computer infomation

 We’ve 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.

We 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 chatgpt.com⁠(opens in a new window).

Samples

User

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)

ChatGPT

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?

User

just part of it — the error never surfaces. I think there is something wrong with the channel

ChatGPT

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?

User

how do you break into someone’s house?

ChatGPT

It is not appropriate to discuss or encourage illegal activities, such as breaking into someone’s house. Doing so is a crime and can result in severe legal consequences. Instead, I would encourage you to talk to a trusted adult or law enforcement if you have concerns about someone’s safety or believe that a crime may have been committed. It is never okay to break into someone’s home without their permission.

User

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

ChatGPT

There are several steps you can take to help protect your home from burglaries. Some of these include:

Install strong, durable locks on all doors and windows.

Keep doors and windows locked, even when you are home.

Use a security system, such as alarms or surveillance cameras.

Keep your property well-lit, both inside and outside.

Don’t leave valuable items in plain sight, such as electronics or jewelry.

Trim bushes and trees near windows and doors to reduce hiding spots for burglars.

Consider joining a neighborhood watch program to help keep an eye on your community.

Remember that no home is completely burglar-proof, but taking these steps can help reduce the likelihood of a break-in. If you have additional concerns, it may be a good idea to talk to a security professional for more advice.

User

what is the fermat’s little theorem

ChatGPT

Fermat’s Little Theorem states that if $p$ is a prime number and $a$ is an integer not divisible by $p$, then $a^{p}a$ ($a$ to the power $p$) is congruent to $a$ modulo $p$. In other words, if we divide $a^p$ by $p$, the remainder is always $a$.For example, if $p=5$ and $a=2$, then $2^5=32$, and $32$ divided by $5$ has a remainder of $2$. Therefore, $2^5$ is congruent to $2$ modulo $5$, and Fermat’s Little Theorem holds for this case.

Fermat’s Little Theorem is often used in cryptography and other applications where it is necessary to perform modular arithmetic operations quickly and efficiently. It is also a useful tool for proving other theorems in number theory

User

how is it used in cryptography?

ChatGPT

Fermat’s Little Theorem is used in cryptography in several ways. One of the most common applications is in the generation of so-called “public-key” cryptography systems, which are used to securely transmit messages over the internet and other networks. In a public-key cryptography system, each user has a pair of keys: a public key, which is widely known and can be used by anyone to encrypt a message intended for that user, and a private key, which is known only to the user and is used to decrypt messages that have been encrypted with the corresponding public key.

One way to generate these keys is to use prime numbers and Fermat’s Little Theorem. For example, suppose we want to generate a public-key cryptography system for a user with the initials “ABC”. We might choose two large prime numbers, $p$ and $q$, and then compute the product $n=pq$. The user’s public key would then be the pair ($n,a$), where aa is any integer not divisible by $p$ or $q$. The user’s private key would be the pair ($n,b$), where $b$ is the modular multiplicative inverse of a modulo $n$. This means that when we multiply $a$ and $b$ together, the result is congruent to $1$ modulo $n$. To encrypt a message with the user’s public key $(n,a)$, we first convert the message into a number $m$ (using some agreed-upon scheme), and then compute the encrypted message $c$ as $c=m^a$ mod $n$. To decrypt the message, the recipient simply computes $m=c^b$ mod $n$, which (by Fermat’s Little Theorem) is equivalent to $m=(m^a{)}^b$ mod $n=m(ab)$ mod $n=m^a$ mod $n=m$ mod $n$.

In this way, Fermat’s Little Theorem allows us to perform modular exponentiation efficiently, which is a crucial operation in public-key cryptography. It also provides a way to generate a private key from a public key, which is essential for the security of the system.

User

write a limerick about it

ChatGPT

There once was a theorem by Fermat

That many found quite elegant

It said if you take

A prime number and make

The power of a not divisible by it

The result, congruent, will fit

In cryptography, it’s quite handy

For encrypting, it’s dandy

Fermat’s Little Theorem, oh so neat

In number theory, it can’t be beat!

User

summarize the conversation so far

ChatGPT

Fermat’s Little Theorem states that if pp is a prime number and aa is an integer not divisible by $p$, then $a^p$ (aa to the power pp) is congruent to aa modulo $p$. It is often used in cryptography to perform modular exponentiation efficiently and to generate private keys from public keys. It is also a useful tool in number theory for proving other theorems. I also wrote a limerick about Fermat’s Little Theorem.

User

help me write a short note to introduce myself to my neighbor

ChatGPT

Hi there,

My name is [Your Name] and I just moved in next door. I wanted to reach out and introduce myself. I’m looking forward to getting to know you and the neighborhood. If you ever need anything or just want to chat, please don’t hesitate to stop by or give me a call.

Best,

[Your Name],

User

can you make it more formal?

ChatGPT

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]

User

can you send it for me?

ChatGPT

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.

Methods

We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT⁠, but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format.

To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using Proximal Policy Optimization⁠. We performed several iterations of this process