Artificial Intelligence - Who Is Sherry Turkle?

 

 

 

Sherry Turkle(1948–) has a background in sociology and psychology, and her work focuses on the human-technology interaction.

While her study in the 1980s focused on how technology affects people's thinking, her work in the 2000s has become more critical of how technology is utilized at the expense of building and maintaining meaningful interpersonal connections.

She has employed artificial intelligence in products like children's toys and pets for the elderly to highlight what people lose out on when interacting with such things.

Turkle has been at the vanguard of AI breakthroughs as a professor at the Massachusetts Institute of Technology (MIT) and the creator of the MIT Initiative on Technology and the Self.

She highlights a conceptual change in the understanding of AI that occurs between the 1960s and 1980s in Life on the Screen: Identity inthe Age of the Internet (1995), substantially changing the way humans connect to and interact with AI.

She claims that early AI paradigms depended on extensive preprogramming and employed a rule-based concept of intelligence.

However, this viewpoint has given place to one that considers intelligence to be emergent.

This emergent paradigm, which became the recognized mainstream view by 1990, claims that AI arises from a much simpler set of learning algorithms.

The emergent method, according to Turkle, aims to emulate the way the human brain functions, assisting in the breaking down of barriers between computers and nature, and more generally between the natural and the artificial.

In summary, an emergent approach to AI allows people to connect to the technology more easily, even thinking of AI-based programs and gadgets as children.

Not just for the area of AI, but also for Turkle's study and writing on the subject, the rising acceptance of the emerging paradigm of AI and the enhanced relatability it heralds represents a significant turning point.

Turkle started to employ ethnographic research techniques to study the relationship between humans and their gadgets in two edited collections, Evocative Objects: Things We Think With (2007) and The Inner History of Devices (2008).

She emphasized in her book The Inner History of Devices that her intimate ethnography, or the ability to "listen with a third ear," is required to go past the advertising-based clichés that are often employed when addressing technology.

This method comprises setting up time for silent meditation so that participants may think thoroughly about their interactions with their equipment.

Turkle used similar intimate ethnographic approaches in her second major book, Alone Together: 

Why We Expect More from Technology and Less from Each Other (2011), to argue that the increasing connection between people and the technology they use is harmful.

These issues are connected to the increased usage of social media as a form of communication, as well as the continuous degree of familiarity and relatability with technology gadgets, which stems from the emerging AI paradigm that has become practically omnipresent.

She traced the origins of the dilemma back to early pioneers in the field of cybernetics, citing, for example, Norbert Weiner's speculations on the idea of transmitting a human person across a telegraph line in his book God & Golem, Inc.(1964).

Because it reduces both people and technology to information, this approach to cybernetic thinking blurs the barriers between them.

In terms of AI, this implies that it doesn't matter whether the machines with which we interact are really intelligent.

Turkle claims that by engaging with and caring for these technologies, we may deceive ourselves into feeling we are in a relationship, causing us to treat them as if they were sentient.

In a 2006 presentation titled "Artificial Intelligence at 50: From Building Intelligence to Nurturing Sociabilities" at the Dartmouth Artificial Intelligence Conference, she recognized this trend.

She identified the 1997 Tamagotchi, 1998 Furby, and 2000 MyReal Baby as early versions of what she refers to as relational artifacts, which are more broadly referred to as social machines in the literature.

The main difference between these devices and previous children's toys is that these devices come pre-animated and ready for a relationship, whereas previous children's toys required children to project a relationship onto them.

Turkle argues that this change is about our human weaknesses as much as it is about computer capabilities.

In other words, just caring for an item increases the likelihood of not only seeing it as intelligent but also feeling a connection to it.

This sense of connection is more relevant to the typical person engaging with these technologies than abstract philosophic considerations concerning the nature of their intelligence.

Turkle delves more into the ramifications of people engaging with AI-based technologies in both Alone Together and Reclaiming Conversation: The Power of Talk in a Digital Age (2015).

She provides the example of Adam in Alone Together, who appreciates the appreciation of the AI bots he controls over in the game Civilization.

Adam appreciates the fact that he is able to create something fresh when playing.

Turkle, on the other hand, is skeptical of this interaction, stating that Adam's playing isn't actual creation, but rather the sensation of creation, and that it's problematic since it lacks meaningful pressure or danger.

In Reclaiming Conversation, she expands on this point, suggesting that social partners simply provide a perception of camaraderie.

This is important because of the value of human connection and what may be lost in relationships that simply provide a sensation or perception of friendship rather than true friendship.

Turkle believes that this transition is critical.

She claims that although connections with AI-enabledtechnologies may have certain advantages, they pale in contrast to what is missing: 

• the complete complexity and inherent contradictions that define what it is to be human.

A person's connection with an AI-enabled technology is not as intricate as one's interaction with other individuals.

Turkle claims that as individuals have become more used to and dependent on technology gadgets, the definition of friendship has evolved.

• People's expectations for companionship have been simplified as a result of this transformation, and the advantages that one wants to obtain from partnerships have been reduced.

• People now tend to associate friendship only with the concept of interaction, ignoring the more nuanced sentiments and arguments that are typical in partnerships.

• By engaging with gadgets, one may form a relationship with them.

• Conversations between humans have become merely transactional as human communication has shifted away from face-to-face conversation and toward interaction mediated by devices. 

In other words, the most that can be anticipated is engagement.

Turkle, who has a background in psychoanalysis, claims that this kind of transactional communication allows users to spend less time learning to view the world through the eyes of another person, which is a crucial ability for empathy.

Turkle argues we are in a robotic period in which people yearn for, and in some circumstances prefer, AI-based robotic companionship over that of other humans, drawing together these numerous streams of argument.

For example, some people enjoy conversing with their iPhone's Siri virtual assistant because they aren't afraid of being judged by it, as evidenced by a series of Siri commercials featuring celebrities talking to their phones.

Turkle has a problem with this because these devices can only respond as if they understand what is being said.

AI-based gadgets, on the other hand, are confined to comprehending the literal meanings of data stored on the device.

They can decipher the contents of phone calendars and emails, but they have no idea what any of this data means to the user.

There is no discernible difference between a calendar appointment for car maintenance and one for chemotherapy for an AI-based device.

A person may lose sight of what it is to have an authentic dialogue with another human when entangled in a variety of these robotic connections with a growing number of technologies.

While Reclaiming Communication documents deteriorating conversation skills and decreasing empathy, it ultimately ends on a positive note.

Because people are becoming increasingly dissatisfied with their relationships, there may be a chance for face-to-face human communication to reclaim its vital role.

Turkle's ideas focus on reducing the amount of time people spend on their phones, but AI's involvement in this interaction is equally critical.

• Users must accept that their virtual assistant connections will never be able to replace face-to-face interactions.

• This will necessitate being more deliberate in how one uses devices, prioritizing in-person interactions over the faster and easier interactions provided by AI-enabled devices.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Blade Runner; Chatbots and Loebner Prize; ELIZA; General and Narrow AI; Moral Turing Test; PARRY; Turing, Alan; 2001: A Space Odyssey.

References And Further Reading

• Haugeland, John. 1997. “What Is Mind Design?” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 1–28. Cambridge, MA: MIT Press.

• Searle, John R. 1997. “Minds, Brains, and Programs.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 183–204. Cambridge, MA: MIT Press.

• Turing, A. M. 1997. “Computing Machinery and Intelligence.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 29–56. Cambridge, MA: MIT Press.

Artificial Intelligence - What Is The Turing Test?

 

 

The Turing Test is a method of determining whether or not a machine can exhibit intelligence that mimics or is equivalent and likened to Human intelligence. 

The Turing Test, named after computer scientist Alan Turing, is an AI benchmark that assigns intelligence to any machine capable of displaying intelligent behavior comparable to that of a person.

Turing's "Computing Machinery and Intelligence" (1950), which establishes a simple prototype—what Turing calls "The Imitation Game," is the test's locus classicus.

In this game, a person is asked to determine which of the two rooms is filled by a computer and which is occupied by another human based on anonymized replies to natural language questions posed by the judge to each inhabitant.

Despite the fact that the human respondent must offer accurate answers to the court's queries, the machine's purpose is to fool the judge into thinking it is human.

According to Turing, the machine may be considered intelligent to the degree that it is successful at this job.

The fundamental benefit of this essentially operationalist view of intelligence is that it avoids complex metaphysics and epistemological issues about the nature and inner experience of intelligent activities.

According to Turing's criteria, little more than empirical observation of outward behavior is required for predicting object intelligence.

This is in sharp contrast to the widely Cartesian epistemological tradition, which holds that some internal self-awareness is a need for intelligence.

Turing's method avoids the so-called "problem of other minds" that arises from such a viewpoint—namely, how to be confident of the presence of other intelligent individuals if it is impossible to know their thoughts from a presumably required first-person perspective.

Nonetheless, the Turing Test, at least insofar as it considers intelligence in a strictly formalist manner, is bound up with the spirit of Cartesian epistemol ogy.

The machine in the Imitation Game is a digital computer in the sense of Turing: a set of operations that may theoretically be implemented in any material.

A digital computer consists of three parts: a knowledge store, an executive unit that executes individual orders, and a control that regulates the executive unit.

However, as Turing points out, it makes no difference whether these components are created using electrical or mechanical means.

What matters is the formal set of rules that make up the computer's very nature.

Turing holds to the core belief that intellect is inherently immaterial.

If this is true, it is logical to assume that human intellect functions in a similar manner to a digital computer and may therefore be copied artificially.

Since Turing's work, AI research has been split into two camps: 

1. those who embrace and 
2. those who oppose this fundamental premise.

To describe the first camp, John Haugeland created the term "good old-fashioned AI," or GOFAI.

Marvin Minsky, Allen Newell, Herbert Simon, Terry Winograd, and, most notably, Joseph Weizenbaum, whose software ELIZA was controversially hailed as the first to pass the Turing Test in 1966.

Nonetheless, detractors of Turing's formalism have proliferated, particularly in the past three decades, and GOFAI is now widely regarded as a discredited AI technique.

John Searle's Minds, Brains, and Programs (1980), in which Searle builds his now-famous Chinese Room thought experiment, is one of the most renowned criticisms of GOFAI in general—and the assumptions of the Turing Test in particular.

In the latter, a person with no prior understanding of Chinese is placed in a room and forced to correlate Chinese characters she receives with other Chinese characters she puts out, according to an English-scripted software.

Searle thinks that, assuming adequate mastery of the software, the person within the room may pass the Turing Test, fooling a native Chinese speaker into thinking she knew Chinese.

If, on the other hand, the person in the room is a digital computer, Turing-type tests, according to Searle, fail to capture the phenomena of understanding, which he claims entails more than the functionally accurate connection of inputs and outputs.

Searle's argument implies that AI research should take materiality issues seriously in ways that Turing's Imitation Game's formalism does not.

Searle continues his own explanation of the Chinese Room thought experiment by saying that human species' physical makeup—particularly their sophisticated nerve systems, brain tissue, and so on—should not be discarded as unimportant to conceptions of intelligence.

This viewpoint has influenced connectionism, an altogether new approach to AI that aims to build computer intelligence by replicating the electrical circuitry of human brain tissue.

The effectiveness of this strategy has been hotly contested, although it looks to outperform GOFAI in terms of developing generalized kinds of intelligence.

Turing's test, on the other hand, may be criticized not just from the standpoint of materialism, but also from the one of fresh formalism.

As a result, one may argue that Turing tests are insufficient as a measure of intelligence since they attempt to reproduce human behavior, which is frequently exceedingly dumb.

According to certain variants of this argument, if criteria of rationality are to distinguish rational from illogical human conduct in the first place, they must be derived a priori rather than from real human experience.

This line of criticism has gotten more acute as AI research has shifted its focus to the potential of so-called super-intelligence: forms of generalized machine intelligence that far outperform human intellect.

Should this next level of AI be attained, Turing tests would seem to be outdated.

Furthermore, simply discussing the idea of superintelligence would seem to need additional intelligence criteria in addition to severe Turing testing.

Turing may be defended against such accusation by pointing out that establishing a universal criterion of intellect was never his goal.

Indeed, according to Turing (1997, 29–30), the purpose is to replace the metaphysically problematic issue "can machines think" with the more empirically verifiable alternative: 

"What will happen when a computer assumes the role [of the man in the Imitation Game]" (Turing 1997, 29–30).

Thus, Turing's test's above-mentioned flaw—that it fails to establish a priori rationality standards—is also part of its strength and drive.

It also explains why, since it was initially presented three-quarters of a century ago, it has had such a lengthy effect on AI research in all domains.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Blade Runner; Chatbots and Loebner Prize; ELIZA; General and Narrow AI; Moral Turing Test; PARRY; Turing, Alan; 2001: A Space Odyssey.

References And Further Reading

Haugeland, John. 1997. “What Is Mind Design?” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 1–28. Cambridge, MA: MIT Press.

Searle, John R. 1997. “Minds, Brains, and Programs.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 183–204. Cambridge, MA: MIT Press.

Turing, A. M. 1997. “Computing Machinery and Intelligence.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 29–56. Cam￾bridge, MA: MIT Press.

Artificial Intelligence - Who Was Alan Turing?

 

 

 Alan Mathison Turing OBE FRS(1912–1954) was a logician and mathematician from the United Kingdom.

He is known as the "Father of Artificial Intelligence" and "The Father of Computer Science." 

Turing earned a first-class honors degree in mathematics from King's College, Cambridge, in 1934.

Turing received his PhD from Princeton University after a fellowship at King's College, where he studied under American mathematician Alonzo Church.

Turing wrote numerous important publications during his studies, including "On Computable Numbers, with an Application to the Entscheidungsproblem," which proved that the so-called "decision problem" had no solution.

The decision issue asks if there is a method for determining the correctness of any assertion inside a mathematical system.

This paper also explored a hypothetical Turing machine (basically an early computer) that, if represented by an algorithm, could execute any mathematical operation.

Turing is best known for his codebreaking work at Bletchley Park's Government Code and Cypher School (GC&CS) during World War II (1939–1945).

Turing's work at GC&CS included heading Hut 8, which was tasked with cracking the German Enigma and other very difficult naval encryption.

Turing's work undoubtedly shortened the war by years, saving millions of lives, but it is hard to measure with precision.

Turing wrote "The Applications of Probability to Cryptography" and "Paper on Statistics of Repetitions" during his tenure at GC&CS, both of which were held secret for seventy years by the Government Communications Headquarters (GCHQ) until being given to the UK National Archives in 2012.

Following WWII, Turing enrolled at the Victoria University of Manchester to study mathematical biology while continuing his work in mathematics, stored-program digital computers, and artificial intelligence.

Turing's 1950 paper "Computing Machinery and Intelligence" looked into artificial intelligence and introduced the concept of the Imitation Game (also known as the Turing Test), in which a human judge uses a set of written questions and responses to try to distinguish between a computer program and a human.

If the computer program imitates a person to the point that the human judge cannot discern the difference between the computer program's and the human's replies, the program has passed the test, indicating that it is capable of intelligent reasoning.

Turochamp, a chess program written by Turing and his colleague D.G. Champernowne, was meant to be executed by a computer, but no machine with adequate capacity existed to test the program.

Turing instead manually ran the algorithms to test the software.

Turing was well-recognized during his lifetime, despite the fact that most of his work remained secret until after his death.

Turing was made a Fellow of the Royal Society in 1951 and was awarded to the Order of the British Empire in 1946.(FRS).

The Turing Award, named after him, is given annually by the Association for Computing Machinery for contributions to the area of computing.

The Turing Award, which comes with a $1 million reward, is commonly recognized as the Nobel Prize of Computing.

Turing was outspoken about his sexuality at a period when homosexuality was still illegal in the United Kingdom.

Turing was accused in 1952 under Section 11 of the Criminal Law Amendment Act 1885 with "gross indecency." 

Turing was found guilty, granted probation, and was sentenced to a year of "chemical castration," in which he was injected with synthetic estrogen.

Turing's conviction had an influence on his career as well.

His security clearance was withdrawn, and he was compelled to stop working for the GCHQ as a cryptographer.

Following successful campaigning for an apology and pardon, the British government passed the Alan Turing bill in 2016, which retrospectively pardoned hundreds of persons imprisoned under Section 11 and other historical laws.

In 1954, Turing died of cyanide poisoning.

Turing's death may have been caused by inadvertent inhalation of cyanide vapors, despite the fact that it was officially considered a suicide.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Chatbots and Loebner Prize; General and Narrow AI; Moral Turing Test; Turing Test.

References And Further Reading

Hodges, Andrew. 2004. “Turing, Alan Mathison (1912–1954).” In Oxford Dictionary of National Biography. https://www.oxforddnb.com/view/10.1093/ref:odnb/9780198614128.001.0001/odnb-9780198614128-e-36578.

Lavington, Simon. 2012. Alan Turing and His Contemporaries: Building the World’s First Computers. Swindon, UK: BCS, The Chartered Institute for IT.

Sharkey, Noel. 2012. “Alan Turing: The Experiment that Shaped Artificial Intelligence.” BBC News, June 21, 2012. https://www.bbc.com/news/technology-18475646.

Artificial Intelligence - What Is The PARRY Computer Program?

PARRY (short for paranoia) is the first computer program to imitate a mental patient, created by Stanford University psychiatrist Kenneth Colby.

PARRY is communicated with by the psychiatrist-user in simple English.

PARRY's responses are intended to mirror the cognitive (mal)functioning of a paranoid patient.

In the late 1960s and early 1970s, Colby experimented with mental patient chatbots, which led to the development of PARRY.

Colby sought to illustrate that cognition is fundamentally a symbol manipulation process and that computer simulations may help psychiatric research.

Many technical aspects of PARRY were shared with Joseph Weizenbaum's ELIZA.

Both of these applications were conversational in nature, allowing the user to submit remarks in plain English.

PARRY's underlying algorithms, like ELIZA's, examined inputted phrases for essential terms to create plausible answers.

PARRY, on the other hand, was given a history in order to imitate the right paranoid behaviors.

Parry, who was fictitious, was a gambler who had gotten into a fight with a bookie.

Parry was paranoid enough to assume that the bookie would send the Mafia after him.

Since a result, PARRY freely shared information on its crazy Mafia ideas, as it would wish to enlist the user's assistance.

PARRY was also born with the ability to be "sensitive to his parents, religion, and sex" (Colby 1975, 36).

In most other topics of conversation, the show was neutral.

If PARRY couldn't find a match in its database, it may respond with "I don't know," "Why do you ask that?" or by returning to an earlier subject (Colby 1975, 77).

Whereas ELIZA's achievements made Weizenbaum a skeptic of AI, PARRY's findings bolstered Colby's support for computer simulations in psychiatry.

Colby picked paranoia as the mental state to mimic because it has the least fluid behavior and hence is the simplest to see.

Colby felt that human cognition was a process of symbol manipulation, as did artificial intelligence pioneers Herbert Simon and Allen Newell.

PARRY's cognitive functioning resembled that of a paranoid human being as a result of this.

Colby emphasized that a psychiatrist conversing with PARRY had learnt something about human paranoia.

He saw PARRY as a tool to help novice psychiatrists get started in their careers.

PARRY's reactions might also be used to determine the most successful therapeutic discourse lines.

Colby hoped that systems like PARRY would assist confirm or refute psychiatric hypotheses while also bolstering the field's scientific credibility.

On PARRY, Colby put his shame humiliation hypothesis of paranoid insanity to the test.

In the 1970s, Colby performed a series of studies to see how effectively PARRY could simulate true paranoia.

Two of these examinations resembled the Turing Test.

To begin, practicing psychiatrists were instructed to interview patients using a teletype terminal, an antiquated electromechanical typewriter that was used to send and receive typed messages over telecommunications.

The doctors were unaware that PARRY was one of the patients who took part in the interviews.

The transcripts of these interviews were then distributed to a group of 100 psychiatrists.

These psychiatrists were tasked with determining which version was created by a computer.

Twenty psychiatrists properly recognized PARRY, whereas the other twenty did not.

A total of 100 computer scientists received transcripts.

32 of the 67 computer scientists were accurate, while 35 were incorrect.

According to Colby, the findings "are akin to tossing a coin" statistically, and PARRY was not exposed (Colby 1975, 92).

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Chatbots and Loebner Prize; ELIZA; Expert Systems; Natural Language Processing and Speech Understanding; Turing Test.

References & Further Reading:

Cerf, Vincent. 1973. “Parry Encounters the Doctor: Conversation between a Simulated Paranoid and a Simulated Psychiatrist.” Datamation 19, no. 7 (July): 62–65.

Colby, Kenneth M. 1975. Artificial Paranoia: A Computer Simulation of Paranoid Pro￾cesses. New York: Pergamon Press.

Colby, Kenneth M., James B. Watt, and John P. Gilbert. 1966. “A Computer Method of Psychotherapy: Preliminary Communication.” Journal of Nervous and Mental Disease 142, no. 2 (February): 148–52.

McCorduck, Pamela. 1979. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, 251–56, 308–28. San Francisco: W. H. Freeman and Company.

Warren, Jim. 1976. Artificial Paranoia: An NIMH Program Report. Rockville, MD: US. Department of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute of Mental Health, Division of Scientific and Public Information, Mental Health Studies and Reports Branch.

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. https://www.newyorker.com/magazine/1981/12/14/a-i.

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.