Tag: Artificial Intelligence

Since AlphaGo won, artificial intelligence has become very popular, and the emergence of ChatGPT has pushed the popularity of artificial intelligence to a new level. However, when people talk about “intelligence”, they rarely focus on “artificial”. It seems that with the help of artificial intelligence, everything can be automated. In fact, behind this intelligence lies a large amount of “manual labor” and a considerable amount of things that cannot be automated. The labor here is mainly reflected in two aspects: 1. Data preparation The basic methods of modern artificial intelligence technology, or machine learning, are not much different from those

The most popular topic this year is ChatGPT, which has sounded the horn of the AI large model, with multiple enterprises and institutions rushing into the large model track bombed out by ChatGPT with acceleration. However, this is ultimately a competition among technology giants. Developing large models requires core capabilities such as great computing power, big data and strong algorithms. The technology threshold is high and the investment is large. Taking GPT-3 as an example, its parameters reach 175 billion, with over 285000 CPU cores, 10000 GPUs, and a 400GB/s GPU server network transmission bandwidth. The computational cost of training

Recognizing handwritten Arabic numbers is very simple for human beings, but it is still somewhat complicated for programs. However, with the popularization of machine learning technology, it is not difficult to implement a program that can recognize handwritten numbers using over 10 lines of code. This is because there are too many machine learning models that can be directly used, such as TensorFlow and Caffe. There are ready-made installation packages available in Python, and for writing a program that recognizes numbers, a little over 10 lines of code is enough. However, what I want to do is to completely implement

There are many tools that can be used for AI modeling and prediction, such as Python, R, SAS and SPSS, where Python is very popular because it is simple, easy to learn, rich in data science libraries, open source and free. However, modeling in Python is still complicated for programmers who are not very familiar with data modeling algorithms. In many cases, they have no idea to start and do not know which algorithm to choose when they have data. In fact, SPL is also a good choice when performing data analysis and modeling task since SPL is simpler and