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Artificial Intelligence - Who Is Hiroshi Ishiguro (1963–)?

Hiroshi Ishiguro is a well-known engineer who is most known for his life-like humanoid robots.

He thinks that the present information culture will eventually develop into a world populated by robot caregivers or helpmates.

Ishiguro also expects that studying artificial people would help us better understand how humans are conditioned to read and comprehend the actions and expressions of their own species.

Ishiguro seeks to explain concepts like relationship authenticity, autonomy, creativity, imitation, reciprocity, and robot ethics in terms of cognitive science.

Ishiguro's study aims to produce robots that are uncannily identical to humans in look and behavior.

He thinks that his robots will assist us in comprehending what it is to be human.

Sonzaikan is the Japanese name for this sense of a human's substantial presence, or spirit.

Success, according to Ishiguro, may be measured and evaluated in two ways.

The first is what he refers to as the complete Turing Test, in which an android passes if 70% of human spectators are unaware that they are seeing a robot until at least two seconds have passed.

The second metric for success, he claims, is the length of time a human stays actively engaged with a robot before discovering that the robot's cooperative eye tracking does not reflect true thinking.

Robovie was one of Ishiguro's earliest robots, launched in 2000.

Ishiguro intended to make a robot that didn't appear like a machine or a pet, but might be mistaken for a friend in everyday life.

Robovie may not seem to be human, but it can perform a variety of innovative human-like motions and interactive activities.

Eye contact, staring at items, pointing at things, nodding, swinging and folding arms, shaking hands, and saying hello and goodbye are all possible with Robovie.

Robo Doll was extensively featured in Japanese media, and Ishiguro was persuaded that the robot's look, engagement, and conversation were vital to deeper, more nuanced connections between robots and humans.

In 2003, Ishiguro debuted Actroid to the general public for the first time.

Sanrio's Kokoro animatronics division has begun manufacturing Actroid, an autonomous robot controlled by AI software developed at Osaka University's Intelligent Robotics Laboratory.

Actroid has a feminine look (in science fiction terms, a "gynoid") with skin constructed of incredibly realistic silicone.

Internal sensors and quiet air actuators at 47 points of physical articulation allow the robot to replicate human movement, breathing, and blinking, and it can even speak.

Movement is done by sensor processing, data files carrying key val ues for degrees of freedom in movement of limbs and joints.

Five to seven degrees of freedom are typical for robot arms.

Arms, legs, torso, and neck of humanoid robots may have thirty or more degrees of freedom.

Programmers create Actroid scenarios in four steps: (1) collect recognition data from sensors activated by contact, (2) choose a motion module, (3) execute a specified series of movements and play an audio file, and (4) return to step 1.

Experiments utilizing irregular random or contingent reactions to human context hints have been shown to be helpful in holding the human subject's attention, but they are made much more effective when planned scenarios are included.

Motion modules are written in XML, a text-based markup language that is simple enough for even inexperienced programmers to understand.

Ishiguro debuted Repliee variants of the Actroid in 2005, which were supposed to be indistinguishable from a human female on first glance.

Repliee Q1Expo is an android replica of Ayako Fujii, a genuine Japanese newscaster.

Repliee androids are interactive; they can use voice recognition software to comprehend human conversations, answer verbally, maintain eye contact, and react quickly to human touch.

This is made possible by a sensor network made up of infrared motion detectors, cameras, microphones, identification tag readers, and floor sensors that is distributed and ubiquitous.

Artificial intelligence is used by the robot to assess whether the human is contacting the robot gently or aggressively.

Ishiguro also debuted Repliee R1, a kid version of the robot that looks identical to his then four-year-old daughter.

Actroids have recently been proven to be capable of imitating human limb and joint movement by observing and duplicating the movements.

Because much of the computer gear that runs the artificial intelligence program is external to the robot, it is not capable of actual movement.

Self-reports of human volunteers' sentiments and moods are captured when robots perform activities in research done at Ishiguro's lab.

The Actroid elicits a wide spectrum of emotions, from curiosity to disgust, acceptance to terror.

Ishiguro's research colleagues have also benefited from real-time neuroimaging of human volunteers in order to better understand how human brains are stimulated in human-android interactions.

As a result, Actroid serves as a testbed for determining why particular nonhuman agent acts fail to elicit the required cognitive reactions in humans.

The Geminoid robots were created in response to the fact that artificial intelligence lags far behind robotics when it comes to developing realistic interactions between humans and androids.

Ishiguro, in particular, admitted that it would be several years before a computer could have a lengthy, intensive spoken discussion with a person.

The Geminoid HI-1, which debuted in 2006, is a teleoperated (rather than totally autonomous) robot that looks similar to Ishiguro.

The name "gemininoid" is derived from the Latin word "twin." Hand fidgeting, blinking, and motions similar with human respiration are all possible for Geminoid.

Motion-capture technology is used to operate the android, which mimics Ishiguro's face and body motions.

The robot can imitate its creator's voice and communicate in a human-like manner.

Ishiguro plans to utilize the robot to teach students through remote telepresence one day.

When he is teleoperating the robot, he has observed that the sensation of immersion is so strong that his brain is fooled into producing phantom perceptions of actual contact when the android is poked.

The Geminoid-DK is a mechanical doppelgänger of Danish psychology professor Henrik Schärfe, launched in 2011.

While some viewers find the Geminoid's look unsettling, many others do not and simply communicate with the robot in a normal way.

In 2010, the Telenoid R1 was introduced as a teleoperated android robot.

Telenoid is 30 inches tall and amorphous, with just a passing resemblance to a human form.

The robot's objective is to transmit a human voice and gestures to a spectator who may use it as a communication or videoconferencing tool.

The Telenoid, like the other robots in Ishiguro's lab, looks to be alive: it simulates breathing and speech gestures and blinks.

However, in order to stimulate creativity, the design limits the amount of features.

In this manner, the Telenoid is analogous to a tangible, real-world avatar.

Its goal is to make more intimate, human-like interactions possible using telecommunications technology.

Ishiguro suggests that the robot might one day serve as a suitable stand-in for a teacher or partner who is otherwise only accessible from afar.

The Elfoid, a tiny version of the robot, can be grasped with one hand and carried in a pocket.

The autonomous persocom dolls that replace smart phones and other electronics in the immensely famous manga series Chobits foreshadowed the Actroid and Telenoid.

Ishiguro is a professor of systems innovation and the director of Osaka University's Intelligent Robotics Laboratory.

He's also a group leader at Kansai Science City's Advanced Telecommunications Research Institute (ATR) and a cofounder of the tech-transfer startup Vstone Ltd.

He thinks that future commercial enterprises will profit from the success of teleoperated robots in order to fund the continued development of his autonomous robots.

Erica, a humanoid robot that became a Japanese television news presenter in 2018, is his most recent creation.

Ishiguro studied oil painting extensively as a young man, pondering how to depict human resemblance on canvas while he worked.

In Hanao Mori's computer science lab at Yamanashi University, he got enthralled with robots.

At Osaka University, Ishiguro pursued his PhD in engineering under computer vision and image recognition pioneer Saburo Tsuji.

At studies done in Tsuji’s lab, he worked on mobile robots capable of SLAM— simultaneous mapping and navigation using panoramic and omni-directional video cameras.

This work led to his doctoral dissertation, which focused on tracking a human subject using active camera control and panning to acquire complete 360-degree views of the surroundings.

Ishiguro believed that his technology and applications may be utilized to provide a meaningful internal map of an interacting robot's surroundings.

His dissertation was rejected by the first reviewer of an article based on it.

Fine arts and technology, according to Ishiguro, are inexorably linked; art inspires new technologies, while technology enables for the creation and duplication of art.

Ishiguro has recently brought his robots to Seinendan, a theatre company founded by Oriza Hirata, in order to put what he's learned about human-robot communication into practice.

Ishiguro's field of cognitive science and AI, which he calls android science, has precedents in Disneyland's "Great Moments with Mr.

Lincoln" robotics animation show and the fictitious robot replacements described in the Bruce Willis film Surrogates (2009).

In the Willis film, Ishiguro has a cameo appearance.

~ Jai Krishna Ponnappan

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

Artificial Intelligence - Who Is Demis Hassabis (1976–)?

Demis Hassabis lives in the United Kingdom and works as a computer game programmer, cognitive scientist, and artificial intelligence specialist.

He is a cofounder of DeepMind, the company that created the AlphaGo deep learning engine.

Hassabis is well-known for being a skilled game player.

His passion for video games paved the way for his career as an artificial intelligence researcher and computer game entrepreneur.

Hassabis' parents noticed his chess prowess at a young age.

At the age of thirteen, he had achieved the status of chess master.

He's also a World Team Champion in the strategic board game Diplomacy, a World Series of Poker Main Event participant, and numerous World Pentamind and World Deca mentathlon Champions in the London Mind Sports Olympiad.

Hassabis began working at Bullfrog Games in Guildford, England, with renowned game designer Peter Molyneux when he was seventeen years old.

Bullfrog was notable for creating a variety of popular computer "god games." A god game is a computer-generated life simulation in which the user has power and influence over semiautonomous people in a diverse world.

Molyneux's Populous, published in 1989, is generally regarded as the first god game.

Has sabis co-designed and coded Theme Park, a simulation management game published by Bullfrog in 1994.

Hassabis dropped out of Bullfrog Games to pursue a degree at Queens' College, Cambridge.

In 1997, he earned a bachelor's degree in computer science.

Following graduation, Hassabis rejoined Molyneux at Lionhead Studios, a new gaming studio.

Hassabis worked on the artificial intelligence for the game Black & White, another god game in which the user reigned over a virtual island inhabited by different tribes, for a short time.

Hassabis departed Lionhead after a year to launch his own video game studio, Elixir Studios.

Hassabis has signed arrangements with major publishers such as Microsoft and Vivendi Universal.

Before closing in 2005, Elixir created a variety of games, including the diplomatic strategy simulation game Republic: The Revolution and the real-time strategy game Evil Genius.

Republic's artificial intelligence is modeled after Elias Canetti's 1960 book People and Authority, which explores problems concerning how and why crowds follow rulers' power (which Hassabis boiled down to force, money, and influence).

Republic required the daily programming efforts of twenty-five programmers over the course of four years.

Hassabis thought that the AI in the game would be valuable to academics.

Hassabis took a break from game creation to pursue additional studies at University College London (UCL).

In 2009, he received his PhD in Cognitive Neuroscience.

In his research of individuals with hippocampal injury, Hassabis revealed links between memory loss and poor imagination.

These findings revealed that the brain's memory systems may splice together recalled fragments of previous experiences to imagine hypothetical futures.

Hassabis continued his academic studies at the Gatsby Computational Neuroscience Unit at UCL and as a Wellcome Trust fellow for another two years.

He was also a visiting researcher at MIT and Harvard University.

Hassabis' cognitive science study influenced subsequent work on unsupervised learning, memory and one-shot learning, and imagination-based planning utilizing generic models in artificial intelligence.

With Shane Legg and Mustafa Suleyman, Hassabis cofounded the London-based AI start-up DeepMind Technologies in 2011.

The organization was focused on interdisciplinary science, bringing together premier academics and concepts from machine learning, neurology, engineering, and mathematics.

The mission of DeepMind was to create scientific breakthroughs in artificial intelligence and develop new artificial general-purpose learning capabilities.

Hassabis has compared the project to the Apollo Program for AI.

DeepMind was tasked with developing a computer capable of defeating human opponents in the abstract strategic board game Go.

Hassabis didn't want to build an expert system, a brute-force computer preprogrammed with Go-specific algorithms and heuristics.

Rather than the chess-playing single-purpose Deep Blue system, he intended to construct a computer that adapted to play ing games in ways comparable to human chess champ Garry Kasparov.

He sought to build a machine that could learn to deal with new issues and have universality, which he defined as the ability to do a variety of jobs.

The reinforcement learning architecture was used by the company's AlphaGo artificial intelligence agent, which was built to compete against Lee Sedol, an eighteen-time world champion Go player.

Agents in the environment (in this example, the Go board) aim to attain a certain objective via reinforcement learning (winning the game).

The agents have perceptual inputs (such as visual input) as well as a statistical model based on environmental data.

The agent creates plans and goes through simulations of actions that will modify the model in order to accomplish the objective while collecting perceptual input and developing a representation of its surroundings.

The agent is always attempting to choose behaviors that will get it closer to its goal.

Hassabis argues that resolving all of the issues of goal-oriented agents in a reinforcement learning framework would be adequate to fulfill artificial general intelligence's promise.

He claims that biological systems work in a similar manner.

The dopamine system in human brains is responsible for implementing a reinforcement learning framework.

To master the game of Go, it usually takes a lifetime of study and practice.

Go includes a significantly broader search area than chess.

On the board, there are more potential Go locations than there are atoms in the cosmos.

It is also thought to be almost hard to develop an evaluation function that covers a significant portion of those places in order to determine where the next stone should be placed on the board.

Each game is essentially unique, and exceptional players describe their decisions as being guided by intuition rather than logic.

AlphaGo addressed these obstacles by leveraging data gathered from thousands of strong amateur games played by human Go players to train a neural network.

After that, AlphaGo played millions of games against itself, predicting how probable each side was to win based on the present board positions.

No specific assessment standards were required in this manner.

In Seoul, South Korea, in 2006, AlphaGo beat Go champion Lee Sedol (four games to one).

The way AlphaGo plays is considered cautious.

It favors diagonal stone placements known as "shoulder hits" to enhance victory while avoiding risk or point spread—thus putting less apparent focus on achieving territorial gains on the board.

In order to play any two-person game, AlphaGo has subsequently been renamed AlphaZero.

Without any human training data or sample games, AlphaZero learns from begin.

It only learns from random play.

After just four hours of training, AlphaZero destroyed Stock fish, one of the best free and open-source chess engines (28 games to 0 with 72 draws).

AlphaZero prefers the mobility of the pieces above their materiality while playing chess, which results in a creative style of play (similar to Go).

Another task the business took on was to develop a versatile, adaptable, and durable AI that could teach itself how to play more than 50 Atari video games just by looking at the pixels and scores on a video screen.

Hassabis introduced deep reinforcement learning, which combines reinforcement learning and deep learning, for this difficulty.

To create a neural network capable of reliable perceptual identification, deep neural networks need an input layer of observations, weighting mechanisms, and backpropagation.

In the instance of the Atari challenge, the network was trained using the 20,000-pixel values that flashed on the videogame screen at any given time.

Under deep learning, reinforcement learning takes the machine from the point where it perceives and recognizes a given input to the point where it can take meaningful action toward a goal.

In the Atari challenge, the computer learnt how to win over hundreds of hours of playtime by doing eighteen distinct exact joystick actions in a certain time-step.

To put it another way, a deep reinforcement learning machine is an end-to-end learning system capable of analyzing perceptual inputs, devising a strategy, and executing the strategy from start.

DeepMind was purchased by Google in 2014.

Hassabis continues to work at Google with DeepMind's deep learning technology.

Optical coherence tomography scans for eye disorders are used in one of these attempts.

By triaging patients and proposing how they should be referred for further treatment, DeepMind's AI system can swiftly and reliably diagnose from eye scans.

AlphaFold is a machine learning, physics, and structural biology system that predicts three-dimensional protein structures simply based on its genetic sequence.

AlphaFold took first place in the 2018 "world championship" for Critical Assessment of Techniques for Protein Structure Prediction, successfully predicting the most accurate structure for 25 of 43 proteins.

AlphaStar is currently mastering the real-time strategy game StarCraft II.

~ Jai Krishna Ponnappan

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

Artificial Intelligence - Gender and Artificial Intelligence.

Artificial intelligence and robots are often thought to be sexless and genderless in today's society, but this is not the case.

Humans, on the other hand, encode gender and stereo types into artificial intelligence systems in a similar way that gender is woven into language and culture.

The data used to train artificial intelligences has a gender bias.

Biased data may cause significant discrepancies in computer predictions and conclusions.

These differences would be said to be discriminating in humans.

AIs are only as good as the people who provide the data that machine learning systems capture, and they are only as ethical as the programmers who create and supervise them.

Machines presume gender prejudice is normal (if not acceptable) human behavior when individuals exhibit it.

When utilizing numbers, text, graphics, or voice recordings to teach algorithms, bias might emerge.

Machine learning is the use of statistical models to evaluate and categorize large amounts of data in order to generate predictions.

Deep learning is the use of neural network topologies that are expected to imitate human brainpower.

Data is labeled using classifiers based on previous patterns.

Classifiers have a lot of power.

By studying data from automobiles visible in Google Street View, they can precisely forecast income levels and political leanings of neighborhoods and cities.

The language individuals employ reveals gender prejudice.

This bias may be apparent in the names of items as well as how they are ranked in significance.

Beginning with the frequency with which their respective titles are employed and they are referred to as men and women vs boys and girls, descriptions of men and women are skewed.

The analogies and words employed are skewed as well.

Biased AI may influence whether or not individuals of particular genders or ethnicities are targeted for certain occupations, whether or not medical diagnoses are correct, whether or not they are able to acquire loans, and even how exams are scored.

"Woman" and "girl" are more often associated with the arts than with mathematics in AI systems.

Similar biases have been discovered in Google's AI systems for finding employment prospects.

Facebook and Microsoft's algorithms regularly correlate pictures of cooking and shopping with female activity, whereas sports and hunting are associated with masculine activity.

Researchers have discovered instances when gender prejudices are purposefully included into AI systems.

Men, for example, are more often provided opportunities to apply for highly paid and sought-after positions on job sites than women.

Female-sounding names for digital assistants on smartphones include Siri, Alexa, and Cortana.

According to Alexa's creator, the name came from negotiations with Amazon CEO Jeff Bezos, who desired a virtual assistant with the attitude and gender of the Enterprise starship computer from the Star Trek television program, which is a woman.

Debo rah Harrison, the Cortana project's head, claims that their female voice arose from studies demonstrating that people react better to female voices.

However, when BMW introduced a female voice to its in-car GPS route planner, it experienced instant backlash from males who didn't want their vehicles to tell them what to do.

Female voices should seem empathic and trustworthy, but not authoritative, according to the company.

Affectiva, a startup that specializes in artificial intelligence, utilizes photographs of six million people's faces as training data to attempt to identify their underlying emotional states.

The startup is now collaborating with automakers to utilize real-time footage of drivers to assess whether or not they are weary or furious.

The automobile would advise these drivers to pull over and take a break.

However, the organization has discovered that women seem to "laugh more" than males, which complicates efforts to accurately estimate the emotional states of normal drivers.

In hardware, the same biases might be discovered.

A disproportionate percentage of female robots are created by computer engineers, who are still mostly male.

The NASA Valkyrie robot, which has been deployed on Shuttle flights, has breasts.

Jia, a shockingly human-looking robot created at China's University of Science and Technology, has long wavy black hair, pale complexion, and pink lips and cheeks.

She maintains her eyes and head inclined down when initially spoken to, as though in reverence.

She wears a tight gold gown that is slender and busty.

"Yes, my lord, what can I do for you?" she says as a welcome.

"Don't get too near to me while you're taking a photo," Jia says when asked to snap a picture.

It will make my face seem chubby." In popular culture, there is a strong prejudice against female robots.

Fembots in the 1997 film Austin Powers discharged bullets from their breast cups, weaponizing female sexuality.

The majority of robots in music videos are female robots.

Duran Duran's "Electric Barbarella" was the first song accessible for download on the internet.

Bjork's video "The Girl And The Robot" gave birth to the archetypal white-sheathed robot seen today in so many places.

Marina and the Diamonds' protest that "I Am Not a Robot" is met by Hoodie Allen's fast answer that "You Are Not a Robot." In "The Ghost Inside," by the Broken Bells, a female robot sacrifices plastic body parts to pay tolls and reclaim paradise.

The skin of Lenny Kravitz's "Black Velveteen" is titanium.

Hatsune Miku and Kagamine Rin are anime-inspired holographic vocaloid singers.

Daft Punk is the notable exception, where robot costumes conceal the genuine identity of the male musicians.

Sexy robots are the principal love interests in films like Metropolis (1927), The Stepford Wives (1975), Blade Runner (1982), Ex Machina (2014), and Her (2013), as well as television programs like Battlestar Galactica and Westworld.

Meanwhile, "killer robots," or deadly autonomous weapons systems, are hypermasculine.

Atlas, Helios, and Titan are examples of rugged military robots developed by the Defense Advanced Research Projects Agency (DARPA).

Achilles, Black Knight, Overlord, and Thor PRO are some of the names given to self-driving automobiles.

The HAL 9000 computer implanted in the spacecraft Discovery in 2001: A Space Odyssey (1968), the most renowned autonomous vehicle of all time, is masculine and deadly.

In the field of artificial intelligence, there is a clear gender disparity.

The head of the Stanford Artificial Intelligence Lab, Fei-Fei Li, revealed in 2017 that her team was mostly made up of "men in hoodies" (Hempel 2017).

Women make up just approximately 12% of the researchers who speak at major AI conferences (Simonite 2018b).

In computer and information sciences, women have 19% of bachelor's degrees and 22% of PhD degrees (NCIS 2018).

Women now have a lower proportion of bachelor's degrees in computer science than they did in 1984, when they had a peak of 37 percent (Simonite 2018a).

This is despite the fact that the earliest "computers," as shown in the film Hidden Figures (2016), were women.

There is significant dispute among philosophers over whether un-situated, gender-neutral knowledge may exist in human society.

Users projected gender preferences on Google and Apple's unsexed digital assistants even after they were launched.

White males developed centuries of professional knowledge, which was eventually unleashed into digital realms.

Will machines be able to build and employ rules based on impartial information for hundreds of years to come? In other words, is there a gender to scientific knowledge? Is it masculine or female? Alison Adam is a Science and Technology Studies researcher who is more concerned in the gender of the ideas created by the participants than the gender of the persons engaged.

Sage, a British corporation, recently employed a "conversation manager" entrusted with building a gender-neutral digital assistant, which was eventually dubbed "Pegg." To help its programmers, the organization has also formalized "five key principles" in a "ethics of code" paper.

According to Sage CEO Kriti Sharma, "by 2020, we'll spend more time talking to machines than our own families," thus getting technology right is critical.

Aether, a Microsoft internal ethics panel for AI and Ethics in Engineering and Research, was recently established.

Gender Swap is a project that employs a virtual reality system as a platform for embodiment experience, a kind of neuroscience in which users may sense themselves in a new body.

Human partners utilize the immersive Head Mounted Display Oculus Rift and first-person cameras to generate the brain illusion.

Both users coordinate their motions to generate this illusion.

The embodiment experience will not operate if one does not correlate to the movement of the other.

It implies that every move they make jointly must be agreed upon by both users.

On a regular basis, new causes of algorithmic gender bias are discovered.

Joy Buolamwini, an MIT computer science graduate student, discovered gender and racial prejudice in the way AI detected individuals' looks in 2018.

She discovered, with the help of other researchers, that the dermatologist-approved Fitzpatrick The datasets for Skin Type categorization systems were primarily made up of lighter-skinned people (up to 86 percent).

The researchers developed a skin type system based on a rebalanced dataset and used it to compare three gender categorization systems available off the shelf.

They discovered that darker-skinned girls are the most misclassified in all three commercial systems.

Buolamwini founded the Algorithmic Justice League, a group that fights unfairness in decision-making software.

~ Jai Krishna Ponnappan

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

Artificial Intelligence - What Are Expert Systems?

Expert systems are used to solve issues that would normally be addressed by humans.

In the early decades of artificial intelligence research, they emerged as one of the most promising application strategies.

The core concept is to convert an expert's knowledge into a computer-based knowledge system.

Dan Patterson, a statistician and computer scientist at the University of Texas in El Paso, differentiates various properties of expert systems:

• They make decisions based on knowledge rather than facts.
• The task of representing heuristic knowledge in expert systems is daunting.
• Knowledge and the program are generally separated so that the same program can operate on different knowledge bases.
• Expert systems should be able to explain their decisions, represent knowledge symbolically, and have and use meta knowledge, that is, knowledge about knowledge.

(Patterson, et al., 2008) Expert systems generally often reflect domain-specific knowledge.

The subject of medical research was a frequent test application for expert systems.

Expert systems were created as a tool to assist medical doctors in their work.

Symptoms were usually communicated by the patient in the form of replies to inquiries.

Based on its knowledge base, the system would next attempt to identify the ailment and, in certain cases, recommend relevant remedies.

MYCIN, a Stanford University-developed expert system for detecting bacterial infections and blood disorders, is one example.

Another well-known application in the realm of engineering and engineering design tries to capture the heuristic knowledge of the design process in the design of motors and generators.

The expert system assists in the initial design phase, when choices like as the number of poles, whether to use AC or DC, and so on are made (Hoole et al. 2003).

The knowledge base and the inference engine are the two components that make up the core framework of expert systems.

The inference engine utilizes the knowledge base to make choices, whereas the knowledge base holds the expert's expertise.

In this way, the knowledge is isolated from the software that manipulates it.

Knowledge must first be gathered, then comprehended, categorized, and stored in order to create expert systems.

It is retrieved to answer issues depending on predetermined criteria.

The four main processes in the design of an expert system, according to Thomson Reuters chief scientist Peter Jackson, are obtaining information, representing that knowledge, directing reasoning via an inference engine, and explaining the expert system's answer (Jackson 1999).

The expert system's largest issue was acquiring domain knowledge.

Human specialists may be challenging to obtain information from.

Many variables contribute to the difficulty of acquiring knowledge, but the complexity of encoding heuristic and experienced information is perhaps the most important.

The knowledge acquisition process is divided into five phases, according to Hayes-Roth et al. (1983).

Identification, or recognizing the problem and the data that must be used to arrive at a solution; conceptualization, or comprehending the key concepts and relationships between the data; formalization, or comprehending the relevant search space; implementation, or converting formalized knowledge into a software program; and testing the rules for completeness and accuracy are among them.

Production (rule-based) or non-production systems may be used to represent domain knowledge.

In rule-based systems, knowledge is represented by rules in the form of IF THEN-ELSE expressions.

The inference process is carried out by iteratively going over the rules, either through a forward or backward chaining technique.

Forward chaining asks what would happen next if the condition and rules were known to be true. Going from a goal to the rules we know to be true, backward chaining asks why this occurred.

Forward chaining is defined as when the left side of the rule is assessed first, that is, when the conditions are verified first and the rules are performed left to right (also known as data-driven inference).

Backward chaining occurs when the rules are evaluated from the right side, that is, when the outcomes are verified first (also known as goal-driven inference).

CLIPS, a public domain example of an expert system tool that implements the forward chaining method, was created at NASA's Johnson Space Center. MYCIN is an expert system that works backwards.

Associative/semantic networks, frame representations, decision trees, and neural networks may be used in expert system designs based on nonproduction architectures.

Nodes make form an associative/semantic network, which may be used to represent hierarchical knowledge.

An example of a system based on an associative network is CASNET.

The most well-known use of CASNET was the development of an expert system for glaucoma diagnosis and therapy.

Frames are structured sets of closely related knowledge in frame architectures.

A frame-based architecture is an example of PIP (Present Illness Program).

MIT and Tufts-New England Clinical Center developed PIP to generate hypotheses regarding renal illness.

Top-down knowledge is represented via decision tree structures.

Blackboard system designs are complex systems in which the inference process's direction may be changed during runtime.

A blackboard system architecture may be seen in DARPA's HEARSAY domain independent expert system.

Knowledge is spread throughout a neural network in the form of nodes in neural network topologies.

Case-based reasoning is attempting to examine and find answers for a problem using previously solved examples.

A loose connection may be formed between case-based reasoning and judicial law, in which the decision of a comparable but previous case is used to solve a current legal matter.

Case-based reasoning is often implemented as a frame, which necessitates a more involved matching and retrieval procedure.

There are three options for manually constructing the knowledge base.

Knowledge may be elicited via an interview with a computer using interactive tools. This technique is shown by the computer-graphics-based OPAL software, which enabled clinicians with no prior medical training to construct expert medical knowledge bases for the care of cancer patients.

Text scanning algorithms that read books into memory are a second alternative to human knowledge base creation.

Machine learning algorithms that build competence on their own, with or without supervision from a human expert, are a third alternative still under development.

DENDRAL, a project started at Stanford University in 1965, is an early example of a machine learning architecture project.

DENDRAL was created in order to study the molecular structure of organic molecules.

While DENDRAL followed a set of rules to complete its work, META-DENDRAL created its own rules.

META-DENDRAL chose the important data points to observe with the aid of a human chemist.

Expert systems may be created in a variety of methods.

User-friendly graphical user interfaces are used in interactive development environments to assist programmers as they code.

Special languages may be used in the construction of expert systems.

Prolog (Logic Programming) and LISP are two of the most common options (List Programming).

Because Prolog is built on predicate logic, it belongs to the logic programming paradigm.

One of the first programming languages for artificial intelligence applications was LISP.

Expert system shells are often used by programmers.

A shell provides a platform for knowledge to be programmed into the system.

The shell is a layer without a knowledge basis, as the name indicates.

The Java Expert System Shell (JESS) is a strong expert shell built in Java.

Many efforts have been made to blend disparate paradigms to create hybrid systems.

Object-oriented programming seeks to combine logic-based and object-oriented systems.

Object orientation, despite its lack of a rigorous mathematical basis, is very useful in modeling real-world circumstances.

Knowledge is represented as objects that encompass both the data and the ways for working with it.

Object-oriented systems are more accurate models of real-world things than procedural programming.

The Object Inference Knowledge Specification Language (OI-KSL) is one way (Mascrenghe et al. 2002).

Although other languages, such as Visual Prolog, have merged object-oriented programming, OI-KSL takes a different approach.

Backtracking in Visual Prolog occurs inside the objects; that is, the methods backtracked.

Backtracking is taken to a whole new level in OI KSL, with the item itself being backtracked.

To cope with uncertainties in the given data, probability theory, heuristics, and fuzzy logic are sometimes utilized.

A fuzzy electric lighting system was one example of a Prolog implementation of fuzzy logic, in which the quantity of natural light influenced the voltage that flowed to the electric bulb (Mascrenghe 2002).

This allowed the system to reason in the face of uncertainty and with little data.

Interest in expert systems started to wane in the late 1990s, owing in part to unrealistic expectations for the technology and the expensive cost of upkeep.

Expert systems were unable to deliver on their promises.

Even today, technology generated in expert systems research is used in various fields like data science, chatbots, and machine intelligence.

Expert systems are designed to capture the collective knowledge that mankind has accumulated through millennia of learning, experience, and practice.

~ Jai Krishna Ponnappan

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Dan Patterson, a statistician and computer scientist at the University of Texas in El Paso, differentiates various properties of expert systems:

(Patterson, et al., 2008) Expert systems generally often reflect domain-specific knowledge.

Many variables contribute to the difficulty of acquiring knowledge, but the complexity of encoding heuristic and experienced information is perhaps the most important.

The inference process is carried out by iteratively going over the rules, either through a forward or backward chaining technique.

Associative/semantic networks, frame representations, decision trees, and neural networks may be used in expert system designs based on nonproduction architectures.

Blackboard system designs are complex systems in which the inference process's direction may be changed during runtime.

DENDRAL, a project started at Stanford University in 1965, is an early example of a machine learning architecture project.

Expert systems may be created in a variety of methods.

Expert system shells are often used by programmers.

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