Artificial Intelligence - Who Was Raj Reddy Or Dabbala Rajagopal "Raj" Reddy?

 

 

Dabbala Rajagopal "Raj" Reddy (1937–) is an Indian American who has made important contributions to artificial intelligence and has won the Turing Award.

He holds the Moza Bint Nasser Chair and University Professor of Computer Science and Robotics at Carnegie Mellon University's School of Computer Science.

He worked on the faculties of Stanford and Carnegie Mellon universities, two of the world's leading colleges for artificial intelligence research.

In the United States and in India, he has received honors for his contributions to artificial intelligence.

In 2001, the Indian government bestowed upon him the Padma Bhushan Award (the third highest civilian honor).

In 1984, he was also given the Legion of Honor, France's highest honor, which was created in 1802 by Napoleon Bonaparte himself.

In 1958, Reddy obtained his bachelor's degree from the University of Madras' Guindy Engineering College, and in 1960, he received his master's degree from the University of New South Wales in Australia.

In 1966, he came to the United States to get his doctorate in computer science at Stanford University.

He was the first in his family to get a university degree, which is typical of many rural Indian households.

He went to the academy in 1966 and joined the faculty of Stanford University as an Assistant Professor of Computer Science, where he stayed until 1969, after working in the industry as an Applied Science Representative at IBM Australia from 1960 to 1963.

He began working at Carnegie Mellon as an Associate Professor of Computer Science in 1969 and will continue to do so until 2020.

He rose up the ranks at Carnegie Mellon, eventually becoming a full professor in 1973 and a university professor in 1984.

In 1991, he was appointed as the head of the School of Computer Science, a post he held until 1999.

Many schools and institutions were founded as a result of Reddy's efforts.

In 1979, he launched the Robotics Institute and served as its first director, a position he held until 1999.

He was a driving force behind the establishment of the Language Technologies Institute, the Human Computer Interaction Institute, the Center for Automated Learning and Discovery (now the Machine Learning Department), and the Institute for Software Research at CMU during his stint as dean.

From 1999 to 2001, Reddy was a cochair of the President's Information Technology Advisory Committee (PITAC).

The President's Council of Advisors on Science and Technology (PCAST) took over PITAC in 2005.

Reddy was the president of the American Association for Artificial Intelligence (AAAI) from 1987 to 1989.

The AAAI has been renamed the Association for the Advancement of Artificial Intelligence, recognizing the worldwide character of the research community, which began with pioneers like Reddy.

The former logo, acronym (AAAI), and purpose have been retained.

Artificial intelligence, or the study of giving intelligence to computers, was the subject of Reddy's research.

He worked on voice control for robots, speech recognition without relying on the speaker, and unlimited vocabulary dictation, which allowed for continuous speech dictation.

Reddy and his collaborators have made significant contributions to computer analysis of natural sceneries, job oriented computer architectures, universal access to information (a project supported by UNESCO), and autonomous robotic systems.

Reddy collaborated on Hearsay II, Dragon, Harpy, and Sphinx I/II with his coworkers.

The blackboard model, one of the fundamental concepts that sprang from this study, has been extensively implemented in many fields of AI.

Reddy was also interested in employing technology for the sake of society, and he worked as the Chief Scientist at the Centre Mondial Informatique et Ressource Humaine in France.

He aided the Indian government in the establishment of the Rajiv Gandhi University of Knowledge Technologies, which focuses on low-income rural youth.

He is a member of the International Institute of Information Technology (IIIT) in Hyderabad's governing council.

IIIT is a non-profit public-private partnership (N-PPP) that focuses on technological research and applied research.

He was on the board of directors of the Emergency Management and Research Institute, a nonprofit public-private partnership that offers public emergency medical services.

EMRI has also aided in the emergency management of its neighboring nation, Sri Lanka.

In addition, he was a member of the Health Care Management Research Institute (HMRI).

HMRI provides non-emergency health-care consultation to rural populations, particularly in Andhra Pradesh, India.

In 1994, Reddy and Edward A. Feigenbaum shared the Turing Award, the top honor in artificial intelligence, and Reddy became the first person of Indian/Asian descent to receive the award.

In 1991, he received the IBM Research Ralph Gomory Fellow Award, the Okawa Foundation's Okawa Prize in 2004, the Honda Foundation's Honda Prize in 2005, and the Vannevar Bush Award from the United States National Science Board in 2006.

Reddy has received fellowships from the Institute of Electronic and Electrical Engineers (IEEE), the Acoustical Society of America, and the American Association for Artificial Intelligence, among other prestigious organizations.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Autonomous and Semiautonomous Systems; Natural Language Processing and Speech Understanding.

References & Further Reading:

Reddy, Raj. 1988. “Foundations and Grand Challenges of Artificial Intelligence.” AI Magazine 9, no. 4 (Winter): 9–21.

Reddy, Raj. 1996. “To Dream the Possible Dream.” Communications of the ACM 39, no. 5 (May): 105–12.

Artificial Intelligence - Person of Interest(2011–2016), The CBS Sci-Fi Series

 

Between 2011 through 2016, the fictitious television program Person of Interest ran on CBS for five seasons.

Although the show's early episodes resembled a serial crime drama, the tale developed into a science fiction genre that probed ethical questions around artificial intelligence development.

The show's central concept revolves upon a monitoring system known as "The Machine," which was developed for the United States by millionaire Harold Finch, portrayed by Michael Emerson.

This technology was created largely to avoid terrorist acts, but it has evolved to the point where it can anticipate crimes before they happen.

However, owing to its architecture, it only discloses the "person of interest's" social security number, which might be either the victim or the offender.

Normally, each episode is centered on a single person of interest number that has been produced.

Although the ensemble increases in size over the seasons, Finch first employs ex-CIA agent John Reese, portrayed by Jim Caviezel, to assist him in investigating and preventing these atrocities.

Person of Interest is renowned for emphasizing and dramatizing ethical issues surrounding both the invention and deployment of artificial intelligence.

Season four, for example, delves deeply into how Finch constructed The Machine in the first place.

Finch took enormous pains to ensure that The Machine had the correct set of values before exposing it to actual data, as shown by flashbacks.

As Finch strove to get the settings just correct, viewers were able to see exactly what might go wrong.

In one flashback, The Machine altered its own programming before lying about it.

When these failures arise, Finch deletes the incorrect code, noting that The Machine will have unrivaled capabilities.

The Machine quickly responds by overriding its own deletion procedures and even attempting to murder Finch.

"I taught it how to think," Finch says as he reflects on the process.

All I have to do now is educate it how to be concerned." Finally, Finch is able to program The Machine successfully with the proper set of ideals, which includes the preservation of human life.

The interaction of numerous AI beings is a second key ethical subject that runs through seasons three through five.

In season three, Samaritan, a competing AI surveillance software, is built.

This system does not care about human life in the same way as The Machine does, and as a result, it causes enormous harm and turmoil in order to achieve its goals, which include sustaining the United States' national security and its own survival.

As a result of their differences, Samaritan and The Machine find themselves at odds.

The Machine finally beats Samaritan, despite the fact that the program implies that Samaritan is more powerful owing to the employment of newer technology.

This program was mainly a critical success; nevertheless, declining ratings led to its cancellation after just thirteen episodes in its fifth season.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Biometric Privacy and Security; Biometric Technology; Predictive Policing.

References & Further Reading:

McFarland, Melanie. 2016. “Person of Interest Comes to an End, but the Technology Central to the Story Will Keep Evolving.” Geek Wire, June 20, 2016. https://www.geekwire.com/2016/person-of-interest/.

Newitz, Annalee. 2016. “Person of Interest Remains One of the Smartest Shows about AI on Television.” Ars Technica, May 3, 2016. https://arstechnica.com/gaming/2016/05/person-of-interest-remains-one-of-the-smartest-shows-about-ai-on-television/.

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 - Who Was John McCarthy?

 

John McCarthy  (1927–2011) was an American computer scientist and mathematician who was best known for helping to develop the subject of artificial intelligence in the late 1950s and pushing the use of formal logic in AI research.

McCarthy was a creative thinker who earned multiple accolades for his contributions to programming languages and operating systems research.

Throughout McCarthy's life, however, artificial intelligence and "formalizing common sense" remained his primary research interest (McCarthy 1990).

As a graduate student, McCarthy first met the concepts that would lead him to AI at the Hixon conference on "Cerebral Mechanisms in Behavior" in 1948.

The symposium was place at the California Institute of Technology, where McCarthy had just finished his undergraduate studies and was now enrolled in a graduate mathematics program.

In the United States, machine intelligence had become a subject of substantial academic interest under the wide term of cybernetics by 1948, and many renowned cyberneticists, notably Princeton mathematician John von Neumann, were in attendance at the symposium.

McCarthy moved to Princeton's mathematics department a year later, when he discussed some early ideas inspired by the symposium with von Neumann.

McCarthy never published the work, despite von Neumann's urging, since he believed cybernetics could not solve his problems concerning human knowing.

McCarthy finished a PhD on partial differential equations at Princeton.

He stayed at Princeton as an instructor after graduating in 1951, and in the summer of 1952, he had the chance to work at Bell Labs with cyberneticist and inventor of information theory Claude Shannon, whom he persuaded to collaborate on an edited collection of writings on machine intelligence.

Automata Studies received contributions from a variety of fields, ranging from pure mathematics to neuroscience.

McCarthy, on the other hand, felt that the published studies did not devote enough attention to the important subject of how to develop intelligent machines.

McCarthy joined the mathematics department at Stanford in 1953, but was fired two years later, maybe because he spent too much time thinking about intelligent computers and not enough time working on his mathematical studies, he speculated.

In 1955, he accepted a position at Dartmouth, just as IBM was preparing to establish the New England Computation Center at MIT.

The New England Computation Center gave Dartmouth access to an IBM computer that was installed at MIT and made accessible to a group of New England colleges.

McCarthy met IBM researcher Nathaniel Rochester via the IBM initiative, and he recruited McCarthy to IBM in the summer of 1955 to work with his research group.

McCarthy persuaded Rochester of the need for more research on machine intelligence, and he submitted a proposal to the Rockefeller Foundation for a "Summer Research Project on Artificial Intelligence" with Rochester, Shannon, and Marvin Minsky, a graduate student at Princeton, which included the first known use of the phrase "artificial intelligence." Despite the fact that the Dartmouth Project is usually regarded as a watershed moment in the development of AI, the conference did not go as McCarthy had envisioned.

The Rockefeller Foundation supported the proposal at half the proposed budget since it was for such an unique field of research with a relatively young professor as author, and because Shannon's reputation carried substantial weight with the Foundation.

Furthermore, since the event took place over many weeks in the summer of 1955, only a handful of the guests were able to attend the whole period.

As a consequence, the Dartmouth conference was a fluid affair with an ever-changing and unpredictably diverse guest list.

Despite its chaotic implementation, the meeting was crucial in establishing AI as a distinct area of research.

McCarthy won a Sloan grant to spend a year at MIT, closer to IBM's New England Computation Center, while still at Dartmouth in 1957.

McCarthy was given a post in the Electrical Engineering department at MIT in 1958, which he accepted.

Later, he was joined by Minsky, who worked in the mathematics department.

McCarthy and Minsky suggested the construction of an official AI laboratory to Jerome Wiesner, head of MIT's Research Laboratory of Electronics, in 1958.

McCarthy and Minsky agreed on the condition that Wiesner let six freshly accepted graduate students into the laboratory, and the "artificial intelligence project" started teaching its first generation of students.

McCarthy released his first article on artificial intelligence in the same year.

In his book "Programs with Common Sense," he described a computer system he named the Advice Taker that would be capable of accepting and understanding instructions in ordinary natural language from nonexpert users.

McCarthy would later define Advice Taker as the start of a study program aimed at "formalizing common sense." McCarthy felt that everyday common sense notions, such as comprehending that if you don't know a phone number, you'll need to look it up before calling, might be written as mathematical equations and fed into a computer, enabling the machine to come to the same conclusions as humans.

Such formalization of common knowledge, McCarthy felt, was the key to artificial intelligence.

McCarthy's presentation, which was presented at the United Kingdom's National Physical Laboratory's "Symposium on Mechansation of Thought Processes," helped establish the symbolic program of AI research.

McCarthy's research was focused on AI by the late 1950s, although he was also involved in a range of other computing-related topics.

In 1957, he was assigned to a group of the Association for Computing Machinery charged with developing the ALGOL programming language, which would go on to become the de facto language for academic research for the next several decades.

He created the LISP programming language for AI research in 1958, and its successors are widely used in business and academia today.

McCarthy contributed to computer operating system research via the construction of time sharing systems, in addition to his work on programming languages.

Early computers were large and costly, and they could only be operated by one person at a time.

McCarthy identified the necessity for several users throughout a major institution, such as a university or hospital, to be able to use the organization's computer systems concurrently via computer terminals in their offices from his first interaction with computers in 1955 at IBM.

McCarthy pushed for study on similar systems at MIT, serving on a university committee that looked into the issue and ultimately assisting in the development of MIT's Compatible Time-Sharing System (CTSS).

Although McCarthy left MIT before the CTSS work was completed, his advocacy with J.C.R.

Licklider, future office head at the Advanced Research Projects Agency, the predecessor to DARPA, while a consultant at Bolt Beranek and Newman in Cambridge, was instrumental in helping MIT secure significant federal support for computing research.

McCarthy was recruited to join what would become the second department of computer science in the United States, after Purdue's, by Stanford Professor George Forsythe in 1962.

McCarthy insisted on going only as a full professor, which he believed would be too much for Forsythe to handle as a young researcher.

Forsythe was able to persuade Stanford to grant McCarthy a full chair, and he moved to Stanford in 1965 to establish the Stanford AI laboratory.

Until his retirement in 2000, McCarthy oversaw research at Stanford on AI topics such as robotics, expert systems, and chess.

McCarthy was up in a family where both parents were ardent members of the Communist Party, and he had a lifetime interest in Russian events.

He maintained numerous professional relationships with Soviet cybernetics and AI experts, traveling and lecturing there in the mid-1960s, and even arranged a chess match between a Stanford chess computer and a Russian equivalent in 1965, which the Russian program won.

He developed many foundational concepts in symbolic AI theory while at Stanford, such as circumscription, which expresses the idea that a computer must be allowed to make reasonable assumptions about problems presented to it; otherwise, even simple scenarios would have to be specified in such exacting logical detail that the task would be all but impossible.

McCarthy's accomplishments have been acknowledged with various prizes, including the 1971 Turing Award, the 1988 Kyoto Prize, admission into the National Academy of Sciences in 1989, the 1990 Presidential Medal of Science, and the 2003 Benjamin Franklin Medal.

McCarthy was a brilliant thinker who continuously imagined new technologies, such as a space elevator for economically transporting stuff into orbit and a system of carts strung from wires to better urban transportation.

In a 2008 interview, McCarthy was asked what he felt the most significant topics in computing now were, and he answered without hesitation, "Formalizing common sense," the same endeavor that had inspired him from the start.

~ 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; Expert Systems; Symbolic Logic.

References & Further Reading:

Hayes, Patrick J., and Leora Morgenstern. 2007. “On John McCarthy’s 80th Birthday, in Honor of His Contributions.” AI Magazine 28, no. 4 (Winter): 93–102.

McCarthy, John. 1990. Formalizing Common Sense: Papers, edited by Vladimir Lifschitz. Norwood, NJ: Albex.

Morgenstern, Leora, and Sheila A. McIlraith. 2011. “John McCarthy’s Legacy.” Artificial Intelligence 175, no. 1 (January): 1–24.

Nilsson, Nils J. 2012. “John McCarthy: A Biographical Memoir.” Biographical Memoirs of the National Academy of Sciences. http://www.nasonline.org/publications/biographical-memoirs/memoir-pdfs/mccarthy-john.pdf.

Artificial Intelligence - What Are Mobile Recommendation Assistants?

 

Mobile Recommendation Assistants, also known as Virtual Assistants, Intelligent Agents, or Virtual Personal Assistants, are a collection of software features that combine a conversational user interface with artificial intelligence to act on behalf of a user.

They may deliver what seems to a user as an agent when they work together.

In this sense, an agent differs from a tool in that it has the ability to act autonomously and make choices with some degree of autonomy.

Many qualities may be included into the design of mobile suggestion helpers to improve the user's impression of agency.

Using visual avatars to represent technology, incorporating features of personality such as humor or informal/colloquial language, giving a voice and a legitimate name, constructing a consistent method of behaving, and so on are examples of such tactics.

A human user can use a mobile recommendation assistant to help them with a wide range of tasks, such as opening software applications, answering questions, performing tasks (operating other software/hardware), or engaging in conversational commerce or entertainment (telling stories, telling jokes, playing games, etc.).

Apple's Siri, Baidu's Xiaodu, Amazon's Alexa, Microsoft's Cortana, Google's Google Assistant, and Xiaomi's Xiao AI are among the mobile voice assistants now in development, each designed for certain companies, use cases, and user experiences.

A range of user interface modali ties are used by mobile recommendation aides.

Some are completely text-based, and they are referred regarded as chatbots.

Business to consumer (B2C) communication is the most common use case for a chatbot, and notable applications include online retail communication, insurance, banking, transportation, and restaurants.

Chatbots are increasingly being employed in medical and psychological applications, such as assisting users with behavior modification.

Similar apps are becoming more popular in educational settings to help students with language learning, studying, and exam preparation.

Facebook Messenger is a prominent example of a chatbot on social media.

While not all mobile recommendation assistants need voice-enabled interaction as an input modality (some, such web site chatbots, may depend entirely on text input), many contemporary examples do.

A Mobile Recommendation Assistant uses a number similar predecessor technologies, including a voice-enabled user interface.

Early voice-enabled user interfaces were made feasible by a command syntax that was hand-coded as a collection of rules or heuristics in advance.

These rule-based systems allowed users to operate devices without using their hands by delivering voice directions.

IBM produced the first voice recognition program, which was exhibited during the 1962 World's Fair in Seattle.

The IBM Shoebox has a limited vocabulary of sixteen words and nine numbers.

By the 1990s, IBM and Microsoft's personal computers and software had basic speech recognition; Apple's Siri, which debuted on the iPhone 4s in 2011, was the first mobile application of a mobile assistant.

These early voice recognition systems were disadvantaged in comparison to conversational mobile agents in terms of user experience since they required a user to learn and adhere to a preset command language.

The consequence of rule-based voice interaction might seem mechanical when it comes to contributing to real humanlike conversation with computers, which is a feature of current mobile recommendation assistants.

Instead, natural language processing (NLP) uses machine learning and statistical inference to learn rules from enormous amounts of linguistic data (corpora).

Decision trees and statistical modeling are used in natural language processing machine learning to understand requests made in a variety of ways that are typical of how people regularly communicate with one another.

Advanced agents may have the capacity to infer a user's purpose in light of explicit preferences expressed via settings or other inputs, such as calendar entries.

Google's Voice Assistant uses a mix of probabilistic reasoning and natural language processing to construct a natural-sounding dialogue, which includes conversational components such as paralanguage ("uh", "uh-huh", "ummm").

To convey knowledge and attention, modern digital assistants use multimodal communication.

Paralanguage refers to communication components that don't have semantic content but are nonetheless important for conveying meaning in context.

These may be used to show purpose, collaboration in a dialogue, or emotion.

The aspects of paralanguage utilized in Google's voice assistant employing Duplex technology are termed vocal segre gates or speech disfluencies; they are intended to not only make the assistant appear more human, but also to help the dialogue "flow" by filling gaps or making the listener feel heard.

Another key aspect of engagement is kinesics, which makes an assistant feel more like an engaged conversation partner.

Kinesics is the use of gestures, movements, facial expressions, and emotion to aid in the flow of communication.

The car firm NIO's virtual robot helper, Nome, is one recent example of the application of face expression.

Nome is a digital voice assistant that sits above the central dashboard of NIO's ES8 in a spherical shell with an LCD screen.

It can swivel its "head" automatically to attend to various speakers and display emotions using facial expressions.

Another example is Dr. Cynthia Breazeal's commercial Jibo home robot from MIT, which uses anthropomorphism using paralinguistic approaches.

Motion graphics or lighting animations are used to communicate states of communication such as listening, thinking, speaking, or waiting in less anthropomorphic uses of kinesics, such as the graphical user interface elements on Apple's Siri or illumination arrays on Amazon Alexa's physical interface Echo or in Xiami's Xiao AI.

The rising intelligence and anthropomorphism (or, in some circumstances, zoomorphism or mechanomorphism) that comes with it might pose some ethical issues about user experience.

The need for more anthropomorphic systems derives from the positive user experience of humanlike agentic systems whose communicative behaviors are more closely aligned with familiar interactions like conversation, which are made feasible by natural language and paralinguistics.

Natural conversation systems have the fundamental advantage of not requiring the user to learn new syntax or semantics in order to successfully convey orders and wants.

These more humanistic human machine interfaces may employ a user's familiar mental model of communication, which they gained through interacting with other people.

Transparency and security become difficulties when a user's judgments about a machine's behavior are influenced by human-to-human communication as machine systems become closer to human-to-human contact.

The establishment of comfort and rapport may obscure the differences between virtual assistant cognition and assumed motivation.

Many systems may be outfitted with motion sensors, proximity sensors, cameras, tiny phones, and other devices that resemble, replicate, or even surpass human capabilities in terms of cognition (the assistant's intellect and perceptive capacity).

While these can be used to facilitate some humanlike interaction by improving perception of the environment, they can also be used to record, document, analyze, and share information that is opaque to a user when their mental model and the machine's interface do not communicate the machine's operation at a functional level.

After a user interaction, a digital assistant visual avatar may shut his eyes or vanish, but there is no need to associate such behavior with the microphone's and camera's capabilities to continue recording.

As digital assistants become more incorporated into human users' daily lives, data privacy issues are becoming more prominent.

Transparency becomes a significant problem to solve when specifications, manufacturer data collecting aims, and machine actions are potentially mismatched with user expectations.

Finally, when it comes to data storage, personal information, and sharing methods, security becomes a concern, as hacking, disinformation, and other types of abuse threaten to undermine faith in technology systems and organizations.

~ 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; Mobile Recommendation Assistants; Natural Language Processing and Speech Understanding.

References & Further Reading:

Lee, Gary G., Hong Kook Kim, Minwoo Jeong, and Ji-Hwan Kim, eds. 2015. Natural Language Dialog Systems and Intelligent Assistants. Berlin: Springer.

Leviathan, Yaniv, and Yossi Matias. 2018. “Google Duplex: An AI System for Accomplishing Real-world Tasks Over the Phone.” Google AI Blog. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html.

Viken, Alexander. 2009. “The History of Personal Digital Assistants, 1980–2000.” Agile Mobility, April 10, 2009.

Waddell, Kaveh. 2016. “The Privacy Problem with Digital Assistants.” The Atlantic, May 24, 2016. https://www.theatlantic.com/technology/archive/2016/05/the-privacy-problem-with-digital-assistants/483950/.

Artificial Intelligence - Who Is Heather Knight?

Heather Knight is a robotics and artificial intelligence specialist best recognized for her work in the entertainment industry.

Her Collaborative Humans and Robots: Interaction, Sociability, Machine Learning, and Art (CHARISMA) Research Lab at Oregon State University aims to apply performing arts techniques to robots.

Knight identifies herself as a social roboticist, a person who develops non-anthropomorphic—and sometimes nonverbal—machines that interact with people.

She makes robots that act in ways that are modeled after human interpersonal communication.

These behaviors include speaking styles, greeting movements, open attitudes, and a variety of other context indicators that assist humans in establishing rapport with robots in ordinary life.

Knight examines social and political policies relating to robotics in the CHARISMA Lab, where he works with social robots and so-called charismatic machines.

The Marilyn Monrobot interactive robot theatre company was founded by Knight.

The Robot Film Festival provides a venue for roboticists to demonstrate their latest inventions in a live setting, as well as films that are relevant to the evolving state of the art in robotics and robot-human interaction.

The Marilyn Monrobot firm arose from Knight's involvement with the Syyn Labs creative collective and her observations of Guy Hoffman, Director of the MIT Media Innovation Lab, on robots built for performance reasons.

Knight's production firm specializes on robot humor.

Knight claims that theatrical spaces are ideal for social robotics research because they not only encourage playfulness—requiring robot actors to express themselves and interact—but also include creative constraints that robots thrive in, such as a fixed stage, trial-and-error learning, and repeat performances (with manipu lated variations).

The usage of robots in entertainment situations, according to Knight, is beneficial since it increases human culture, imagination, and creativity.

At the TEDWomen conference in 2010, Knight debuted Data, a stand-up comedy robot.

Aldebaran Robotics created Data, an Nao robot (now SoftBank Group).

Data performs a comedy performance (with roughly 200 pre-programmed jokes) while gathering input from the audience and fine-tuning its act in real time.

The robot was created at Carnegie Mellon University by Scott Satkin and Varun Ramakrisha.

Knight is presently collaborating with Ginger the Robot on a comedic project.

The development of algorithms for artificial social intelligence is also fueled by robot entertainment.

In other words, art is utilized to motivate the development of new technologies.

To evaluate audience responses and understand the noises made by audiences, Data and Ginger use a microphone and a machine learning system (laughter, chatter, clap ping, etc.).

After each joke, the audience is given green and red cards to hold up.

Green cards indicate to the robots that the audience enjoys the joke.

Red cards are given out when jokes fall flat.

Knight has discovered that excellent robot humor doesn't have to disguise the fact that it's about a robot.

Rather, Data makes people laugh by drawing attention to its machine-specific issues and making self-deprecating remarks about its limits.

In order to create expressive, captivating robots, Knight has found improvisational acting and dancing skills to be quite useful.

In the process, she has changed the original Robotic Paradigm's technique of Sense-Plan-Act, preferring Sensing-Character-Enactment, which is more similar to the procedure utilized in theatrical performance in practice.

Knight is presently experimenting with ChairBots, which are hybrid robots made by gluing IKEA wooden chairs to Neato Botvacs (a brand of intelligent robotic vacuum cleaner).

The ChairBots are being tested in public places to see how a basic robot might persuade people to get out of the way using just rudimentary gestures as a mode of communication.

They've also been used to persuade prospective café customers to come in, locate a seat, and settle down.

Knight collaborated on the synthetic organic robot art piece Public Anemone for the SIGGRAPH computer graphics conference while pursuing degrees at the MIT Media Lab with Personal Robots group head Professor Cynthia Breazeal.

The installation consisted of a fiberglass cave filled with glowing creatures that moved and responded to music and people.

The cave's centerpiece robot, also known as "Public Anemone," swayed and interacted with visitors, bathed in a waterfall, watered a plant, and interacted with other cave attractions.

Knight collaborated with animatronics designer Dan Stiehl to create capacitive sensor-equipped artificial tube worms.

The tubeworm's fiberoptic tentacles drew into their tubes and changed color when a human observer reached into the cave, as though prompted by protective impulses.

The team behind Public Anemone defined the initiative as "a step toward fully embodied robot theatrical performance" and "an example of intelligent staging." Knight also helped with the mechanical design of the Smithsonian/Cooper-Hewitt Design Museum's "Cyberflora" kinetic robot flower garden display in 2003.

Her master's thesis at MIT focused on the Sensate Bear, a huggable robot teddy bear with full-body capacitive touch sensors that she used to investigate real-time algorithms incorporating social touch and nonverbal communication.

In 2016, Knight received her PhD from Carnegie Mellon University.

Her dissertation focused on expressive motion in robots with a reduced degree of freedom.

Humans do not require robots to closely resemble humans in appearance or behavior to be treated as close associates, according to Knight's research.

Humans, on the other hand, are quick to anthropomorphize robots and offer them autonomy.

Indeed, she claims, when robots become more human-like in appearance, people may feel uneasy or anticipate a far higher level of humanlike conduct.

Professor Matt Mason of the School of Computer Science and Robotics Institute advised Knight.

She was formerly a robotic artist in residence at Alphabet's X, Google's parent company's research lab.

Knight has previously worked with Aldebaran Robotics and NASA's Jet Propulsion Laboratory as a research scientist and engineer.

While working as an engineer at Aldebaran Robotics, Knight created the touch sensing panel for the Nao autonomous family companion robot, as well as the infrared detection and emission capabilities in its eyes.

Syyn Labs won a UK Music Video Award for her work on the opening two minutes of the OK Go video "This Too Shall Pass," which contains a Rube Goldberg machine.

She is now assisting Clearpath Robotics in making its self-driving, mobile-transport robots more socially conscious. 

~ Jai Krishna Ponnappan

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

See also: 

RoboThespian; Turkle, Sherry.

Further Reading:

Biever, Celeste. 2010. “Wherefore Art Thou, Robot?” New Scientist 208, no. 2792: 50–52.

Breazeal, Cynthia, Andrew Brooks, Jesse Gray, Matt Hancher, Cory Kidd, John McBean, Dan Stiehl, and Joshua Strickon. 2003. “Interactive Robot Theatre.” Communications of the ACM 46, no. 7: 76–84.

Knight, Heather. 2013. “Social Robots: Our Charismatic Friends in an Automated Future.” Wired UK, April 2, 2013. https://www.wired.co.uk/article/the-inventor.

Knight, Heather. 2014. How Humans Respond to Robots: Building Public Policy through Good Design. Washington, DC: Brookings Institute, Center for Technology Innovation.