Food or drink competitions reward products primarily for their "gustative worth" in the form of an award or a medal. These competitions can be classified into different categories, and most awards are product-specific, such as for wines, beers, and cheeses. Others focus on local, regional and/or national products. Finally, the international competitions test all kinds of foods and drinks available to consumers on a retail basis. Wine is probably the most tested drink, followed by beer and spirits.

There is a variety of such food and drink competitions worldwide, where the taste and quality of products are assessed. Most evaluations are carried out blind, sometimes complemented by laboratory analysis. The jury usually consists of a panel of different types of "experts" in tastings such as chefs or sommeliers.

The history of degustation tests is unclear; however, one of the earliest taste awards recorded for drinks is the Brewing Industry International Awards, dating back to 1888. For food, Monde Selection is the oldest award recorded, first run in 1961. Since then, as the variety of food products on the market has grown, so has the number of competitions and evaluations.

Monde Selection states that "This bronze, silver, gold or grand gold quality label can be compared to the quality stars of a hotel or those of the Michelin Guide." However, whilst both processes are anonymous, Michelin chooses the hotels and restaurants it reviews and pays for everything. Only products who pay the Euro 1,100 entry fee are reviewed by Monde Selection. Unlike Olympic gold, silver and bronze medals, where only one of each is awarded per event, there are no limits to how many can be awarded in each category. Other competitions use a similar business model.

Most awarding organizations charge an entry fee. Alternatively, for some awards, producers or retailers pay a fee only if their products are awarded and/or they decide to communicate about the award (payment for the right to show the award on packaging).

In both cases, one might think that these recognitions can be bought. However, tastings are carried out independently and are blind-tested, where judges focus exclusively on sensory (organoleptic) features, such as taste, olfaction, vision, aspect, mouth-feel, after-taste, without being influenced by packaging, branding or marketing. Of course, only producers who pay the entry fee can make use of any award made.

Competitions
These are all pay to enter:
 International Wine Contest
 Monde Selection
 New York International Olive Oil Competition
 Superior Taste Award
 World Beer Cup
 A.A. Taste Award

See also

 Wine competition
 Wine tasting
 List of food and drink awards

References

Competition
Food and drink awards

Food competition


Alejandro Ferrant y Fischermans (9 September 1843 – 20 January 1917) was a Spanish painter.

Life

He was born in Madrid, and studied with his uncle, Luis Ferrant Llausàs, and at the Real Academia de Bellas Artes de San Fernando. He also received a fellowship to study in Rome, where he was among the first students to attend the "Academia Española de Bellas Artes en Roma", together with Francisco Pradilla, Casto Plasencia, Manuel Castellano, Eduardo Sánchez Solá and Jaime Morera. He was director of the Museum of Modern Art in Madrid, which was merged into the Museo del Prado in 1971. His son Ángel Ferrant was a leading sculptor of the Spanish vanguard.

Selected works
He mainly painted religious and historical themes, but also devoted to genre painting and decorative art:
The Last Communion of San Fernando.
Sibyls and prophets on the inside of the dome of San Francisco el Grande, Madrid.
The Burial of Saint Sebastian.
Cisneros, founder of Illescas hospital - won the first medal of the National Exhibition of Fine Arts of 1892.

Sources
 Diccionario de Arte, Pintores del siglo XIX, Editorial LIBSA, 2001.

External links

ArtNet: More works by Ferrant.

1843 births
1917 deaths
19th-century Spanish painters
Spanish male painters
20th-century Spanish painters
20th-century Spanish male artists
19th-century Spanish male artists

Alejandro Ferrant y Fischermans


Kidnapped (1938) is an adventure film directed by Otto Preminger (who was uncredited) and Alfred L. Werker, starring Warner Baxter and Freddie Bartholomew, and based on the 1886 novel Kidnapped by Robert Louis Stevenson.

The copyright of the film was renewed.

Plot
In 1747, David Balfour's evil uncle arranges for him to be kidnapped and sent to sea where he meets exiled Alan Breck. The two make their way back to Scotland and justice.

Cast
 Warner Baxter as Alan Breck
 Freddie Bartholomew as David Balfour
 Arleen Whelan as Jean MacDonald
 C. Aubrey Smith as Duke of Argyle
 Reginald Owen as Captain Hoseason
 John Carradine as Gordon
 Nigel Bruce as Neil MacDonald
 Miles Mander as Ebenezer Balfour
 Ralph Forbes as James
 H. B. Warner as Angus Rankeillor
 Arthur Hohl as Riach
 E. E. Clive as Minister MacDougall
 Halliwell Hobbes as Dominie Campbell
 Montagu Love as Colonel Whitehead
 Moroni Olsen as Douglas
 Leonard Mudie as Red Fox
 Mary Gordon as Mrs. MacDonald
 C. Montague Shaw as Scotch Statesman
 Russell Hicks as Bailiff
 Holmes Herbert as Judge
 Brandon Hurst as Doomster
 Harry Tenbrook as Crewman (uncredited)

Production notes
 Production Dates: 3 Jan-mid-Mar 1938
 The film's credits open with a picture of Robert Louis Stevenson lying in bed writing.
 The screen credit for the title reads, "Robert Louis Stevenson's Kidnapped: The Adventures of David Balfour."
 In his autobiography, Otto Preminger recounts that he was surprised when, after having directed only two films in Hollywood, he was assigned to Kidnapped, a big-budget film. Preminger, who was not familiar with the book, read the script and complained to his friend, Gregory Ratoff, who was acting as Zanuck's assistant, that he did not want to direct the film because he had no understanding of the people in the part of the world where the story took place. Ratoff, however, persuaded him to accept. When Zanuck saw some of the rushes, he accused Preminger of cutting out a portion of the script without permission. During a heated argument, Preminger denied the charge, and when Zanuck yelled at him, Preminger yelled back. Preminger subsequently refused to apologize, and, according to the autobiography, because of the incident, he was prevented from working in Hollywood. He returned to stage direction and did not direct another film until 1943.
 Other films based on the same book include a 1948 Monogram release starring Roddy McDowell and Dan O'Herlihy and directed by William Beaudine; a 1960 Walt Disney production directed by Robert Stevenson and starring Peter Finch and James MacArthur; and a 1971 American International Pictures version, directed by Delbert Mann and starring Michael Caine and Lawrence Douglas.

See also
 List of American films of 1938
 Kidnapped (1960 film)

References

External links

 Radio Times review of the film

1938 films
Films directed by Otto Preminger
Films directed by Alfred L. Werker
Films based on Kidnapped (novel)
1938 adventure films
American adventure films
20th Century Fox films
Films set in the 1740s
American black-and-white films
1930s English-language films
1930s American films

Kidnapped (1938 film)


A wig-wag (also wigwag, wig wag) is the red light (also known as a "red-eye") near each door of a motion picture sound stage. It flashes to indicate that cameras are rolling inside the stage and no one should enter or exit the stage for any reason, and all people and vehicles outside should remain quiet. A flashing wig-wag is accompanied by one long buzzer when the cameras start rolling, and two shorter buzzers when cameras stop rolling.

References

Film and video technology

Wig-wag (sound stage)


Jatra () is a type of Odia theater performance in the Indian state of Odisha. The Jatra season begins from autumn and continues until the monsoon months of June. The Jatra is usually performed on an open-air stage with an audience seated on all sides. The performance includes play, music and dance.

Origin
The origin of Jatra is variously attributed to the Natya Shastra and the rise of Vaishnavism in Odisha by 16th cen CE. The Odia jatra tradition has evolved and diversified through centuries from stories based on the epics with theater performance in Odisha based on stories of Mahabharata and Ramayana and later modern plays were also included. Jatra was revived during the period of Baishṇaba Pāṇi with the introduction of "gitinatya" (musical performance).

The Odishan jatras are very popular across Odisha across rural settings mostly focused on mythological themes.

References

Odia culture
Odia language

Jatra (Odisha)


Angela Hewitt,  (born July 26, 1958) is a Canadian classical pianist. She is best known for her Bach interpretations.

Career
Hewitt was born in Ottawa, Ontario, daughter of the Yorkshire-born Godfrey Hewitt (thus she also has British nationality) who was choirmaster at Christ Church Cathedral in Ottawa. She began piano studies at the age of three with her mother. She earned a scholarship at the age of five. She studied violin with Walter Prystawski, recorder with Wolfgang Grunsky, and ballet with Nesta Toumine in Ottawa. Her first full-length recital was at the age of nine, in The Royal Conservatory of Music in Toronto, where she studied from 1964 to 1973 with Earle Moss and Myrtle Guerrero.  She then went on to be the student of French pianist Jean-Paul Sevilla at the University of Ottawa.

Hewitt has performed around the world in recital and as soloist with orchestra. She is best known for her cycle of Bach recordings which she began in 1994 and finished in 2005—covering all of the major keyboard works of J. S. Bach. Her recording of Bach's The Art of Fugue was released on October 17, 2014. Her discography also includes works by Louis Couperin, Jean-Philippe Rameau, Olivier Messiaen, Emmanuel Chabrier, Maurice Ravel, Robert Schumann, Ludwig van Beethoven, Frédéric Chopin, Claude Debussy and Gabriel Fauré. She has recorded two discs of Mozart concertos with the Orchestra da Camera di Mantova, and a third with Ottawa's National Arts Centre Orchestra, conducted by Hannu Lintu. With the DSO Berlin and Lintu, she also recorded the Schumann Piano Concerto.

Her entire 2007–08 season was devoted to complete performances of Bach's The Well-Tempered Clavier in major cities all over the world.  Her Hyperion DVD on Bach performance on the piano was released to coincide with the tour.

In July 2005, Hewitt launched the Trasimeno Music Festival in Umbria near Perugia, of which she is artistic director.

Hewitt switched to Fazioli pianos in 2002. Her unique four-pedal F278 Fazioli was dropped by instrument movers in January 2020 and considered unsalvageable by Paolo Fazioli, the company's founder. She chose a new Fazioli (out of five made available for her from which to choose) in January 2021.

Recognition
In 1975, Hewitt won the Chopin Young Pianists' Competition in Buffalo, New York, and a Bach competition in Washington, D.C. In 1979, she won third prize in the Robert Casadesus International Piano Competition, since renamed the Cleveland International Piano Competition.  In 1978, she won piano division in the CBC Radio Competition and in 1980 the Dino Ciani Competition in Milan, Italy. The same year, she won an honorable mention at the X International Chopin Piano Competition in Warsaw. In 1985, she won first prize in the Toronto International Bach Piano Competition, which led to a recording with Deutsche Grammophon. In 1986, she was named artist of the year by the Canadian Music Council.

In 2000, she was made an Officer of the Order of Canada (OC). In 2002, Hewitt was awarded the National Arts Centre Award, a companion award to the Governor General's Performing Arts Awards, given to an artist or group who has had an exceptional performance year.

Hewitt was named an Officer of the Order of the British Empire (OBE) on June 17, 2006 and Gramophone Artist of the Year in 2006. She received the MIDEM Classical Award for Instrumentalist of the Year in 2010, and was awarded the first ever BBC Radio 3 Listener's Award (Royal Philharmonic Society Awards) in 2003. She is also a fellow of the Royal Society of Canada, and has honorary degrees from the University of Ottawa, the University of Toronto, Queen's University (Kingston), The Open University (Milton Keynes, UK), Mount Saint Vincent University (Halifax), the University of Saskatchewan and Carleton University (Ottawa).

On December 30, 2015, Hewitt was promoted to Companion of the Order of Canada, the highest grade of the honour.

Personal life
After living in Paris from 1978 to 1985, Hewitt moved to London, which has been her principal residence ever since.

Selected discography
Johann Sebastian Bach: Goldberg Variations (Hyperion Records, 2016)
Maurice Ravel: The Complete Piano Works (Hyperion Records, 2002)
Domenico Scarlatti: Sonatas (Hyperion Records, 2016)
Franz Liszt: Piano Sonata & other works (Hyperion Records, 2015)
Johann Sebastian Bach: The Art of Fugue (Hyperion Records, 2014)
Johann Sebastian Bach: Flute Sonatas – with Andrea Oliva (flute) (Hyperion Records, 2013)
Gabriel Fauré: Piano Music (Hyperion Records, 2013)
Claude Debussy: Solo Piano Music (Hyperion Records, 2012)
Johann Sebastian Bach: Angela Hewitt plays Bach (Hyperion Records, 2010)

References

External links
 Official site
 Angela Hewitt on Hyperion Records
 Trasimeno Music Festival
 Bach World Tour

 Interview with Angela Hewitt on The Next Track podcast

1958 births
Living people
Women classical pianists
Place of birth missing (living people)
Companions of the Order of Canada
Officers of the Order of the British Empire
Canadian expatriates in England
The Royal Conservatory of Music alumni
Canadian classical pianists
Canadian women pianists
Musicians from Ottawa
Juno Award for Classical Album of the Year – Large Ensemble or Soloist(s) with Large Ensemble Accompaniment winners
Cleveland International Piano Competition prize-winners
Canadian Officers of the Order of the British Empire
Juno Award for Classical Album of the Year – Solo or Chamber Ensemble winners
21st-century classical pianists
Bach musicians
21st-century women pianists
Governor General's Award winners

Angela Hewitt


Hot pants, or 'short shorts', are a fashion item from the 1960s which is still popular today. They first appeared as a design item in so-called 'Swinging London'. The trend for shorter, brighter shorts for young women was started by designers in London such as Mary Quant.

Running or gym shorts are worn by girls as normal wear in some places. Many gym shorts have an inlay made of a comfortable fabric such as cotton, similar to swim shorts (swimming trunks). These are designed to be worn without underwear.

If a person cuts off the legs of a pair of jeans, that's known as a 'cut-off'.

The character Daisy Duke from The Dukes of Hazzard was known for wearing low cut blue jeans. The popularity of the character led to the term "Daisy Dukes" being used for short shorts.

References

1960s
Trousers and shorts

Hot pants


Edgeroi is a small township in the northwest of New South Wales, Australia, located between Narrabri and Moree. The Newell Highway and the North-West railway line pass through the township. The now-disused railway station opened in 1897. The War Memorial plaque from the railway station has been relocated to the vicinity of the Fuel Stop. The main industry of the district is agriculture and the village serves as a community centre for the local farmers as well as the railway grain silo.

There is a small CWA hall, a Memorial Hall and a windpump watering trough located behind the silo.
The only street in Edgeroi is Queen Street. The township is 24 km, or 15 mins from Narrabri.

It takes its name from Edgeroi Station, one of the largest stations in north west New South Wales, which was divided into soldier settler blocks after World War II.

Edgeroi Post Office opened on 25 April 1910 and closed in 1987.

Edgeroi railway station

References

External links

Towns in New South Wales
Newell Highway
Narrabri Shire
Localities in New South Wales
Geography of New South Wales
Central West (New South Wales)

Edgeroi


Anne Buydens (born Hannelore Marx; April 23, 1919 – April 29, 2021) was a German-born American philanthropist and film producer. She was the wife of actor Kirk Douglas from 1954 until his death in 2020. She received a Jefferson Award for Public Service in 2003.

Early life

Buydens was born Hannelore Marx in Hanover, Weimar Republic (now Germany), on April 23, 1919, the daughter of Siegfried Marx, a textile merchant who imported silk for making parachutes, and Paula Marx, a socialite.  After her parents were divorced, she was sent to a boarding school in Switzerland, where she learned English, French and Italian.  She later studied in Brussels but fled the bombed city for Paris during World War II.  As her German identity was a problem, she became a Belgian citizen by marrying a Belgian, Albert Buydens, whom she later divorced.

Career

When the Nazis in Paris insisted that films be shown with German subtitles, her language skills allowed her to find a job adding subtitles for a French film distributor. In 1948, she was hired to produce an NBC program, Paris Cavalcade of Fashion.  She also worked as a location scout on the 1952 production of Moulin Rouge for John Huston.  From 1953, she scheduled celebrity parties at the Cannes Film Festival.

When Buydens met Kirk Douglas in Paris in 1953, he was divorced from his first wife, Diana Dill, with whom he had two sons, Michael and Joel. They worked on the film Act of Love, directed by Anatole Litvak, he as an actor and she as a publicist. He offered her a job as his publicist, which she first refused, but eventually accepted, described by him later: "She finally agreed to work with me on a trial basis, making it clear our relationship would be strictly business".

Personal life

Buydens and Douglas married in Las Vegas on May 29, 1954. The couple had two sons: Peter (born 1955) and Eric (1958–2004), who both went into the film business.  Douglas credited Buydens with saving his life in 1958 when she had insisted that he not travel via private plane with director Mike Todd; the plane crashed the next day, killing all four people aboard.

Buydens became a United States citizen in 1959.  She converted to Judaism when she renewed her wedding vows with Douglas on their 50th wedding anniversary in 2004.

In 2017, they co-wrote a memoir, Kirk and Anne: Letters of Love, Laughter, and a Lifetime in Hollywood, including letters they had exchanged. She and her husband both became centenarians; he died aged 103 on February 5, 2020.

Death
Buydens died on April 29, 2021, aged 102, at their longtime Beverly Hills home. She was interred at the Westwood Village Memorial Park Cemetery next to her husband and their son Eric.

In a statement released by the family, her stepson Michael Douglas paid tribute:

Philanthropy

One of Buydens' first forays into philanthropy came on the heels of recovering from breast cancer: With six fellow survivors, Buydens established the "Research for Women's Cancers" charity, which raised millions of dollars to help finance a research facility at Cedars-Sinai Medical Center in Los Angeles.

The two started the Douglas Foundation in 1964, which has since donated roughly 118 million USD to institutions such as the Children's Hospital Los Angeles and the Motion Picture & Television Fund (MPTF).  They were also behind the establishment of Harry's Haven, an Alzheimer's disease unit named after Douglas's father at the MPTF Home in Woodland Hills.

The couple was known for their efforts to rebuild playgrounds in the Los Angeles Unified School District, attending opening ceremonies in person.  In recognition of her compassion toward homeless women, the Anne Douglas Center for Women was named after her.

Awards

Buydens was inducted into the International Best-Dressed Hall of Fame List in 1970.

In recognition of her philanthropic deeds as a private citizen, Buydens received a Jefferson Award for Public Service in 2003.  Buydens and Douglas are the only married couple to have each received an individual Jefferson Award; Douglas was awarded in 1983.

References

External links

1919 births
2021 deaths
American centenarians
Businesspeople from Hanover
Belgian emigrants to the United States
Burials at Westwood Village Memorial Park Cemetery
Bryna Productions people
German emigrants to Belgium
German emigrants to the United States
Converts to Judaism
American film producers
American Jews
American philanthropists
Douglas family
People with acquired American citizenship
Women centenarians

Anne Buydens


Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can effectively generalize and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to surpass many previous approaches in performance. Machine learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine, where it is too costly to develop algorithms to perform the needed tasks.

The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning.

ML is known in its application across business problems under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field's methods.

History and relationships to other fields

The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used in this time period.

By the early 1960s an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize patterns and equipped with a "goof" button to cause it to re-evaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P,  improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".

Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions.

Artificial intelligence

As a scientific endeavor, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. By 1980, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval. Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart, and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.

Machine learning (ML), reorganized and recognized as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory.

Data mining
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).

Generalization
The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms.

Statistics
Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field.

Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured model; rather, the data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be.

Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random Forest.

Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.

Physics
Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyze the weight space of deep neural networks. Statistical physics is thus finding applications in the area of medical diagnostics.

Theory

A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the Probably Approximately Correct Learning (PAC) model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error.

For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.

In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

Approaches

Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system:
 Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
 Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
 Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize. Although each algorithm has advantages and limitations, no single algorithm works for all problems.

Supervised learning

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal.  In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function allows the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.

Types of supervised-learning algorithms include active learning, classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.

Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.

Unsupervised learning

Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. Though unsupervised learning encompasses other domains involving summarizing and explaining data features. Unsupervised learning algorithms streamlined the process of survey and graph large indel based haplotypes of a gene of interest from pan-genome.

Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.

Semi-supervised learning

Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.

In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.

Reinforcement learning

Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In reinforcement learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcements learning algorithms use dynamic programming techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.

Dimensionality reduction
Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). This results in a smaller dimension of data (2D instead of 3D), while keeping all original variables in the model without changing the data.
The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption, leading to the area of manifold learning and manifold regularization.

Other types
Other approaches have been developed which do not fit neatly into this three-fold categorization, and sometimes more than one is used by the same machine learning system. For example, topic modeling, meta-learning.

Self-learning
Self-learning, as a machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array (CAA). It is learning with no external rewards and no external teacher advice. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.
The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine:
  in situation s perform action a
  receive consequence situation s  compute emotion of being in consequence situation v(s')
  update crossbar memory  w'(a,s) = w(a,s) + v(s')

It is a system with only one input, situation, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behavior, in an environment that contains both desirable and undesirable situations.

 Feature learning

Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal component analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.

Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data.  Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization and various forms of clustering.

Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.

Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.

 Sparse dictionary learning

Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.

 Anomaly detection

In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.

In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods (in particular, unsupervised algorithms) will fail on such data unless aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.

Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.

 Robot learning
Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning, and finally meta-learning (e.g. MAML).

 Association rules

Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".

Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.

Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule  found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.

Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.

Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs.

Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.Shapiro, Ehud Y. Inductive inference of theories from facts , Research Report 192, Yale University, Department of Computer Science, 1981. Reprinted in J.-L. Lassez, G. Plotkin (Eds.), Computational Logic, The MIT Press, Cambridge, MA, 1991, pp. 199–254. Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples. The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set.

 Models
Performing machine learning can involve creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.

 Artificial neural networks

Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times.

The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.

Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.

 Decision trees

Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels, and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision-making.

 Support-vector machines

Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category.  An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

 Regression analysis

Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space.

 Bayesian networks

A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.

 Gaussian processes

A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate normal distribution, and it relies on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other depending on their locations.

Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as function of its input data can be directly computed by looking like the observed points and the covariances between those points and the new, unobserved point.

Gaussian processes are popular surrogate models in Bayesian optimization used to do hyperparameter optimization.

 Genetic algorithms

A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.

Belief functions

The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and  imprecise probability theories. These theoretical frameworks can be thought of as a kind of learner and have some analogous properties of how evidence is combined (e.g.,  Dempster's rule of combination), just like how in a pmf-based Bayesian approach would combine probabilities. However, there are many caveats to these beliefs functions when compared to Bayesian approaches in order to incorporate ignorance and Uncertainty quantification. These belief function approaches that are implemented within the machine learning domain typically leverage a fusion approach of various ensemble methods to better handle the learner's decision boundary, low samples, and ambiguous class issues that standard machine learning approach tend to have difficulty resolving. However, the computational complexity of these algorithms are dependent on the number of propositions (classes), and can lead a much higher computation time when compared to other machine learning approaches.

 Training models
Typically, machine learning models require a high quantity of reliable data in order for the models to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a corpus of text, a collection of images, sensor data, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Bias models may result in detrimental outcomes thereby furthering the negative impacts on society or objectives. Algorithmic bias is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and notably be integrated within machine learning engineering teams.

 Federated learning

Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.

 Applications
There are many applications for machine learning, including:

 Agriculture
 Anatomy
 Adaptive website
 Affective computing
 Astronomy
 Automated decision-making
 Banking
 Behaviorism
 Bioinformatics
 Brain–machine interfaces
 Cheminformatics
 Citizen Science
 Climate Science
 Computer networks
 Computer vision
 Credit-card fraud detection
 Data quality
 DNA sequence classification
 Economics
 Financial market analysis
 General game playing
 Handwriting recognition
 Healthcare
 Information retrieval
 Insurance
 Internet fraud detection
 Knowledge graph embedding
 Linguistics
 Machine learning control
 Machine perception
 Machine translation
 Marketing
 Medical diagnosis
 Natural language processing
 Natural language understanding
 Online advertising
 Optimization
 Recommender systems
 Robot locomotion
 Search engines
 Sentiment analysis
 Sequence mining
 Software engineering
 Speech recognition
 Structural health monitoring
 Syntactic pattern recognition
 Telecommunication
 Theorem proving
 Time-series forecasting
 Tomographic reconstruction
 User behavior analytics

In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists. In 2019 Springer Nature published the first research book created using machine learning. In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19. Machine learning was recently applied to predict the pro-environmental behavior of travelers. Recently, machine learning technology was also applied to optimize smartphone's performance and thermal behavior based on the user's interaction with the phone.

 Limitations
Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.

In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.

Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves.

 Bias

Machine learning approaches in particular can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on human-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society. Language models learned from data have been shown to contain human-like biases. Machine learning systems used for criminal risk assessment have been found to be biased against black people. In 2015, Google photos would often tag black people as gorillas, and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all. Similar issues with recognizing non-white people have been found in many other systems. In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language. Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There's nothing artificial about AI...It's inspired by people, it's created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility."

 Explainability

Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. XAI may be an implementation of the social right to explanation.

 Overfitting

Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data but penalizing the theory in accordance with how complex the theory is.

 Other limitations and vulnerabilities

Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers often do not primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies.

Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification. Machine learning models are often vulnerable to manipulation and/or evasion via adversarial machine learning.

Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g., for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access.

 Model assessments
Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.

In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).

 Ethics

Machine learning poses a host of ethical questions. Systems that are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and this program had denied nearly 60 candidates who were found to be either women or had non-European sounding names. Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants. Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.

AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on objectivity and logical reasoning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.

Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated.

 Hardware
Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units. By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. OpenAI estimated the hardware computing used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.

Neuromorphic/Physical Neural Networks

A physical neural network or Neuromorphic computer  is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.

Embedded Machine LearningEmbedded Machine Learning''' is a sub-field of machine learning, where the machine learning model is run on embedded systems with limited computing resources such as wearable computers, edge devices and microcontrollers. Running machine learning model in embedded devices removes the need for transferring and storing data on cloud servers for further processing, henceforth, reducing data breaches and privacy leaks happening because of transferring data, and also minimizes theft of intellectual properties, personal data and business secrets. Embedded Machine Learning could be applied through several techniques including hardware acceleration, using approximate computing, optimization of machine learning models and many more.

 Software
Software suites containing a variety of machine learning algorithms include the following:

 Free and open-source software

 Caffe
 Deeplearning4j
 DeepSpeed
 ELKI
 Google JAX
 Infer.NET
 Keras
 Kubeflow
 LightGBM
 Mahout
 Mallet
 Microsoft Cognitive Toolkit
 ML.NET
 mlpack
 MXNet
 OpenNN
 Orange
 pandas (software)
 ROOT (TMVA with ROOT)
 scikit-learn
 Shogun
 Spark MLlib
 SystemML
 TensorFlow
 Torch / PyTorch
 Weka / MOA
 XGBoost
 Yooreeka

 Proprietary software with free and open-source editions
 KNIME
 RapidMiner

 Proprietary software

 Amazon Machine Learning
 Angoss KnowledgeSTUDIO
 Azure Machine Learning
 IBM Watson Studio
 Google Cloud Vertex AI
 Google Prediction API
 IBM SPSS Modeler
 KXEN Modeler
 LIONsolver
 Mathematica
 MATLAB
 Neural Designer
 NeuroSolutions
 Oracle Data Mining
 Oracle AI Platform Cloud Service
 PolyAnalyst
 RCASE
 SAS Enterprise Miner
 SequenceL
 Splunk
 STATISTICA Data Miner

 Journals
 Journal of Machine Learning Research
 Machine Learning
 Nature Machine Intelligence
 Neural Computation
 IEEE Transactions on Pattern Analysis and Machine Intelligence

 Conferences
 AAAI Conference on Artificial Intelligence
 Association for Computational Linguistics (ACL)
 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
 International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)
 International Conference on Machine Learning (ICML)
 International Conference on Learning Representations (ICLR)
 International Conference on Intelligent Robots and Systems (IROS)
 Conference on Knowledge Discovery and Data Mining (KDD)
 Conference on Neural Information Processing Systems (NeurIPS)

 See also

 Force control

 References

 Sources

 .

 Further reading

  Nils J. Nilsson, Introduction to Machine Learning .
 Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001). The Elements of Statistical Learning , Springer. .
 Pedro Domingos (September 2015), The Master Algorithm, Basic Books,
 Ian H. Witten and Eibe Frank (2011). Data Mining: Practical machine learning tools and techniques Morgan Kaufmann, 664pp., .
 Ethem Alpaydin (2004). Introduction to Machine Learning, MIT Press, .
 David J. C. MacKay. Information Theory, Inference, and Learning Algorithms  Cambridge: Cambridge University Press, 2003.
 Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, .
 Christopher Bishop (1995). Neural Networks for Pattern Recognition, Oxford University Press. .
 Stuart Russell & Peter Norvig, (2009). Artificial Intelligence – A Modern Approach . Pearson, .
 Ray Solomonoff, An Inductive Inference Machine, IRE Convention Record, Section on Information Theory, Part 2, pp., 56–62, 1957.
 Ray Solomonoff, An Inductive Inference Machine  A privately circulated report from the 1956 Dartmouth Summer Research Conference on AI.
 Kevin P. Murphy (2021). Probabilistic Machine Learning: An Introduction '', MIT Press.

External links

International Machine Learning Society
mloss is an academic database of open-source machine learning software.

Cybernetics
Learning

Machine learning


Sweet Mud () is a 2006 Israeli satirical drama film written and directed by Dror Shaul.  The semi-autobiographical film was shot on the kibbutzim of Ruhama and Nir Eliyahu, and draws on Shaul's memories of growing up on a kibbutz with his mentally unstable and widowed mother.

Plot
Set in 1974, Dvir (Tomer Steinhof) is soon to turn 13 and lives with his mother Miri (Ronit Yudkevitz) at a progressive kibbutz populated by people who take pride in their open-minded attitudes. However, they're not so easygoing when it comes to Miri; she's been sent to a mental hospital more than once, and her instability is more than most of the residents want to deal with, leaving Dvir to look after his mother with the help of his older brother Eyal (Pini Tavger). Miri persuades her Swiss boyfriend Stephan (Henri Garcin) to join her at the kibbutz, even though he isn't Jewish, but he isn't welcomed by other residents, and an unpleasant incident involving a neighbor's dog turns the couple into outcasts. In the midst of all this, Dvir is trying to prepare for his bar mitzvah, which at the kibbutz is combined with a severe regimen of survival training; he also gets a crash course in his ongoing maturity when he develops a crush on Maya (Daniel Kitsis), a cute girl his age.

Cast
 Tomer Steinhof as Dvir
 Ronit Yudkevitz as Miri
  as Avraham
 Pini Tavger as Eyal
 Gal Zaid as Shimshon
 Henri Garcin as Stephan
 Daniel Kitsis as Maya
 Idit Tzur as Hanna
 Yosef Carmon as Zvi
 Sharon Zuckerman as Etty
 Rivka Neumann as Zila
 Ami Weil as Uzi
 Hila Ofer as Linda
 Omer Berger as Ronen
 Natan Sgan-Cohen as Avi

Critical reception
Sweet Mud received generally favorable reviews from critics. At Rotten Tomatoes, the film holds a rating of 82%, based on 11 reviews and an average rating of 7/10. The Toronto Star called it "an appealing coming-of-age tale that takes on the difficult issues of mental illness and conformity", providing two contrasting views of kibbutz life: on one hand, "an idyllic pastoral life where the fruits of labour and a strong sense of community are shared by all," and on the other, "a place of rigid rules, where children sleep in segregated quarters away from their parents, baby bottles are dispensed in a regimented maternity ward-type system and where disapproval of individual idiosyncrasies can easily become a communal decision that isolates and ostracizes."

Awards
The film received four 2006 Ophir Awards from the Israeli Academy of Film and Television (Best Film, Best Music, Best Production Design, Best Sound) and six other nominations (Best Actor, Best Actress, Best Director, Best Screenplay, Best Costume Design, Best Editing). It also won the World Cinema Jury Prize (Dramatic) at the 2007 Sundance Film Festival and was Israel's  official submission for Best Foreign Language Film at the 2007 Academy Awards.

References

External links

2006 films
2006 drama films
2000s Hebrew-language films
Films about the kibbutz
Sundance Film Festival award-winning films
Films set in 1974
Israeli satirical films
2000s satirical films
Israeli drama films

Sweet Mud


Per Allan Magnus Claësson Uggla (pronounced ; born 18 June 1954) is a Swedish artist, composer, actor, and occasional radio host. He is known for his satirical lyrics.

Uggla was born in Stockholm. He was the lead singer of the hard rock band JUSO (later renamed Alexander Lucas) before going solo in the early 1970s. Their influences were Black Sabbath, The Groundhogs and Alice Cooper, among others. Uggla's first two glam- and art rock-inspired albums Om Bobbo Viking and Livets teaterˈ did not sell well, the first only about 500 copies in Sweden and Livets teater even less. But his departure from glam rock with the punk and hard rock inspired Va ska man ta livet av sig för när man ändå inte får höra snacket efteråt gained him much popularity and the album sold about 150,000 copies. In the 1970s, several music magazines gave Magnus the name "The Swedish Ian Hunter". In 1979, he was one of the members in Magnus Uggla Band. Povel Ramel awarded him the Karamelodiktstipendiet in 1991.

Uggla's solo influences includes Karl Gerhard, David Bowie, The Clash, Marc Bolan, Wendy Carlos and Mott the Hoople. In an interview with Aftonbladet, Uggla said that the Clash "was a really good band" and also created the song "Du ska va' glad" after he saw them perform at Gröna Lund.

Personal life
Uggla is married to Louise Uggla since 1990, with whom he has one son and a daughter. He also has a daughter from an earlier relationship.

Uggla is diagnosed with attention deficit hyperactivity disorder (ADHD). He decided to do a psychiatric assessment for ADHD as an adult after one of his children had been diagnosed.

In popular culture
Uggla participated in the fourth semi-final of Melodifestivalen 2007 with the song "För kung och fosterland", competing for the opportunity to represent Sweden in the Eurovision Song Contest in Helsinki, Finland. On February 24, 2007, Uggla performed his song, and advanced to the Second Chance round the following Saturday, where he lost to Sonja Aldén in the last round of voting. Earlier that week, he had received complaints about the lyrics of the song which contain a line that could be interpreted as degrading to Polish people, but the Swedish Chancellor of Justice decided not to try this as a violation of the hate speech act.
Plastic Bertrand has recorded a French version of Uggla's song "Vittring". It's called "Rock'n'Roll je te hais" and is found on Bertrand's LP L'Album.

Discography
Studio albums
1975: Om Bobbo Viking
1976: Livets teater
1977: Va ska man ta livet av sig för när man ändå inte får höra snacket efteråt
1978: Vittring
1980: Den ljusnande framtid är vår
1983: Välkommen till folkhemmet
1986: Den döende dandyn
1987: Allting som ni gör kan jag göra bättre
1989: 35-åringen
1993: Alla får påsar
1997: Karaoke
2000: Där jag är e're alltid bäst
2004: Den tatuerade generationen
2006: Ett bedårande barn av sin tid (Karl Gerhard covers)
2007: Pärlor åt svinen
2010: Karl Gerhard passerar i revy
2011: Innan filmen tagit slut

Collections
1985: Retrospektivt collage
1986: Collection (only released in Finland)
1994: 100% Uggla
2002: Klassiska mästerverk
2008: Magnus Uggla 1975-2008

Live albums
1981: Godkänd pirat – Live
2013: Magnus den store – Live

Maxi EP
1979: Magnus Uggla band sjunger schlagers

International singles
Singles released in France, Spain, United Kingdom and Germany.
1979: Everything You Do/Concrete Kid
1981: Ain't About To Go Back/Scandal Beauties
1981: Body Love/The Other Side (also released in Sweden)

International covers
Magnus Uggla has recorded several covers. These are the international ones, for which Uggla wrote new Swedish lyrics:
The Rolling Stones' "Star Star" became Uggla's "Stjärn…r" (or actually "Stjärnluder")
The Crystals' "Then He Kissed Me" became Uggla's "Å, han kysste mej"
Nick Gilder's "Metro Jets" became Uggla's "Centrumhets"
Bruce Woolley's "Blue Blue (Victoria)" became Uggla's "IQ"
Nick Gilder's "Worlds Collide" became Uggla's "Herr servitör"
Recorded with original lyrics:
David Bowie's "Rebel Rebel"
Steppenwolf's "Born to Be Wild"
Ramones' "Sheena Is a Punk Rocker"

Swedish covers
Uggla has covered the following Swedish songs by other artists on officially released studio records (the artists mentioned are those who first recorded the songs, not the songwriters/composers):
"Jazzgossen" by Karl Gerhard
"Leva livet" by Lill-Babs (originally "It's My Party" by Lesley Gore)
"Ring ring" (Swedish version) by ABBA
"Mälarö kyrka" by Sven Lindahl
"Livet är en fest" by Nationalteatern
"Påtalåten" by Ola Magnell
"Vem kan man lita på?" by Hoola Bandoola Band
"Häng med på party" by Ulf Neidemar
"I natt är jag din" by Tomas Ledin
"Hög standard" by Peps Persson
"Vi måste höja våra röster" by Margareta Garpe, Suzanne Osten and Gunnar Edander (since nobody seems to know exactly who recorded it first, these are the composers and songwriters)
"Tusen systrar" by Jösses Flickor
"In kommer Gösta" by Philemon Arthur & the Dung
"Speedy Gonzales" by Nationalteatern
"Hog farm" by Pugh Rogefeldt
"Ska vi gå hem till dig" by Lasse Tennander

Others
"Jag och min far" (from EP Så mycket bättre (Säsong 3 – Olles dag – Program 2))
"Har hört om en tjej (I Heard of a Girl)" (from EP Säsong 3 – Miss Lis dag – Program 3)

Bibliography
 Jag hade en gång en blogg (2008) with Jonathex Dan

See also
List of Swedes in music

References

External links

Official Magnus Uggla website

1954 births
Living people
Singers from Stockholm
Swedish nobility
Swedish people of Jewish descent
Swedish male songwriters
Swedish-language singers
Swedish male bloggers
Melodifestivalen contestants of 2007
Melodifestivalen contestants of 2006
People with attention deficit hyperactivity disorder

Magnus Uggla


John Washington Baird (February 22, 1852 – 1923) was a minor American chess master, who played in a number of American and international chess tournaments between 1880 and 1906. He was the brother of David Graham Baird, who was also an American chess master.

A writer in the New York Times, describing the players in the Sixth American Chess Congress (1889), portrayed Baird and his brother as follows:
Of the Baird brothers, David G. is the better player by far. He plays with characteristic Scotch carefulness, for he is of Scotch descent. Of medium height, he is inclined to stoutness, and is of light complexion. His brother John W. is very thin, although he looks like his brother in the face. He was one of the slowest players in the tournament.

The date and place of Baird's death are unknown.

Chess career

Baird played in a number of American and international chess tournaments between 1880 and 1906. He scored 9/13, finished tied for 4th-5th place, in the Minor Section of the Fifth American Chess Congress. He finished 4th in the Fifth Manhattan Chess Club championship (1883). Baird finished 3rd with 6.15/12 in the Eighth Manhattan Chess Club championship (1883).

At the Sixth American Chess Congress at New York 1889, a double round robin that was one of the longest tournaments in history, Baird scored 7/38, finishing 19th out of 20 players. Baird's brother David Graham Baird scored 16/38, finishing 14th; Mikhail Chigorin and Max Weiss tied for first with 29 points, edging out Isidor Gunsberg (28.5).

Baird played in the 1892 Hauptturnier in Dresden, scoring 4/6 (2nd-3rd place of 7 players) and qualifying for the final, where he scored 4/8, finishing tied 4th-6th of 9 players. At a strong tournament at New York 1893, Baird finished with 4.5/9 (a half point more than his brother), tying for 5th-6th. Harry Nelson Pillsbury won with 7/9.) Baird played in the very strong Meisterturnier (Master Tournament) at Leipzig 1894, scoring a respectable 7.5/17 (12th place out of 18 players); Siegbert Tarrasch won. Baird also competed in international tournaments at Barmen 1905 and Nuremberg 1906.

References

External links

American chess players
1852 births
1923 deaths
19th-century chess players

John Washington Baird


George William Goddard (June 15, 1889 – September 20, 1987) was a United States Air Force brigadier general and a pioneer in aerial photography.

Early life and education
George William Goddard was born in London, England, in 1889. He moved to Rochester, New York, in June 1904 to live with his aunt and uncle. He was naturalized on April 27, 1918.

Goddard graduated from Washing Irving Preparatory School in New York in 1910 and attended Keuka Institute, now Keuka College in Keuka Park, New York, for two years.  While at Keuka College, he witnessed early flights by Glenn Curtiss.

Goddard then studied commercial art in Rochester for a year. He was a free-lance cartoonist in Rochester until January 1916, when he became a staff artist for Coke and Iron Monthly in Chicago, Illinois.  While in Chicago, he saw an exhibition by aviator Ruth Law which inspired him to learn to fly.

Career

World War I
Originally Goddard intended to serve the war effort in the Corps of Engineers, but persuaded by a pilot he met en route to this job, he decided to join the Signal Corps Air Service to become a pilot instead. On December 14, 1917, Goddard enlisted in the Aviation Section of the U.S. Army Signal Corps and entered the aerial photography course at the School of Military Aeronautics at Cornell University. Upon completion of the three-month course, he became an instructor in aerial photographic interpretation.

Goddard was commissioned a second lieutenant in the Aviation Section Reserve on August 8, 1918, and assigned to Taliaferro Field, Fort Worth, Texas, to organize and take to France the 43rd, 44th and 45th Aerial Photographic sections.

Despite plenty of informal flying, Goddard did not become a rated pilot until two years after joining up. He had been assigned to photography because he had admitted to being an amateur photographer in civilian life.

Between the world wars
When the Armistice was declared, ending World War I, Goddard was transferred to Carlstrom Field, Arcadia, Florida, where he finally graduated from flying school and was rated a pilot in May 1919. At the personal urging of General Billy Mitchell, who was impressed with Goddard's camera experimentation, he was assigned to McCook Field, Ohio, as officer in charge of aerial photographic research. In that capacity, he started developments in the infra-red and long-range photography, special aerial cameras, photographic aircraft and portable field laboratory equipment and formed the nucleus of the Photographic Laboratory at Wright-Patterson Air Force Base, Ohio.

On July 1, 1920, Goddard received his regular commission as a second lieutenant in the Air Service, and was promoted immediately to first lieutenant the same date. He then was appointed officer in charge of aerial photography in the Office of the Chief of the Air Corps, Washington, D.C. While on that assignment, he received a presidential appointment as Air Corps representative on the Federal Board of Surveys and Maps. He organized the first Army aerial photographic mapping units that pioneered in mapping Muscle Shoals, Tennessee River Basin, Teapot Dome, Mississippi River and many other areas. As chief photographic officer, Goddard created the first aerial mapping units, directed photo coverage of General Billy Mitchell's 1921 warship bombings, and made mosaic maps of many cities and land area.

Returning to McCook Field, Goddard pioneered the development of nighttime reconnaissance photography in 1925. One night in 1925, he stunned Rochester, N.Y., by igniting an 80-pound flash powder bomb to light up the whole city. The result was the first aerial night photograph.

On a trip to the Philippines, Goddard mapped unexplored areas, and subsequently became Director of the photographic school at Chanute Field, Illinois.

Goddard personally developed and held the patents on the Air Force's system for taking night pictures. Developed in 1926, the system, with improvements, was still in use through the 1950s.

In 1934, Goddard directed aerial mapping in Alaska. As chief photographic officer at Wright Field, Ohio, he pioneered in stereoscopic, high altitude, and color photography and developed the film strip camera (see strip aerial photography).

Before and during World War II, Goddard went to England on several occasions to learn British methods of reconnaissance and exchange technical information. During these trips, he met with his counterpart in the Royal Air Force (RAF), Group Commander Frederick Laws, who had pioneered British aerial reconnaissance as early as 1913.

World War II
During World War II, Goddard promoted aerial reconnaissance, aided the Navy in use of the strip camera and color photography, and introduced the moving film magazine.

Goddard also directed the design of reconnaissance aircraft and equipment. At Wright Field, Goddard and his staff were preoccupied with preparing hitherto much-neglected reconnaissance aircraft types for the coming war. The biggest job was in modifying 100 P-38 Lightnings to F-4 standard. In 1941, Goddard got into a fierce bureaucratic and personal dispute with the then USAAC Director of Photography, Lieutenant Colonel Minton Kaye. It was exacerbated by Goddard's single-minded promotion of the continuously open-shutter strip-camera, which he revealed to the public in an extensive photo coverage in Life magazine. In punishment, for a time, the USAAC relegated Goddard to combat venereal disease at a base in Charlotte, NC. Using his connections, he was then detached to work on naval tasks, since the Navy thought the strip camera would be useful for amphibious operations in the Pacific.

In February 1944, Assistant Secretary of War for Air Robert Lovell sent Colonel Goddard to England to assist friend Colonel Elliott Roosevelt, the son of the president, in setting up the reconnaissance program for the 325th Reconnaissance Wing. Goddard helped modify F-8 Mosquitos for radar photography, and assisted in the development of night photography using the Edgerton D-2 skyflash. He also successfully interested the RAF in the strip camera, which Roosevelt had initially refused because it required very low altitude flight for best results. Goddard said later that the two collaborated in sending a letter to the President demanding that Colonel Kaye be removed from his posts in Washington. Kaye then fell in disfavor and was sent to India just short of his promotion to flag rank, and Goddard returned to the good graces of General Henry Arnold, the chief of the Air Force.

After the liberation of Paris, Goddard set up headquarters there and led in reconnaissance development for the U.S. air forces in theater. That winter, he finally succeeded in getting a stereo-strip camera installed in an F-6 Mustang, trying it out over Paris. With the occupation of Germany, Goddard received special clearance to take over and recover useful scientific and technical information from the Schneider Optical Plant at Bad Kreuznach and the renowned Carl Zeiss and Schott AG plants at Jena. Goddard was able to seize much data and material, and persuaded many optical scientists to move to the West just before the Red Army replaced American troops, but he complained that the State Department had countermanded much of this effort and returned equipment to the Soviets. During the war period, Goddard (along with Roosevelt) continued to stridently advocate for a dedicated reconnaissance aircraft, as opposed to the use of modified types.

By mid-August 1945, Colonel Goddard returned to Wright Field, restored as Director of Aerial Research and Development.

Cold War
In August 1945, Goddard was appointed chief of the Photographic Laboratory at Wright Field. Goddard was sent to Bikini Atoll in 1946 in connection with the atomic bomb test. He retired June 30, 1949, but was recalled to active duty the following day by General Hoyt Vandenberg, Air Force Chief of Staff, retaining his position as chief of the Photographic Laboratory.

In July 1950, Goddard was awarded the Thurman H. Bane Award for his development in low-altitude high-speed night photography.

During the early part of the Korean War in 1950, Goddard went to Korea to introduce this new system of night photography and the latest type strip camera, which has been highly successful in the low-altitude jet aircraft operations under adverse weather conditions.

In August 1951, he was awarded the honorary degree of master of photography by the Photographers Association of America, and also received the Progress Medal for 1951 at the annual convention of the Photographic Society of America, held in Detroit, Michigan.

In May 1952, Goddard was transferred to headquarters of NATO's Allied Air Forces in Central Europe at Fontainebleau, France, for duty as director of reconnaissance, Operations Division. While there, he worked with Central European countries to develop unified standards in aerial photographic methods.

In July 1953, Goddard was assigned to Headquarters Air Materiel Command, Wright-Patterson Air Force Base, Ohio.

Goddard was presented the George W. Harris award by the Photographers' Association of America at its 61st annual national convention in Chicago. Goddard received the award, the highest single honor the profession of photographers can bestow, for his contributions to the art of aerial photography in supervising development of aerial cameras, equipment and techniques.

"Aerial photography has come a long way even since World War II, General Goddard said. "Present advances in cameras, equipment and particularly in night photography, have greatly restricted maneuvers of the enemy in Korea."

"Fast jets, traveling at 600 miles an hour and at either 3,000 or 40,000 feet, are able to take continuous film strips of miles of territory that are as clear as day-time pictures. They are so clear that on pictures taken from 40,000 feet, you can count the ties in a railroad track, or the rivets on the wings of an airplane.

"Efforts to camouflage installations also are detected by new electronic aerial photo equipment," the general added.

General Goddard declared that advances in aerial photography have been greatly speeded by Congress' recognition of its value and its willingness to provide funds. Helpful, also, the general said, are the research experiments of three leading American universities. He referred to Boston University, Ohio State, and Wesleyan University as contributing significant advances.

Goddard's Law states that "in reconnaissance, there is no substitute for focal length."

Awards and decorations
Brigadier General Goddard received a lot of decorations during his military service. Here is the list:

Later life and death
Goddard retired as a brigadier general in 1953.  In 1962, during the Cuban Missile Crisis, Goddard served as an Air Force consultant on detecting and interpreting the Soviet missile sites in Cuba, especially by advocating the use of his strip camera for low-altitude flights.

Goddard was inducted into the National Aviation Hall of Fame in 1976.

Goddard died on September 20, 1987, at the age of 98.  He was buried with his wife Elizabeth (1898–1984) at Arlington National Cemetery.

Legacy

The Society of Photographic Instrumentation Engineers (SPIE) has awarded the George W. Goddard award annually since 1961 in recognition of "exceptional achievement in optical or photonic instrumentation for aerospace, atmospheric science, or astronomy. The award is for the invention and development of a new technique, photonic instrumentation, instrument, or system."  Goddard was the first recipient of the award in 1961.

Goddard's autobiography, written with DeWitt S. Copp and published in 1969, is a piece of reconnaissance literature and an important and detailed source to the history of the field in the United States.

In 1976, Goddard was inducted into the International Air & Space Hall of Fame.

References

Bibliography
 Goddard, George. Overview: A Lifelong Adventure in Aerial Photography. Doubleday, Garden City, 1969.
 Hansen, Chris. Enfant Terrible: The Times and Schemes of General Elliott Roosevelt. Able Baker Press, Tucson, 2012.

External links
 National Aviation Hall of Fame

 George William Goddard at ArlingtonCemetery.net, an unofficial website

1889 births
1987 deaths
United States Army personnel of World War I
United States Army Air Forces personnel of World War II
United States Air Force personnel of the Korean War
National Aviation Hall of Fame inductees
United States Air Force generals
Aerial reconnaissance pioneers
Recipients of the Distinguished Service Medal (US Army)
Recipients of the Legion of Merit
Burials at Arlington National Cemetery
Cornell University alumni
Wesleyan University people
British emigrants to the United States
Keuka College alumni
SPIE

George William Goddard