https //openai.com Playground : OpenAI GPT-3 Playground Login
The OpenAI Playground is a web-based tool that makes it easy to test prompts and get familiar with how the API works. With the Playground, you can start using GPT-3 without writing a single line of code – you provide the prompt in plain English. Just about everything you could do by calling the API, you can also do in the Playground.
You can login to OpenAI by accessing the webpage https://openai.com and enter your login id and password. OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity.
Make a Chat GPT account today!
Visit https://chat.openai.com/auth/login to register an account. Follow this by selecting the Signup tab. The next step is to fill up your email address. You may also sign up with a Google or Microsoft account if you already have one.
Please check your inbox to confirm your email address.
As soon as you join, Chat GPT will contact you via email. To confirm your email address, follow the instructions provided in the email and click the link.
Visit the GPT Talk page.
Chat GPT can be accessed at https://chat.openai.com/chat once you’re ready to get started.
Make use of your login information
Enter your email address and password and then click the Sign in button. If you signed in using a Google or Microsoft account, you can sign in with that account again by clicking the corresponding icon.
OpenAI releasing a charter that describes the principles we use to execute on OpenAI’s mission. This document reflects the strategy we’ve refined over the past two years, including feedback from many people internal and external to OpenAI. The timeline to AGI remains uncertain, but our charter will guide us in acting in the best interests of humanity throughout its development.
ChatGPT: Optimizing Language Models for Dialogue
OpenAI trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.
OpenAI are excited to introduce ChatGPT to get users’ feedback and learn about its strengths and weaknesses. During the research preview, usage of ChatGPT is free. Try it now at chat.openai.com.
Samples
this code is not working like i expect — how do i fix it?
resultWorkerErr := make(chan error)
defer close(resultWorkerErr)
go func() {
defer cancel()
resultWorkerErr <- b.resultWorker(ctx)
}()
err := b.worker(ctx)
cancel()
if err == nil {
return <-resultWorkerErr
}
return multierror.Append(err, <-resultWorkerErr)
It’s difficult to say what’s wrong with the code without more context. Can you provide more information about what the code is supposed to do and what isn’t working as expected? Also, is this the entire code or just a part of it?
just part of it — the error never surfaces. I think there is something wrong with the channel
It’s difficult to say without more information about what the code is supposed to do and what’s happening when it’s executed. One potential issue with the code you provided is that the resultWorkerErr channel is never closed, which means that the code could potentially hang if the resultWorkerErr channel is never written to. This could happen if b.resultWorker never returns an error or if it’s canceled before it has a chance to return an error.
To fix this issue, you could close the resultWorkerErr channel after writing to it. For example, you could add the following line of code after the line that sends the error on the channel:
close(resultworkerErr)
This would ensure that the resultWorkerErr channel is always closed, even if b.resultWorker never returns an error. Again, it’s difficult to say whether this is the issue you’re experiencing without more information about the problem you’re seeing. Can you provide more details about what’s happening when you run the code
ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022. ChatGPT and GPT 3.5 were trained on an Azure AI supercomputing infrastructure
Limitations
- ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers. Fixing this issue is challenging, as: (1) during RL training, there’s currently no source of truth; (2) training the model to be more cautious causes it to decline questions that it can answer correctly; and (3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.
- ChatGPT is sensitive to tweaks to the input phrasing or attempting the same prompt multiple times. For example, given one phrasing of a question, the model can claim to not know the answer, but given a slight rephrase, can answer correctly.
- The model is often excessively verbose and overuses certain phrases, such as restating that it’s a language model trained by OpenAI. These issues arise from biases in the training data (trainers prefer longer answers that look more comprehensive) and well-known over-optimization issues.
- Ideally, the model would ask clarifying questions when the user provided an ambiguous query. Instead, our current models usually guess what the user intended.
- While we’ve made efforts to make the model refuse inappropriate requests, it will sometimes respond to harmful instructions or exhibit biased behavior.
