Deep Learning is a class of machine learning algorithms that primarily learns the inherent patterns and hierarchical representations of sample data, enabling machines to possess analysis and learning capabilities similar to humans. The ultimate goal of deep learning is to allow machines to recognize and interpret various data, such as text, images, and sounds, thereby achieving the objectives of artificial intelligence.
In today’s rapidly advancing technology, deep learning has become a shining star in the field of artificial intelligence. It excels not only in traditional AI problems such as image classification, image generation, and Go but has also achieved remarkable accomplishments in cutting-edge scientific fields like protein structure prediction. All of this stems from the unique advantages of deep learning in handling high-dimensional problems.
From a mathematical perspective, the problems faced by deep learning actually involve a series of approximations of high-dimensional functions, probability distributions, and sampling, as well as solving the Bellman equation. These issues have existed in the field of applied mathematics, particularly in computational mathematics, but deep learning has found new solutions for them in its unique way.
In the problem of image classification, each pixel can be viewed as a degree of freedom. This means that a 32×32 pixel color image has 3072 degrees of freedom, which is an extremely high dimension. However, deep learning can navigate this high-dimensional space with ease, providing effective solutions for image classification problems.
The success of deep neural networks in handling these high-dimensional issues is due to their potential as a more effective tool for approximating high-dimensional functions. Although there is currently no complete mathematical theory of deep learning, scientists have made significant progress and intuitive understanding.
The success of deep learning is not limited to the field of image processing; it has also demonstrated strong potential in biology, materials science, and other fields. One of the most notable examples is the AlphaFold algorithm for predicting protein structures. Protein structure is one of the fundamental problems in biology, but traditional research methods face enormous challenges. The emergence of the AlphaFold2 algorithm has completely changed this situation. It elegantly solves the protein structure prediction problem through a deep learning model, astonishing the world.
The success of the AlphaFold2 algorithm is not a coincidence; it reflects the rise of a new research paradigm known as AI for Science. AI for Science aims to develop more efficient algorithms or approximate models for fundamental scientific principles using artificial intelligence methods. In the field of molecular dynamics, deep learning has also shown tremendous potential. By training deep potential molecular dynamics models, scientists can simulate the properties of molecules and materials with first-principles accuracy, greatly advancing fields such as materials science and biology.
In addition to basic principle models, artificial intelligence methods can provide more efficient and accurate inversion algorithms, thereby enhancing experimental characterization capabilities. This means that artificial intelligence can not only provide us with more realistic data but also help us design optimized experimental plans, improving experimental efficiency.
Moreover, artificial intelligence methods may change the way we utilize literature and existing scientific knowledge. With tools like intelligent databases and large language models, we can acquire and integrate relevant information more quickly and accurately, significantly increasing the efficiency of scientific research.
With these new possibilities emerging, we can explore a new research paradigm—”Android Paradigm”. In this new paradigm, the scientific community will work together to establish a new infrastructure, including artificial intelligence algorithms for fundamental principles, AI-enabled experimental facilities, and new knowledge databases. These platforms will form the “Android Platform” for scientific research, providing robust support for various specific scientific studies.
This horizontal integration perspective will also help break down disciplinary barriers, strengthening interdisciplinary research and education. With the new tools and platforms provided by artificial intelligence methods, researchers from different fields can share knowledge and resources more effectively, promoting the overall advancement of science.
In summary, deep learning, as a new key to unlock the high-dimensional world, is leading a revolutionary transformation in scientific research. It not only provides us with new perspectives and methods but also reveals the infinite possibilities of scientific research. In the future, we have reason to believe that deep learning will continue to demonstrate strong potential in more fields, advancing scientific research continuously.
Source: Learning Power. Text and images produced by: Liu YanInitial review: Li Fengyun. Second review: Bao XuejingThird review: Lai Fuquan