Both machine learning, which is often used in AI, and annealing machines, which perform combinatorial optimization, are computers that perform difficult processing on behalf of humans. What are the differences between the two? Actually, there is a crucial difference. While machine learning infers the optimal choice from accumulated experience, an annealing machine makes the optimal combinatorial choice in the current situation based on a set of defined rules.
What AI can do is learn from past data and infer situations from that data. For example, what kind of processing would take place if we were to use AI to create work shifts? Based on the data accumulated in the past, the AI would infer this kind of work style would be better for person A, and that kind of work style would be better for person B, and then decide on the shifts.
When you need to consider various patterns, you need the AI to use the appropriate historical data to learn.
In contrast, what an annealing machine can do is to determine the optimal combination of parameters to the given situation. For example, it will create a shift that is considered optimal for person A, who has these wishes, and person B, who has those wishes, as well as the work rules and circumstances at that workplace.
Everything that needs to be considered should be defined.
As noted above, annealing needs to consider all the conditions and constraints that should be there and define them as cost functions or constraints. AI does not need to define conditions and constraints, but it does need to learn all the situations it wants to consider.
For example, suppose you need to create a shift for someone who has never taken paid vacation on a Tuesday before. With an annealing machine, you can create a shift by defining that the person will take paid vacation pay on Tuesdays, and it will take that into account. In contrast, an AI cannot learn to put a person on a shift with paid vacation on a Tuesday that the person has no experience with, so adjustments and innovations are required.
Let us assume that we need to set up a shift; it is possible to know empirically how many people are needed at any given time, so you can use AI to predict the number of staff needed. On the other hand, you can use an annealing machine to create shifts which consider the precisely defined individual circumstances of the workers.
By combining AI and annealing machines, it is possible to create shifts with each person's preferences in accordance with the expectations of how many people should work based on previous experience.
As described above, there are significant differences between AI and annealing machines. By understanding the differences, they can be effective when used in combination.
Combinatorial optimization will be explained by using two easy-to-understand examples of familiar combinatorial optimization problems. The importance of finding and using fast solution methods for combinatorial optimization processes in an IoT society will also be discussed.
In the wake of the COVID-19 epidemic, we present a use case in which an annealing machine is used to create shifts for researchers under constraints that take into account the risk of infection.
This article presents a use case in which an annealing machine is used to formulate a reinsurance portfolio that leverages the vast amount of data held by an insurance company to address natural catastrophe risks.
For beginners, this article explains the procedures and key points of the process of solving optimization problems with an annealing machine. The process involves dividing the problem into four stages: "organizing problem and defining problem," "formulation," "input data preparation," and "executing CMOS annealing machine".
This article provides an easy-to-understand explanation of the mathematical assumptions you need to understand, focusing on "quadratic" and "discrete," which are some of the characteristics of the problems that annealing machines excel at solving.