Human-computer collaboration is difficult because it mainly involves semantic understanding and inference, human-computer communication, task assignment and cooperation, trust and privacy protection, and social and ethical considerations.Among them, the factors caused by the difference in fact and value of information of different granularity are an important aspect.
Physical phenomena at different scales of information
Physical phenomena at different information scales have specific laws, although the specific laws may vary.Information scales refer to the amount of information needed to describe and process physical systems.Macroscale: At macroscale, for example, the movement of objects in everyday life follows the laws of classical mechanics, such as Newton's laws of motion and conservation of energy.At this scale, factors such as the speed, mass, and magnitude of the force of an object's motion are important. Micro-scale: The behavior of atoms and molecules at micro-scale, such as atoms and molecules, follows the laws of quantum mechanics, such as wave-particle duality, the uncertainty principle, and the evolution of wave functions.At this scale, a particle's wave function describes its possible state, and observations lead to the collapse of the wave function. Time Scale: The laws of physical phenomena can vary from one time scale to another.For example, for very short timescales, such as nanoseconds or picoseconds, the effects of quantum mechanics become significant, while on long timescales, the laws of classical mechanics are more applicable. Spatial scales: The laws of physical phenomena vary at different spatial scales.For example, physical phenomena at the microscopic scale can be described through quantum mechanics, while at the macro scale, classical mechanics can be described.In addition, the relativistic effect has become significant on a cosmic scale. In short, physical phenomena at different information scales have specific laws, and understanding and revealing these laws is one of the core goals of physics.With the development of science, the study of the laws of physical phenomena under different scales is also deepening.
The facts and values contained in information of different granularity are indeed different.
In coarse-grained information, a wider range of facts is often included.These facts are often more macro- and comprehensive, providing a more comprehensive perspective.For example, a country's GDP data is a coarse-grained information that reflects the overall state of a country's economy.Such information is of great value to the government in formulating macroeconomic policies and investment decisions.
Information of finer granularity usually contains more specific, local facts.These facts may more specifically describe a particular event or the characteristics of an individual.For example, a company's daily sales data is a finer-grained piece of information that helps businesses understand their daily sales to adjust their sales strategies.Such information is of great value to the operation decision-making and marketing of enterprises.Therefore, the difference in facts and values contained in information of different granularity reflects the difference in the level and scope of information.In the process of information processing and decision-making, depending on the specific needs, we need to find useful facts in information of different granularities to support decision-making and implementation.
Human facts and values are often influenced by social, cultural, and personal experiences and may change over time.For example, scientific knowledge and ideas also change over time.In the past, the earth was thought to be flat, and modern science has proven it to be an ellipsoid.Similarly, our understanding of the universe, natural phenomena, and human body functions is constantly updated as scientific research progresses.Machines, by contrast, operate on a programmatic and algorithmic basis, without independent thinking and subjective consciousness, and without emotion or morality.Machines can only perform tasks according to predetermined rules, and their decisions are based on predetermined logic and algorithms and do not change spontaneously over time.Machine decisions are predictable, consistent and reproducible.Nevertheless, humans can design machines through programming and algorithms to adapt them to changing facts and values.For example, machine learning and artificial intelligence technologies can adapt models to new data and situations by training and adapting them.However, these adjustments are still made by humans, and machines themselves do not have the ability to autonomously alter their facts and values.
Human-machine cooperation refers to the process of cooperation and interaction between humans and machines.
In this process, different granularity of information can contain different facts and values.For example, suppose a team is working on a new software project.In human-machine collaboration, there can be multiple levels of information exchange.
High-level information: In the high-level exchange of information on a project, people and machines may discuss the objectives, strategies, and market needs of the entire project.This level of information contains the overall planning and strategy of the project, as well as important decision-making bases.The facts and values are about the objectives of the project, market trends and business opportunities.
Medium-level information: In the middle-level information exchange of a project, people and machines may discuss specific functional requirements, technical solutions, and development progress.This level of information includes specific implementation plans and technical implementation plans for the project.The facts and values are about the accuracy of functional requirements, technical feasibility, and the effectiveness of development progress.
Low-level information: In a project's low-level information exchange, people and machines may discuss specific coding implementations, bug fixes, and test reports.This level of information contains specific details and technical issues in the implementation of the project.The facts and values are about the correctness of the code, the timeliness of bug fixes, and the validity of the test results.
As can be seen from the above examples, there are differences in facts and values contained in information of different granularity.High-level information focuses more on the overall planning and strategy of the project, its fact and value are related to the market and business opportunities, its fact and value are related to the project's implementation and technical solutions.Different levels of information exchange require people and machines to cooperate with each other in order to achieve the smooth progress of the project.
One of the biggest problems with human-machine collaboration is the difficulty of communication and understanding at different scales of information.
There are differences in information processing between humans and computers, where humans tend to communicate using natural language, context, and background knowledge, while computers are better at handling structured data and symbols.
In human-machine collaboration, humans may have difficulty accurately communicating their meaning or may not be able to understand computer-generated results.This can lead to misunderstandings, errors, or invalid interactions.In addition, because computers process faster and more powerful than humans, the speed at which information is transmitted between humans and computers can also be mismatched, resulting in delays or incongruities in communication.In addition, different fields and tasks require different information scales.For example, in the task of large-scale data analysis, humans need to take on higher levels of decision-making and judgment, while computers need to process huge amounts of data.This mismatch in information scales can also lead to difficulties and conflicts.
In order to solve these problems, communication and understanding between the two sides need to be strengthened in human-computer cooperation.For example, natural language processing and emotion recognition techniques can be used to improve computers' ability to understand human language.At the same time, humans can also work better with computers by learning and adapting to the way they work.In addition, methods such as visualization and interaction design can be used to improve communication between the two parties.
There are different cognitive and understanding abilities between humans and machines.Humans are rich in emotions, intuition, and creativity, while machines are better at handling big data, logical analysis, and fast operations.In collaborative work, humans and machines often need to communicate and accomplish tasks together through communication.However, due to cognitive differences between humans and machines, they tend to communicate using different languages and symbols.Humans may use natural language and emotional expressions to communicate intent, while machines are more likely to use code and numerical representations.This difference leads to barriers to communication, making it difficult for humans and machines to accurately understand each other's intentions and needs.There are also differences in the way humans and machines perceive and solve problems.Humans tend to think from multiple perspectives, while machines may only be able to analyze based on pre-programmed rules.This difference leads to confusion and misunderstanding in solving problems.To solve this problem, it is necessary to improve the efficiency and accuracy of human-computer interaction and communication.By designing more intelligent and human interfaces, humans and machines can understand each other's intentions more easily.At the same time, the machine's cognitive and understanding skills can be improved through technologies such as deep learning and natural language processing, so that it can better communicate and work together with humans.