[alechoey]

Hi geoduck14, I'm sorry that this article isn't resonating with you.

While I'm not 100% sure what you mean by over sensationalized, you're accurate in saying it's strange to write a full length technical blog post on sending some words to an API. I actually write about that feeling in the article.

This article is meant to help those interested in getting started with GPT-3, especially those without a background using language models. Starting out, I had a hard time understanding what to do, so I wanted to document some of my process. Do you have working experience with GPT-3? Anything interesting you can share?

[geoduck14]

Oh, hey. The author! Allow me elaborate on my original comment.

First and foremost, my initial comment was made after spending 30 seconds reading the article. It was a quick judgement and (unfairly) condescending. My initial comment was based in the assumption you were a fly-by-night blogger trying to cram buzz words unto your post to get clicks.

I was wrong. And I apologize.

I have now read more of the post. It IS a document on implementing GPT-3, and it IS a good foundation for people who are not familiar with it. I applaud you for making ML stuff more accessible to HNers. I recently took a crash course in deploying ML models (like GPT-3), and one question I kept in my head was "How can we make this accessible to EVERYONE".

Please keep documenting and democratizing cool models.

[alechoey]

Awesome. I'm glad you're able to see how this post could be useful to others.

It's great to hear that you thought this makes ML concepts a little bit more accessible. Hopefully others can see GPT-3 as more than a black box and that with a little bit of intuition, they can get the results they're looking for.

[ryohkyo]

Perhaps he meant overhyped; I am just guessing. The creator of GPT-3 also said that the model is more hyped than it deserves because it's simply looking at the existing data to forecast the next words.

Hype or not, there are some questions that deserve some serious consideration. It is hard to quantify how "personalized" the content GPT-3 spits out actually is. It may output the same content on every N-th try on average. If so, we have copyright issues at hand.

Two people using GPT-3 who happened to feed it similar parameters and got similar results can sue each other for copywrite infringement. On the other hand, an author who genuinely created an article can sue someone using GPT-3 created content without that person knowing he has infringed on the author's copyright, and vice versa.

It's all wonderful on paper. In practice, a whole host of issues can pop up when using this indiscriminately.

[robbedpeter]

It's looking at the existing data in the context of everything it's been trained on. It's forecasting in a very sophisticated way. We're a couple years away from seeing fully realized applications with these models that exploit the most clever and nuanced uses. Moore's law is trundling along in the meantime, and we're 6-8 years from seeing these massive transformer models show up in a flagship phone.

It might be overhyped, but it's also a massively broad-use tool that is going to provide value for many years to come. We've hardly scratched the surface of its utility.