What is the structure and function of human-computer interaction in the future?

Future human-computer interaction should emphasize the structural flexibility, capability and functional coordination between humans and machines, and achieve more intelligent and efficient decision-making and innovation by combining human cognitive (creative thinking, emotional understanding and abstract reasoning of complex problems) capabilities with machine computing (large-scale data processing, high speed and accuracy). It is a state of continuous evolution, continuous creation and uncertainty. With the continuous advancement of technology and changes in application scenarios, the new generation of human-computer interaction needs to flexibly adapt to uncertainty and complexity. Both humans and machines need to continuously and dynamically learn and adjust their own functional allocation capabilities and strategic structures to meet new challenges and needs.

In human-computer interaction, the structure of interaction does play an important role in determining the distribution of human-computer functions, including information flow, control distribution, and decision-making process. These structures determine the distribution of roles and responsibilities between humans and machines, as well as the way they interact with each other:

Information flow: The interaction structure determines how information flows from people to machines or from machines to people. For example, in a question-answering system, humans provide questions and machines provide answers; while in an intelligent assistant, humans provide instructions and machines perform tasks and provide results. The difference in information flow determines the roles and function allocation of people and machines in the interaction process.

Distribution of control: The interaction structure determines who has control over the interaction process. For example, in a voice assistant, humans issue voice commands and the machine performs the corresponding operations. The distribution of control can affect the power relationship and decision-making process between humans and machines.

Decision-making process: The interaction structure determines the distribution of roles and functions between humans and machines in the decision-making process. For example, in a self-driving car, the machine is responsible for perception and control, while the human is responsible for supervision and making necessary decisions. The structure of the decision-making process can determine the roles and responsibilities of humans and machines in decision-making.

The new interaction structure is very important for the allocation of human-machine functions. It determines the roles, responsibilities and powers of humans and machines in the interaction process, as well as the way they interact with each other. When designing and implementing a new generation of human-machine interaction systems, it is necessary to reasonably consider the interaction structure to achieve effective, efficient and safe human-machine symbiosis.

While structure affects function, the allocation of human-computer functions and capabilities can also improve the structure of human-computer interaction, thereby enhancing the effect of human-computer interaction and user experience. By reasonably allocating human-computer functions and capabilities, the following aspects of human-computer interaction structure can be improved:

Expertise: Both humans and machines have unique strengths and expertise in their respective fields. By allocating the functions of humans and machines to the party that is best at them, the efficiency and accuracy of task execution can be improved. For example, in self-driving cars, machines are responsible for sophisticated perception and control, while humans are responsible for complex decision-making and responding to emergencies. This expertise can improve the performance of the entire system.

Collaboration: By rationally dividing the functions and capabilities of humans and machines, better collaboration and complementary advantages can be achieved. Humans and machines can complement each other and form synergies. For example, in an intelligent assistant, humans provide goals and instructions, while machines perform specific tasks through calculations and execution. This kind of collaboration can improve the effectiveness and efficiency of human-machine interaction.

Improved user experience: Through reasonable allocation of human-machine functional capabilities, the user experience can be optimized. According to the needs and preferences of users, the functions of the machine are designed as tools to assist and support users, providing personalized services and assistance. This can enhance the user's satisfaction and trust in the human-machine interaction system.

Humanized design: By rationally allocating human-machine functional capabilities, human-machine interaction can be made closer to human cognition and behavior, making human-machine interaction more natural and easy to use. For example, in voice assistants, through voice recognition and natural language processing technology, users can directly communicate with intelligent assistants using natural language without having to learn complex commands and operations.

The ability to allocate human-machine functions plays an important role in improving the structure of human-machine interaction. Through reasonable allocation, we can give full play to the advantages of humans and machines, achieve collaborative cooperation, improve user experience, and achieve a more intelligent and humanized interaction method. At the same time, by reasonably considering the structural relationship between the state, potential, perception, and cognition of humans and machines, we can better determine the roles, responsibilities, and functional capabilities between humans and machines to achieve an efficient, collaborative, and optimized human-machine interaction experience:

State: Each component of the human-machine system has different states, that is, their current situation or condition. These states can be the switch state of the device, the running state of the software, the identity of the user, etc. In human-machine interaction, in a certain state, different functional capabilities may need to be assigned to humans or machines to complete the corresponding tasks. The state of the human-machine system can determine which tasks require human participation and which tasks can be completed by machines. For example, in some facts that require judgment and decision-making, humans may have more dominance, while machines are often more efficient when performing large-scale data processing and calculations.

Potential: There are differences between humans and machines in terms of capabilities, skills, and knowledge. Humans have flexible thinking, judgment, and creativity, while machines are good at calculations, processing big data, and performing precise operations, and generating results in a shorter time. Based on different potential energy distributions, tasks can be assigned to the party that is more suitable for the task. The differences in capabilities and skills between humans and machines will also affect the distribution of facts and values. Humans have the abilities of emotion, moral judgment, and ethical standards, and can participate in decision-making that comprehensively considers facts and values.

Perception: Perception is the process by which humans and machines acquire information from the outside world. Humans acquire information through their senses, such as vision, hearing, and touch. Machines acquire information through sensors and input devices. Depending on the perceptual capabilities of the human-machine system, it can be determined whether the information is acquired by a machine or a human, and functional capabilities are allocated accordingly. These differences in perceptual capabilities between humans and machines also affect the cognition and understanding of specific facts and values.

Knowledge: Knowledge is the intelligent content mastered by humans and machines. Humans can acquire knowledge through learning and experience accumulation, while machines can acquire knowledge through training and data-driven methods. Based on the knowledge differences between humans and machines, tasks involving professional knowledge or complex calculations can be assigned to machines, while tasks that require human experience and judgment can be left to humans. The scope and depth of knowledge mastered by humans and machines will also affect the distribution of facts and values. Humans acquire rich knowledge through learning, education, and experience accumulation, and can comprehensively consider more factors in decision-making.

Looking further, there are often overlaps and entanglements between the factual states, potentials, feelings, and perceptions of different granularities between humans and machines and the value states, potentials, feelings, and perceptions. Through reasonable design and collaboration, a more effective, accurate, and demand-oriented human-machine interaction experience can be achieved. In specific situations, the granularity of facts can include different levels from details to the whole, while the granularity of values ​​can cover different levels from individuals to groups. In this case, the interaction between humans and machines often involves comprehensive consideration and trade-offs of multiple factors. For example, in the decision-making process, humans may consider detailed specific facts and more macro values ​​at the same time, weigh benefits and risks, and adopt different ways of thinking and decision-making bases. Generally speaking, machines may provide decision support by analyzing large amounts of data and algorithmic models, but may be relatively limited when it comes to value judgments and ethical standards. This superposition and entanglement requires sufficient flexibility and adaptability in the interaction between humans and machines. Human-machine systems need to play their strengths in their respective fields and achieve effective collaboration and integration. At the same time, transparency and explainability are also very important so that humans can understand and evaluate the recommendations or decisions of machines and adjust them when necessary.