Post by yamanhosen8564 on Feb 14, 2024 18:28:25 GMT 10
Many-layered, weighted algorithm modeled after the human brain, called a deep learning neural network. It's what allows GPT-3 to understand patterns and relationships in the text data and tap into the ability to create human-like responses. GPT-3's neural network has 175 billion parameters (or variables) that allow it to take an input—your prompt—and then, based on the values and weightings it gives to the different parameters (and a small amount of randomness), outputs whatever it thinks best matches your request. Pairing AI with automation will change how you work Learn more GPT's network uses the transformer architecture—it's the "T" in GPT. At the core of transformers is a process called "self-attention." Older recurrent neural networks (RNNs) read text from left-to-right. Transformer-based networks, on the other hand, read every token in a sentence at the same time and compare each token to all the others. This allows them to direct their "attention" to the most relevant tokens, no matter where they are in the text.
Of course, this is all vastly simplifying things. GPT can't really understand anything. Instead, every token is encoded as a vector (a number with position and direction). The closer together that two token-vectors are, the more closely related GPT thinks they are. This is why it's able to process Faroe Islands Email List the difference between brown bears, the right to bear arms, and ball bearings. While all use the string of letters "bear," it's encoded in such a way that the neural network can tell from context what meaning is most likely to be relevant. It's a lot, I know, and that's just a basic overview.
For more details on how AI, LLMs, and GPT work, check out these articles: What is AI? AI vs. machine learning What is natural language processing? How does ChatGPT work? Is GPT safe? Given that GPT is trained on the open internet, including a lot of toxic, harmful, and just downright incorrect content, OpenAI has put a lot of work into making it as safe as possible for people to use. OpenAI calls the process "alignment." The idea is that AI systems should align with human values and follow human intent, not do their own thing and go rogue. A big part of this is a process called reinforcement learning with human feedback (RLHF). The basics of it are that AI trainers at OpenAI created demonstration data showing GPT how to respond to typical prompts.
Of course, this is all vastly simplifying things. GPT can't really understand anything. Instead, every token is encoded as a vector (a number with position and direction). The closer together that two token-vectors are, the more closely related GPT thinks they are. This is why it's able to process Faroe Islands Email List the difference between brown bears, the right to bear arms, and ball bearings. While all use the string of letters "bear," it's encoded in such a way that the neural network can tell from context what meaning is most likely to be relevant. It's a lot, I know, and that's just a basic overview.
For more details on how AI, LLMs, and GPT work, check out these articles: What is AI? AI vs. machine learning What is natural language processing? How does ChatGPT work? Is GPT safe? Given that GPT is trained on the open internet, including a lot of toxic, harmful, and just downright incorrect content, OpenAI has put a lot of work into making it as safe as possible for people to use. OpenAI calls the process "alignment." The idea is that AI systems should align with human values and follow human intent, not do their own thing and go rogue. A big part of this is a process called reinforcement learning with human feedback (RLHF). The basics of it are that AI trainers at OpenAI created demonstration data showing GPT how to respond to typical prompts.