How to know and understand artificial intelligence
The knowledge and understanding of artificial intelligence are: 1. Covering many research fields; 2. Trending towards deep learning, reinforcement learning, and natural language processing; 3. Wide range of future application fields; 3. Becoming a new economic influencing factor .
Knowledge and understanding of artificial intelligence:
Since the 1950s, computer scientists have been committed to research and development Programs similar to human intelligence. When these programs are developed to a certain level, they can replace humans in some specific application scenarios. They are called artificial intelligence (AI) and cognitive computing.
In the 1980s, people hyped this concept, but then ushered in the "winter of artificial intelligence", announcing the demise of this good wish. Until recently, some groundbreaking successes in this field have finally convinced the academic community that artificial intelligence will not only bring about intelligent machines that liberate human power, but also breed new technologies.
1. Covers many research fields: Computer science, psychology, philosophy, neuroscience, sociology, mathematics, biology
In computer science, artificial intelligence It is an interdisciplinary research field. Depending on the research objectives, it involves solutions in disciplines such as mathematics, informatics, speech recognition, computer vision and robotics. And since researchers began teaching computers to understand emotions such as sympathy, happiness and a desire to help, psychological and philosophical models have also been included in the research. In addition, since the computer program is also required to make decisions in tasks such as autonomous driving or the management of insurance companies, it must also be able to answer questions in the legal field, especially when it comes to liability claims.
2. Artificial Intelligence Research Trend
Trend One: Deep Learning
Deep learning refers to learning through multi-layer artificial neural networks. This network model is based on the human nervous system. In the human brain, neural pathways become more active the more they are used, and the same applies to software networks.
Trend 2: Reinforcement Learning
Traditional machine learning models focus on finding fixed patterns in data, while reinforcement learning programs go one step further. They make decisions to achieve a specific goal as much as possible. This reflects the transition from predictive analytics to guided analytics.
Trend Three: Natural Language Processing
Natural language processing and automatic language recognition are both the most widely used artificial intelligence technologies. Whether it's Google's search function, Siri's voice commands or Amazon Alexa's control of home appliances, they are all based on speech recognition and understanding.
3. Future application areas of artificial intelligence: Industrial and service robots, office software, interconnected electric transportation, medical diagnostic software
In the future, with the assistance of artificial intelligence Robots that can learn simple processes will be used to support workers in factories, warehouses, hospitals and nursing homes. And programs capable of autonomous decision-making will soon be able to handle simple administrative tasks like archiving and standardized program communications. In connected e-mobility, autonomous vehicles coordinate with each other to optimize road traffic. This could save big cities from traffic gridlock and make them more livable. Experts have also found that smart medical diagnostic systems can bring huge benefits to people. Once trained, these systems can be used to detect anomalies and provide preliminary analysis.
4. New economic influencing factors
Experts believe that artificial intelligence technology is bringing fundamental changes to economic activities. Now, the key factor in economic growth is no longer capital or labor, but depends on how industrial countries make full use of the opportunities brought by artificial intelligence technology. The foundation of this new growth model is data. In the future, data will be as valuable to business and the workplace as mineral oil was to both in the 1970s. At the same time, data is the basis of machine learning: the more data a program processes, the more accurately it can complete operations such as fault detection, prediction, speech recognition or motion.
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