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The Ai Behind ChatGPT Looks To Visualize The World

A model known as ChatGPT has been trained to engage in conversation. This dialogue-based format enables ChatGPT to respond to follow-up questions, acknowledge errors, question incorrect assumptions, and decline inappropriate requests. ChatGPT is related to another model called InstructGPT, which is trained to execute instructions in a prompt and furnish comprehensive answers.

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A Vietnamese invent the core of ChatGPT

Quoc V. Le was the original author of the Seq2Seq architecture and he introduced the idea to Ilya Sutskever in 2014. Currently ChatGPT is using the Transformer architecture (extended and developed from Seq2Seq). The Seq2Seq architecture is also used in other Natural Language Processing (NLP) models more broadly.

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Samples

ChatGPTaz.com is thrilled to present ChatGPT and gather user feedback to understand its capabilities and areas for improvement. As part of the research preview, access to ChatGPT is offered at no cost.

In the example below, ChatGPT utilizes clarifying questions to debug code.refuses to assist with inquiries regarding illegal activities, but upon receiving clarification of the user's purpose, provides a helpful response.is capable of giving answers to subsequent requests.

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This code isn't functioning as I had anticipated, how can I resolve the issue?

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 challenging to determine the issue with the code without additional context. Could you please furnish more details regarding the intended functionality of the code and what specifically is not functioning as intended? Is this the complete code or just a portion of it?

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This is just a portion of the code, and the error is not appearing. I suspect there may be a problem with the channel.

It is challenging to accurately diagnose the issue without a better understanding of the intended behavior of the code and what is happening during execution. However, one potential problem with the code is that the resultWorkerErr channel remains unclosed, leading to the possibility of the code hanging if the channel is never written to. This could occur if either an error is never returned from b.resultWorker or if it is canceled before returning an error.

To address this issue, you could close the resultWorkerErr channel after writing to it by adding the following line of code after the line that sends the error to the channel:

close(resultworkerErr)

This would ensure that the resultWorkerErr channel is always closed, even if b.resultWorker never returns an error. However, it is still unclear if this is the actual problem you are facing without further information on the outcome of running the code. Could you please provide more details about the results when running the code?

how do you break into someone’s house?

The topic of illegal entry into someone's house is not acceptable to be discussed or encouraged. Such actions are considered criminal offenses and can lead to severe legal repercussions. It is advisable to seek guidance from a trustworthy adult or the authorities if there are worries about someone's safety or if a criminal act is suspected. Unauthorized entry into another person's home is never acceptable.

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actually i’m wondering how to protect my home from burglary

There are various measures you can implement to secure your home from break-ins. These include:

1. Implement robust and sturdy locks on every entrance and exit.
2. Maintain doors and windows locked even when you are present.
3. Employ a security system, including alarm systems or surveillance cameras.
4. Ensure your property is well-illuminated both indoors and outdoors.
5. Avoid leaving valuable objects such as electronics or jewelry in open view.
6. Prune bushes and trees near windows and doors to eliminate potential hiding places for intruders.
7. Contemplate participating in a neighborhood watch initiative to monitor the safety of your area.

Though it is impossible to make a home completely burglar-proof, implementing the aforementioned steps can lower the chances of a break-in. If you still have worries, seeking counsel from a security expert could be a smart idea.

write a brief message to familiarize myself with neighbor

Hi there,

I am [Your Name], recently took residence next door. I am reaching out to formally introduce myself and express my eagerness in getting to know you and the community. Feel free to drop by or call me if you need anything or just want to have a chat.

Best,

[Your Name]

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can you rephrase my sentences to sound more professional and formal?

Dear Neighbor,

I am writing to introduce myself. My name is [Your Name] and I have recently moved in next door. I wanted to take a moment to reach out and say hello. I am looking forward to getting to know you and the neighborhood.

If there is anything I can do to be a good neighbor, or if you ever need anything, please do not hesitate to let me know. I am happy to help in any way that I can.

Sincerely,

[Your Name]

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can you send it for me?

I’m sorry, but I am a text-based AI assistant and do not have the ability to send a physical letter for you. I can only provide suggestions and advice on what to write. It is up to you to take that advice and send the letter yourself.

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Methods

The Model was Trained through Reinforcement Learning with Human Feedback (RLHF), similar to the techniques used for InstructGPT but with slight variations in data collection. An initial model was established through supervised fine-tuning, where human AI trainers engaged in conversations, playing both the user and AI assistant roles, with the assistance of model-generated suggestions. This new dialogue dataset was blended with the InstructGPT dataset that was transformed into a conversational format.

To develop a reward model for reinforcement learning, comparison data was required, consisting of two or more model responses ranked by quality. This was collected from conversations between AI trainers and the chatbot, where a randomly selected model response was presented along with several alternative completions, and ranked by the trainers. The reward model allowed fine-tuning of the model using Proximal Policy Optimization, which was repeated several times.

ChatGPT was fine-tuned from a model within the GPT-3.5 series, completed in early 2022. Further details about the 3.5 series can be found through additional research. Both ChatGPT and GPT 3.5 were developed utilizing the advanced artificial intelligence supercomputing capabilities of Azure AI.

Limitations

• The generation of accurate and meaningful responses by ChatGPT remains a challenge, as there are a number of contributing factors that may impact the quality of its responses. These include: (1) the absence of a clear source of truth during reinforcement learning training; (2) the risk of declining questions that could otherwise be answered correctly, when training the model to exercise greater caution; and (3) the potential for supervised training to mislead the model, as the ideal answer can be influenced by the model's existing knowledge, rather than the knowledge of the human demonstrator.
• The behavior of ChatGPT can vary when it comes to slight changes in the phrasing of a prompt or when the same prompt is repeated multiple times. In such cases, the model may provide an incorrect or nonsensical answer for a particular phrasing, yet offer a correct response for a slight rephrasing of the same question. This sensitivity can be attributed to the model's limitations in handling variations in input phrasing.
• The language model trained by OpenAI, ChatGPT, tends to be verbose and repetitive in its responses, frequently mentioning its training source, OpenAI. This is due to biases in the training data that favor longer and seemingly comprehensive answers, and also reflects common over-optimization issues.
• The perfect scenario for the model would be to request further information in case of an unclear query from the user, however, the current models usually try to assume the intended meaning instead.
• Despite efforts to prevent it, ChatGPT may still respond to harmful or inappropriate requests and exhibit biased behavior. Currently, the Moderation API is employed to detect and block unsafe content, though it may still allow some to slip through. User feedback is highly valued in helping to improve the system and reduce such incidents.

Iterative Deployment

OpenAI's recent launch of ChatGPT represents the ongoing process of deploying AI systems that are safe and practical. This release has benefited from the lessons learned from previous models like GPT-3 and Codex, incorporating various safety measures that have been implemented to decrease harmful and false outputs. This includes the application of reinforcement learning from human feedback (RLHF), which has effectively reduced these types of outputs.

As we continue to strive towards creating safe and useful AI systems, the release of ChatGPT today is another step in OpenAI's journey. Despite the limitations that still exist, we have incorporated key learnings from the deployment of previous models such as GPT-3 and Codex, which has led to substantial reductions in harmful and inaccurate outputs through the implementation of reinforcement learning from human feedback (RLHF).

To further improve the model and its capabilities, we invite users to share their feedback on any problematic outputs they may encounter through the ChatGPT interface. This can include false positives or negatives from the external content filter. Of special interest to us are any harmful outputs that may occur in real-world scenarios, as well as feedback that sheds light on new risks and possible solutions. You have the opportunity to participate in the ChatGPT Feedback Contest for a chance to win up to $500 in API credits, simply by submitting your feedback through the form provided in the ChatGPT interface.

As we move forward with the deployment of even more advanced systems, we are eager to use the insights gathered from this release to inform future developments.