Artificial Intelligence
This work gives a brief review of the history of artificial intelligence, and analyzes the current status of the field. The main principles and methodologies of the major branches in AI included symbolism and connectionism. Furthermore, the history, and booming reasons and major applications of deep learning are introduced as well.
This work introduces the application status and prospect of artificial intelligence in disease prediction comprising two aspects: public health prevention and control, personal disease screening and health management. The drawbacks of traditional methods for disease prevention and control are analyzed. The breakthroughs and developments of disease prediction brought by artificial intelligence are summarized in view of data sources and techniques. Finally, this work gives some examples of the productions of disease prediction by artificial intelligence.
Spatial analysis is a method of quantitative analysis of spatial phenomena, which becomes the core competitiveness of supporting the development of GIS. Especially with the rapid development of smart phones, mobile payment systems and shared bicycles, spatial analysis has been brought to a new stage of artificial intelligence in just a few years. In order to draw a blueprint for the coming artificial intelligence spatial analysis (AISA), this paper first briefly combs the key nodes of the development of spatial analysis: the computerized work mode from 0 to 1, global visual calculation, hidden LBS and intelligent applications, cloud GIS and artificial intelligence spatial-temporal decision-making. Secondly, the five schools of machine learning, the evolution characteristics of dominant algorithms and spatial analysis are summarized. Thirdly, the definition, modeling principles and technical framework of AISA are proposed. Finally, several hot area of AISA in the future are proposed, such as intelligent spatial computing, hyperparametric spatial optimization, intelligent spatial planning robot, full-sample spatial-temporal prediction and spatial neural network analysis. By combing, analyzing, summarizing and predicting the history and trend of artificial inerlligence spatial analysis, this paper aims to provide systematic theoretical and applied data for artificial intelligence spatial analysis.
Today, artificial intelligence has been significant progress. The breakthrough development of machine learning has promoted the adaptation of artificial intelligence with a wide range of applications. Artificial intelligence become a new highland for the development of science and technology in the world, and all countries have made strategic investment. At the same time, the development of artificial intelligence has also presented us with a new challenge, introducing new topics in our ethics and social governance. While looking forward to the bright future of artificial intelligence, we must also clearly recognize the limitation of current state-of-the-art in artificial intelligence, especially machine learning. The basic methods and basic ideas are relatively still simple and rough. Today’s artificial intelligence is mainly focusing on the emulation of external function of the human intelligence. The development of artificial intelligence still needs to be continuously progressed in the understanding of the intelligent connotation. This article addresses the approaches for future development of artificial intelligence, especially the direction of machine learning research by emphasising a knowledge support and data driven methodology.
Artificial Intelligence
