Talk to Transformer

示例：
输入：A modern neural network admitting gauge symmetry
补全：In an earlier article by myself we discussed the fact that a neural network model that has a function that satisfies two conditions for it to operate is also a neural network model that has gauge symmetry.
In other words, a featureful structure that is always a symmetric network but never has any gauge symmetry (with some exceptions), is a neural network model with a gauge symmetry.
I was initially interested in how closely gauge symmetry can be maintained in this case. I decided to take a closer look at the case that there is a binary tree with only a couple of leaves and an element with two properties. By looking at other features such as the number of non-zero elements and some properties of the tree, I can decide that a neural network model with gauge symmetry in this case can be considered stable.
In addition there are some other features which we can make use of as well. If my graph is given a set of nodes, I can be confident that the set of leaves is always equal to the set. This can be very useful, if it is assumed that my feature list is only used in combination with the trees in place of graph nodes.

输入：Symbolism and Connectionism are two different approaches of AI.
补全：I have talked about several times that the two approaches have very different definitions and are very different in their strengths and weaknesses. I want to make this point again. AI is not a new phenomenon. It is one of the oldest developments in a century or so and that means it has been around for long and, therefore, has its own specific challenges which need to be tackled at all levels.
I mentioned earlier that AI is not just about being fast and calculating numbers. AI is about making decisions about how to manage the network we have assembled. This will lead to changes in how we operate across various domains. There are certain problems that AI has to address which need to be solved in order for AI's full potential to be realized.
The first problem is that AI should not be reactive but actively adaptive. AI has to be a system that recognizes the environment, knows what is possible or could happen and how to be ready for the situations with the least amount of stress on the system. I will also refer to as our "systematic nature" of AI and the "meta nature" of AI since it is the underlying systems which are involved.
Secondly, AI must maintain its focus on the task at hand. AI is not a game or a program which is being presented to you but a system which decides what it wants

各位可以把这个程序与自己的人工智能理论进行比较。