Herbert A. Simon
Herbert Alexander Simon (June 15, 1916 – February 9, 2001) was an American political scientist, economist, sociologist, psychologist, and professor—most notably at Carnegie Mellon University—whose research ranged across the fields of cognitive psychology, cognitive science, computer science, public administration, economics, management, philosophy of science, sociology, and political science. With almost a thousand very highly-cited publications, he was one of the most influential social scientists of the twentieth century.[4] Simon was among the founding fathers of several of today's important scientific domains, including artificial intelligence, information processing, decision-making, problem-solving, attention economics, organization theory, complex systems, and computer simulation of scientific discovery. He also received many top-level honors later in life. Early life and education[edit] Herbert Alexander Simon was born in Milwaukee, Wisconsin on June 15, 1916.
His Ideas
Cognitivism (psychology)
In psychology, cognitivism is a theoretical framework for understanding the mind that gained credence in the 1950s. The movement was a response to behaviorism, which cognitivists said neglected to explain cognition. Cognitive psychology derived its name from the Latin cognoscere, referring to knowing and information, thus cognitive psychology is an information-processing psychology derived in part from earlier traditions of the investigation of thought and problem solving.[1][2] Behaviorists acknowledged the existence of thinking, but identified it as a behavior. Cognitivists argued that the way people think impacts their behavior and therefore cannot be a behavior in and of itself. Cognitivism has two major components, one methodological, the other theoretical. Cognitivism became the dominant force in psychology in the late-20th century, replacing behaviorism as the most popular paradigm for understanding mental function. Costall, A. and Still, A. Jump up ^ Mandler, G. (2002).
Allen Newell
Allen Newell (March 19, 1927 – July 19, 1992) was a researcher in computer science and cognitive psychology at the RAND Corporation and at Carnegie Mellon University’s School of Computer Science, Tepper School of Business, and Department of Psychology. He contributed to the Information Processing Language (1956) and two of the earliest AI programs, the Logic Theory Machine (1956) and the General Problem Solver (1957) (with Herbert A. Simon). He was awarded the ACM's A.M. Turing Award along with Herbert A. Simon in 1975 for their basic contributions to artificial intelligence and the psychology of human cognition.[1][2] Early studies[edit] Newell completed his Bachelor's degree from Stanford in 1949. Afterwards, Newell "turned to the design and conduct of laboratory experiments on decision making in small groups" (Simon). Artificial intelligence[edit] His work came to the attention of economist (and future nobel laureate) Herbert A. Later achievements[edit] Awards and honors[edit]
Generative grammar
Early versions of Chomsky's theory were called transformational grammar, and this term is still used as a general term that includes his subsequent theories. There are a number of competing versions of generative grammar currently practiced within linguistics. Chomsky's current theory is known as the Minimalist program. Chomsky has argued that many of the properties of a generative grammar arise from an "innate" universal grammar. Most versions of generative grammar characterize sentences as either grammatically correct (also known as well formed) or not. Frameworks[edit] There are a number of different approaches to generative grammar. Historical development of models of transformational grammar[edit] Chomsky, in an award acceptance speech delivered in India in 2001, claimed "The first generative grammar in the modern sense was Panini's grammar".This work, called the Ashtadhyayi, was composed in 6th century BC. Standard Theory (1957–1965)[edit] Extended Standard Theory (1965–1973)[edit]
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Activation function
In computational networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard computer chip circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on input. This is similar to the behavior of the linear perceptron in neural networks. However, it is the nonlinear activation function that allows such networks to compute nontrivial problems using only a small number of nodes. Functions[edit] In biologically inspired neural networks, the activation function is usually an abstraction representing the rate of action potential firing in the cell. , where is the Heaviside step function. A line of positive slope may also be used to reflect the increase in firing rate that occurs as input current increases. is the slope. All problems mentioned above can be handled by using a normalizable sigmoid activation function. , where the hyperbolic tangent function can also be any sigmoid. where
Profiles: The Devil’s Accountant
PROFILE of Noam Chomsky... Writer describes the scene during Chomsky’s Thursday evening M.I.T. class about politics... When Chomsky likened the September 11th attacks to Clinton’s bombing of a factory in Khartoum, many found the comparison not only absurd but repugnant: how could he speak in the same breath of an attack intended to maximize civilian deaths and one intended to minimize them?
free variables and bound variables
A bound variable is a variable that was previously free, but has been bound to a specific value or set of values. For example, the variable x becomes a bound variable when we write: 'For all x, (x + 1)2 = x2 + 2x + 1.' or 'There exists x such that x2 = 2.' In either of these propositions, it does not matter logically whether we use x or some other letter. Examples[edit] Before stating a precise definition of free variable and bound variable, the following are some examples that perhaps make these two concepts clearer than the definition would: In the expression n is a free variable and k is a bound variable; consequently the value of this expression depends on the value of n, but there is nothing called k on which it could depend. y is a free variable and x is a bound variable; consequently the value of this expression depends on the value of y, but there is nothing called x on which it could depend. Variable-binding operators[edit] The following are variable-binding operators. for sums or where
