In 2002, Small Pieces Loosely Joined argued that our experience of the web oddly mirrors Heidegger’s understanding of the world we experience, including one of his central ideas: Our experience is always shaped by what we care about, what matters to us. Caring comes in all sizes—I’m hanging a painting, I’m taking my child to school, I’m kicking a rock as I walk. In 2002, before the corporate takeover, the web was a purer expression of what we—each of us—cared about, for everything on the web was created by a person who cared enough to build it. The internet is built out of care, but now, far too often, it’s care about money.
2007’s Everything Is Miscellaneous countered the West’s insistence that everything must have an essence—or at least a fixed category—of some sort. That’ s handy if you’re trying to organize physical things on bookshelves, but the internet’ s invention of tagging showed just how limiting that old method was. Internet metadata in general allows for multiple classifications of items, as if what a thing is depends on what we’re looking at it as. More classifications, more connections, more meaning.
2012’s Too Big to Know was a critique of knowledge that grew up in two and a half millennia of relying on written, and then printed, texts. The physicality of books requires giving books a single linear order, which stifles knowledge and meaning, because both of those things thrive as messy and multiple connections among all.
2019’s Everyday Chaos was about how the chaos of the internet, and the coming AI wave, are already sweeping us along to embrace the overflow of connections and meaning.
Now, 7 years later, and after 5 years of writing, Beautiful Particulars is out. It tries to think about how AI might affect our ideas, as tech typically does. It makes a case for thinking about AI primarily as a way in which the differences among things determine what they are, not a smoothed-down view of things according to what they have in common.
For example, AI designed to identify animals in photos isn’t told elephants are gray and have trunks or that zebras are striped. It doesn’t point out their commonalities, such as both having four legs. Instead, machine learning AI examines photos labeled as elephants and zebras to find patterns in the pixels that differentiate them from one another. The lesson the book draws is that the world’s meaning consists of differences more than of
similarities, as other philosophers have argued. In fact, if you want to find what physical things are by looking at what they have in common, you’ll have to reduce them to saying that they all consist of matter—an idea so general that it misses everything interesting and meaningful about the things of the world.
The book asks this question: If differences are more important to our under- standing of things, how might that change how we think about morality, success, decision making, creativity, knowledge, reality, and other such light-hearted topics? But it’s a short book of short chapters, written for people with no special background in philosophy or technology.
It was by far my hardest book to write. But it was also a series of “duh” moments, realizing that I had come back to my beginning, and, in fact, had never entirely left it.