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Cybernetics
Cybernetics is the transdisciplinary study of circular causal processes such as feedback and recursion, where the effects of a system's actions (its outputs) return as inputs to that system, influencing subsequent actions. It is concerned with general principles that are relevant across multiple contexts, including engineering, ecological, economic, biological, cognitive and social systems and also in practical activities such as designing, learning, and managing. Cybernetics' transdisciplinary character means that it intersects with a number of other fields, resulting in a wide influence and diverse interpretations. The field is named after an example of circular causal feedback—that of steering a ship (the ancient Greek κυβερνήτης (kybernḗtēs) refers to the person who steers a ship). In steering a ship, the position of the rudder is adjusted in continual response to the effect it is observed as having, forming a feedback loop through which a steady course can be maintained in a changing environment, responding to disturbances from cross winds and tide. Cybernetics has its origins in exchanges between numerous disciplines during the 1940s. Initial developments were consolidated through meetings such as the Macy conferences and the Ratio Club. Early focuses included purposeful behaviour, neural networks, heterarchy, information theory, and self-organising systems. As cybernetics developed, it became broader in scope to include work in design, family therapy, management and organisation, pedagogy, sociology, the creative arts and the counterculture. == Definitions == Cybernetics has been defined in a variety of ways, reflecting "the richness of its conceptual base". One of the best known definitions is that of the American scientist Norbert Wiener, who characterised cybernetics as concerned with "control and communication in the animal and the machine". Another early definition is that of the Macy cybernetics conferences, where cybernetics was understood as the study of "circular causal and feedback mechanisms in biological and social systems". Margaret Mead emphasised the role of cybernetics as "a form of cross-disciplinary thought which made it possible for members of many disciplines to communicate with each other easily in a language which all could understand". Other definitions include: "the art of governing or the science of government" (André-Marie Ampère); "the art of steersmanship" (Ross Ashby); "the study of systems of any nature which are capable of receiving, storing, and processing information so as to use it for control" (Andrey Kolmogorov); and "a branch of mathematics dealing with problems of control, recursiveness, and information, focuses on forms and the patterns that connect" (Gregory Bateson). == Etymology == The Ancient Greek term κυβερνητικός (kubernētikos, '(good at) steering') appears in Plato's Republic and Alcibiades, where the metaphor of a steersman is used to signify the governance of people. The French word cybernétique was also used in 1834 by the physicist André-Marie Ampère to denote the sciences of government in his classification system of human knowledge. According to Norbert Wiener, the word cybernetics was coined by a research group involving himself and Arturo Rosenblueth in the summer of 1947. It has been attested in print since at least 1948 through Wiener's book Cybernetics: Or Control and Communication in the Animal and the Machine. In the book, Wiener states: After much consideration, we have come to the conclusion that all the existing terminology has too heavy a bias to one side or another to serve the future development of the field as well as it should; and as happens so often to scientists, we have been forced to coin at least one artificial neo-Greek expression to fill the gap. We have decided to call the entire field of control and communication theory, whether in the machine or in the animal, by the name Cybernetics, which we form from the Greek κυβερνήτης or steersman. Moreover, Wiener explains, the term was chosen to recognize James Clerk Maxwell's 1868 publication on feedback mechanisms involving governors, noting that the term governor is also derived from κυβερνήτης (kubernḗtēs) via a Latin corruption gubernator. Finally, Wiener motivates the choice by steering engines of a ship being "one of the earliest and best-developed forms of feedback mechanisms". == History == === First wave === The initial focus of cybernetics was on parallels between regulatory feedback processes in biological and technological systems. Two foundational articles were published in 1943: "Behavior, Purpose and Teleology" by Arturo Rosenblueth, Norbert Wiener, and Julian Bigelow – based on the research on living organisms that Rosenblueth did in Mexico – and the paper "A Logical Calculus of the Ideas Immanent in Nervous Activity" by Warren McCulloch and Walter Pitts. The foundations of cybernetics were then developed through a series of transdisciplinary conferences funded by the Josiah Macy, Jr. Foundation, between 1946 and 1953. The conferences were chaired by McCulloch and had participants that included Ross Ashby, Gregory Bateson, Heinz von Foerster, Margaret Mead, John von Neumann, and Norbert Wiener. In the UK, similar focuses were explored by the Ratio Club, an informal dining club of young psychiatrists, psychologists, physiologists, mathematicians and engineers that met between 1949 and 1958. Wiener introduced the neologism cybernetics to denote the study of "teleological mechanisms" and popularized it through the book Cybernetics: Or Control and Communication in the Animal and the Machine. During the 1950s, cybernetics was developed as a primarily technical discipline, such as in Qian Xuesen's 1954 "Engineering Cybernetics". The text was quickly translated into multiple languages and became a foundational text on automation. In the Soviet Union, Cybernetics was initially considered with suspicion but became accepted from the mid to late 1950s. By the 1960s and 1970s, however, cybernetics' transdisciplinarity fragmented, with technical focuses separating into separate fields. Artificial intelligence (AI) was founded as a distinct discipline at the Dartmouth workshop in 1956, differentiating itself from the broader cybernetics field. After some uneasy coexistence, AI gained funding and prominence. Consequently, cybernetic sciences such as the study of artificial neural networks were downplayed. Similarly, computer science became defined as a distinct academic discipline in the 1950s and early 1960s. === Second wave === The second wave of cybernetics came to prominence from the 1960s onwards, with its focus shifting away from technology toward social, ecological, and philosophical concerns. It was still grounded in biology, notably Maturana and Varela's autopoiesis, and built on earlier work on self-organising systems and the presence of anthropologists Mead and Bateson in the Macy meetings. The Biological Computer Laboratory, founded in 1958 and active until the mid-1970s under the direction of Heinz von Foerster at the University of Illinois at Urbana–Champaign, was a major incubator of this trend in cybernetics research. Focuses of the second wave of cybernetics included management cybernetics, such as Stafford Beer's biologically inspired viable system model; work in family therapy, drawing on Bateson; social systems, such as in the work of Niklas Luhmann; epistemology and pedagogy, such as in the development of radical constructivism. Cybernetics' core theme of circular causality was developed beyond goal-oriented processes to concerns with reflexivity and recursion, notably in Mead's invocation at the inaugural meeting of the American Society for Cybernetics (ASC) to apply cybernetics to the activities of the ASC itself. This focus on reflexivity was especially prominent in the development of second-order cybernetics (or the cybernetics of cybernetics), developed and promoted by Heinz von Foerster, which focused on questions of observation, cognition, epistemology, and ethics. The 1960s onwards also saw cybernetics begin to develop exchanges with the creative arts, design, and architecture, notably with the Cybernetic Serendipity exhibition (ICA, London, 1968), curated by Jasia Reichardt, and the unrealised Fun Palace project (London, unrealised, 1964 onwards), where Gordon Pask was consultant to architect Cedric Price and theatre director Joan Littlewood. In 1962, Qian Xuesen recruited Song Jian and Guan Zhaozhi to establish China's first cybernetics laboratory with him. Following the Sino-Soviet split, cybernetics was deemed disreputable in China. The field was again favored in the 1970s and 1980s following Deng Xiaoping's emphasis on modernisation. === Third wave === From the 1990s onwards, there has been a renewed interest in cybernetics from a number of directions. Early cybernetic work on artificial neural networks has been returned to as a paradigm in machine learning and artifi
Sasha Luccioni
Alexandra Sasha Luccioni (née Vorobyova; born 1990) is a computer scientist specializing in the intersection of artificial intelligence (AI) and climate change. Her work focuses on quantifying the environmental impact of AI technologies and promoting sustainable practices in machine learning development. == Early life and education == Alexandra Sasha Vorobyova was born in the Ukrainian Soviet Socialist Republic in 1990. When she was four years old, her family relocated to Ontario, Canada. Her interest in science is influenced by her family's history; her mother, grandmother, and great-grandmother all pursued careers in scientific fields. Luccioni earned a B.A. in language science from University of Paris III: Sorbonne Nouvelle in 2010. Subsequently, she completed a M.S. in cognitive science, with a minor in natural language processing, at École normale supérieure in Paris in 2012. Luccioni obtained her PhD in cognitive computing from Université du Québec à Montréal (UQAM) in 2018. == Career == Luccioni began her professional career at Nuance Communications in 2017, where she focused on natural language processing (NLP) and machine learning (ML) techniques to enhance conversational agents. She then joined Morgan Stanley’s AI/ML Center of Excellence in 2018, working on explainable artificial intelligence (AI) and decision-making systems. In 2019, she became a postdoctoral researcher at Université de Montréal and Mila, collaborating with computer scientist Yoshua Bengio on a project titled This Climate Does Not Exist. This initiative used generative adversarial networks to visualize the effects of climate change. During this time, she also contributed to integrating fairness and accountability into machine learning education at Mila. Luccioni briefly worked with the United Nations Global Pulse in 2021, developing tools to monitor COVID-19 misinformation. Later that year, she joined Hugging Face as a research scientist. Her role includes quantifying the carbon footprint of AI systems, co-chairing the carbon working group in the Big Science project, and advancing responsible machine learning practices. She helped create "CodeCarbon," an open-source software tool that estimates the carbon emissions produced during the training and operation of machine learning models. In addition to her research, she has developed tools to measure the environmental impact of AI models, communicated findings through media engagements, and presented at international conferences, including a TED Talk. In 2024, she was listed on BBC 100 Women and Time 100 AI.
Luca Maria Gambardella
Luca Maria Gambardella (born 4 January 1962) is an Italian computer scientist and author. He is the former director of the Dalle Molle Institute for Artificial Intelligence Research in Lugano, in the Ticino canton of Switzerland. He is currently the prorector of Università della Svizzera italiana, where he directs the Master of Science in Artificial Intelligence degree course. Several of his papers have been extensively cited, with his collaborators including Marco Dorigo, with whom he has published papers on the application of ant colony optimization theory to the traveling salesman problem, and Jürgen Schmidhuber with whom he has published research on deep neural networks.. Beside working in research, Gambardella explores the potentials of AI applied for the generation of art. Some of his artistic installations received significant media coverage. As a novelist, the genres he approached broad from Bildungsroman of his first book "Sei vite" ("Six lives"), to romance of his second book "Il suono dell'alba" ("The sound of sunrise").
Top 10 AI Sales Assistants Compared (2026)
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Groover
Groover is an online platform, record label and distributor, connecting artists and musicians with music professionals and media outlets. The service was founded in 2018 in France and operates from offices in Paris and New York. The platform has over 3,000 active contacts, including SPIN Magazine and Sofar Sounds. Groover uses a micro-payment model. Among the platform's over 500,000 regular users are record labels such as Ninja Tune, Ba Da Bing Records, Dance To The Radio, Roche Musique, Wagram Music, Secret City Records, and artists including Bonobo, Michael Bolton, Aloe Blacc, Haddaway, Passenger, La Femme and Chinese Man. == History == Groover was launched at the MaMA Music Convention in October 2018. It was co-founded by Dorian Perron, Romain Palmieri, and Rafaël Cohen while they were students at UC Berkeley. Initially growing in France, the company has expanded to the United States, Canada, the United Kingdom, Brazil, Italy, and elsewhere in Europe. In March 2019, Groover was part of the Business France delegation at the South by Southwest (SXSW) festival. In June 2019, Groover raised €1.3 million from various angel investors. In April 2021, Groover acquired the platform Soonvibes, which had 70,000 users at the time, in order to strengthen its community in the electronic music space. In November 2021, Groover announced a €6 million funding round from Bpifrance Creative Industries and Partech. Between 2023 and 2025, Groover entered strategic partnerships with major artist service providers, including CD Baby, TuneCore, SoundCloud, UnitedMasters, Symphonic Distribution, Audiomack and SACEM. In February 2024, Groover announced a Series A funding round of $8 million from OneRagTime, Trind, Techmind, and Mozza Angels. == Function == Using a micro-payment system, professionals listen to tracks and provide written feedback. These professionals retain full editorial independence and are under no obligation to share the track or contact the artist. == Awards == 2nd Prize for Music Innovation 2023 from the Centre national de la musique (France) "Future Creator" Award at the Petit Poucet Competition 2019 Jury's Special Mention at the MaMA Invent 2019 competition 1st Prize for Digital Initiative in Culture, Communication & Media 2019 awarded by Audiens "Start-up of the Year" at the Social Music Awards 2020 French American Entrepreneurship Award 2022 at the French Consulate in New York
Separating words problem
In theoretical computer science, the separating words problem is the problem of finding the smallest deterministic finite automaton that behaves differently on two given strings, meaning that it accepts one of the two strings and rejects the other string. It is an open problem how large such an automaton must be, in the worst case, as a function of the length of the input strings. == Example == The two strings 0010 and 1000 may be distinguished from each other by a three-state automaton in which the transitions from the start state go to two different states, both of which are terminal in the sense that subsequent transitions from these two states always return to the same state. The state of this automaton records the first symbol of the input string. If one of the two terminal states is accepting and the other is rejecting, then the automaton will accept only one of the strings 0010 and 1000. However, these two strings cannot be distinguished by any automaton with fewer than three states. == Simplifying assumptions == For proving bounds on this problem, it may be assumed without loss of generality that the inputs are strings over a two-letter alphabet. For, if two strings over a larger alphabet differ then there exists a string homomorphism that maps them to binary strings of the same length that also differ. Any automaton that distinguishes the binary strings can be translated into an automaton that distinguishes the original strings, without any increase in the number of states. It may also be assumed that the two strings have equal length. For strings of unequal length, there always exists a prime number p whose value is logarithmic in the smaller of the two input lengths, such that the two lengths are different modulo p. An automaton that counts the length of its input modulo p can be used to distinguish the two strings from each other in this case. Therefore, strings of unequal lengths can always be distinguished from each other by automata with few states. == History and bounds == The problem of bounding the size of an automaton that distinguishes two given strings was first formulated by Goralčík & Koubek (1986), who showed that the automaton size is always sublinear. Later, Robson (1989) proved the upper bound O(n2/5(log n)3/5) on the automaton size that may be required. This was improved by Chase (2020) to O(n1/3(log n)7). There exist pairs of inputs that are both binary strings of length n for which any automaton that distinguishes the inputs must have size Ω(log n). Closing the gap between this lower bound and Chase's upper bound remains an open problem. Jeffrey Shallit has offered a prize of 100 British pounds for any improvement to Robson's upper bound. == Special cases == Several special cases of the separating words problem are known to be solvable using few states: If two binary words have differing numbers of zeros or ones, then they can be distinguished from each other by counting their Hamming weights modulo a prime of logarithmic size, using a logarithmic number of states. More generally, if a pattern of length k appears a different number of times in the two words, they can be distinguished from each other using O(k log n) states. If two binary words differ from each other within their first or last k positions, they can be distinguished from each other using k + O(1) states. This implies that almost all pairs of binary words can be distinguished from each other with a logarithmic number of states, because only a polynomially small fraction of pairs have no difference in their initial O(log n) positions. If two binary words have Hamming distance d, then there exists a prime p with p = O(d log n) and a position i at which the two strings differ, such that i is not equal modulo p to the position of any other difference. By computing the parity of the input symbols at positions congruent to i modulo p, it is possible to distinguish the words using an automaton with O(d log n) states.