GPT-3 Will Accelerate The Privatization of Internet Communities

Two Thursdays ago, I was sitting alone in my SoMa loft, screaming at my computer.

“Noooo wayyyyy!”

I was reading something that can only be described as magic. It was a post about how to run an effective board meeting. An excerpt describing the second step of a 3-step process:

I’m not looking to build a board of directors right now, but it provided a useful framework in case I ever did. But what made me yell out loud at my computer wasn’t the insightful process of creating a target list to recruit board members. I’m a nerd, but I’m not that much of a nerd.

Rather, I was screaming at my computer because this document was generated entirely by a machine learning language model called GPT-3.

Yes, that’s right. A computer created a 3-step process on how to recruit board members, all on its own. Are you yelling at your computer yet?

While GPT-3 is not self-aware — the true test of general artificial intelligence — it fools us more than anything else ever created. It can deceive us so well that it will forever change the nature of public digital spaces.

In this article, I’ll be attempting to inform and persuade you that GPT-3 is the beginning of the end of the digital public commons.

Care to join along?

What is GPT-3?

GPT-3 is a predictive language model developed by OpenAI, a research lab founded by Sam Altman, Elon Musk and others. OpenAI has the state goal of “promoting and developing friendly AI in a way that benefits humanity as a whole.” A few weeks ago, they opened up GPT-3 to the world for the first time through an invite-only API and website.

So how does it work? GPT-3 (well, really any machining learning model) tries to predict what’s going to happen next based on what’s already happened. To train GPT-3, the model would try to predict the next word in a given sentence. Then the model would compare that sentence against “correct” sentences pulled from the massive amount of text data found on the internet. If the model got the word wrong, it would update it’s guess and try again until it got the word right. Then it did this over and over again until it became really likely that it got the next word right. There’s more complicated math under the hood to figure out the probabilities of getting the correct next words, but from my layman’s understanding that’s how GPT-3 was trained. (For additional technical explanations check out these articles: 1, 2, 3.)

Now, the unique part about GPT-3 isn’t necessarily the method for generating the model, but rather the volume of data it was trained on. GPT-3 used 175 billion parameters, which is ten times greater than the previous best of 17 billion parameters.

It’s incredibly powerful, but probably the coolest part about GPT-3 is its meta-learning skills: not only can it complete the second half of a sentence or paragraph, but it can actually follow instructions written in plain English. It wasn’t hyperbole when I said I was screaming in awe at my computer: you can give it directions just like you would tell your dog to sit, and GPT-3 will do what you say.