- Title：Manifold Learning and Artificial Intelligence - Open source solution replicates ChatGPT Training Process
- Date：09:00pm US East time, 02/25/2023
- Date：10:00am Beijing time, 02/26/2023
- Zoom ID：933 1613 9423
- Zoom PWD：416262
-
Momiao Xiong, Ph. D, Professor in Department of Biostatistics snd Data Science , University of Texas, School of Public Health. Dr. Xiong graduated from the Department of Statistics at the University of Georgia in 1993. From 1993 to 1995, Dr. Xiong was postdoctoral fellow at the University of Southern California working with Michael Waterman.
-
Research Interest： Causal Inference, Artificial Intelligence , Manifold Learning, Statistic Genetics and Bioinformatics .
-
https://theaisummer.com/diffusion-models/#:~:text=Diffusion%20models%20are%20a%20new,to%20train%20large%2Dscale%20models.
-
https://towardsdatascience.com/understanding-graph-convolutional-networks-for-node-classification-a2bfdb7aba7b
-
https://medium.com/red-buffer/implementation-and-understanding-of-graph-neural-networks-gnn-54084c8a0e24
Background
Replicating the ChatGPT training process requires access to a large dataset of text, high-performance computing resources, and expertise in machine learning and natural language processing. The training process is also highly proprietary and specific to OpenAI’s technology stack, which may not be fully available to the public.
However, there are several open source deep learning frameworks and libraries available that can be used to build and train language models. Some popular options include TensorFlow, PyTorch, and Keras.
To replicate the ChatGPT training process, you would need to:
Acquire a large dataset of text. This could include web pages, news articles, books, and other sources of text. The quality and diversity of the data is critical to the success of the language model.
Preprocess the data to prepare it for training. This includes tokenizing the text, normalizing it, and encoding it in a format that can be used by the deep learning framework.
Choose a deep learning framework and set up a high-performance computing environment to train the model. This may involve using GPU-accelerated hardware, cloud computing resources, or a cluster of machines.
Build a language model architecture based on the transformer architecture used in ChatGPT. This involves designing the model architecture, including the number of layers, attention mechanisms, and other hyperparameters.
Train the model on the dataset using the chosen deep learning framework. This may involve using techniques such as gradient descent, backpropagation, and regularization to optimize the model.
Evaluate the model to determine its accuracy and performance. This may involve using metrics such as perplexity, BLEU score, and human evaluation to assess the model’s ability to generate coherent and meaningful text.
Overall, replicating the ChatGPT training process requires significant expertise in machine learning and natural language processing, as well as access to large amounts of data and computing resources. While there are open source tools and frameworks available, reproducing the ChatGPT model in its entirety may be challenging for most researchers and developers.
我们正在经历人工智能的巨大革命性变化。人们逐渐认识到语言模型和其他人工智能工具实际上是模拟人的智能活动。ChatGPT 不仅能理解自然语言，写作，而且能解答数学问题，编写和执行程序。人工智能一定会改变我们生活和工作的方式，提供解决科学和工程技术问题的新思路，改变从小学，中学到大学，研究生的所有教学内容和教学方式。本次讲课除了介绍一些大型语言模型的一些知识外，主要讨论我们在科学和工程技术方面工作的朋友们如何迎接人工智能的巨大挑战。
