I've not kept track of where large transformers like this have gotten to, GPT3 and the like - has GPT3 made any real difference to the world? Are people using it? Has it vastly improved any software?
It's a safe bet that Google is using transformers at scale for search and translations - the full extent isn't public but they release a fair amount of research papers, e.g. the current article, or https://ai.googleblog.com/2020/06/recent-advances-in-google-...
Transformers are state of the art for many tasks so they are likely to be used for "intelligent" processing of text or speech data, but due to practical limitations you are probably interacting with them mostly through web services.
I don't know about world changing but it's saved me hundreds of hours. I use it to help read academic papers, put formatting on things like markdown and subtitles, and creative writing. A lot of the things that take it 15 seconds to do take me 2 minute and drain me mentally for about 15 mins.
If anything, it's being used in force for social media marketing, where you're trying to say "buy this thing" in different ways every day.
I recently used GPT-J to create some handouts (fake 1930's newspaper articles) for a roleplaying game. I wrote a headline and byline, then have the model suggest some text. I change the text to include the details I want the players to have + enforce consistency, then reuse the text-so-far as a prompt, and repeat.
I definitely cranked out the newspaper text much faster than I would have on my own, and the model actually made some really nice embellishments and added a couple ideas that I kept in the final text.
It's a funny question, like asking how to write a book with a computer, but perfectly valid.
You can access GPT-3 directly now. There's no waitlist, but there still are restrictions. There's some examples here: https://beta.openai.com/examples
You don't even need the API. Once you get access, it comes with access to the playground, which is enough to do anything you like.
If you look at the examples, it's very "no code". You literally tell the AI what you're trying to do and it tries its best. Most of the work in prompt engineering is writing something that can't be misunderstood. But you just have to explain to it what you want like you would to a child.
Seconding someone else's comment : what is your workflow for those tasks ? How does it help you to read academic papers ? Or to put formatting on markdown ?
For formatting, I copy from our spreadsheet instruction, and copy what the "markdown" format looks like.
Convert the following text to markdown format.
Text:
1. Open the app
2. Enter your USERNAME and PASSWORD
3. Go to the menu and click "Foobar"
4. Enter the following into the field [CORRECT INPUT HERE]
```
Markdown:
# How to Use Foobar
## Subtitle
- Open the app
- Enter your USERNAME and PASSWORD
- Select the menu and click <b>Foobar</b>
- Enter the following into the field <b><span style="color:green;">{correctInput}</span></b>
```
Text:
(your input here)
Yes, I know it's not really markdown. But you give it an example of input and output and it will figure it out as easily as a human can.
The problem we faced was that it was meant to be markdown but it's not easy to write the parser, so we had a different format of "markdown" on different front ends. You'd have something a little different on Android, iOS, HTML.
But the beautiful thing is we can keep the same input and change the output to whatever the parser needs. And instead of having to write up regex that detects [CAPITAL LETTER INPUT] and converts that, the AI can just recognize it.
Github Copilot is definitely GPT-3-based and is seeing real-world use https://copilot.github.com
Transformers are state of the art for many tasks so they are likely to be used for "intelligent" processing of text or speech data, but due to practical limitations you are probably interacting with them mostly through web services.