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Artificial Intelligence (AI) : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

The long-term goal of artificial intelligence (AI) is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models: one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

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混合-增强智能：协作与认知

A
significant recent technological development concerns the automation of knowledge
and service work as a result of advances in Artificial Intelligence (AI) and
its sub-fields. We use the term Intelligent Automation to describe this
phenomenon. This development presents
organisations with a new strategic opportunity to increase business value. However, academic research contributions that examine these developments are spread across a wide range of scholarly disciplines resulting
in a lack of consensus regarding key findings and implications. We conduct the first interdisciplinary literature review that systematically characterises the intellectual state and development of Intelligent
Automation technologies in the knowledge and service sectors. Based on this
review, we provide three significant contributions. First, we conceptualise
Intelligent Automation and its associated technologies. Second, we provide a business value-based model of Intelligent Automation for knowledge and service work and identify
twelve research gaps that hinder a complete understanding of the business value
realisation process. Third, we provide a research agenda to address these gaps

The strategic impacts of Intelligent Automation for knowledge and service work: An interdisciplinary review

Flexible pressure sensors with microstructures

Based on the improved genetic algorithm and the BP neural network, the financial data analysis model is built on the basis of the data research problem in the financial industry, especially the stock market economy Genetic algorithm (GA) is a bionic algorithm that simulates the evolutionary mechanism of “natural selection and survival of the fittest” Through a suitable fitness function, it enables the high-quality individuals to inherit the next generation with a larger probability Using this method to select variables, we can optimize the variables that affect the stock price and effectively solve the selection problem of the input layer variables of the neural network On the basis of genetic algorithm, a new GA-BP model based on genetic algorithm and neural network is proposed, and it is applied to the prediction and analysis of securities price or trend in the securities intelligence analysis system By selecting the Shanghai and Shenzhen 300 index data, the model based on genetic algorithm is used to carry out the experiment, and the experimental results show that the method can effectively reduce the number of variables while ensuring the accuracy of the prediction

Research on intelligence analysis technology of financial industry data based on genetic algorithm

The aim of artificial intelligence is to discover mechanisms of adaptation in a changing environment with utilisation of intelligence, for instance in the ability to exclude unlikely solutions. Artificial intelligence methods have extensive application in different fields such as medicine, games, transportation, or heavy industry. This paper deals with interdisciplinary issues – interconnection of artificial intelligence and finance. The paper briefly describes techniques of data mining, expert systems and agent based computation intelligence and specifies the types of tasks solved by these techniques in the context of financial tasks. It provides deeper insight into potential usage of intelligent systems on financial markets.

Peter Koncz

Papers

Application of Artificial Intelligence and Data Mining Techniques to Financial Markets