AI. Positive. Negative. One. Zero.

Advertisements
Photo by Elizaveta Dushechkina from Pexels

Classifying things is critical for our daily lives. For example, we have to detect spam mail, fake political news, as well as more mundane things such as objects or faces. When using AI, such tasks are based on “classification technology” in machine learning – having the computer learn using the boundary separating positive and negative data. For example, “positive” data would be photos including a happy face, and “negative” data photos that include a sad face. Once a classification boundary is learned, the computer can determine whether a certain data is positive or negative. The difficulty with this technology is that it requires both positive and negative data for the learning process, and negative data are not available in many cases (for instance, it is hard to find photos with the label, “this photo includes a sad face,” since most people smile in front of a camera.)

In terms of real-life programs, when a retailer is trying to predict who will make a purchase, it can easily find data on customers who purchased from them (positive data), but it is basically impossible to obtain data on customers who did not purchase from them (negative data), since they do not have access to their competitors’ data. Another example is a common task for app developers: they need to predict which users will continue using the app (positive) or stop (negative). However, when a user unsubscribes, the developers lose the user’s data because they have to completely delete data regarding that user in accordance with the privacy policy to protect personal information.

According to lead author Takashi Ishida from RIKEN AIP, “Previous classification methods could not cope with the situation where negative data were not available, but we have made it possible for computers to learn with only positive data, as long as we have a confidence score for our positive data, constructed from information such as buying intention or the active rate of app users. Using our new method, we can let computers learn a classifier only from positive data equipped with confidence.”

Ishida proposed, together with researcher Niu Gang from his group and team leader Masashi Sugiyama, that they let computers learn well by adding the confidence score, which mathematically corresponds to the probability whether the data belongs to a positive class or not. They succeeded in developing a method that can let computers learn a classification boundary only from positive data and information on its confidence (positive reliability) against classification problems of machine learning that divide data positively and negatively. (1)

Computers trying to learn based on negative feedback.

And when such not exists, trying to compensate for that based on the positive one.

But can there be any feedback which is either positive or negative?

Can anything not be something else?

Can anything not be part of nothing?

In a cosmos full of everything, where can you seek nothingness? Which result can be negative in a cosmos where every negative element creates an equally positive one? Which result can be positive in a cosmos leading to death in every possible scenario in place? How can the computer learn anything in a world where humans have forgotten how they started learning in the first place, at a time when there was nothing to learn?

Look at that child.

Learning every passing minute.

By not learning anything…

Author: skakos

Spiros Kakos (or Spyros Kakos or Spyridon Kakos) [Σπύρος Κάκος] is a thinker located in Athens, Greece. He has been the Chief Editor of Harmonia Philosophica since its inception. Spiros has a diploma in Chemical Engineering, an MSc in Advanced Materials' Technology, an MBA in Decisions' Science, a phD in the use of conductive polymers in PCB industry and is still learning. He also worked as a technical advisor and a researcher in the Advanced Materials sector for many years in the past. In his free time he develops software solutions and contributes to the open source community. He is the creator of Huo Chess, one of the smallest micro-chess programs ever that is perfect for educational purposes. He believes that science and religion are two sides of the same coin and is profoundly interested in Religion and Science philosophy, as well as the philosophy of the irrational. His philosophical work is mainly concentrated on an effort to free thinking of "logic" and reconcile all philosophical opinions under the umbrella of the "One" that Parmenides - one of the first thinkers - visualized. Since our thought is dictated by our assumptions, the only way to free it and know cosmos as it is, is to think irrationally and destroy everything we have built. The "Harmonia Philosophica" articles program is the tool that will accomplish that. Life's purpose is to be defeated by greater things. And the most important things in life are illogical. We must fight the dogmatic belief in "logic" if we are to stay humans. We should stop thinking in order to think. Credo quia absurdum! View all posts by skakos