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.
The Birth of Behavioral Psychology - Author: Dave Grossman "Behavioral Psychology" The Birth of Behavioral Psychology Around the turn of the century, Edward Thorndike attempted to develop an objective experimental method for testing the mechanical problem solving ability of cats and dogs. Thorndike's initial aim was to show that the anecdotal achievement of cats and dogs could be replicated in controlled, standardized circumstances. Thorndike was particularly interested in discovering whether his animals could learn their tasks through imitation or observation. By 1910 Thorndike had formalized this notion into the "Law of Effect," which essentially states that responses that are accompanied or followed by satisfaction (i.e., a reward, or what was later to be termed a reinforcement) will be more likely to reoccur, and those which are accompanied by discomfort (i.e., a punishment) will be less likely to reoccur. In the 1920s behaviorism began to wane in popularity. © 1999 by Academic Press.
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]
Humanism In modern times, humanist movements are typically aligned with secularism, and today "Humanism" typically refers to a non-theistic life stance centred on human agency, and looking to science instead of religious dogma in order to understand the world.[2] Background The word "Humanism" is ultimately derived from the Latin concept humanitas, and, like most other words ending in -ism, entered English in the nineteenth century. However, historians agree that the concept predates the label invented to describe it, encompassing the various meanings ascribed to humanitas, which included both benevolence toward one's fellow humans and the values imparted by bonae litterae or humane learning (literally "good letters"). In the second century A.D, a Latin grammarian, Aulus Gellius (c. 125– c. 180), complained: Gellius says that in his day humanitas is commonly used as a synonym for philanthropy – or kindness and benevolence toward one's fellow human being. History Predecessors Asia Ancient Greece Types
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
Cognition Cognition is a faculty for the processing of information, applying knowledge, and changing preferences. Cognition, or cognitive processes, can be natural or artificial, conscious or unconscious.[4] These processes are analyzed from different perspectives within different contexts, notably in the fields of linguistics, anesthesia, neuroscience, psychiatry, psychology, philosophy, anthropology, systemics, and computer science.[5][page needed] Within psychology or philosophy, the concept of cognition is closely related to abstract concepts such as mind, intelligence. It encompasses the mental functions, mental processes (thoughts), and states of intelligent entities (humans, collaborative groups, human organizations, highly autonomous machines, and artificial intelligences).[3] Etymology[edit] Origins[edit] Wilhelm Wundt (1832-1920) heavily emphasized the notion of what he called introspection; examining the inner feelings of an individual. Psychology[edit] Social process[edit] Serial position
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
Empiricism John Locke, a leading philosopher of British empiricism Empiricism is a theory which states that knowledge comes only or primarily from sensory experience.[1] One of several views of epistemology, the study of human knowledge, along with rationalism and skepticism, empiricism emphasizes the role of experience and evidence, especially sensory experience, in the formation of ideas, over the notion of innate ideas or traditions;[2] empiricists may argue however that traditions (or customs) arise due to relations of previous sense experiences.[3] Empiricism, often used by natural scientists, says that "knowledge is based on experience" and that "knowledge is tentative and probabilistic, subject to continued revision and falsification Etymology[edit] The English term "empirical" derives from the Greek word ἐμπειρία, which is cognate with and translates to the Latin experientia, from which we derive the word "experience" and the related "experiment". History[edit] Background[edit] Pragmatism[edit]
Spiking neural network Spiking neural networks (SNNs) fall into the third generation of neural network models, increasing the level of realism in a neural simulation. In addition to neuronal and synaptic state, SNNs also incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather fire only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value. When a neuron fires, it generates a signal which travels to other neurons which, in turn, increase or decrease their potentials in accordance with this signal. In the context of spiking neural networks, the current activation level (modeled as some differential equation) is normally considered to be the neuron's state, with incoming spikes pushing this value higher, and then either firing or decaying over time. Beginnings[edit] Applications[edit] CoDi