“I learn,” “you learn,” “she learns,” “they learn,” yet, according to a surprising new linguistic study, in countries where the dominant language allows personal pronouns such as ‘I’ to be omitted, learning suffers. (1)
A more or less logical conclusion. Learning is about you increasing your knowledge. While being, on the other hand, is about increasing your ignorance to the point that you become one with the cosmos.
Question everything.
Even your ability to question anything.
Do you feel wise? Are you ready to accept that you are not? It is only when you are ready to accept that you are nothing, that you become everything. A cup of tea is not useful when it is full…
Only the wisest of men admitted that they learnt nothing…
Only the most arrogant of men advertised that they know something…
I am. Therefore, I learn.
I am no one.
Therefore, I already know everything…
Not because I know them.
But because I accept that I am already part of nothing…
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?
In order to learn about the world, an animal needs to do more than just pay attention to its surroundings. It also needs to learn which sights, sounds and sensations in its environment are the most important and monitor how the importance of those details change over time. Yet how humans and other animals track those details has remained a mystery.
Now, Stanford biologists report in Science, they think they’ve figured out how animals sort through the details. A part of the brain called the paraventricular thalamus, or PVT, serves as a kind of gatekeeper, making sure that the brain identifies and tracks the most salient details of a situation.
The results are a surprise, Chen said, in part because few had suspected the thalamus could do something so sophisticated. “We showed thalamic cells play a very important role in keeping track of the behavioral significance of stimuli, which nobody had done before”, said Chen, who is also a member of Stanford Bio-X and the Wu Tsai Neurosciences Institute. (1)
Jason Chan makes a point to periodically interrupt his lecture and ask students a question about the material they’ve covered. The associate professor of psychology at Iowa State University does this to regain students’ attention, but more importantly, to enhance their ability to learn new information.
Researchers know the retrieval process
is beneficial for new learning and a new meta-analysis by Chan and his
colleagues confirms that but found there are limits. The research shows the
frequency and difficulty of questions can reverse the effect and be detrimental
to learning. It also is not enough to simply ask a question; Chan says students
must respond to see a positive effect on learning. The work is published in the
Psychological Bulletin, an American Psychological Association journal. (1)
This is something well known.
Only when the student wants, does the
master appear…
Children differ substantially in their mathematical abilities. In fact, some children cannot routinely add or subtract, even after extensive schooling. This new paper proposes that math disability arises from abnormalities in brain areas supporting procedural memory. Procedural memory is a learning and memory system that is crucial for the automation of non-conscious skills, such as driving or grammar. (1)
We learn rules.
We then learn math based on rules which we memorize.
Failure to do so makes us “bad” at math. And yet why should that be a problem? Why should we “learn rules” and memorize them? Why should we interpret or measure the cosmos based on these rules?
In a world where everything is One and non-dividable we try to learn the rules of division. In a world made out of oblivion, we try to base our civilization on remembering…
