"The Domino Effect: How machine logic infects our tastes"
From Real Life:
Americans order a lot of pizza: A 2014 survey
by the U.S. Department of Agriculture suggested that one in eight
Americans eats pizza on any given day. Nowadays it feels as if there are
as many options for having a pizza delivered as there are available
toppings. If picking up the phone fills you with anxiety, Pizza Hut offers
a pair of sneakers with a built-in button for ordering pizza. “Host
Hungrybot in your Twitch channel to let your fans order pizza delivery
right from your stream,” a startup targeting eSports fans trumpeted.
Twitch is one of the few digital realms left untouched by Domino’s,
which now offers a series of apps, chatbots, and even the option of
tweeting an order using the pizza emoji. Some of these ordering options
may exist primarily as marketing gimmicks, but their aggregate effect
remains notable: Any interface to which you have access can likely be
used to order pizza.
This in part stems from pizza’s popularity, but taste is only a small
part of the story: The delivery pizza is highly adaptable to the logic
and formatted language of communication interfaces. The typical
consumer’s mental model of a pizza — dough with sauce, cheese, and
toppings baked in an oven — is quite similar to a machine’s conception
of pizza, which is quite similar to how a pizza is actually made. The
algorithm for pizza is not complex. Ordering a pizza through a chatbot
or within a Twitch stream is possible because all parties in the
transaction are imagining the same simple process and speaking from the
same restricted phrasebook.
Because it is streamlined to be easy to assemble, Pizza (and not the Verace Pizza Napoletana-certified
kind) is well-suited for digital abstraction. The fast food burger and
the burrito have undergone similar transformations, along with plenty of
foods desirable not only for their taste but because they are
rationalized and efficient, capable of individual customization without
requiring any special trust in the person preparing it at the other end
of the interface. Do these interfaces make it simpler to satisfy our tastes, or do they subtly simplify them?
After a bad day at work, you return home to find a turnip, some
lettuce, and a desultory chicken breast. That problem was the basic
premise of the British cooking show Ready Steady Cook: Members
of the public would throw together bags of groceries for a few pounds,
and chefs would then make a serviceable meal out of these ingredients.
This premise lasted 16 years and 1,895 episodes. Beyond their knife
skills, what the chefs on Ready Steady Cook really offer is improvisational intelligence: the ability to come up with solutions to new problems on the spot.
Improvisational intelligence, or the appearance thereof, is a dream of consumer technology. DARPA hired a jazz musician to help teach an AI system to improvise. IBM engineers fed Watson, of Jeopardy! fame, the entirety of Bon Appétit’s
archive, combined with insights into human taste and analysis of what
ingredients tend to be used together. “With Watson’s help, I cooked some
eggplant fritters that made convenient use of every sad, wrinkling root
in my refrigerator’s crisper,” Alexandra Kleeman wrote in the New Yorker.
But Watson is not in your kitchen yet, and may never be; instead, its
example is used to show what the current range of culinary companions
cannot do.
The Allrecipes skill for Amazon’s Alexa claims to “quickly
[find] recipes that match your desired dish type, ingredients you have
on hand, your available cooking time, and/or your preferred cooking
method.” But it’s just an interface over a simple dataset — the recipes
written and documented by contributors to allrecipes.com — and the
appearance of improvisational intelligence is purely a function of the
search terms a user enters. JULIA, a chatbot that aims to be “your new
BFF in the kitchen” by demonstrating the improvisational power of a
master chef, can only answer questions one ingredient at a time — it can
provide a recipe for turnip, lettuce, or chicken breast, but not all at
once. You can feel your new BFF querying a database in the background.
The app regularly responds to queries with “I don’t think I’m qualified
to answer that yet,” before linking to a page of tips about how best to
chat with the bot.
Generally, predictive services are not predictive so much as reliant
on someone else having been in your position before — if you search for
the contents of your fridge, odds are someone else will have previously
cooked these items together, but you will have sift through their
results yourself. The appealing complications of actually cooking a meal
remain messy, inconvenient, and human. Apps encourage us not to
improvise and trust ourselves, but to undertake the process of itemizing
and analyzing our ingredients as data for machines to process; to think
of ourselves as a component of the machine itself. These tools simplify
our lives on the condition that we simplify ourselves for them.
Ordering in is meant to outsource problems — and labor — to other
parties: You pay for other people to buy ingredients, prepare them, and
bring them to your door. That workforce is largely invisible, and
interfaces like those employed by Seamless or UberEats are designed to
conceal the labors of the unknowable number of people involved in
preparing your food, making the process appear as little more than a
hand-off at the door. These apps make a contradictory promise: to
simplify the multipart process involved in creating a meal to a series
of clicks, while offering enough options to satisfy an infinite number
of cravings. You agree to meet the interface somewhere between what you
want and what it knows how to offer, until you want what it knows how to
offer...MUCH MORE.
