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

In future human-computer interactions, we can also try to use probability distribution to represent machine factuality and human value. Machine factuality distribution M can be used to represent the probability distribution of the machine's judgment and prediction of a certain event or situation, while human value distribution H represents the probability distribution of human value judgments on the same event or situation. By optimizing cross entropy, machines can better understand and meet human value judgments and needs in human-computer interactions.

Let's take a simple example to illustrate this concept. Suppose there is an intelligent customer service robot. When a user asks a question, the robot needs to give an answer based on its own knowledge base and algorithm. In this case, the machine factual distribution M can represent the probability distribution of the machine for each possible answer, and the human value distribution H can represent the probability distribution of human evaluation of the quality or accuracy of these answers.

For example, when a user asks the question "What will the weather be like tomorrow?", the intelligent customer service robot will predict tomorrow's weather conditions based on various data sources and algorithms, and give a corresponding answer. The machine factual distribution M represents the probability of each weather condition that the machine believes will occur, such as [0.6, 0.2, 0.1, 0.1], where the first element represents the probability that the machine believes tomorrow will be sunny, the second element represents the probability that the machine believes it will be cloudy, and so on. The human value distribution H represents the probability of human evaluation of these weather conditions, such as [0.8, 0.1, 0.05, 0.05], where the first element represents the probability that humans believe that sunny days have more benefits, and the second element represents the probability that humans believe that cloudy days have more benefits. By comparing the cross entropy between the machine factual distribution M and the human value distribution H, the accuracy and deviation of the machine's value judgment of humans can be evaluated. If the two distributions are similar and the cross entropy is small, it means that the machine's answer can meet human expectations; if the two distributions are very different and the cross entropy is large, it means that there is a certain deviation between the machine's answer and human expectations.

Appendix Description:

Cross entropy is a concept in information theory that measures the similarity or difference between two probability distributions. When understanding cross entropy, you can start from the following aspects:

Probability distribution: First, cross entropy involves two probability distributions, such as the true distribution and the model prediction distribution. These distributions represent the probability of an event occurring.

Information: In information theory, information refers to the degree of surprise or uncertainty about the occurrence of an event. Events with lower probabilities provide more information, while events with higher probabilities provide less information.

Relative Entropy (Kullback-Leibler Divergence): Cross entropy is built on the basis of relative entropy. Relative entropy is a measure of the difference between two probability distributions. It measures the average amount of extra information observed under a certain distribution, and more extra information is observed when we use the wrong distribution to represent the true distribution.

Measuring Difference: Cross entropy quantifies the difference between two probability distributions by combining the relative entropy with the entropy of a reference distribution. When the two distributions are exactly the same, the cross entropy is 0; when the difference between the distributions increases, the cross entropy also increases.

In general, cross entropy provides a measure of the difference between two probability distributions. By minimizing the cross entropy, the distribution predicted by the model can be made closer to the true distribution, thereby improving the performance of the model. In machine learning, cross entropy is often used as a loss function to guide the training and optimization process of the model. The details are as follows:

Assume that there are two probability distributions, namely the true distribution P and the distribution predicted by the model Q. The following is a simple example to illustrate the concept of cross entropy.

Suppose we have a binary classification problem dataset containing 4 samples, two of which belong to category A and the other two belong to category B. The true distribution P can be expressed as [0.5, 0.5], which means that the probability of occurrence of category A and category B is 0.5. The distribution Q predicted by the model can be expressed as [0.8, 0.2], which means that the model believes that the probability of category A is 0.8 and the probability of category B is 0.2.

We can calculate the cross entropy between the true distribution P and the model predicted distribution Q. The calculation formula for cross entropy is:

H(P, Q) = -Σ P(i) * log(Q(i))

Substitute the true distribution P and the model predicted distribution Q into the formula:

H(P, Q) = -(0.5 * log(0.8) + 0.5 * log(0.2))

Through calculation, we get the result of cross entropy to be 0.693.

Cross entropy measures the difference between the distribution predicted by the model and the true distribution. When the distribution predicted by the model is exactly the same as the true distribution, the cross entropy is 0; when the two distributions are very different, the value of cross entropy increases. By minimizing cross entropy, we can train the model to make its predicted distribution closer to the true distribution, thereby improving the performance and accuracy of the model.