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 Warburg, the 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.
Quam Proxime | As near as may be 