IA WRITER TEMPLAYTES MOD

ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers.We performed several iterations of this process. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them.

To collect this data, we took conversations that AI trainers had with the chatbot. 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. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format. We gave the trainers access to model-written suggestions to help them compose their responses. 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 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. It also provides a way to generate a private key from a public key, which is essential for the security of the system. In this way, Fermat’s Little Theorem allows us to perform modular exponentiation efficiently, which is a crucial operation in public-key cryptography.

IA WRITER TEMPLAYTES MOD

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

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