During the past year, I've made several fascinating discoveries while hoarding information on the internet. This has led to a whole slew of ideas that have been brewing for months, and I finally feel I can put them into words.
In the next few paragraphs, I'll share my findings, explain their importance, how they connect with each other, and how their symbiosis results in a sum larger than its parts. I truly think this will revolutionize how we discover, consume, and produce information.
Without further ado, let's go!
Pillar 1: GPT-3 and AI-based tools.
GPT-3 was the talk of the town in Silicon Valley a few months ago. If you haven't heard of it, it's basically "an AI that is better at creating content that has a language structure – human or machine language – than anything that has come before it."
"GPT-3 can create anything that has a language structure – which means it can answer questions, write essays, summarize long texts, translate languages, take memos, and even create computer code."
Here are the functionalities we're most interested in:
1.a The summarization and expansion of information.
Here's GPT-3 in action:
Think about this for a second:
GPT-3 (and its successors) can zoom in and zoom out of the knowledge graph to your preferred level of complexity—it can put things into context, your context.
"Okay, so what?"
Let's take a programming tutorial for instance. When the material is too shallow, the verbosity can be overwhelming and boring ("just get to the point already! Which part do I copy-paste?!!"); but when the author assumes you know too much, it can feel like you're reading Latin. When you have to look up every other term, the connection between the strings of words is severed, thus their meaning is lost (on you).
GPT-n will be able to not only ELI5, but also ELI35, because it'll understand what you know and don't know (through Pillar 2). We'll have a tireless translator, teacher, and navigator of maps!
1.b personalize information delivery
Not only can GPT-n match your vocabulary, it can also craft an entertaining story/format that works best for you, optimized for both understanding and remembering.
We can solve one of the biggest problems of e-learning through interactivity and personalization of virtual material. Imagine using data about your tastes and/or the Big 5 or cognitive functions, to present a trigonometry lesson that is as gripping as a Harry Potter book. What if you were a biology student, and GPT generated mnemonics or memory palace ideas for remembering the different parts of the brain?
A taste of this from Mckay Wrigley:
Automated story-telling + limitless heroes and professors— how's that for the future of e-learning?!
1.c It can blend a million voices to create epistemological harmony.
How do we know that a piece of information is the best? How can multiple people employ their skills in tandem with each other to improve information?
Let's take a look at open source software. There, collaborative improvement occurs through pull-requests, where people can freely use/fork/remix code from a single source of information (the source repository code).
Then, to make improvements, the code is edited at various parts— changing one statement, one function, one character at a time— and then we create pull-requests to merge the changes into the main repo.
But what about improving English texts? The equivalent would be Wikipedia, but as (edit) history has demonstrated, this is not efficient— you usually can't just edit one word at a time, and there's a million ways to say the same thing. What now?
Well, what if we improved the written word by getting a maximum of people to interpret a subject, and then used AI to summarize their answers into a single entry? In other words, we let AI find the meme— an automated version of The Flip Side.
This isn't a new idea by any means. The best current representation of this I could find was Google Play and Google Maps reviews:
Now imagine an AI-boosted long-form version of this, applied on, say, book reviews. Or even better, book notes. Or on lecture notes produced by students, for the next batch of students (a tweet I read also proposed that the teacher could use this to learn which parts of their lesson were memorable (or memeable) and which parts need reworking).
While this principle is applicable to virtually any subject, it comes with a pitfall: the wisdom of the crowds isn't always right, but I touch on addressing this later on.
1.d cut out the wheat from the chaff
With GPT-n, information distillation is vastly augmented.
Within a few years, business books might become obsolete because GPT-n cuts out all the anecdotal fluff in a business book, and summarizes the main topics for the reader beforehand.
GPT-n can enable a better, faster process for picking out our best, timeless works from years of consistent practice— for e.g., your best blog posts in the last 10 years automatically get compressed into a book.
Another application is summarizing long-form content like a Youtube lecture (both through the text transcript and video content analysis), which you could read to decide if it's worth watching the lecture— or maybe just drop the lecture altogether and use the generated notes.
There aren't enough hours in the day to listen to podcasts— what if we could have automated podcast summaries? The list goes on.
2. Roam-like tools for building explicit knowledge graphs;
Roam Research is "a note-taking tool for networked thought". It allows building modular second brains, reusable chunks of information, finding the connections in between them, and so much more. Much has been said about it, but using it is the easiest way to understand its significance— this study guide for A Hero with a Thousand Faces is an easy way to start.
Features like block/page transclusion and block-level version control will be absolutely crucial to distill knowledge. This is the shortest section in this piece, but really, I just can't find the words to convey the importance and uses of Roam.
3. social networks that connect knowledge graphs, and 4. "graphical" and sensible search engines
These two items could easily be baked into the same product. Essentially, they will be used to find and navigate the connections between graphs— both user-graphs and topic-graphs.
Creating graphs of this sort will also allow us to easily accredit ideas and go back to the source—reducing chances of misattributions. (E.g. implementation = Quotebacks)
Networks of Roam graphs are slowly taking form, as we see with Roam Library and Roam Public. @ec_anderson even made a graph of graphs:
Meanwhile, Roam Portal by @DharamKapila is an up and coming search engine, more for stats really, but it's slowly getting "graphical":
Maybe it'll look more like this:
Our search engine must be capable of understanding the difference between convergent and divergent queries, and that can use dual-authority ranking (explained below).
What do I mean by convergent vs divergent queries?
A divergent query would be:
Thailand; I entered a single word— tell me everything about it, Wikipedia style, from every angle; I want to know everything that's related to this topic and more.
A convergent query, on the other hand, would be
cheapest hotels & restaurants in Thailand; I want you to go through various information sources and give me a convergent result – which in this case is just a list of hotels.
The twist: I want to be able to specify whose list I want, because the opinions/presentation style of frequent travel bloggers versus booking.com could be different! More on this (dual-authority ranking) in 6.
5. a social platform that simultaneously builds "taste profiles" and leverages the internet to make information distillation, as a social task, more efficient.
In the past, social information distillation worked well through memes and various mental biases, but the internet broke this mechanism, resulting in inaccurate, clickbait, redundant information that multiplies cancerously. But we can fix this.
i) social "journal"/consumption tracking. You are what you feed your mind, whether it's books, articles, tweets, or videos. Over time, this can be excellent for delving into who you are and what you know, especially when combined with your social circles.
Most of the information we consume/curate today is virtual, and this allows the creation of a consumption-tracking social platform.
Your consumption can then be used for "reputation building", i.e. Strava for "taste", as Julian Lehr puts it.
The user experience here would look like a mix of Haptic, Twitter, and Yourstack:
ii) global, public annotation.
We need to critique, review and connect and build upon everything we learn/absorb, sort of like Hypothesis.
Within the item itself, we could use tools resembling Kindle Popular Highlights and Hypothesis, while on the item listing page, it would look more like Genius and Google Play/Google Map reviews.
Your annotations connect 3 ways: to your knowledge graph, to your profile/taste/clout-meter, and the page for the item you're annotating, be it a movie/article/book/whatever. Your critiques/annotations will be fed to GPT-3-like tools to build profile sets others can "relate to", i.e. the relevant information that will be supplied through curation bubbles.
This leads us to:
6. Tools that give us control over filter bubbles—that allow us see the consumption bubbles of others, to expose curation, especially in the context of searches.
An stellar example would be their.tube by Tomo Kihara:
This will be important for dual-authority judgement of information, by which I mean:
judging what is said,—the information as it is—by creating an average/summary that dissolves the individual nuances, but also judging information by who conveys it, either individually or by circle (psychologists, virologists, lawyers, libertarians...).
A system of this sort would've been especially useful throughout this year, where we saw the same people become experts in climate change, virology, and—more recently— in political forecasts.
This creates a free hiearchy, which I think is critical.
We already have this sort of thing in place, except in different mediums, and bubble selection is done implicitly.
For instance, look at the huge amount of Twitter and Reddit threads where we ask questions like "what are the best books you've read?". For "the wisdom of the crowds, we might select a niche (through the OP's following or the subreddit we post in) or ask in a completely anonymous fashion (e.g. r/all, or "basic" people with millions of followers);
Then there's newsletters, blogs, read-next.com (by Dhvanil), anti-libraries, and library.json (by Tom Critchlow)... In essence, these are all personal taste-aggregations. We don't necessarily consume them because they're the best X, but because the best X people say they're the best.
By putting all of these together, a network can be created where people can collectively curate the best content, have skin in the game, learn at lightning speeds, and eliminate huge portions of information redundancy in the world.
This is the future. Forget flying cars— let's build maps of meaning. Let the Great Information Distillation begin.
P.S. As you know, I'm just another person on the internet. I barely understand most of these concepts, especially AI, but I wanted to share my findings and excitement. If you happen to know more and can help improve this piece, I'd really appreciate a comment or DM from you.
A few more things related to these ideas: