AI intelligence is a technology that achieves human-like intelligence through computer programs and algorithms. It can be divided into two types: weak artificial intelligence and strong artificial intelligence. Weak artificial intelligence refers to algorithms that are specially designed to complete specific tasks, such as speech recognition, image recognition, etc. Strong artificial intelligence refers to a level of intelligence similar to human intelligence, capable of making decisions in various situations and learning and adapting to new environments. AI intelligence has been applied in many fields, such as natural language processing, robotics, medical care, finance and smart home, etc.
GPT (Generative Pre-trained Transformer) is a neural network model based on the Transformer framework, which was first proposed by the OpenAI R&D team in 2018. GPT is a pre-trained language model designed to understand the semantics of natural language, so it can be used for tasks such as natural language processing, machine translation, and question answering systems.
The following is the detailed technology of GPT:
1. Transformer architecture: GPT uses the Transformer framework, which is an advanced model architecture for natural language processing. The Transformer framework uses Self-Attention to encode the input sequence to capture the interactions and dependencies in the sequence, while avoiding the use of recurrent models such as RNN or LSTM.
2. Large-scale pre-training: GPT is pre-trained through a large-scale corpus, enabling it to fully understand the structure and laws of language data and generate meaningful sentences. The size of the GPT-3 model pre-trained data set exceeds 45TB.
<br><br>3. Unsupervised learning: Like many other natural language processing tasks, GPT is an unsupervised learning model. This means it is trained without a labeled dataset.
4. Fine-tuning: GPT uses the fine-tuning mechanism to fine-tune specific tasks. In the fine-tuning stage, the model can be trained with labeled data to better adapt to the specific requirements of the task.
5. Multi-layer stacking: The GPT model is composed of multi-layer Transformer encoders, and each layer can capture different language features. These layers are stacked together to generate a more accurate language.
6. State preservation: GPT also has the characteristics of state preservation, which means that the output result is generated according to the previous input and state, so it is different from other models that generate results according to the input.
In general, GPT is an advanced language model that utilizes techniques such as Transformer architecture and unsupervised learning to complete a variety of natural language processing tasks through pre-training and fine-tuning mechanisms.