AI News, NIPS Proceedingsβ

NIPS Proceedingsβ

Part of: Advances in Neural Information Processing Systems 27 (NIPS 2014) Basic decisions, such as judging a person as a friend or foe, involve categorizing novel stimuli.

In light of this capacity limitation, recent work finds that idealizing training items, such that the saliency of ambiguous cases is reduced, improves human performance on novel test items.

As predicted, to the extent that the learning model used by the machine teacher conforms to the true nature of human learners, the recommendations of the machine teacher prove effective.

Learning curve

The first person to describe the learning curve was Hermann Ebbinghaus in 1885, in the field of the psychology of learning, although the name wasn't used until 1909.[2][3] In 1936, Theodore Paul Wright described the effect of learning on production costs in the aircraft industry.[4] This form, in which unit cost is plotted against total production, is sometimes called an experience curve.

He also notes that the score can decrease, or even oscillate.[6][7][8] The first known use of the term learning curve is from 1910: 'Bryan and Harter (6) found in their study of the acquisition of the telegraphic language a learning curve which had the rapid rise at the beginning followed by a period of retardation, and was thus convex to the vertical axis.'[3][6] Psychologist Arthur Bills gave a more detailed description of learning curves in 1934.

He named this particular version the experience curve.[10][11] Research by BCG in the 1970s observed experience curve effects for various industries that ranged from 10 to 25 percent.[12] The economic learning of productivity and efficiency generally follows the same kinds of experience curves and have interesting secondary effects.

Performance is the error rate or accuracy of the learning system, while experience may be the number of training examples used for learning or the number of iterations used in optimizing the system model parameters.[17] The machine learning curve is useful for many purposes including comparing different algorithms,[18] choosing model parameters during design,[19] adjusting optimization to improve convergence, and determining the amount of data used for training.[20] Initially introduced in educational and behavioral psychology, the term has acquired a broader interpretation over time, and expressions such as 'experience curve', 'improvement curve', 'cost improvement curve', 'progress curve', 'progress function', 'startup curve', and 'efficiency curve' are often used interchangeably.

It has now also become associated with the evolutionary theory of punctuated equilibrium and other kinds of revolutionary change in complex systems generally, relating to innovation, organizational behavior and the management of group learning, among other fields.[21] These processes of rapidly emerging new form appear to take place by complex learning within the systems themselves, which when observable, display curves of changing rates that accelerate and decelerate.

Such limits generally present themselves as increasing complications that slow the learning of how to do things more efficiently, like the well-known limits of perfecting any process or product or to perfecting measurements.[22] These practical experiences match the predictions of the second law of thermodynamics for the limits of waste reduction generally.

Instead, it can be understood as a matter of preference related to ambition, personality and learning style.) The term learning curve with meanings of easy and difficult can be described with adjectives like short and long rather than steep and shallow.[5] If two products have similar functionality then the one with a 'steep' curve is probably better, because it can be learned in a shorter time.

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