, as the many people cannot afford to just throw things away.

To keep costs low, of course, requires “getting good results with small or limited resources.” Which, by logical necessity, leads to subsequent propositions about the cost of capital and inventory management. Kamprad says that “expensive solutions to any kind of problem are usually the work of mediocrity.” Rigorous pragmatism, and having a sense for the entire supply chain of costs to create and distribute a product, is a core part of IKEA’s culture. Considering waste of resources to be “one of the greatest diseases of mankind,” Kamprad builds the cultural mindset that leads to the compact, do-it-yourself assembly packaging for which IKEA is famous.

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IKEA created this Poäng series in 1976, the same year Kamrad wrote   The Testament of a Furniture Dealer

And the brilliance continues. Kamprad pivots from simplicity in design and production to simplicity as a virtue in decision making, claiming, like Bezos, that “exaggerated planning is the most common cause of corporate death” and extolling a rigorous curiosity that always asks why things are done a given way to open the critical curiosity required to identify opportunities to do things differently. He extols concentration, the discipline required to focus on the specific range of products that defines core identity, the type of focus Steve Jobs instilled to usher Apple to its current level of success. And finally, Kamprad closes his mission by celebrating incompletion and process:

“The feeling of having finished something is an effective sleeping pill. A person who retires feeling that he has done his bit will quickly wither away. A company which feels it has reached its goal will quickly stagnate and lose its vitality.

Happiness is not reaching your goal. Happiness is being on the way. It is our wonderful fate to be just at the beginning. In all areas. We will move ahead only by constantly asking ourselves how what we are doing today can be done better tomorrow. The positive joy of discovery must be our inspiration in the future too.

The word impossible has been deleted from our dictionary and must remain so.”

Note that he wrote this in 1976, a good 35 years before the current new-age thinking focusing on the joy of the process was commonplace wisdom. Note the parallels with Bezos, how both leaders focus on creativity, discipline, and the ruthless awareness and avoidance of biases as a means to keep innovation alive. IKEA has grown from being a low-cost furniture provider in Europe to being a global franchise business with operations in many markets. Time will tell what it will mean for them to serve the many people in the future, and how they will expand their product range while adhering to the focus and discipline required to keep their identity and mission intact.

Commitment to Diversity at Fast Forward Labs

On February 23, 2017, Hilary Mason, the founder and CEO of Fast Forward Labs, sent the following email to our team:

Subject: Maintaining a Respectful Environment

Body:

It’s very important to me that our office is respectful and comfortable for everyone who works here and for our visitors, many of whom we are trying to engage as customers and collaborators.

To that end: PUT THE TOILET SEAT DOWN.

There is writing on the seat to remind you.

Thank you,

Hilary
Diversity can easily become a compliance checklist item or a pat-yourself-on-the-back politically correct platitude. Practicing it fully takes vigilance and effort. Mason actively promotes diversity and equality in just about every aspect of her professional (and private) existence. Fast Forward Labs is a small company, but there are many women on the leadership team and research interns from international creeds, races, and backgrounds. I’ve sometimes overlooked gender equality (even though I’m a woman myself!) when recommending speakers for conferences, only to have Mason remind me to be mindful in my choices next time around to help build the future we want and can be proud of. We make sure to include a section on ethics in each of our machine learning research reports and have actively turned down business with organizations whose values contrast highly with our own.
The vast majority of the technology world is still run by white men, leading to narratives about the singularity and the superintelligent future that distract us from the real-world ethical problems we face today. We need more women-run companies like Fast Forward Labs to bring more voices to the table and, pragmatically, to make sure character traits like empathy are keeping us on track to solve the right problems and encourage AI adoption (as I discussed in a recent interview on TWiML). This is certainly not to say that empathy is a uniquely feminine trait; but it is to say that no large enterprise will adopt AI successfully without navigating the emotional and people challenges that attend any change management initiative.
(Mason read this post, and told me she doesn’t consider herself to be promoting diversity, but to be creating the world she wants to live in.)

Culture as Product at Asana

The final example focuses on practices to make culture a critical component of a business as opposed to an office decoration or afterthought at the company party. Asana, which offers a SaaS project-management tool, has received multiple accolades for its positive culture, including a rare near-perfect rating on Glassdoor. Short of using Putin-style coercion and manipulation techniques, how did they achieve such positive employee ratings on culture and experience?

According to a recent Fast Company article, by “treating culture as a product that needed to be carefully designed, tested, debugged, and iterated on, like any other product they released.” Just as Amazon analyzes feedback from their external market, so too does Asana analyze feedback from their internal market, soliciting feedback from employees and “debugging” issues like false empowerment as soon as they arise. The company also offers the standard cool office perks that are commonplace in the valley, offering each employee $10,000 (!!!) to set up customized workplaces that can include anything from standing desks to treadmills.

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Asana states its cultural values on its company page.

It’s likely risky to adopt Asana’s culture as product model given the gimmick of hacking phenomena–like our minds and cultural practices–that aren’t computer code. But the essence here is to manage culture using the tools you know well. Agile software development practices aren’t universal across all companies, so it would be a stretch to apply them in environments where they’re not a great fit. But if we take this to a more abstract and general level, it does make sense to treat culture as a living, moving, dynamic product that requires work and discipline just like the products a business offers to its customers, and to manage it accordingly.

Conclusion

The older I get, the more I’m convinced that the how of activity is more important to happiness than the what of activity. Culture is the big how of a company that emerges from the little hows of each individual’s daily activities. When norms were mores, the how was inherited and given, habits and manners we had to adopt to align civil interaction. Now that norms are culture, we’re empowered to create our how, to have it trickle down from mission into operations or work actively to build it and debug it like a product. This requires vigilance, mindfulness, responsibility. It requires humbleness and, as Ingvar Kamprad concludes, “the ambition to develop ourselves as human beings and co-workers.”

The image is from Soup Addict’s recipe for a wild yeast sourdough starter. It’s valuable for us to remember the agricultural roots of the world culture, to bring things back to earth and remember the hard work required to care for something larger than and different from ourselves, which, once it reaches maturity, can feed and nourish us.

Point : Counterpoint

she woke
she blinked
it rained
she stretched
she peed
she ground
she brew
she drank
she read
she shat
she breathed
she wandered
she ran
she wondered
she stretched
she showered
she sang
she ate
she dressed
she walked

he woke
she sat
he peed
she read
he showered
she typed
he ate
she typed
he trained
she laughed
he elevatored
she listened
he sat
she disagreed
he read
she acquiesced
he typed
she reflected
he presented
she worried
he nodded
rain abetted
she breathed
he answered
she typed
he typed
she typed
he typed

she smiled
he ate
she gossiped
he bragged
she nibbled
he texted
she sipped
he slurped
clouds tiptoed
she noticed
he returned
she presented
he typed
she surged
he furrowed
she calmed
he called
she elevatored
he yelled
she walked
he regretted
she sat
he walked

she nodded
he sauntered
she saw
he sat
she averted
he texted
she reverted
he saw
she felt
he waited
she tingled
he approached
she blushed
he offered
she accepted
he asked
she answered
he answered
she asked
he probed
she allowed
sun set
he dared
she walked
he walked

she hinted
he touched
she coiled
he doubted
she opened
he mirrored
she undressed
he watched
she slithered
he followed
she touched
he entered
she winced
he worried
she arrived
he thrust
she followed
he retained
she overtook
he watched
she came
he smiled
she embraced
he continued
she nourished
he came
she smiled
he breathed
she peed
he lay
she washed
he slept
she observed
he slept
she dressed
he slept
night hummed
she left
he awoke
she walked
he noticed
she mulled
he turned
she walked
he slept
she itched
he slept
she relived
he slept
she glistened
he slept
she slept

love happened

The image is Magritte’s The Lovers, from 1928. Many say the work represents the difficulty of achieving true intimacy with another, as we retain ourselves behind veils and barriers. Perhaps that’s right. Perhaps it’s not. 

Education in the Age of AI

There’s all this talk that robots will replace humans in the workplace, leaving us poor, redundant schmucks with nothing to do but embrace the glorious (yet terrifying) creative potential of opiates and ennui. (Let it be noted that bumdom was all the rage in the 19th century, leading to the surging ecstasies of Baudelaire, Rimbaud, and the crown priest of hermeticism (and my all-time favorite poet besides Sappho*), Stéphane Mallarmé**).

As I’ve argued in a previous post, I think that’s bollocks. But I also think it’s worth thinking about what cognitive, services-oriented jobs could and should look like in the next 20 years as technology advances. Note that I’m restricting my commentary to professional services work, as the manufacturing, agricultural, and transportation (truck and taxi driving) sectors entail a different type of work activity and are governed by different economic dynamics. They may indeed be quite threatened by emerging artificial intelligence (AI) technologies.

So, here we go.

I’m currently reading Yuval Noah Harari’s latest book, Homo Deusand the following passage caught my attention:

“In fact, as time goes by it becomes easier and easier to replace humans with computer algorithms, not merely because the algorithms are getting smarter, but also because humans are professionalizing. Ancient hunter-gatherers mastered a very wide variety of skills in order to survive, which is why it would be immensely difficult to design a robotic hunter-gatherer. Such a robot would have to know how to prepare spear points from flint stones, find edible mushrooms in a forest, track down a mammoth and coordinate a charge with a dozen other hunters, and afterwards use medicinal herbs to bandage any wounds. However, over the last few thousand years we humans have been specializing. A taxi driver or a cardiologist specializes in a much narrower niche than a hunter-gatherer, which makes it easier to replace them with AI. As I have repeatedly stressed, AI is nowhere near human-like existence. But 99 per cent of human qualities and abilities are simply redundant for the performance of most modern jobs. For AI to squeeze humans out of the job market it needs only to outperform us in the specific abilities a particular profession demands.”

duchamp toilet
Harari is at his best critiquing liberal humanism. He features Duchamp’s ready-made art as the apogee of humanist aesthetics, where beauty is in the eye of the beholder.

This is astute. I love how Harari debunks the false impression that the human race progresses over time. We tend to be amazed upon seeing the technical difficulty of ancient works of art at the Met or the Louvre, assuming History (big H intended) is a straightforward, linear march from primitivism towards perfection. While culture and technologies are passed down through language and traditions from generation to generation, shaping and changing how we interact with one another and with the physical world, how we interact as a collective and emerge into something way beyond our capacities to observe, this does not mean that the culture and civilization we inhabit today is morally superior to those that came before, or those few that still exist in the remote corners of the globe. Indeed, primitive hunter-gatherers, given the broad range of tasks they had to carry out to survive prior to Adam Smith’s division of labor across a collective, may have a skill set more immune to the “cognitive” smarts of new technologies than a highly educated, highly specialized service worker!

This reveals something about both the nature of AI and the nature of the division of labor in contemporary capitalism arising from industrialism. First, it helps us understand that intelligent systems are best viewed as idiot savants, not Renaissance Men. They are specialists, not generalists. As Tom Mitchell explains in the opening of his manifesto on machine learning:

“We say that a machine learns with respect to a particular task T, performance metric P, and type of experience E, if the system reliably improves its performance P at task T, following experience E. Depending on how we specify T, P, and E, the learning task might also be called by names such as data mining, autonomous discovery, database updating, programming by example, etc.”

Confusion about super-intelligent systems stems from the popular misunderstanding of the word “learn,” which is a term of art with a specific meaning in the machine learning community. The learning of machine learning, as Mitchell explains, does not mean perfecting a skill through repetition or synthesizing ideas to create something new. It means updating the slope of your function to better fit new data. In deep learning, these functions need not be simple, 2-D lines like we learn in middle school algebra: they can be incredibly complex curves that transverse thousands of dimensions (which we have a hard time visualizing, leading to tools like t-SNE that compress multi-dimensional math into the comfortable space-time parameters of human cognition).

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t-SNE reminds me of Edwin Abbott’s Flatland, where dimensions signify different social castes.

The AI research community is making baby steps in the dark trying to create systems with more general intelligence, i.e., systems that reliably perform more than one task. OpenAI Universe and DeepMind Lab are the most exciting attempts. At the Future Labs AI Summit this week, Facebook’s Yann LeCun discussed (largely failed) attempts to teach machines common sense. We tend to think that highly skilled tasks like diagnosing pneumonia from an X-ray or deeming a tax return in compliance with the IRS code require more smarts than intuiting that a Jenga tower is about to fall or perceiving that someone may be bluffing in a poker game. But these physical and emotional intuitions are, in fact, incredibly difficult to encode into mathematical models and functions. Our minds are probabilistic, plastic approximation machines, constantly rewiring themselves to help us navigate the physical world. This is damn hard to replicate with math, no matter how many parameters we stuff into a model! It may also explain why the greatest philosophers in history have always had room to revisit and question the givens of human experience****, infinitely more interesting and harder to describe than the specialized knowledge that populates academic journals.

Next, it is precisely this specialization that renders workers susceptible to being replaced by machines. I’m not versed enough in the history of economics to know how and when specialization arose, but it makes sense that there is a tight correlation between specialization, machine coordination, and scale, as R. David Dixon recently discussed in his excellent Medium article about machines and the division of labor. Some people are drawn to startups because they are the antithesis of specialization. You get to wear multiple hats, doubling, as I do in my role at Fast Forward Labs, as sales, marketing, branding, partnerships, and even consulting and services delivery. Guild work used to work this way, as in the nursery rhyme Rub-a-dub-dub: the butcher prepared meat from end to end, the baker made bread from end to end, and the candlestick maker made candles from end to end. As Dixon points out, tasks and the time it takes to do tasks become important once the steps in a given work process are broken apart, leading to theories of economic specialization as we see in Adam Smith, Henry Ford, and, in their modern manifestation, the cold, harsh governance of algorithms and KPIs. The corollary of scale is mechanism, templates, repetition, efficiency. And the educational system we’ve inherited from the late 19th century is tailored and tuned to farm out skilled, specialized automatons who fit nicely into the specific roles required by corporate machines like Google or Goldman Sachs.

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Frederick Taylor pioneered the scientific management theories that shaped factories in the 20th century, culminating in process methodologies like Lean Six Sigma

This leads to the core argument I’d like to put forth in this post: the right educational training and curriculum for the AI-enabled job market of the 21st century should create generalists, not specialists. Intelligent systems will get better and better at carrying out specific activities and specific tasks on our behalf. They’ll do them reliably. They won’t get sick. They won’t have fragile egos. They won’t want to stay home and eat ice cream after a breakup. They can and should take over this specialized work to drive efficiencies and scale. But, machines won’t be like startup employees any time soon. They won’t be able to reliably wear multiple hats, shifting behavior and style for different contexts and different needs. They won’t be creative problem solvers, dreamers, or creators of mission. We need to educate the next generation of workers to be more like startup employees. We need to bring back respect for the generalist. We need the honnête homme of the 17th century or Arnheim*** in Robert Musil’s Man Without Qualities. We need hunter-gatherers who may not do one thing fabulously, but have the resiliency to do a lot of things well enough to get by.

What types of skills should these AI-resistant generalists have and how can we teach them?

Flexibility and Adaptability

Andrew Ng is a pithy tweeter. He recently wrote: “The half-life of knowledge is decreasing. That’s why you need to keep learning your whole life, not only through college.”

This is sound. The apprenticeship model we’ve inherited from the guild days, where the father-figure professor passes down his wisdom to the student who becomes assistant professor then associate professor then tenured professor then stays there for the rest of his life only to repeat the cycle in the next generation, should probably just stop. Technologies are advancing quickly, which open opportunities to automate tasks that we used to do manually or do new things we couldn’t do before (like summarizing 10,000 customer reviews on Amazon in a second, as the system my colleagues at Fast Forward Labs built). Many people fear change and there are emotional hurdles to having to break out of habits and routine and learn something new. But honing the ability to recognize that new technologies are opening new markets and new opportunities will be seminal to succeeding in a world where things constantly change. This is not to extol disruption. That’s infantile. It’s to accept and embrace the need to constantly learn to stay relevant. That’s exciting and even meaningful. Most people wait until they retire to finally take the time to paint or learn a new hobby. What if work itself offered the opportunity to constantly expand and take on something new? That doesn’t mean that everyone will be up to the challenge of becoming a data scientist over night in some bootcamp. So the task universities and MOOCs have before them is to create curricula that will help laymen update their skills to stay relevant in the future economy.

Interdisciplinarity

From the late 17th to mid 18th centuries, intellectual giants like Leibniz, D’Alembert, and Diderot undertook the colossal task of curating and editing encyclopedias (the Greek etymology means “in the circle of knowledge”) to represent and organize all the world’s knowledge (Google and Wikipedia being the modern manifestations of the same goal). These Enlightenment powerhouses all assumed that the world was one, and that our various disciplines were simply different prisms that refracted a unified whole. The magic of the encyclopedia lay in the play of hyperlinks, where we could see the connections between things as we jumped from physics to architecture to Haitian voodoo, all different lenses we mere mortals required to view what God (for lack of a better name) would understand holistically and all at once.

Contemporary curricula focused on specialization force students to grow myopic blinders, viewing phenomena according to the methodologies and formalisms unique to a particular course of study. We then mistake these different ways of studying and asking questions for literally different things and objects in the world and in the process develop prejudices against other tastes, interests, and preferences.

There is a lot of value in doing the philosophical work to understand just what our methodologies and assumptions are, and how they shape how we view problems and ask and answer questions about the world. I think one of the best ways to help students develop sensitivities for methodologies is to have them study a single topic, like climate change, energy, truth, beauty, emergence, whatever it may be, from multiple disciplinary perspectives. So understanding how physics studies climate change; how politicians study climate change; how international relations study climate change; how authors have portrayed climate change and its impact on society in recent literature. Stanford’s Thinking Matters and the University of Chicago’s Social Thought programs approach big questions this way. I’ve heard Thinking Matters has not helped humanities enrollment at Stanford, but still find the approach commendable.

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The 18th-century Encyclopédie placed vocational knowledge like embroidery on equal footing with abstract knowledge of philosophy or religion.

Model Thinking

Michael Lewis does a masterful job narrating the lifelong (though not always strong) partnership between Daniel Kahneman and Amos Tversky in The Undoing Project. Kahneman and Tversky spent their lives showing how we are horrible probabilistic thinkers. We struggle with uncertainty and have developed all sorts of narrative and heuristic mental techniques to make our world make more concrete sense. Unfortunately, we need to improve our statistical intuitions to succeed in the world of AI, which are probabilistic systems that output responses couched in statistical terms. While we can hide this complexity behind savvy design choices, really understanding how AI works and how it may impact our lives requires that we develop intuitions for how models, well, model the world. At least when I was a student 10 years ago, statistics was not required in high school or undergrad. We had to take geometry, algebra, and calculus, not stats. It seems to make sense to make basic statistics a mandatory requirement for contemporary curricula.

Synthetic and Analogical Reasoning

There are a lot of TED Talks about brains and creativity. People love to hear about the science of making up new things. Many interesting breakthroughs in the history of philosophy or physics came from combining together two strands of thought that were formerly separate: the French psychoanalyst Jacques Lacan, whose unintelligibility is besides the point, cleverly combined linguistic theory from Ferdinand Saussure with psychoanalytic theory from Sigmund Freud to make his special brand of analysis; the Dutch physicist Erik Verlinde cleverly combined Newton and Maxwell’s equations with information theory to come to the stunning conclusion that gravity emerges from entropy (which is debated, but super interesting).

As we saw above, AI systems aren’t analogical or synthetic reasoners. In law, for example, they excel at classification tasks to identify if a piece of evidence is relevant for a given matter, but they fail at executing other types of reasoning tasks like identifying that the facts of a particular case are similar to the facts of another to merit a comparison using precedent. Technology cases help illustrate this. Data privacy law, for example, frequently thinks about our right to privacy in the virtual world through reference back to Katz v. United Statesa 1967 case featuring a man making illegal gambling bets from a phone booth. Topic modeling algorithms would struggle to recognize that words connoting phones and bets had a relationship to words connoting tracking sensors on the bottom of trucks (as in United States v. Jones). But lawyers and judges use Katz as precedent to think through this brave new world, showing how we can see similarities between radically different particulars from a particular level of abstraction.

Does this mean that, like stats, everyone should take a course on the basics of legal reasoning to make sure they’re relevant in the AI-enabled world? That doesn’t feel right. I think requiring coursework in the arts and humanities could do the trick.

Framing Qualitative Ideas as Quantitative Problems

A final skill that seems paramount for the AI-enabled economy is the ability to translate an idea into something that can be measured. Not everyone needs to be able to this, but there will be good jobs–and more and more jobs–for the people who can.

This is the data science equivalent of being able to go from strategy to tactical execution. Perhaps the hardest thing in data science, in particular as tooling becomes more ubiquitous and commoditized, is to figure out what problems are worth solving and what products are worth building. This requires working closely with non-technical business leaders who set strategy and have visions about where they’d like to go. But it takes a lot of work to break down a big idea into a set of small steps that can be represented as a quantitative problem, i.e., translated into some sort of technology or product. This is also synthetic and interdisciplinary thinking. It requires the flexibility to speak human and speak machine, to prioritize projects and have a sense for how long it will take to build a system that does what need it to do, to render the messy real-world tractable for computation. Machines won’t be automating this kind of work anytime soon, so it’s a skill set worth building. The best way to teach this is through case studies. I’d advocate for co-op training programs alongside theoretical studies, as Waterloo provides for its computer science students.

Conclusion

While our culture idealizes and extols polymaths like Da Vinci or Galileo, it also undervalues generalists who seem to lack the discipline and rigor to focus on doing something well. Our academic institutions prize novelty and specialization, pushing us to focus on earning the new leaf at the edge of a vast tree wizened with rings of experience. We need to change this mindset to cultivate a workforce that can successfully collaborate with intelligent machines. The risk is a world without work; the reward is a vibrant and curious new humanity.


The featured image is from Émile, Jean-Jacques Rousseau’s treatise on education. Rousseau also felt educational institutions needed to be updated to better match the theories of man and freedom developed during the Enlightenment. Or so I thought! Upon reading this, one of my favorite professors (and people), Keith Baker, kindly insisted that “Rousseau’s goal in Emile was not to show how educational institutions could be improved (which he didn’t think would be possible without a total reform of the social order) but how the education of an individual could provide an alternative (and a means for an individual to live free in a corrupt society).” Keith knows his stuff, and recalling that Rousseau is a misanthropic humanist makes things all the more interesting. 

*Sappho may be the sexiest poet of all time. An ancient lyric poet from Lesbos, she left fragments that pulse with desire and eroticism. Randomly opening a collection, for example, I came across this:

Afraid of losing you

I ran fluttering/like a little girl/after her mother

**I’m stretching the truth here for rhetorical effect. Mallarmé actually made a living as an English teacher, although he was apparently horrible at both teaching and speaking English. Like Knausgaard in Book 2 of My StruggleMallarmé frequently writes poems about how hard it is for him to find a block of silence while his kids are screaming and needing attention. Bourgeois family life sublimated into the ecstasy of hermeticism. Another fun fact is that the French Symbolists loved Edgar Allen Poe, but in France they drop the Allen and just call him Edgar Poe.

***Musil modeled Arnheim after his nemesis Walther Rathenau, the German Foreign Minister during the Weimar Republic. Rathenau was a Jew, but identified mostly as a German. He wrote some very mystical works on the soul that aren’t worth reading unless you’d like to understand the philosophical and cocktail party ethos of the Habsburg Empire.

****I’m a devout listener of the Partially Examined Life podcast, where they recently discussed Wilfrid Sellars’s Empiricism and the Philosophy of Mind. Sellars critiques what he calls “the myth of the given” and has amazing thoughts on what it means to tell the truth.

Whales, Fish, and Paradigm Shifts

I never really liked the 17th-century English philosopher Thomas Hobbes, but, as with Descartes, found myself continuously drawn to his work. The structure of Leviathan, the seminal founding work of the social contract theory tradition (where we willingly abdicate our natural rights in exchange for security and protection from an empowered government, so we can devote our energy to meaningful activities like work rather than constantly fear that our neighbors will steal our property in a savage war of of all against all)*, is so 17th-century rationalist and, in turn, so strange to our contemporary sensibilities. Imagine beginning a critique of the Trump administration by defining the axioms of human experience (sensory experience, imagination, memory, emotions) and imagining a fictional, pre-social state of affairs where everyone fights with one another, and then showing not only that a sovereign monarchy is a good form of government, but also that it must exist out of deductive logical necessity, and!, that it is formed by a mystical, again fictional, moment where we come together and willing agree it’s rational and in our best interests to hand over some of our rights, in a contract signed by all for all, that is then sublimated into a representative we call government! I found the form of this argument so strange and compelling that I taught a course tracing the history of this fictional “state of nature” in literature, philosophy, and film at Stanford.

Long preamble. The punch line is, because Hobbes haunted my thoughts whether I liked it or not, I was intrigued when I saw a poster advertising Trying Leviathan back in 2008. Given the title, I falsely assumed the book was about the contentious reception of Hobbesian thought. In fact, Trying Leviathan is D. Graham Burnett‘s intellectual history of Maurice v. Judd, an 1818 trial where James Maurice, a fish oil inspector who collected taxes for the state of New York, sought penalty against Samuel Judd, who had purchased three barrels of whale oil without inspection. Judd pleaded that the barrels contained whale oil, not fish oil, and so were not subject to the fish oil legislation. As with any great case**, the turnkey issue in Maurice v. Judd was much more profound than the matter that brought it to court: at stake was whether a whale is a fish, turning a quibble over tax law into an epic fight pitting new science against sedimented religious belief.

Indeed, in Trying Leviathan Burnett shows how, in 1818, four different witnesses with four very different backgrounds and sets of experiences answered what one would think would be a simple, factual question in four very different ways. The types of knowledge they espoused were structured differently and founded on different principles:

  • The Religious Syllogism: The Bible says that birds are in heaven, animals are on land, and fish are in the sea. The Bible says no wrong. We can easily observe that whales live in the sea. Therefore, a whale is a fish.
  • The Linnaean Taxonomy: Organisms can classified into different types and subtypes given a set of features or characteristics that may or may not be visible to the naked eye. Unlike fish, whales cannot breathe underwater because they have lungs, not gills. That’s why they come to the ocean surface and spout majestic sea geysers. We may not be able to observe the insides of whales directly, but we can use technology to help us do so.
    • Fine print: Linnaean taxonomy was a slippery slope to Darwinism, which throws meaning and God to the curb of history (see Nietzsche)
  • The Whaler’s Know-How: As tested by iterations and experience, I’ve learned that to kill a whale, I place my harpoon in a different part of the whale’s body than where I place my hook when I kill a fish. I can’t tell you why this is so, but I can certainly tell you that this is so, the proof being my successful bounty. This know-how has been passed down from whalers I apprenticed with.
  • The Inspector’s Orders: To protect the public from contaminated oil, the New York State Legislature had enacted legislation requiring that all fish oil sold in New York be gauged, inspected and branded. Oil inspectors were to impose a penalty on those who failed to comply. Better to err of the side of caution and count a whale as a fish than not obey the law.

From our 2017 vantage point, it’s easy to accept and appreciate the way the Linnaean taxonomist presented categories to triage species in the world. 200 years is a long time in the evolution of an idea: unlike genes, culture and knowledge can literally change from one generation to the next through deliberate choices in education. So we have to do some work to imagine how strange and unfamiliar this would have seemed to most people at the time, to appreciate how the Bible’s simple logic made more sense. Samuel Mitchell, who testified for Judd and represented the Linnaean strand of thought, likely faced the same set of social forces as Clarence Darrow in the Scopes Trial or Hilary Clinton in last year’s election. American mistrust of intellectuals runs deep.

But there’s a contemporary parallel that can help us relive and revive the emotional urgency of Maurice v. Judd: the rise of artificial intelligence (A.I.). The type of knowledge A.I. algorithms provide is different than the type of knowledge acquired by professionals whose activity they might replace. And society’s excited, confused, and fearful reaction to these new technologies is surfacing a similar set of epistemological collisions as those at play back in 1818.

Consider, for example, how Siddharta Mukherjee describes using deep learning algorithms to analyze medical images in a recent New Yorker article, A.I. versus M.D. Early in the article, Mukherjee distinguishes contemporary deep learning approaches to computer vision from earlier expert systems based on Boolean logic and rules:

“Imagine an old-fashioned program to identify a dog. A software engineer would write a thousand if-then-else statements: if it has ears, and a snout, and has hair, and is not a rat . . . and so forth, ad infinitum.”

With deep learning, we don’t list the features we want our algorithm to look for to identify a dog as a dog or a cat as a cat or a malignant tumor as a malignant tumor. We don’t need to be able to articulate the essence of dog or the essence of cat. Instead, we feed as many examples of previously labeled pieces of data into the algorithm and leave it to its own devices, as it tunes the weights linking together pockets of computing across a network, playing Marco Polo until it gets the right answer, so it can then make educated guesses on new data it hasn’t yet seen before. The general public understanding that A.I. can just go off and discern patterns in data, bootstrapping their way to superintelligence, is incorrect. Supervised learning algorithms take precipitates of human judgments and mimic them in the form of linear algebra and statistics. The intelligence behind the classifications or predictions, however, lies within a set of non-linear functions that defy any attempt at reduction to the linear, simple building blocks of analytical intelligence. And that, for many people, is a frightening proposition.

But it need not be. In the four knowledge categories sampled from Trying Leviathan above, computer vision using deep learning is like a fusion between a Linnaean Taxonomy and the Whaler’s Know-How. These algorithms excel at classification tasks, dividing the world up into parts. And they do it without our cleanly being able to articulate why – they do it by distilling, in computation, the lessons of apprenticeship, where the teacher is a set of labeled training data that tunes the worldview of the algorithm. As Mukherjee points out in his article, classification systems do a good job saying that something is the case, but do a horrible job saying why.*** For society to get comfortable with these new technologies, we should first help everyone understand what kinds of truths they are able (and not able) to tell. How they make sense of the world will be different from the tools we’ve used to make sense of the world in the past. But that’s not a bad thing, and it shouldn’t limit adoption. We’ll need to shift our standards for evaluating them else we’ll end up in the age old fight pitting the old against the new.

 

*Hobbes was a cynical, miserable man whose life was shaped by constant bloodshed and war. He’s said to have been born prematurely on April 5, 1588, at a moment when the Spanish Armada was invading England. He later reported that “my mother gave birth to twins: myself and fear.” Hobbes was also a third-rate mathematician whose insistence that he be able to mentally picture objects of inquiry stunted his ability to contribute to the more abstract and formal developments of the day, like the calculus developed simultaneously by Newton and Leibniz (to keep themselves entertained, as founding a new mathematical discipline wasn’t stimulating enough, they communicated the fundamental theorem of calculus to one another in Latin anagrams!)

**Zubulake v. UBS Warburgthe grandmother case setting standards for evidence in the age of electronic information, started off as a sexual harassment lawsuit. Lola v. Skadden started as an employment law case focused on overtime compensation rights, but will likely shape future adoption of artificial intelligence in law firms, as it claims that document review is not the practice of law because this is the type of activity a computer could do.

***There’s research on using algorithms to answer questions about causation, but many perception based tools simply excel at correlating stuff to proxies and labels for stuff.