Peering with the unwavering focus of a watchful overlord.
A cat could seem to be many different things, and Brendan Wenzel’s recent picture book They All Saw a Cat conveys these vagrancies of perception beautifully. Though we share the world, we all see and hear and taste it differently. Each creature’s mind filters a torrential influx of information into manageable experience; we all filter the world differently.
They All Saw a Cat ends with a composite image. We see the various components that were focused on by each of the other animals, amalgamated into something approaching “cat-ness.” A human child noticed the cat’s soft fur, a mouse noticed its sharp claws, a fox noticed its swift speed, a bird noticed that it can’t fly.
All these properties are essential descriptors, but so much is blurred away by our minds. When I look at a domesticated cat, I tend to forget about the sharp claws and teeth. I certainly don’t remark on its lack of flight – being landbound myself, this seems perfectly ordinary to me. To be ensnared by gravity only seems strange from the perspective of a bird.
There is another way of developing the concept of “cat-ness,” though. Instead of compiling many creatures’ perceptions of a single cat, we could consider a single perceptive entity’s response to many specimens. How, for instance, do our brains learn to recognize cats?
When a friend (who teaches upper-level philosophy) and I were talking about Ludwig Wittgenstein’s Philosophical Investigations, I mentioned that I felt many of the aims of that book could be accomplished with a description of principal component analysis paired with Gideon Lewis-Kraus’s lovely New York Times Magazine article on Google Translate.
My friend looked at me with a mix of puzzlement and pity and said, “No.” Then added, as regards Philosophical Investigations, “You read it too fast.”
One of Wittgenstein’s aims is to show how humans can learn to use language… which is complicated by the fact that, in my friend’s words, “Any group of objects will share more than one commonality.” He posits that no matter how many red objects you point to, they’ll always share properties other than red-ness in common.
Or cats… when you’re teaching a child how to speak and point out many cats, will they have properties other than cat-ness in common?
In some ways, I agree. After all, I think the boundaries between species are porous. I don’t think there is a set of rules that could be used to determine whether a creature qualifies for personhood, so it’d be a bit silly if I also claimed that cat-ness could be clearly defined.
But when I point and say “That’s a cat!”, chances are that you’ll think so too. Even if no one had ever taught us what cats are, most people in the United States have seen enough of them to think “All those furry, four-legged, swivel-tailed, pointy-eared, pouncing things were probably the same type of creature!”
Even a computer can pick out these commonalities. When we learn about the world, we have a huge quantity of sensory data to draw upon – cats make those noises, they look like that when they find a sunny patch of grass to lie in, they look like that when they don’t want me to pet them – but a computer can learn to identify cat-ness using nothing more than grainy stills from Youtube.
Quoc Le et al. fed a few million images from Youtube videos to a computer algorithm that was searching for commonalities between the pictures. Even though the algorithm was given no hints as to the nature of the videos, it learned that many shared an emphasis on oblong shapes with triangles on top… cat faces. Indeed, when Le et al. made a visualization of the patterns that were causing their algorithm to cluster these particular videos together, we can recognize a cat in that blur of pixels.
The computer learns in a way vaguely analogous to the formation of social cliques in a middle school cafeteria. Each kid is a beautiful and unique snowflake, sure, but there are certain properties that cause them to cluster together: the sporty ones, the bookish ones, the D&D kids. For a neural network, each individual is only distinguished by voting “yes” or “no,” but you can cluster the individuals who tend to vote “yes” at the same time. For a small grid of black and white pixels, some individuals will be assigned to the pixels and vote “yes” only when their pixels are white… but others will watch the votes of those first responders and vote “yes” if they see a long line of “yes” votes in the top quadrants, perhaps… and others could watch those votes, allowing for layers upon layers of complexity in analysis.
And I should mention that I feel indebted to Liu Cixin’s sci-fi novel The Three-Body Problem for thinking to humanize a computer algorithm this way. Liu includes a lovely description of a human motherboard, with triads of trained soldiers hoisting red or green flags forming each logic gate.
In the end, the algorithm developed by Le et al. clustered only 75% of the frames from Youtube cat videos together – it could recognize many of these as being somehow similar, but it was worse at identifying cat-ness than the average human child. But it’s pretty easy to realize why: after all, Le et al. titled their paper “Building high-level features using large scale unsupervised learning.”
When Wittgenstein writes about someone watching builders – one person calls out “Slab!”, the other brings a large flat rock – he is also considering unsupervised learning. And so it is easy for Wittgenstein to imagine that the watcher, even after exclaiming “Now I’ve got it!”, could be stymied by a situation that went beyond the training.
Many human cultures have utilized unsupervised learning as a major component of childrearing – kids are expected to watch their elders and puzzle out on their own how to do everything in life – but this potential inflexibility that Wittgenstein alludes to underlies David Lancy’s advice in The Anthropology of Childhood that children will fair best in our modern world when they have someone guiding their education and development.
Unsupervised learning may be sufficient to prepare children for life in an agrarian village. Unsupervised learning is sufficient for chimpanzees learning how to crack nuts. And unsupervised learning is sufficient to for a computer to develop an idea about what cats are.
But the best human learning employs the scientific method – purposefully seeking out “no.”
I assume most children reflexively follow the scientific method – my daughter started shortly after her first birthday. I was teaching her about animals, and we started with dogs. At first, she pointed primarily to creatures that looked like her Uncle Max. Big, brown, four-legged, slobbery.
Eventually she started pointing to creatures that looked slightly different: white dogs, black dogs, small dogs, quiet dogs. And then the scientific method kicked in.
She’d point to a non-dog, emphatically claiming it to be a dog as well. And then I’d explain why her choice wasn’t a dog. What features cause an object to be excluded from the set of correct answers?
Eventually she caught on.
Seems toddler & I will just have to agree to disagree whether certain animals are Canis lupus (“Daa!”) or Sus scrofa (“Naw, that’s a pig!”).
— Frank Brown Cloud (@FCBrownCloud) April 10, 2015
Many adults, sadly, are worse at this style of thinking than children. As we grow, it becomes more pressing to seem competent. We adults want our guesses to be right – we want to hear yes all the time – which makes it harder to learn.
The New York Times recently presented a clever demonstration of this. They showed a series of numbers that follow a rule, let readers type in new numbers to see if their guesses also followed the rule, and asked for readers to describe what the rule was.
A scientist would approach this type of puzzle by guessing a rule and then plugging in numbers that don’t follow it – nothing is ever really proven in science, but we validate theories by designing experiments that should tell us “no” if our theory is wrong. Only theories that all “falsifiable” fall under the purvey of science. And the best fields of science devote considerable resources to seeking out opportunities to prove ourselves wrong.
But many adults, wanting to seem smart all the time, fear mistakes. When that New York Times puzzle was made public, 80% of readers proposed a rule without ever hearing that a set of numbers didn’t follow it.
Wittgenstein’s watcher can’t really learn what “Slab!” means until perversely hauling over some other type of rock and being told, “no.”
We adults can’t fix the world until we learn from children that it’s okay to look ignorant sometimes. It’s okay to be wrong – just say “sorry” and “I’ll try to do better next time.”
Otherwise we’re stuck digging in our heels and arguing for things we should know to be ridiculous.
It doesn’t hurt so bad. Watch: nope, that one’s not a cat.