Artificial Intelligence - Generative Design.

 

Any iterative rule-based technique used to develop several choices that fulfill a stated set of objectives and constraints is referred to as generative design.

The end result of such a process may be anything from complicated architectural models to works of art, and it could be used in a number of industries, including architecture, art, engineering, and product design, to mention a few.

A more conventional design technique involves evaluating a very small number of possibilities before selecting one to develop into a finished product.

The justification for utilizing a generative design framework is that the end aim of a project is not always known at the start.

As a result, the goal should not be to come up with a single proper solution to an issue, but rather to come up with a variety of feasible choices that all meet the requirements.

Using a computer's processing capacity, multiple variations of a solution may be quickly created and analyzed, much more quickly than a person could.

As the designer/aims user's and overall vision become clearer, the input parameters are fine-tuned to refine the solution space.

This avoids the problem of being locked into a single solution too early in the design phase, allowing for creative exploration of a broad variety of possibilities.

The expectation is that by doing so, the odds of achieving a result that best meets the defined design requirements will increase.

It's worth noting that generative design doesn't have to be a digital process; an iterative approach might be created in a physical environment.

However, since a computer's processing capacity (i.e., the quantity and speed of calculations) greatly exceeds that of a person, generative design approaches are often equated with digital techniques.

The creative process is being aided by digital technologies, particularly artificial intelligence-based solutions.

Generative art and computational design in architecture are two examples of artificial intelligence applications.

The term "generative art," often known as "computer art," refers to artwork created in part with the help of a self-contained digital system.

Decisions that would normally be made by a human artist are delegated to an automated procedure in whole or in part.

Instead, by describing the inputs and rule sets to be followed, the artist generally maintains some influence over the process.

Georg Nees, Frieder Nake, and A. Michael Noll are usually acknowledged as the inventors of visual computer art.

The "3N" group of computer pioneers is sometimes referred to as a unit.

Georg Nees is widely credited with the founding of the first generative art exhibition, Computer Graphic, in Stuttgart in 1965.

In the same year, exhibitions by Nake (in cooperation with Nees) and Noll were held in Stuttgart and New York City, respectively (Boden and Edmonds 2009).

In their use of computers to generate works of art, these early examples of generative art in the visual media are groundbreaking.

They were also constrained by the existing research methodologies at the time.

In today's world, the availability of AI-based technology, along with exponential advances in processing power, has resulted in the emergence of new forms of generative art.

Computational creativity, described as "a discipline of artificial intelligence focused on designing agents that make creative goods autonomously," is an intriguing subset of these new efforts (Davis et al. 2016).

When it comes to generative art, the purpose of computational creativity is to use machine learning methods to tap into a computer's creative potential.

In this approach, the creativity process shifts away from giving a computer step-by-step instructions (as was the case in the early days) and toward more abstract procedures with unpredictable outputs.

The DeepDream computer vision software, invented by Google developer Alexander Mordvintsev in 2015, is a modern example of computational innovation.

A convolutional neural network is used in this project to purposefully over-process a picture.

This brings forward patterns that correspond to how a certain layer in the network interprets an input picture based on the image types it has been taught to recognize.

The end effect is psychedelic reinterpretations of the original picture, comparable to what one may see in a restless night's sleep.

Mordvintsev demonstrates how a neural network trained on a set of animals can take images of clouds and convert them into rough animal representations that match the detected features.

Using a different training set, the network would transform elements like horizon lines and towering vertical structures into squiggly representations of skyscrapers and buildings.

As a result, these new pictures might be regarded unexpected unique pieces of art made entirely by the computer's own creative process based on a neural network.

Another contemporary example of computational creativity is My Artificial Muse.

Unlike DeepDream, which depends entirely on a neural network to create art, Artificial Muse investigates how an AI-based method might cooperate with a human to inspire new paintings (Barqué-Duran et al. 2018).

The neural network is trained using a massive collection of human postures culled from existing photos and rendered as stick figures.

The data is then used to build an entirely new position, which is then given back into the algorithm, which reconstructs what it believes a painting based on this stance should look like.

As a result, the new stance might be seen as a muse for the algorithm, inspiring it to produce an entirely unique picture, which is subsequently executed by the artist.

Two-dimensional computer-aided drafting (CAD) systems were the first to integrate computers into the field of architecture, and they were used to directly imitate the job of hand sketching.

Although using a computer to create drawings was still a manual process, it was seen to be an advance over the analogue method since it allowed for more accuracy and reproducibility.

More complicated parametric design software, which takes a more programmed approach to the construction of an architectural model, soon exceeded these rudimentary CAD applications (i.e., geometry is created through user-specified variables).

Today, the most popular platform for this sort of work is Grasshopper (a plugin for the three-dimensional computer-aided design software Rhino), which was created by David Rutten in 2007 while working at Robert McNeel & Associates.

Take, for example, defining a rectangle, which is a pretty straightforward geometric problem.

The length and breadth values would be created as user-controlled parameters in a parametric modeling technique.

The program would automatically change the final design (i.e., the rectangle drawing) based on the parameter values provided.

Imagine this on a bigger scale, where a set of parameters connects a complicated collection of geometric representations (e.g., curves, surfaces, planes, etc.).

As a consequence, basic user-specified parameters may be used to determine the output of a complicated geometric design.

An further advantage is that parameters interact in unexpected ways, resulting in results that a creator would not have imagined.

Despite the fact that parametric design uses a computer to produce and display complicated results, the process is still manual.

A set of parameters must be specified and controlled by a person.

The computer or program that performs the design computations is given more agency in generative design methodologies.

Neural networks may be trained on examples of designs that meet a project's general aims, and then used to create multiple design proposals using fresh input data.

A recent example of generative design in an architectural environment is the layout of the new Autodesk headquarters in Toronto's MaRS Innovation District (Autodesk 2016).

Existing workers were polled as part of this initiative, and data was collected on six quantifiable goals: work style preference, adjacency preference, degree of distraction, interconnection, daylight, and views to the outside.

All of these requirements were taken into account by the generative design algorithm, which generated numerous office arrangements that met or exceeded the stated standards.

These findings were analyzed, and the highest-scoring ones were utilized to design the new workplace arrangement.

In this approach, a huge quantity of data was utilized to build a final optimal design, including prior projects and user-specified data.

The data linkages would have been too complicated for a person to comprehend, and could only be fully explored through a generative design technique.

In a broad variety of applications where a designer wants to explore a big solution area, generative design techniques have shown to be beneficial.

It avoids the issue of concentrating on a single solution too early in the design phase by allowing for creative explorations of a variety of possibilities.

As AI-based computational approaches develop, generative design will find new uses.

~ Jai Krishna Ponnappan

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

See also: 

Computational Creativity.

Further Reading:

Autodesk. 2016. “Autodesk @ MaRS.” Autodesk Research. https://www.autodeskresearch.com/projects/autodesk-mars.

Barqué-Duran, Albert, Mario Klingemann, and Marc Marzenit. 2018. “My Artificial Muse.” https://albertbarque.com/myartificialmuse.

Boden, Margaret A., and Ernest A. Edmonds. 2009. “What Is Generative Art?” Digital Creativity 20, no. 1–2: 21–46.

Davis, Nicholas, Chih-Pin Hsiao, Kunwar Yashraj Singh, Lisa Li, and Brian Magerko. 2016. “Empirically Studying Participatory Sense-Making in Abstract Drawing with a Co-Creative Cognitive Agent.” In Proceedings of the 21st International Conference on Intelligent User Interfaces—IUI ’16, 196–207. Sonoma, CA: ACM Press.

Menges, Achim, and Sean Ahlquist, eds. 2011. Computational Design Thinking: Computation Design Thinking. Chichester, UK: J. Wiley & Sons.

Mordvintsev, Alexander, Christopher Olah, and Mike Tyka. 2015. “Inceptionism: Going Deeper into Neural Networks.” Google Research Blog. https://web.archive.org/web/20150708233542/http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html.

Nagy, Danil, and Lorenzo Villaggi. 2017. “Generative Design for Architectural Space Planning.” https://www.autodesk.com/autodesk-university/article/Generative-Design-Architectural-Space-Planning-2019.

Picon, Antoine. 2010. Digital Culture in Architecture: An Introduction for the Design Professions. Basel, Switzerland: Birkhäuser Architecture.

Rutten, David. 2007. “Grasshopper: Algorithmic Modeling for Rhino.” https://www.grasshopper3d.com/.

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.

See also: 

Algorithmic Bias and Error; Explainable AI.

Further Reading:

Buolamwini, Joy and Timnit Gebru. 2018. “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.” Proceedings of Machine Learning Research: Conference on Fairness, Accountability, and Transparency 81: 1–15.

Hempel, Jessi. 2017. “Melinda Gates and Fei-Fei Li Want to Liberate AI from ‘Guys With Hoodies.’” Wired, May 4, 2017. https://www.wired.com/2017/05/melinda-gates-and-fei-fei-li-want-to-liberate-ai-from-guys-with-hoodies/.

Leavy, Susan. 2018. “Gender Bias in Artificial Intelligence: The Need for Diversity and Gender Theory in Machine Learning.” In GE ’18: Proceedings of the 1st International Workshop on Gender Equality in Software Engineering, 14–16. New York: Association for Computing Machinery.

National Center for Education Statistics (NCIS). 2018. Digest of Education Statistics. https://nces.ed.gov/programs/digest/d18/tables/dt18_325.35.asp.

Roff, Heather M. 2016. “Gendering a Warbot: Gender, Sex, and the Implications for the Future of War.” International Feminist Journal of Politics 18, no. 1: 1–18.

Simonite, Tom. 2018a. “AI Is the Future—But Where Are the Women?” Wired, August 17, 2018. https://www.wired.com/story/artificial-intelligence-researchers-gender-imbalance/.

Simonite, Tom. 2018b. “AI Researchers Fight Over Four Letters: NIPS.” Wired, October 26, 2018. https://www.wired.com/story/ai-researchers-fight-over-four-letters-nips/.

Søraa, Roger Andre. 2017. “Mechanical Genders: How Do Humans Gender Robots?” Gender, Technology, and Development 21, no. 1–2: 99–115.

Wosk, Julie. 2015. My Fair Ladies: Female Robots, Androids, and Other Artificial Eves. New Brunswick, NJ: Rutgers University Press.

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

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

See also: 

Clinical Decision Support Systems; Computer-Assisted Diagnosis; DENDRAL; Expert Systems.

Further Reading:

Hayes-Roth, Frederick, Donald A. Waterman, and Douglas B. Lenat, eds. 1983. Building Expert Systems. Teknowledge Series in Knowledge Engineering, vol. 1. Reading, MA: Addison Wesley.

Hoole, S. R. H., A. Mascrenghe, K. Navukkarasu, and K. Sivasubramaniam. 2003. “An Expert Design Environment for Electrical Devices and Its Engineering Assistant.” IEEE Transactions on Magnetics 39, no. 3 (May): 1693–96.

Jackson, Peter. 1999. Introduction to Expert Systems. Third edition. Reading, MA: Addison-Wesley.

Mascrenghe, A. 2002. “The Fuzzy Electric Bulb: An Introduction to Fuzzy Logic with Sample Implementation.” PC AI 16, no. 4 (July–August): 33–37.

Mascrenghe, A., S. R. H. Hoole, and K. Navukkarasu. 2002. “Prototype for a New Electromagnetic Knowledge Specification Language.” In CEFC Digest. Perugia, Italy: IEEE.

Patterson, Dan W. 2008. Introduction to Artificial Intelligence and Expert Systems. New Delhi, India: PHI Learning.

Rich, Elaine, Kevin Knight, and Shivashankar B. Nair. 2009. Artificial Intelligence. New Delhi, India: Tata McGraw-Hill.

Artificial Intelligence - What Is Explainable AI Or XAI?

 

AI that can be explained Explainable AI (XAI) refers to approaches or design decisions used in automated systems such that artificial intelligence and machine learning produce outputs with a logic that humans can understand and explain.

The extensive usage of algorithmically assisted decision-making in social situations has raised considerable concerns about the possibility of accidental prejudice and bias being encoded in the choice.

Furthermore, the application of machine learning in domains that need a high degree of accountability and transparency, such as medicine or law enforcement, emphasizes the importance of outputs that are easy to understand.

The fact that a human operator is not involved in automated decision-making does not rule out the possibility of human bias being embedded in the outcomes produced by machine computation.

Artificial intelligence's already limited accountability is exacerbated by the lack of due process and human logic.

The consequences of algorithmically driven processes are often so complicated that even their engineering designers are unable to understand or predict them.

The black box of AI is a term that has been used to describe this situation.

To address these flaws, the General Data Protection Regulation (GDPR) of the European Union contains a set of regulations that provide data subjects the right to an explanation.

Article 22, which deals with automated individual decision-making, and Articles 13, 14, and 15, which deal with transparency rights in relation to automated decision-making and profiling, are the ones to look out for.

When a decision based purely on automated processing has "legal implications" or "similarly substantial" effects on a person, Article 22 of the GDPR reserves a "right not to be subject to a decision based entirely on automated processing" (GDPR 2016).

It also provides three exceptions to this right, notably when it is required for a contract, when a member state of the European Union has approved a legislation establishing an exemption, or when a person has expressly accepted to algorithmic decision-making.

Even if an exemption to Article 22 applies, the data subject has the right to "request human involvement on the controller's side, to voice his or her point of view, and to challenge the decision" (GDPR 2016).

Articles 13 through 15 of the GDPR provide a number of notification rights when personal data is obtained (Article 13) or from third parties (Article 14), as well as the ability to access such data at any time (Article 15), including "meaningful information about the logic involved" (GDPR 2016).

Recital 71 protects the data subject's right to "receive an explanation of the conclusion taken following such evaluation and to contest the decision" where an automated decision is made that has legal consequences or has a comparable impact on the person (GDPR 2016).

Recital 71 is not legally binding, but it does give advice on how to interpret relevant provisions of the GDPR.

The question of whether a mathematically interpretable model is sufficient to account for an automated judgment and provide transparency in automated decision-making is gaining traction.

Ex-ante/ex-post auditing is an alternative technique that focuses on the processes around machine learning models rather than the models themselves, which may be incomprehensible and counterintuitive.

~ Jai Krishna Ponnappan

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

See also: 

Algorithmic Bias and Error; Deep Learning.

Further Reading:

Brkan, Maja. 2019. “Do Algorithms Rule the World? Algorithmic Decision-Making in the 

Framework of the GDPR and Beyond.” International Journal of Law and Information Technology 27, no. 2 (Summer): 91–121.

GDPR. 2016. European Union. https://gdpr.eu/.

Goodman, Bryce, and Seth Flaxman. 2017. “European Union Regulations on Algorithmic Decision-Making and a ‘Right to Explanation.’” AI Magazine 38, no. 3 (Fall): 50–57.

Kaminski, Margot E. 2019. “The Right to Explanation, Explained.” Berkeley Technology Law Journal 34, no. 1: 189–218.

Karanasiou, Argyro P., and Dimitris A. Pinotsis. 2017. “A Study into the Layers of Automated Decision-Making: Emergent Normative and Legal Aspects of Deep Learn￾ing.” International Review of Law, Computers & Technology 31, no. 2: 170–87.

Selbst, Andrew D., and Solon Barocas. 2018. “The Intuitive Appeal of Explainable Machines.” Fordham Law Review 87, no. 3: 1085–1139.

Artificial Intelligence - Who Is J. Doyne Farmer?

 

J. Doyne Farmer (1952–) is a leading expert in artificial life, artificial evolution, and artificial intelligence in the United States.

He is most known for being the head of a group of young people who utilized a wearable computer to get an edge while playing on the roulette wheel at various Nevada casinos.

Farmer founded Eudaemonic Enterprises with boyhood buddy Norman Packard and others in graduate school in order to conquer the game of roulette in Las Vegas.

Farmer felt that by understanding the mechanics of a roulette ball in motion, they could design a computer to anticipate which numbered pocket it would end up in.

After releasing the ball on the spinning roulette wheel, the group identified and exploited the fact that it takes around 10 seconds for a croupier to settle bets.

The findings of their research were finally encoded into a little computer buried within a shoe's sole.

The shoe's user entered the ball's location and velocity information with his big toe, and a second person placed the bets when the signal was given.

The gang did not win big quantities of money while gambling due to frequent hardware issues, and they left after approximately a dozen excursions to different casinos.

According to the gang, they had a 20 percent edge over the house.

Several breakthroughs in chaos theory and complexity systems research are ascribed to the Eudaemonic Enterprises group.

Farmer's metadynamics AI algorithms have been used to model the beginning of life and the human immune system's operation.

While at the Santa Fe Institute, Farmer became regarded as a pioneer of complexity economics, or "econophysics." Farmer demonstrated how, similar to a natural food chain, enterprises and groupings of firms build a market ecology of species.

The growth and earnings of individual enterprises, as well as the groups to which they belong, are influenced by this web and the trading methods used by the firms.

Trading businesses, like natural predators, take advantage of these patterns of influence and diversity.

He observed that trading businesses might use both stabilizing and destabilizing techniques to help or hurt the whole market ecology.

• Farmer cofounded the Prediction Company in order to create advanced statistical financial trading methods and automated quantitative trading in the hopes of outperforming the stock market and making quick money. UBS ultimately bought the firm.

• He is now working on a book on the rational expectations approach to behavioral economics, and he proposes that complexity economics, which is made up of common "rules of thumb" or heuristics discovered in psychological tests and sociological studies of humans, is the way ahead. In chess, for example, "a queen is better than a rook" is an example heuristic.

Farmer is presently Oxford University's Baillie Gifford Professor of Mathematics.

• He earned his bachelor's degree in physics from Stanford University and his master's degree in physics from the University of California, Santa Cruz, where he studied under George Blumenthal.

• He is a cofounder of the journal Quantitative Finance and an Oppenheimer Fellow.

• Farmer grew up in Silver City, New Mexico, where he was motivated by his Scoutmaster, scientist Tom Ingerson, who had the lads looking for abandoned Spanish gold mines and plotting a journey to Mars.

• He credits such early events with instilling in him a lifelong passion for scientific research.

~ Jai Krishna Ponnappan

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

See also: 

Newell, Allen.

Further Reading:

Bass, Thomas A. 1985. The Eudaemonic Pie. Boston: Houghton Mifflin Harcourt.

Bass, Thomas A. 1998. The Predictors: How a Band of Maverick Physicists Used Chaos Theory to Trade Their Way to a Fortune on Wall Street. New York: Henry Holt.

Brockman, John, ed. 2005. Curious Minds: How a Child Becomes a Scientist. New York: Vintage Books.

Freedman, David H. 1994. Brainmakers: How Scientists Are Moving Beyond Computers to Create a Rival to the Human Brain. New York: Simon & Schuster.

Waldrop, M. Mitchell. 1992. Complexity: The Emerging Science at the Edge of Order and Chaos. New York: Simon & Schuster.