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Quote by Pooja Agnihotri

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Pooja Agnihotri

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“When you change your mind, it just shows that now you have got some new information and because of that new information, now your current understanding of the issue has changed and that’s why you now think differently.”

“The first obstacle [to liberal education] is the learning situation itself. What is the ideal learning situation? It is the more or less continuous contact between a student and his teacher, who is another student, more advanced in many ways, but still learning himself. This situation usually does not prevail; in fact, it is extremely rare. Since the immemorial, institutions of learning, especially higher learning, have been established, called „schools“ — and the ambiguity of the term becomes immediately apparent. Institutionalization means ordering activities into certain patters; in the case of learning activities, into classes, schedules, courses, curriculums, examinations, degress, and all the venerable and sometimes ridiculous paraphernalia of academic life. The point is that such institutionalization cannot be avoided: both the gregarious and the rational character of man compel him to impose upon himself laws and regulations. Moreover, the discipline of learning itself seems to require an orderly and planned procedure. And yet we all know how this schedule routine can interfere with the spontaneity of questioning and of leaning and the occurrence of genuine wonderment. A student may even never become aware that there is the possibility of spontaneous learning which depends merely on himself and on nobody and nothing else. Once the institutional character of learning tends to prevail, the goal of liberal education may be completely lost sight of, whatever other goals may be successfully reached. And I repeat, this obstacle is not extraneous to learning. It is prefigured in the methodical and systematic character of exploratory questioning. It has to be faced over and over again.”

“Yann LeCun's strategy provides a good example of a much more general notion: the exploitation of innate knowledge. Convolutional neural networks learn better and faster than other types of neural networks because they do not learn everything. They incorporate, in their very architecture, a strong hypothesis: what I learn in one place can be generalized everywhere else. The main problem with image recognition is invariance: I have to recognize an object, whatever its position and size, even if it moves to the right or left, farther or closer. It is a challenge, but it is also a very strong constraint: I can expect the very same clues to help me recognize a face anywhere in space. By replicating the same algorithm everywhere, convolutional networks effectively exploit this constraint: they integrate it into their very structure. Innately, prior to any learning, the system already “knows” this key property of the visual world. It does not learn invariance, but assumes it a priori and uses it to reduce the learning space-clever indeed!”