Showing posts with label Dartmouth AI Conference. Show all posts
Showing posts with label Dartmouth AI Conference. Show all posts

Artificial Intelligence - Who Was Herbert A. Simon?


Herbert A. Simon (1916–2001) was a multidisciplinary scholar who contributed significantly to artificial intelligence.

He is largely regarded as one of the twentieth century's most prominent social scientists.

His contributions at Carnegie Mellon University lasted five decades.

Early artificial intelligence research was driven by the idea of the computer as a symbol manipulator rather than a number cruncher.

Emil Post, who originally wrote about this sort of computational model in 1943, is credited with inventing production systems, which included sets of rules for symbol strings used to establish conditions—which must exist before rules can be applied—and the actions to be done or conclusions to be drawn.

Simon and his Carnegie Mellon colleague Allen Newell popularized these theories regarding symbol manipulation and production systems by praising their potential benefits for general-purpose reading, storing, and replicating, as well as comparing and contrasting various symbols and patterns.

Simon, Newell, and Cliff Shaw's Logic Theorist software was the first to employ symbol manipulation to construct "intelligent" behavior.

Theorems presented in Bertrand Russell and Alfred North Whitehead's Principia Mathematica (1910) might be independently proved by logic theorists.

Perhaps most notably, the Logic Theorist program uncovered a shorter, more elegant demonstration of Theorem 2.85 in the Principia Mathematica, which was subsequently rejected by the Journal of Symbolic Logic since it was coauthored by a machine.

Although it was theoretically conceivable to prove the Principia Mathematica's theorems in an exhaustively detailed and methodical manner, it was impractical in reality due to the time required.

Newell and Simon were fascinated by the human rules of thumb for solving difficult issues for which an extensive search for answers was impossible due to the massive quantities of processing necessary.

They used the term "heuristics" to describe procedures that may solve issues but do not guarantee success.

A heuristic is a "rule of thumb" used to solve a problem that is too difficult or time consuming to address using an exhaustive search, a formula, or a step-by-step method.

Heuristic approaches are often compared with algorithmic methods in computer science, with the result of the method being a significant differentiating element.

According to this contrast, a heuristic program will provide excellent results in most cases, but not always, while an algorithmic program is a clear technique that guarantees a solution.

This is not, however, a technical difference.

In fact, a heuristic procedure that consistently yields the best result may no longer be deemed "heuristic"—alpha-beta pruning is an example of this.

Simon's heuristics are still utilized by programmers who are trying to solve issues that demand a lot of time and/or memory.

The game of chess is one such example, in which an exhaustive search of all potential board configurations for the proper solution is beyond the human mind's or any computer's capabilities.

Indeed, for artificial intelligence research, Herbert Simon and Allen Newell referred to computer chess as the Drosophila or fruit fly.

Heuristics may also be used to solve issues that don't have a precise answer, such as in medical diagnosis, when heuristics are applied to a collection of symptoms to determine the most probable diagnosis.

Production rules are derived from a class of cognitive science models that apply heuristic principles to productions (situations).

In practice, these rules reduce down to "IF-THEN" statements that reflect specific preconditions or antecedents, as well as the conclusions or consequences that these preconditions or antecedents justify.

"IF there are two X's in a row, THEN put an O to block," is a frequent example offered for the application of production rules to the tic-tac-toe game.

These IF-THEN statements are incorporated into expert systems' inference mechanisms so that a rule interpreter can apply production rules to specific situations lodged in the context data structure or short-term working memory buffer containing information supplied about that situation and draw conclusions or make recommendations.

Production rules were crucial in the development of artificial intelligence as a discipline.

Joshua Lederberg, Edward Feigenbaum, and other Stanford University partners would later use this fundamental finding to develop DENDRAL, an expert system for detecting molecular structure, in the 1960s.

These production guidelines were developed in DENDRAL after discussions between the system's developers and other mass spectrometry specialists.

Edward Shortliffe, Bruce Buchanan, and Edward Feigenbaum used production principles to create MYCIN in the 1970s.

MYCIN has over 600 IFTHEN statements in it, each reflecting domain-specific knowledge about microbial illness diagnosis and treatment.

PUFF, EXPERT, PROSPECTOR, R1, and CLAVIER were among the several production rule systems that followed.

Simon, Newell, and Shaw demonstrated how heuristics may overcome the drawbacks of classical algorithms, which promise answers but take extensive searches or heavy computing to find.

A process for solving issues in a restricted, clear sequence of steps is known as an algorithm.

Sequential operations, conditional operations, and iterative operations are the three kinds of fundamental instructions required to create computable algorithms.

Sequential operations perform tasks in a step-by-step manner.

The algorithm only moves on to the next job when each step is completed.

Conditional operations are made up of instructions that ask questions and then choose the next step dependent on the response.

One kind of conditional operation is the "IF-THEN" expression.

Iterative operations run "loops" of instructions.

These statements tell the task flow to go back and repeat a previous series of statements in order to solve an issue.

Algorithms are often compared to cookbook recipes, in which a certain order and execution of actions in the manufacture of a product—in this example, food—are dictated by a specific sequence of set instructions.

Newell, Shaw, and Simon created list processing for the Logic Theorist software in 1956.

List processing is a programming technique for allocating dynamic storage.

It's mostly utilized in symbol manipulation computer applications like compiler development, visual or linguistic data processing, and artificial intelligence, among others.

Allen Newell, J. Clifford Shaw, and Herbert A. Simon are credited with creating the first list processing software with enormous, sophisticated, and flexible memory structures that were not reliant on subsequent computer/machine memory.

List processing techniques are used in a number of higher-order languages.

IPL and LISP, two artificial intelligence languages, are the most well-known.

Simon and Newell's Generic Problem Solver (GPS) was published in the early 1960s, and it thoroughly explains the essential properties of symbol manipulation as a general process that underpins all types of intelligent problem-solving behavior.

GPS formed the foundation for decades of early AI research.

To arrive at a solution, General Problem Solver is a software for a problem-solving method that employs means-ends analysis and planning.

GPS was created with the goal of separating the problem-solving process from information particular to the situation at hand, allowing it to be used to a wide range of issues.

Simon is an economist, a political scientist, and a cognitive psychologist.

Simon is known for the notions of limited rationality, satisficing, and power law distributions in complex systems, in addition to his important contributions to organizational theory, decision-making, and problem-solving.

Computer and data scientists are interested in all three themes.

Human reasoning is inherently constrained, according to bounded rationality.

Humans lack the time or knowledge required to make ideal judgments; problems are difficult, and the mind has cognitive limitations.

Satisficing is a term used to describe a decision-making process that produces a solution that "satisfies" and "suffices," rather than the most ideal answer.

Customers use satisficing in market conditions when they choose things that are "good enough," meaning sufficient or acceptable.

Simon described how power law distributions were obtained from preferred attachment mechanisms in his study on complex organizations.

When a relative change in one variable induces a proportionate change in another, power laws, also known as scaling laws, come into play.

A square is a simple illustration; when the length of a side doubles, the square's area quadruples.

Power laws may be found in biological systems, fractal patterns, and wealth distributions, among other things.

Preferential attachment processes explain why the affluent grow wealthier in income/wealth distributions: Individuals' wealth is dispersed according on their current level of wealth; those with more wealth get proportionately more income, and hence greater overall wealth, than those with less.

When graphed, such distributions often create so-called long tails.

These long-tailed distributions are being employed to explain crowdsourcing, microfinance, and online marketing, among other things.

Simon was born in Milwaukee, Wisconsin, to a Jewish electrical engineer with multiple patents who came from Germany in the early twentieth century.

His mother was a musical prodigy. Simon grew interested in the social sciences after reading books on psychology and economics written by an uncle.

He has said that two works inspired his early thinking on the subjects: Norman Angell's The Great Illusion (1909) and Henry George's Progress and Poverty (1879).

Simon obtained his doctorate in organizational decision-making from the University of Chicago in 1943.

Rudolf Carnap, Harold Lasswell, Edward Merriam, Nicolas Rashevsky, and Henry Schultz were among his instructors.

He started his career as a political science professor at the Illinois Institute of Technology, where he taught and conducted research.

In 1949, he transferred to Carnegie Mellon University, where he stayed until 2001.

He progressed through the ranks of the Department of Industrial Management to become its chair.

He has written twenty-seven books and several articles that have been published.

In 1959, he was elected a member of the American Academy of Arts and Sciences.

In 1975, Simon was awarded the coveted Turing Award, and in 1978, he was awarded the Nobel Prize in Economics.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Dartmouth AI Conference; Expert Systems; General Problem Solver; Newell, Allen.

References & Further Reading:

Crowther-Heyck, Hunter. 2005. Herbert A. Simon: The Bounds of Reason in Modern America. Baltimore: Johns Hopkins Press.

Newell, Allen, and Herbert A. Simon. 1956. The Logic Theory Machine: A Complex Information Processing System. Santa Monica, CA: The RAND Corporation.

Newell, Allen, and Herbert A. Simon. 1976. “Computer Science as Empirical Inquiry: Symbols and Search.” Communications of the ACM 19, no. 3: 113–26.

Simon, Herbert A. 1996. Models of My Life. Cambridge, MA: MIT Press.

Artificial Intelligence - Who Was Allen Newell?


Allen Newell (1927–1992) was an American writer who lived from 1927 to 1992.

 Allen In the late 1950s and early 1960s, Newell collaborated with Herbert Simon to develop the earliest models of human cognition.

The Logic Theory Machine depicted how logical rules might be used in a proof, the General Problem Solver modeled how basic problem solving could be done, and an early chess software mimicked how to play chess (the Newell-Shaw-Simon chess program).

Newell and Simon demonstrated for the first time in these models how computers can modify symbols and how these manipulations may be used to describe, produce, and explain intelligent behavior.

Newell began his career at Stanford University as a physics student.

He joined to the RAND Corporation to work on complex system models after a year of graduate studies in mathematics at Princeton.

He met and was inspired by Oliver Selfridge while at RAND, who led him to modeling cognition.

He also met Herbert Simon, who would go on to receive the Nobel Prize in Economics for his work on economic decision-making processes, particularly satisficing.

Simon persuaded Newell to attend Carnegie Institute of Technology (now Carnegie Mellon University).

For the most of his academic career, Newell worked with Simon.

Newell's main goal was to simulate the human mind's operations using computer models in order to better comprehend it.

Newell earned his PhD at Carnegie Mellon, where he worked with Simon.

He began his academic career as a tenured and chaired professor.

He was a founding member of the Department of Computer Science (today known as the school), where he held his major position.

With Simon, Newell examined the mind, especially problem solving, as part of his major line of study.

Their book Human Problem Solving, published in 1972, outlined their idea of intelligence and included examples from arithmetic problems and chess.

To assess what resources are being utilized in cognition, they employed a lot of verbal talk-aloud proto cols, which are more accurate than think-aloud or retrospective protocols.

Ericsson and Simon eventually documented the science of verbal protocol data in more detail.

In his final lecture ("Desires and Diversions"), he stated that if you're going to be distracted, you should make the most of it.

He accomplished this via remarkable accomplishments in the areas of his diversions, as well as the use of some of them in his final effort.

One of the early hypertext systems, ZOG, was one of these diversions.

Newell also collaborated with Digital Equipment Corporation (DEC) founder Gordon Bell on a textbook on computer architectures and worked on voice recognition systems with CMU colleague Raj Reddy.

Working with Stuart Card and Thomas Moran at Xerox PARC to develop ideas of how people interact with computers was maybe the longest-running and most fruitful diversion.

The Psychology of Human-Computer Interaction documents these theories (1983).

Their study resulted in the Keystroke Level Model and GOMS, two models for representing human behavior, as well as the Model Human Processor, a simplified description of the mechanics of cognition in this domain.

Some of the first work in human-computer interface was done here (HCI).

Their strategy advocated for first knowing the user and the task, then employing technology to assist the user in completing the job.

In his farewell talk, Newell also said that scientists should have a last endeavor that would outlive them.

Newell's last goal was to advocate for unified theories of cognition (UTCs) and to develop Soar, a proposed UTC and example.

His idea imagined what it would be like to have a theory that combined all of psychology's restrictions, facts, and theories into a single unified outcome that could be implemented by a computer program.

Soar continues to be a successful continuing project, despite the fact that it is not yet completed.

While Soar has yet fully unify psychology, it has made significant progress in describing problem solving, learning, and their interactions, as well as how to create autonomous, reactive entities in huge simulations.

He looked into how learning could be modeled as part of his final project (with Paul Rosenbloom).

Later, this project was merged with Soar.

Learning, according to Newell and Rosenbloom, follows a power law of practice, in which the time to complete a task is proportional to the practice (trial) number raised to a small negative power (e.g., Time trial -).

This holds true across a broad variety of activities.

Their explanation was that when tasks were completed in a hierarchical order, what was learnt at the lowest level had the greatest impact on reaction time, but as learning progressed up the hierarchy, it was less often employed and saved less time, thus learning slowed but did not cease.

Newell delivered the William James Lectures at Harvard in 1987.

He detailed what it would take to develop a unified theory in psychology in these lectures.

These lectures were taped and are accessible in CMU's library.

He gave them again the following autumn and turned them into a book (1990).

Soar's representation of cognition is based on searching through issue spaces.

It takes the form of a manufacturing system (using IF-THEN rules).

It makes an effort to use an operator.

Soar recurses with an impasse to solve the issue if it doesn't have one or can't apply it.

As a result, knowledge is represented as operator parts and issue spaces, as well as how to overcome impasses.

As a result, the architecture is how these choices and information may be organized.

Soar models have been employed in a range of cognitive science and AI applications, including military simulations, and systems with up to one million rules have been constructed.

Kathleen Carley, a social scientist at CMU, and Newell discussed how to use these cognitive models to simulate social agents.

Work on Soar continues, notably at the University of Michigan under the direction of John Laird, with a concentration on intelligent agents presently.

In 1975, the ACM A. M. Turing Award was given to Newell and Simon for their contributions to artificial intelligence, psychology of human cognition, and list processing.

Their work is credited with making significant contributions to computer science as an empirical investigation.

Newell has also been inducted into the National Academies of Sciences and Engineering.

He was awarded the National Medal of Science in 1992.

Newell was instrumental in establishing a productive and supportive research group, department, and institution.

His son said at his memorial service that he was not only a great scientist, but also a great father.

His weaknesses were that he was very intelligent, that he worked really hard, and that he had the same opinion of you.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Dartmouth AI Conference; General Problem Solver; Simon, Herbert A.

References & Further Reading:

Newell, Allen. 1990. Unified Theories of Cognition. Cambridge, MA: Harvard University Press.

Newell, Allen. 1993. Desires and Diversions. Carnegie Mellon University, School of Computer Science. Stanford, CA: University Video Communications.

Simon, Herbert A. 1998. “Allen Newell: 1927–1992.” IEEE Annals of the History of Computing 20, no. 2: 63–76.

Artificial Intelligence - What Were The Macy Conferences?


The Macy Conferences on Cybernetics, which ran from 1946 to 1960, aimed to provide the framework for developing multidisciplinary disciplines such as cybernetics, cognitive psychology, artificial life, and artificial intelligence.

Famous twentieth-century scholars, academics, and researchers took part in the Macy Conferences' freewheeling debates, including psychiatrist W.

Ross Ashby, anthropologist Gregory Bateson, ecologist G. Evelyn Hutchinson, psychologist Kurt Lewin, philosopher Donald Marquis, neurophysiologist Warren McCulloch, cultural anthropologist Margaret Mead, economist Oskar Morgenstern, statistician Leonard Savage, physicist Heinz von Foerster McCulloch, a neurophysiologist at the Massachusetts Institute of Technology's Research Laboratory for Electronics, and von Foerster, a professor of signal engineering at the University of Illinois at Urbana-Champaign and coeditor with Mead of the published Macy Conference proceedings, were the two main organizers of the conferences.

All meetings were sponsored by the Josiah Macy Jr. Foundation, a nonprofit organization.

The conferences were started by Macy administrators Frank Fremont-Smith and Lawrence K. Frank, who believed that they would spark multidisciplinary discussion.

The disciplinary isolation of medical research was a major worry for Fremont-Smith and Frank.

A Macy-sponsored symposium on Cerebral Inhibitions in 1942 preceded the Macy meetings, during which Harvard physiology professor Arturo Rosenblueth presented the first public discussion on cybernetics, titled "Behavior, Purpose, and Teleology." The 10 conferences conducted between 1946 and 1953 focused on biological and social systems' circular causation and feedback processes.

Between 1954 and 1960, five transdisciplinary Group Processes Conferences were held as a result of these sessions.

To foster direct conversation amongst participants, conference organizers avoided formal papers in favor of informal presentations.

The significance of control, communication, and feedback systems in the human nervous system was stressed in the early Macy Conferences.

The contrasts between analog and digital processing, switching circuit design and Boolean logic, game theory, servomechanisms, and communication theory were among the other subjects explored.

These concerns belong under the umbrella of "first-order cybernetics." Several biological issues were also discussed during the conferences, including adrenal cortex function, consciousness, aging, metabolism, nerve impulses, and homeostasis.

The sessions acted as a forum for discussing long-standing issues in what would eventually be referred to as artificial intelligence.

(At Dartmouth College in 1955, mathematician John McCarthy invented the phrase "artificial intelligence.") Gregory Bateson, for example, gave a lecture at the inaugural Macy Conference that differentiated between "learning" and "learning to learn" based on his anthropological research and encouraged listeners to consider how a computer might execute either job.

Attendees in the eighth conference discussed decision theory research, which was led by Leonard Savage.

Ross Ashby suggested the notion of chess-playing automatons at the ninth conference.

The usefulness of automated computers as logic models for human cognition was discussed more than any other issue during the Macy Conferences.

In 1964, the Macy Conferences gave rise to the American Society for Cybernetics, a professional organization.

The Macy Conferences' early arguments on feedback methods were applied to topics as varied as artillery control, project management, and marital therapy.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Cybernetics and AI; Dartmouth AI Conference.

References & Further Reading:

Dupuy, Jean-Pierre. 2000. The Mechanization of the Mind: On the Origins of Cognitive Science. Princeton, NJ: Princeton University Press.

Hayles, N. Katherine. 1999. How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago: University of Chicago Press.

Heims, Steve J. 1988. “Optimism and Faith in Mechanism among Social Scientists at the Macy Conferences on Cybernetics, 1946–1953.” AI & Society 2: 69–78.

Heims, Steve J. 1991. The Cybernetics Group. Cambridge, MA: MIT Press.

Pias, Claus, ed. 2016. The Macy Conferences, 1946–1953: The Complete Transactions. Zürich, Switzerland: Diaphanes.

Artificial Intelligence - Who Was Marvin Minsky?


Donner Professor of Natural Sciences Marvin Minsky (1927–2016) was a well-known cognitive scientist, inventor, and artificial intelligence researcher from the United States.

At the Massachusetts Institute of Technology, he cofounded the Artificial Intelligence Laboratory in the 1950s and the Media Lab in the 1980s.

His renown was such that the sleeping astronaut Dr.

Victor Kaminski (killed by the HAL 9000 sentient computer) was named after him when he was an adviser on Stanley Kubrick's iconic film 2001: A Space Odyssey in the 1960s.

At the conclusion of high school in the 1940s, Minsky got interested in intelligence, thinking, and learning machines.

He was interested in neurology, physics, music, and psychology as a Harvard student.

On problem-solving and learning ideas, he collaborated with cognitive psychologist George Miller, and on perception and brain modeling theories with J.C.R. Licklider, professor of psychoacoustics and later father of the internet.

Minsky started thinking about mental ideas while at Harvard.

"I thought the brain was made up of tiny relays called neurons, each of which had a probability linked to it that determined whether the neuron would conduct an electric pulse," he later recalled.

"Technically, this system is now known as a stochastic neural network" (Bern stein 1981).

This hypothesis is comparable to Donald Hebb's Hebbian theory, which he laid forth in his book The Organization of Behavior (1946).

In the mathematics department, he finished his undergraduate thesis on topology.

Minsky studied mathematics as a graduate student at Princeton University, but he became increasingly interested in attempting to build artificial neurons out of vacuum tubes like those described in Warren McCulloch and Walter Pitts' famous 1943 paper "A Logical Calculus of the Ideas Immanent in Nervous Activity." He thought that a machine like this might navigate mazes like a rat.

In the summer of 1951, he and fellow Princeton student Dean Edmonds created the system, termed SNARC (Stochastic Neural-Analog Reinforcement Calculator), with money from the Office of Naval Research.

There were 300 tubes in the machine, as well as multiple electric motors and clutches.

Making it a learning machine, the machine employed the clutches to adjust its own knobs.

The electric rat initially walked at random, but after learning how to make better choices and accomplish a wanted objective via reinforcement of probability, it learnt how to make better choices and achieve a desired goal.

Multiple rats finally gathered in the labyrinth and learnt from one another.

Minsky built a second memory for his hard-wired neural network in his dissertation thesis, which helped the rat recall what stimulus it had received.

When confronted with a new circumstance, this enabled the system to explore its memories and forecast the optimum course of action.

Minsky had believed that by adding enough memory loops to his self-organizing random networks, conscious intelligence would arise spontaneously.

In 1954, Minsky finished his dissertation, "Neural Nets and the Brain Model Problem." After graduating from Princeton, Minsky continued to consider how to create artificial intelligence.

In 1956, he organized and participated in the DartmouthSummer Research Project on Artificial Intelligence with John McCarthy, Nathaniel Rochester, and Claude Shannon.

The Dartmouth workshop is often referred to as a watershed moment in AI research.

Minsky started replicating the computational process of proving Euclid's geometric theorems using bits of paper during the summer workshop since no computer was available.

He realized he could create an imagined computer that would locate proofs without having to tell it precisely what it needed to accomplish.

Minsky showed the results to Nathaniel Rochester, who returned to IBM and asked Herbert Gelernter, a new physics hire, to write a geometry-proving program on a computer.

Gelernter built a program in FORTRAN List Processing Language, a language he invented.

Later, John McCarthy combined Gelernter's language with ideas from mathematician Alonzo Church to develop LISP, the most widely used AI language (List-Processing).

Minsky began his studies at MIT in 1957.

He started worked on pattern recognition difficulties with Oliver Selfridge at the university's Lincoln Laboratory.

The next year, he was hired as an assistant professor in the mathematics department.

He founded the AI Group with McCarthy, who had transferred to MIT from Dartmouth.

They continued to work on machine learning concepts.

Minsky started working with mathematician Seymour Papert in the 1960s.

Perceptrons: An Introduction to Computational Geometry (1969) was a joint publication describing a kind of artificial neural network described by Cornell Aeronautical Lab oratory psychologist Frank Rosenblatt.

The book sparked a decades-long debate in the AI field, which continues to this day in certain aspects.

The mathematical arguments provided in Minsky and Papert's book pushed the field to shift toward symbolic AI (also known as "Good Old-Fashioned AI" or GOFAI) in the 1980s, when artificial intelligence researchers rediscovered perceptrons and neural networks.

Time-shared computers were more widely accessible on the MIT campus in the 1960s, and Minsky started working with students on machine intelligence issues.

One of the first efforts was to teach computers how to solve problems in basic calculus using symbolic manipulation techniques such as differentiation and integration.

In 1961, his student James Robert Slagle built a software for symbol manipulation.

SAINT was the name of the application, which operated on an IBM 7090 transistorized mainframe computer (Symbolic Automatic INTegrator).

Other students applied the technique to any symbol manipulation that their software MACSYMA would demand.

Minsky's pupils also had to deal with the challenge of educating a computer to reason by analogy.

Minsky's team also worked on issues related to computational linguistics, computer vision, and robotics.

Daniel Bobrow, one of his pupils, taught a computer how to answer word problems, an accomplishment that combined language processing and mathematics.

Henry Ernst, a student, designed the first computer-controlled robot, a mechanical hand with photoelectric touch sensors for grasping nuclear materials.

Minsky collaborated with Papert to develop semi-independent programs that could interact with one another to address increasingly complex challenges in computer vision and manipulation.

Minsky and Papert combined their nonhierarchical management techniques into a natural intelligence hypothesis known as the Society of Mind.

Intelligence, according to this view, is an emergent feature that results from tiny interactions between programs.

After studying various constructions, the MIT AI Group trained a computer-controlled robot to build structures out of children's blocks by 1970.

Throughout the 1970s and 1980s, the blocks-manipulating robot and the Society of Mind hypothesis evolved.

Minsky finally released The Society of Mind (1986), a model for the creation of intelligence through individual mental actors and their interactions, rather than any fundamental principle or universal technique.

He discussed consciousness, self, free will, memory, genius, language, memory, brainstorming, learning, and many other themes in the book, which is made up of 270 unique articles.

Agents, according to Minsky, do not require their own mind, thinking, or feeling abilities.

They are not intelligent.

However, when they work together as a civilization, they develop what we call human intellect.

To put it another way, understanding how to achieve any certain goal requires the collaboration of various agents.

Agents are required by Minsky's robot constructor to see, move, locate, grip, and balance blocks.

"I'd like to believe that this effort provided us insights into what goes on within specific sections of children's brains when they learn to 'play' with basic toys," he wrote (Minsky 1986, 29).

Minsky speculated that there may be over a hundred agents collaborating to create what we call mind.

In the book Emotion Machine, he expanded on his views on Society of Mind (2006).

He argued that emotions are not a separate kind of reasoning in this section.

Rather, they reflect different ways of thinking about various sorts of challenges that people face in the real world.

According to Minsky, the mind changes between different modes of thought, thinks on several levels, finds various ways to represent things, and constructs numerous models of ourselves.

Minsky remarked on a broad variety of popular and significant subjects linked to artificial intelligence and robotics in his final years via his books and interviews.

The Turing Option (1992), a book created by Minsky in partnership with science fiction novelist Harry Harrison, is set in the year 2023 and deals with issues of artificial intelligence.

In a 1994 article for Scientific American headlined "Will Robots Inherit the Earth?" he said, "Yes, but they will be our children" (Minsky 1994, 113).

Minsky once suggested that a superintelligent AI may one day spark a Riemann Hypothesis Catastrophe, in which an agent charged with answering the hypothesis seizes control of the whole planet's resources in order to obtain even more supercomputing power.

He didn't think this was a plausible scenario.

Humans could be able to converse with intelligent alien life forms, according to Minsky.

They'd think like humans because they'd be constrained by the same "space, time, and material constraints" (Minsky 1987, 117).

Minsky was also a critic of the Loebner Prize, the world's oldest Turing Test-like competition, claiming that it is detrimental to artificial intelligence research.

To anybody who could halt Hugh Loebner's yearly competition, he offered his own Minsky Loebner Prize Revocation Prize.

Both Minsky and Loebner died in 2016, yet the Loebner Prize competition is still going on.

Minsky was also responsible for the development of the confocal microscope (1957) and the head-mounted display (HMD) (1963).

He was awarded the Turing Award in 1969, the Japan Prize in 1990, and the Benjamin Franklin Medal in 1991. (2001). Daniel Bobrow (operating systems), K. Eric Drexler (molecular nanotechnology), Carl Hewitt (mathematics and philosophy of logic), Danny Hillis (parallel computing), Benjamin Kuipers (qualitative simulation), Ivan Sutherland (computer graphics), and Patrick Winston (computer graphics) were among Minsky's doctoral students (who succeeded Minsky as director of the MIT AI Lab).

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

AI Winter; Chatbots and Loebner Prize; Dartmouth AI Conference; 2001: A Space Odyssey.

References & Further Reading:

Bernstein, Jeremy. 1981. “Marvin Minsky’s Vision of the Future.” New Yorker, December 7, 1981.

Minsky, Marvin. 1986. The Society of Mind. London: Picador.

Minsky, Marvin. 1987. “Why Intelligent Aliens Will Be Intelligible.” In Extraterrestrials: Science and Alien Intelligence, edited by Edward Regis, 117–28. Cambridge, UK: Cambridge University Press.

Minsky, Marvin. 1994. “Will Robots Inherit the Earth?” Scientific American 271, no. 4 (October): 108–13.

Minsky, Marvin. 2006. The Emotion Machine. New York: Simon & Schuster.

Minsky, Marvin, and Seymour Papert. 1969. Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: Massachusetts Institute of Technology.

Singh, Push. 2003. “Examining the Society of Mind.” Computing and Informatics 22, no. 6: 521–43.

Artificial Intelligence - How Is AI Contributing To Cybernetics?


The study of communication and control in live creatures and machines is known as cybernetics.

Although the phrase "cybernetic thinking" is no longer generally used in the United States, it pervades computer science, engineering, biology, and the social sciences today.

Throughout the last half-century, cybernetic connectionist and artificial neural network approaches to information theory and technology have often clashed, and in some cases hybridized, with symbolic AI methods.

Norbert Wiener (1894–1964), who coined the term "cybernetics" from the Greek word for "steersman," saw the field as a unifying force that brought disparate topics like game theory, operations research, theory of automata, logic, and information theory together and elevated them.

Winer argued in Cybernetics, or Control and Communication in the Animal and the Machine (1948), that contemporary science had become too much of a specialist's playground as a consequence of tendencies dating back to the early Enlightenment.

Wiener envisioned a period when experts might collaborate "not as minions of some great administrative officer, but united by the desire, indeed by the spiritual imperative, to comprehend the area as a whole, and to give one another the power of that knowledge" (Weiner 1948b, 3).

For Wiener, cybernetics provided researchers with access to many sources of knowledge while maintaining their independence and unbiased detachment.

Wiener also believed that man and machine should be seen as basically interchangeable epistemologically.

The biological sciences and medicine, according to Wiener, would remain semi-exact and dependent on observer subjectivity until these common components were discovered.

In the setting of World War II (1939– 1945), Wiener developed his cybernetic theory.

Operations research and game theory, for example, are interdisciplinary sciences rich in mathematics that have previously been utilized to identify German submarines and create the best feasible solutions to complex military decision-making challenges.

Wiener committed himself into the job of implementing modern cybernetic weapons against the Axis countries in his role as a military adviser.

To that purpose, Wiener focused on deciphering the feedback processes involved in curvilinear flight prediction and applying these concepts to the development of advanced fire-control systems for shooting down enemy aircraft.

Claude Shannon, a long-serving Bell Labs researcher, went even further than Wiener in attempting to bring cybernetic ideas to life, most notably in his experiments with Theseus, an electromechanical mouse that used digital relays and a feedback process to learn how to navigate mazes based on previous experience.

Shannon created a slew of other automata that mimicked the behavior of thinking machines.

Shannon's mentees, including AI pioneers John McCarthy and Marvin Minsky, followed in his footsteps and labeled him a symbolic information processor.

McCarthy, who is often regarded with establishing the field of artificial intelligence, studied the mathematical logic that underpins human thought.

Minsky opted to research neural network models as a machine imitation of human vision.

The so-called McCulloch-Pitts neurons were the core components of cybernetic understanding of human cognitive processing.

These neurons were strung together by axons for communication, establishing a cybernated system encompassing a crude simulation of the wet science of the brain, and were named after Warren McCulloch and Walter Pitts.

Pitts admired Wiener's straightforward analogy of cerebral tissue to vacuum tube technology, and saw these switching devices as metallic analogues to organic cognitive components.

McCulloch-Pitts neurons were believed to be capable of mimicking basic logical processes required for learning and memory.

Pitts perceived a close binary equivalence between the electrical discharges produced by these devices and the electrochemical nerve impulses generated in the brain in the 1940s.

McCulloch-Pitts inputs may be either a zero or a one, and the output can also be a zero or a one in their most basic form.

Each input may be categorized as excitatory or inhibitory.

It was therefore merely a short step from artificial to animal memory for Pitts and Wiener.

Donald Hebb, a Canadian neuropsychologist, made even more significant contributions to the research of artificial neurons.

These were detailed in his book The Organization of Behavior, published in 1949.

Associative learning is explained by Hebbian theory as a process of neural synaptic cells firing and connecting together.

In his study of the artificial "perceptron," a model and algorithm that weighted inputs so that it could be taught to detect particular kinds of patterns, U.S.

Navy researcher Frank Rosenblatt expanded the metaphor.

The eye and cerebral circuitry of the perceptron could approximately discern between pictures of cats and dogs.

The navy saw the perceptron as "the embryo of an electronic computer that it anticipates to be able to walk, speak, see, write, reproduce itself, and be cognizant of its existence," according to a 1958 interview with Rosenblatt (New York Times, July 8, 1958, 25).

Wiener, Shannon, McCulloch, Pitts, and other cyberneticists were nourished by the famed Macy Conferences on Cybernetics in the 1940s and 1950s, which attempted to automate human comprehension of the world and the learning process.

The gatherings also acted as a forum for discussing artificial intelligence issues.

The divide between the areas developed over time, but it was visible during the 1956 Dartmouth Summer Research Project on ArtificialIntelligence.

Organic cybernetics research was no longer well-defined in American scientific practice by 1970.

Computing sciences and technology evolved from machine cybernetics.

Cybernetic theories are now on the periphery of social and hard scientific disciplines such as cognitive science, complex systems, robotics, systems theory, and computer science, but they were critical to the information revolution of the twentieth and twenty-first centuries.

In recent studies of artificial neural networks and unsupervised machine learning, Hebbian theory has seen a resurgence of attention.

Cyborgs—beings made up of biological and mechanical pieces that augment normal functions—could be regarded a subset of cybernetics (which was once known as "medical cybernetics" in the 1960s).

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Dartmouth AI Conference; Macy Conferences; Warwick, Kevin.

Further Reading

Ashby, W. Ross. 1956. An Introduction to Cybernetics. London: Chapman & Hall.

Galison, Peter. 1994. “The Ontology of the Enemy: Norbert Weiner and the Cybernetic Vision.” Critical Inquiry 21, no. 1 (Autumn): 228–66.

Kline, Ronald R. 2017. The Cybernetics Moment: Or Why We Call Our Age the Information Age. Baltimore, MD: Johns Hopkins University Press.

Mahoney, Michael S. 1990. “Cybernetics and Information Technology.” In Companion to the History of Modern Science, edited by R. C. Olby, G. N. Cantor, J. R. R. Christie, and M. J. S. Hodge, 537–53. London: Routledge.

“New Navy Device Learns by Doing; Psychologist Shows Embryo of Computer Designed to Read and Grow Wiser.” 1958. New York Times, July 8, 25.

Weiner, Norbert. 1948a. “Cybernetics.” Scientific American 179, no. 5 (November): 14–19.

Weiner, Norbert. 1948b. Cybernetics, or Control and Communication in the Animal and the Machine. Cambridge, MA: MIT Press.

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