Noise-Canceling Filters for the Internet are Coming

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I’m writing this at a busy cafe, but I can’t hear anything except my music, thanks to the algorithms that power my noise-canceling headphones.

When I turn my attention towards Twitter, it’s not so easy to tune out the noise. For each interesting tweet I see, my feed shows me roughly three vapid threads, two reposted TikToks, and maybe one semi-funny meme. Ok, sometimes they’re pretty good:

derek guy@dieworkwear

me wearing my expensive online purchases only to realize the horrifying void is still in me

May 6th 2023, 4:21pm EST

15k Retweets102k Likes

But still, when I see too much content like this, I start to feel bad in a way that is hard to explain. I call it “viral malaise”—that feeling when you’ve been exposed to too much viral content and your brain feels numb and slightly sad. Perhaps you can relate.

It makes me wonder: thanks to LLMs like GPT-4, is it now possible to build tools that can effectively de-noise our information diets, the same way AirPods can silence the noise in our physical surroundings? How would they work? How would they make us feel? What first- and second-order consequences would widespread use of these tools have on culture, media, and politics?

I’m convinced AI noise filters are coming, even if social media companies are resistant at first. They will be effective, and they could have as large an impact on society as social media itself. Here’s why.

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The spark that got me excited about this idea was, ironically, a tweet:

Courtland Allen@csallen

I set my moderation bot loose!

It's banning people on its own. Which means near-instant bans for spammers, so their content is never seen at all.

It still notifies me, and I click the links to double check so I can reverse a bad decision…

…but it's right 99% of the time 🔥

May 7th 2023, 7:45pm EST

161 Likes

Courtland built this bot using GPT-4 to help him moderate the discussions on a community for entrepreneurs he runs called Indie Hackers. Apparently there was a huge increase in quality when he switched from smaller models to more powerful ones:

Courtland Allen@csallen

Replying to @edgarasben

@edgarasben Yeah I tried using babbage or something cheaper than davinci and got mediocre results.

Then I tried GPT-4 with a few example prompts and it was instantly amazing. Way simpler to use, too.

But yeah, expensive. Might go back to give the fine-tuning models another try.

May 8th 2023, 2:05am EST

This might not seem like much, but to me it’s a big deal. Today most algorithms that recommend or suppress content act purely on the basis of inferred popularity. They look at how much time people spend engaging with a piece of content, and boost it to more people if the numbers look good. The content itself is almost purely a black box. Some algorithms try to classify content with tags like “food” or “funny meme” so that it can boost more of the same type of content you’ve engaged with in the past, but it has nowhere near the level of semantic understanding as an LLM does. If you want to change the algorithm, you have to train it all over again, which is time-consuming and expensive.

With LLM-based content filtering and sorting, the underlying model is completely agnostic about what content to promote or suppress. It is a general-purpose reasoning engine. If you want to change its behavior, you don’t need to train a new model. All you need to do is update the prompt.

The consequences of this are subtle, but incredibly important. For the first time, each user could have their own recommendation algorithm, because the “algorithm” is really just a prompt to an LLM. Even better, these prompts are written in natural language, so users can understand the prompt and update it however they want, without the need for any special training.