Как работает ChatGPT: объясняем нейросети просто
RationalAnswer | Павел Комаровский・2 minutes read
Neural networks like ChatGPT predict the next word in text, operating on probabilities to improve accuracy and generate text word by word. Models like GPT-3, with billions of parameters, have revolutionized AI by learning various skills and displaying problem-solving abilities.
Insights
- Language models like ChatGPT and T9 operate on probabilities to predict the next word accurately, using equations to determine word dependencies akin to predicting weight based on height.
- The development of GPT models, from GPT-1 to GPT-3.5, showcases a significant increase in size, parameters, and capabilities, with GPT-3.5 prioritizing user satisfaction and GPT chat gaining immense popularity, emphasizing the importance of user-friendly interfaces in technology adoption and engagement.
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Recent questions
What are neural networks like ChatGPT?
Neural networks like ChatGPT are advanced language models that predict the next word in a sequence of text. They operate on probabilities to generate text word by word, improving text generation accuracy.
How do models like T9 and ChatGPT predict the next word?
Models like T9 and ChatGPT are trained to predict the next word based on existing text by using equations to determine word dependencies, similar to predicting weight based on height.
What is the significance of Large Language Models (LLM)?
Large Language Models (LLM) have many parameters, enhancing text generation capabilities by processing vast amounts of data and improving predictive accuracy.
What revolutionized AI in text generation?
Transformers, like GPT, revolutionized AI by processing data more efficiently and generating higher-quality text, showcasing scalability and efficiency in text generation tasks.
How did GPT-3.5 prioritize user satisfaction?
GPT-3.5, trained on feedback from humans, was the first model to prioritize user satisfaction in its responses, learning various skills like translation, arithmetic, and step-by-step reasoning to enhance user experience and engagement.
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Summary
00:00
"Language Models Predict Words with Probabilities"
- Neural networks like ChatGPT are language models that predict the next word following existing text.
- T9 from old phones and ChatGPT are both language models, with the latter being more advanced.
- Models like T9 and ChatGPT operate on probabilities to predict the next word accurately.
- T9 and ChatGPT are trained to predict the next word based on the existing text.
- Neural networks, like T9, use equations to predict word dependencies, similar to predicting weight based on height.
- Large Language Models (LLM) have many parameters, improving text generation.
- Neural networks generate text word by word, predicting the next word based on probabilities.
- Transformers, like GPT, revolutionized AI by processing data more efficiently and generating better text.
- GPT-1 in 2018 showcased the scalability and efficiency of transformer architecture in text generation.
- Large language models, like GPT-2, can be trained on vast text data without prior labeling, enhancing their predictive capabilities.
13:28
"OpenAI's GPT models revolutionize AI research"
- OpenAI researchers lacked official training datasets for AI research initially.
- OpenAI collected hyperlinks from Reddit posts with 3+ likes to create a dataset.
- The dataset comprised 8 million links and 40 gigabytes of text.
- The volume of text collected was 7300 times more than Shakespeare's works.
- GPT-2 model had 1.5 billion parameters in its equations.
- The model doesn't need to memorize text verbatim, just patterns and rules.
- GPT-2 model was unexpectedly proficient, even feared for potential misuse.
- GPT-2 excelled in writing essays and solving text ambiguity tasks.
- GPT-2 self-learned to solve problems with 70% accuracy without specific training.
- GPT-3, released in 2020, had 175 billion parameters and 700 gigabytes in size.
26:23
GPT-3.5: Advancements in Neural Network Technology
- GPT-3 of 2020 was significantly larger than its predecessor, with 100 times more parameters and 10 times more training data.
- Adding the phrase "Let's think step by step" to prompts significantly improves the neural network's problem-solving abilities.
- Companies are beginning to hire industrial engineers to communicate effectively with models like ChatGPT.
- GPT-3 learned various skills like translation, arithmetic, and step-by-step reasoning due to its increased size.
- Language models often require explicit instructions due to their inability to predict human expectations accurately.
- GPT-3.5, trained on feedback from humans, was the first model to prioritize user satisfaction in its responses.
- GPT chat, released in November 2022, gained immense popularity due to its user-friendly interface and public accessibility.
- GPT chat attracted 1 million users in the first 5 days and over 100 million users in 2 months, leading to significant investments from companies like Microsoft and Google.
- The success of GPT chat highlighted the importance of a convenient interface in technology adoption and user engagement.
- Future discussions will explore the latest GPT-4 model, its advancements, potential threats from artificial intelligence, and the ability of neural network language models to think.
