A Future Query
Welcome to Ohm, the arbitrarily named search engine of the soon-enough future.
Instead of diving into the puzzle pieces required for next-level search—API standards, interoperability, fine-grained differential privacy—we start at the end: a query.
I have been hosting book clubs on and off for years. The process has lots of friction and only a small fraction of people who could meet around common interests actually do. There is potential intellectual energy in the system—how can we release more of it?
We could reframe “hosting a book club” as a search problem:
Me: Hey Ohm, who is currently in Cambridge, UK and has read both “The Beginning of Infinity” and “Antifragile”?
Ohm: List of 85 Persons: [Pablo S., Maria R., Markus E., Xing Z., A. P., T. G., details hidden, …]
Me: Who of the above is open for “learning groups” or “book discussions” or “coffee meetings”?
Ohm: List of 40 Persons: […]
Me: Filter out people with an RSVP/show-up ratio under 0.8 (flakes)
Ohm: This attribute about Persons is aggregated between event hosts and anonymized. 25 Persons have non-null entries. You’ll be charged 15 credits; data is only usable for this query context. Proceeds go to a clean water charity.b b This is tricky—the data is about you, but you don’t control it. Yet it’s information an event creator has a right to share. One solution: entries about a Person expire after N days.
Me: Yes, alright.
Ohm: List of 28 Persons: […]
Me: Pick the set of 15, including myself, that maximizes variance among reading histories.
Ohm: List of 15 Persons: […]
Me: Great, send them invites for the best common date in the next three weeks. Location: Waterstone’s Bookstore. Title: ”The Beginning of Infinity meets Antifragile”
Ohm: Invites cost 1 credit per person, restored when accepted. Invites flagged as inappropriate result in a penalty.
Me: Yes, I agree.
Queries like this are not far out. The raw data to answer them is online in different places. We already intend this exact information exchange, just not effectively.
If we have clear laws about data ownership and ways to stream ours from app APIs, we can decide to expose slices of it. Call it YOU-DB—a service that exposes your merged data streams with highly granular, sane permission sets.
It only becomes spooky if access to personal data is not controlled by the individual.
Where does the data come from?
Location: Cambridge, UK—updated from your phone once a week.
Read book X: Gathered from Goodreads, Amazon/Kindle/Audible orders, uploaded photo of bookshelf, or entered manually.c c If you replaced ”read” with ”interested in,” it could query your wishlist, the fact you follow the author, or an article you clipped or annotated.
Name visibility: The sanest default is names hidden or first-name only, even for peer-to-peer queries.
Openness settings: You choose how open you are to messages.
Best date: Find the most common open spot for people who expose parts of their calendar.
Invites: Similar to LinkedIn’s InMail—if it’s spam, the sender pays.
Going deeper
We could make our book club query more meaningful. So far we searched in rough semantic categories like “books.” But books just point to interest. A book might have hundreds of features of varying interest to us.
We can build a model trained on many books and query its feature embeddings.d d The feature activations of a trained neural network don’t map cleanly to notions like “contemporary” or “dystopian,” but for every trained model we could batch and manually label some dimension X as roughly corresponding to human notion Y. Now we can go hyper-granular in finding awesome meetup guests—e.g., who has spent hours reading ”contemporary tech-dystopian sci-fi“?
For anybody who feels this is far-fetched: when we put information about ourselves on the web, we often want to be discovered—by the right person or community.
Right means above some threshold for mutual context.e e It’s possible to single out individuals from aggregate queries, but there are hacks around it—injecting minimal noise in the result, for example. A 3am hand-waving definition of context is the distance between you and that person in some feature space.
The way we get to the next level in search is through personal privacy. With that, our knowledge, hobbies, interests, worries, feelings can be queried if we wish—and connected to others vastly better.