Previously, our methodology involved creating an equation based on the input and the process, and plugging x into the equation to figure out the result. Or, alternatively, taking the deductive approach of studying phenomena, physics and sociology, and making a prediction.

It is now possible to use AI to enter both the conditional data at the entrance, and the results, and determine the logic between the entrance and exit, even if we don't already know the intermediary process. With just this advancement, we can put in voice or images, like a game board for go, and the results of "won or lost game," "how many squares have been won," and by inputting a lot of data like recorded games and various opponents, we can deduce how to win. Of course, this would not be possible with only voice recognition, image recognition, and automatic translation. But with human support, the possibilities have increased.

With these processes made possible, the most important thing will be the quantitative effect on business administration, or in other words the economy. We can input the kind of numbers that we want to increase as a business, and the data that goes along with them. And by inputting both of these, we will be able not only to logically predict things like "Under what conditions can we achieve this result," but to extrapolate it from previous data. We have achieved a methodology that will allow us to engage more systematically with societal and human issues that, unlike physics, are not readily solvable by equations.

Hitachi, Ltd. has been working to solve these issues for a very long time. Hitachi AI Technology/H is Hitachi's artificial intelligence specialized for making the most impact in the question of how best to improve the numbers of a business. What makes it technologically special is that by providing, for example, the sales and profits of one day of a business branch as the exit, the entrance would have an enormous amount of many types of data, and it would not be possible to match them individually, as it would with the relationship between the photograph and the cat in the "supervised learning" example. In "supervised learning," the constraints are too rigid to be useful for the purpose of improving business results.

What makes Hitachi AI Technology/H unique is its ability to deal with problems not on a "one-on-one" basis, but on a "one-on-many" basis. This is necessary to increase business results, and Hitachi has already secured a patent for this technology, known as "leap learning." It is already being tested in real situations for many business clients. For example, when it automatically optimized shipping warehouses, raising their productivity by 8%, or when it optimized worker placement in a store, leading to a 15% increase in sales per customer. It has also reduced the amount of electricity used by a railroad company in simulations. It is being used in securities companies to decide the optimal pricing from data when lending stocks. It's even being used in shipping, so that packages make it to their destinations right on time.

Hitachi AI Technology/H is being put to use in these diverse applications to solve all of the problems faced by businesses, but there are conditions: Clearly defining numerical results, and inputting all of the relevant source data. It is also necessary for a human to teach the AI where to create feedback. We believe that in this way, we have created the world's leading "multi-purpose AI," useful in many different situations.