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17 Reasons Why Businesses Fail :Unscrew Yourself From Business Failure

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Pooja Agnihotri

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“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.”

PhilosophyLearningLiberal EducationJacob Klein
Book:Lectures and Essays
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“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!”

LearningIntelligenceArtificial IntelligenceA Priori
Book:How We Learn: Why Brains Learn Better Than Any Machine . . . for Now
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“The moral here is that nature and nurture should not be opposed. Pure learning, in the absence of any innate constraints, simply does not exist. Any learning algorithm contains, in one way or another, a set of assumptions about the domain to be learned. Rather than trying to learn everything from scratch, it is much more effective to rely on prior assumptions that clearly delineate the basic laws of the domain that must be explored, and integrate these laws into the very architecture of the system. The more innate assumptions there are, the faster learning is (provided, of course, that these assumptions are correct!). This is universally true. It would be wrong, for example, to think that the AlphaGo Zero software, which trained itself in Go by playing against itself, started from nothing: its initial representation included, among other things, knowledge of the topography and symmetries of the game, which divided the search space by a factor of eight. Our brain too is molded with assumptions of all kinds. Shortly, we will see that, at birth, babies' brains are already organized and knowledgeable. They know, implicitly, that the world is made of things that move only when pushed, without ever interpenetrating each other (solid objects)—and also that it contains much stranger entities that speak and move by themselves (people). No need to learn these laws: since they are true everywhere humans live, our genome hardwires them into the brain, thus constraining and speeding up learning. Babies do not have to learn everything about the world: their brains are full of innate constraints, and only the specific parameters that vary unpredictably (such as face shape, eye color, tone of voice, and individual tastes of the people around them) remain to be acquired.”

LearningIntelligenceStatisticsArtificial IntelligenceMachine LearningA PrioriA Priori Conceptualization
Book:How We Learn: Why Brains Learn Better Than Any Machine . . . for Now
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“It’s not simply a matter of accumulating data points, though in the early stages there is a necessity to take the raw material on-board. If anything, rather than carrying a large inventory, knowledge arrives “just in time”, without the expensive overhead of warehousing, in response to the needs of time, place, people and circumstances.”

KnowledgeLearning
Book:Secret Friends: The Ramblings of a Madman in Search of a Soul
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