Martin Hilbert

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Martin Hilbert
Martin Hilbert.jpg
Hilbert presenting at Puerto de Ideas
Born1977 (age 4647)
NationalityGerman - USA
Alma mater University of Southern California (PhD)
University of Erlangen–Nuremberg (Dr. rer.pol.)
Known for Big Data [1]
Information explosion
eLAC Action Plans. [2]
Scientific career
Fields Computational Social Science, Information Theory, Complex Systems, Information Society
Institutions University of California, Davis
Doctoral advisors Manuel Castells (2012)
Karl Albrecht Schachtschneider (2006)

Martin Hilbert (born in 1977) is a social scientist who is a professor at the University of California where he chairs the campus-wide emphasis on Computational Social Science. [3] He studies societal digitalization. His work is recognized in academia for the first study that assessed how much information there is in the world; [4] in public policy for having designed the first digital action plan with the governments of Latin America and the Caribbean at the United Nations (eLAC Action Plans); and in the popular media for having alerted about the intervention of Cambridge Analytica a year before the scandal broke. [5]

Contents

Career and research

Hilbert served as Economic Affairs Officer of the United Nations Secretariat for 15 years (UN ECLAC), where he created the Information Society Program for Latin America and the Caribbean [6] He conceptualized the design of the eLAC Action Plans, which has led to six consecutive generations of digital development agendas for Latin America and the Caribbean (2005–2025). [7]

Hilbert studies the conditions and effects of digitalization (information & communication) and algorithmification (knowledge) [8] on human processes and societal dynamics. His research has found audiences in communication science, [9] information science, [10] international development, [11] evolution and ecology, [12] technological forecasting, [13] complexity science, [14] [15] network science, [16] economics, [17] [18] physics, [19] psychology, [20] women's studies [21] and multidisciplinary science. [22]

Consulting

Hilbert has provided technical assistance in the field of digital development to more than 20 countries and contributed to publicly traded companies as digital strategist. He has consulted with governments and companies, especially in Latin America, which has earned him media-titles like “guru of big data”. [23] [24]

Teaching

Hilbert's university courses are available as MOOCs on Coursera. His teachings on "Digital Technology & Social Change" consists of an introduction to the digital age, being informed by his hands-on experience at the United Nations and his regular consultancy work. [25] His methods course is an introduction to the scientific method, informed by complexity science, executed with computational tools and called “University of California Computational Social Science”. It was the first UC-wide online course that involves faculty members from all 10 UC campuses (17 different lecturers). [26]

Awards & Recognitions

Hilbert's numerous peer recognitions span from awards for visual infographics, [27] and written interviews, [28] to an endowed chair position at the Library of Congress, [29] and ranked at the 'Top-100 Best Online Courses of ALL TIMES, [30] as well as two awards for online teaching from the University of California Office of the President's Innovative Learning Technology Initiative (ILTI). [31]

Related Research Articles

<span class="mw-page-title-main">Computational chemistry</span> Branch of chemistry

Computational chemistry is a branch of chemistry that uses computer simulations to assist in solving chemical problems. It uses methods of theoretical chemistry incorporated into computer programs to calculate the structures and properties of molecules, groups of molecules, and solids. The importance of this subject stems from the fact that, with the exception of some relatively recent findings related to the hydrogen molecular ion, achieving an accurate quantum mechanical depiction of chemical systems analytically, or in a closed form, is not feasible. The complexity inherent in the many-body problem exacerbates the challenge of providing detailed descriptions of quantum mechanical systems. While computational results normally complement information obtained by chemical experiments, it can occasionally predict unobserved chemical phenomena.

<span class="mw-page-title-main">Digital data</span> Discrete, discontinuous representation of information

Digital data, in information theory and information systems, is information represented as a string of discrete symbols, each of which can take on one of only a finite number of values from some alphabet, such as letters or digits. An example is a text document, which consists of a string of alphanumeric characters. The most common form of digital data in modern information systems is binary data, which is represented by a string of binary digits (bits) each of which can have one of two values, either 0 or 1.

<span class="mw-page-title-main">Quantum computing</span> Technology that uses quantum mechanics

A quantum computer is a computer that exploits quantum mechanical phenomena. On small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior using specialized hardware. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the art is largely experimental and impractical, with several obstacles to useful applications.

<span class="mw-page-title-main">Data storage</span> Recording of information in a storage medium

Data storage is the recording (storing) of information (data) in a storage medium. Handwriting, phonographic recording, magnetic tape, and optical discs are all examples of storage media. Biological molecules such as RNA and DNA are considered by some as data storage. Recording may be accomplished with virtually any form of energy. Electronic data storage requires electrical power to store and retrieve data.

<span class="mw-page-title-main">Information Age</span> Industrial shift to information technology

The Information Age is a historical period that began in the mid-20th century. It is characterized by a rapid shift from traditional industries, as established during the Industrial Revolution, to an economy centered on information technology. The onset of the Information Age has been linked to the development of the transistor in 1947 and the optical amplifier in 1957. These technological advances have had a significant impact on the way information is processed and transmitted.

An information society is a society or subculture where the usage, creation, distribution, manipulation and integration of information is a significant activity. Its main drivers are information and communication technologies, which have resulted in rapid growth of a variety of forms of information. Proponents of this theory posit that these technologies are impacting most important forms of social organization, including education, economy, health, government, warfare, and levels of democracy. The people who are able to partake in this form of society are sometimes called either computer users or even digital citizens, defined by K. Mossberger as “Those who use the Internet regularly and effectively”. This is one of many dozen internet terms that have been identified to suggest that humans are entering a new and different phase of society.

An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

Digital broadcasting is the practice of using digital signals rather than analogue signals for broadcasting over radio frequency bands. Digital television broadcasting is widespread. Digital audio broadcasting is being adopted more slowly for radio broadcasting where it is mainly used in Satellite radio.

A quantum Turing machine (QTM) or universal quantum computer is an abstract machine used to model the effects of a quantum computer. It provides a simple model that captures all of the power of quantum computation—that is, any quantum algorithm can be expressed formally as a particular quantum Turing machine. However, the computationally equivalent quantum circuit is a more common model.

<span class="mw-page-title-main">Spiking neural network</span> Artificial neural network that mimics neurons

Spiking neural networks (SNNs) are artificial neural networks (ANN) that more closely mimic natural neural networks. These models leverage timing of discrete spikes as the main information carrier.

<span class="mw-page-title-main">Information</span> Facts provided or learned about something or someone

Information is an abstract concept that refers to something which has the power to inform. At the most fundamental level, it pertains to the interpretation of that which may be sensed, or their abstractions. Any natural process that is not completely random and any observable pattern in any medium can be said to convey some amount of information. Whereas digital signals and other data use discrete signs to convey information, other phenomena and artifacts such as analogue signals, poems, pictures, music or other sounds, and currents convey information in a more continuous form. Information is not knowledge itself, but the meaning that may be derived from a representation through interpretation.

<span class="mw-page-title-main">Big data</span> Extremely large or complex datasets

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Though used sometimes loosely partly due to a lack of formal definition, the best interpretation is that it is a large body of information that cannot be comprehended when used in small amounts only.

<span class="mw-page-title-main">GFAJ-1</span> Strain of bacteria

GFAJ-1 is a strain of rod-shaped bacteria in the family Halomonadaceae. It is an extremophile that was isolated from the hypersaline and alkaline Mono Lake in eastern California by geobiologist Felisa Wolfe-Simon, a NASA research fellow in residence at the US Geological Survey. In a 2010 Science journal publication, the authors claimed that the microbe, when starved of phosphorus, is capable of substituting arsenic for a small percentage of its phosphorus to sustain its growth. Immediately after publication, other microbiologists and biochemists expressed doubt about this claim, which was robustly criticized in the scientific community. Subsequent independent studies published in 2012 found no detectable arsenate in the DNA of GFAJ-1, refuted the claim, and demonstrated that GFAJ-1 is simply an arsenate-resistant, phosphate-dependent organism.

Culturomics is a form of computational lexicology that studies human behavior and cultural trends through the quantitative analysis of digitized texts. Researchers data mine large digital archives to investigate cultural phenomena reflected in language and word usage. The term is an American neologism first described in a 2010 Science article called Quantitative Analysis of Culture Using Millions of Digitized Books, co-authored by Harvard researchers Jean-Baptiste Michel and Erez Lieberman Aiden.

Information technology (IT) is a set of related fields that encompass computer systems, software, programming languages, and data and information processing, and storage. IT forms part of information and communications technology (ICT). An information technology system is generally an information system, a communications system, or, more specifically speaking, a computer system — including all hardware, software, and peripheral equipment — operated by a limited group of IT users, and an IT project usually refers to the commissioning and implementation of an IT system. IT systems play a vital role in facilitating efficient data management, enhancing communication networks, and supporting organizational processes across various industries. Successful IT projects require meticulous planning, seamless integration, and ongoing maintenance to ensure optimal functionality and alignment with organizational objectives.

<i>BioSystems</i> Academic journal

BioSystems is a monthly peer-reviewed scientific journal covering experimental, computational, and theoretical research that links biology, evolution, and the information processing sciences. It was established in 1967 as Currents in Modern Biology by Robert G. Grenell and published by North-Holland Publishing Company out of Amsterdam until North-Holland merged with Elsevier in 1970. Grenell wrote of his purpose in founding the journal,

It has become necessary to develop a new language of biology; a new mathematics, and to strip biological theory and experiment of their classical approaches, assumptions and limitations. It is such considerations which underlie the establishment of this journal.

Computational social science is an interdisciplinary academic sub-field concerned with computational approaches to the social sciences. This means that computers are used to model, simulate, and analyze social phenomena. It has been applied in areas such as computational economics, computational sociology, computational media analysis, cliodynamics, culturomics, nonprofit studies. It focuses on investigating social and behavioral relationships and interactions using data science approaches, network analysis, social simulation and studies using interactive systems.

The social genome is the collection of data about members of a society that is captured in ever-larger and ever-more complex databases. Some have used the term digital footprint to refer to individual traces.

In quantum computing, quantum supremacy or quantum advantage is the goal of demonstrating that a programmable quantum computer can solve a problem that no classical computer can solve in any feasible amount of time, irrespective of the usefulness of the problem. The term was coined by John Preskill in 2012, but the concept dates to Yuri Manin's 1980 and Richard Feynman's 1981 proposals of quantum computing.

Continuous-variable (CV) quantum information is the area of quantum information science that makes use of physical observables, like the strength of an electromagnetic field, whose numerical values belong to continuous intervals. One primary application is quantum computing. In a sense, continuous-variable quantum computation is "analog", while quantum computation using qubits is "digital." In more technical terms, the former makes use of Hilbert spaces that are infinite-dimensional, while the Hilbert spaces for systems comprising collections of qubits are finite-dimensional. One motivation for studying continuous-variable quantum computation is to understand what resources are necessary to make quantum computers more powerful than classical ones.

References

  1. Hilbert, Martin; López, Priscila (2011). "The World's Technological Capacity to Store, Communicate, and Compute Information". Science . 332 (6025): 60–65. Bibcode:2011Sci...332...60H. doi: 10.1126/science.1200970 . PMID   21310967. S2CID   206531385.
  2. eLAC Action Plans: A personal account; http://www.martinhilbert.net/elac-action-plans-a-personal-account
  3. "Computational Social Science at UC Davis". css.ucdavis.edu. Retrieved 12 July 2024.
  4. Hilbert M, López P (April 2011). "The world's technological capacity to store, communicate, and compute information" (PDF). Science. 332 (6025): 60–5. Bibcode:2011Sci...332...60H. doi:10.1126/science.1200970. PMID   21310967. S2CID   206531385. Archived (PDF) from the original on 19 August 2019. Retrieved 11 May 2019.
  5. "Journalism of Excellence Award". MartinHilbert.net. Retrieved 12 July 2024.
  6. "Information and communications technologies (ICTs)". www.cepal.org. Retrieved 12 July 2024.
  7. "ELAC Action Plans: A personal account".
  8. Martin Hilbert (11 July 2023). DTSC: 2.4 What is Digitalization & Algorithmification? . Retrieved 12 July 2024 via YouTube.
  9. Hilbert, M., & Darmon, D. (2020). Largescale Communication Is More Complex and Unpredictable with Automated Bots. Journal of Communication, 70(5).
  10. Hilbert, Martin; Thakur, Arti; Flores, Pablo M.; Zhang, Xiaoya; Bhan, Jee Young; Bernhard, Patrick; Ji, Feng (2024). "8–10% of algorithmic recommendations are 'bad', but… an exploratory risk-utility meta-analysis and its regulatory implications". International Journal of Information Management. 75: 102743. doi:10.1016/j.ijinfomgt.2023.102743. ISSN   0268-4012.
  11. Hilbert, Martin (2010). "When is Cheap, Cheap Enough to Bridge the Digital Divide? Modeling Income Related Structural Challenges of Technology Diffusion in Latin America". World Development. 38 (5): 756–770. doi:10.1016/j.worlddev.2009.11.019. ISSN   0305-750X.
  12. Gillings, Michael R.; Hilbert, Martin; Kemp, Darrell J. (2016). "Information in the Biosphere: Biological and Digital Worlds". Trends in Ecology & Evolution. 31 (3): 180–189. Bibcode:2016TEcoE..31..180G. doi:10.1016/j.tree.2015.12.013. ISSN   0169-5347. PMID   26777788.
  13. Hilbert, Martin; Miles, Ian; Othmer, Julia (2009). "Foresight tools for participative policy-making in inter-governmental processes in developing countries: Lessons learned from the eLAC Policy Priorities Delphi". Technological Forecasting and Social Change. 76 (7): 880–896. doi:10.1016/j.techfore.2009.01.001. ISSN   0040-1625.
  14. Hilbert, Martin (2014). "Scale-free power-laws as interaction between progress and diffusion". Complexity. 19 (4): 56–65. Bibcode:2014Cmplx..19d..56H. doi:10.1002/cplx.21485. ISSN   1076-2787.
  15. Hilbert, Martin (2017). "Complementary Variety: When Can Cooperation in Uncertain Environments Outperform Competitive Selection?". Complexity. 2017: 1–15. doi: 10.1155/2017/5052071 . ISSN   1076-2787.
  16. Hilbert, Martin; Oh, Poong; Monge, Peter (1 October 2016). "Evolution of what? A network approach for the detection of evolutionary forces". Social Networks. 47: 38–46. doi:10.1016/j.socnet.2016.04.003. ISSN   0378-8733.
  17. Hilbert, Martin (1 September 2016). "Formal definitions of information and knowledge and their role in growth through structural change". Structural Change and Economic Dynamics. Complexity and Economic Development. 38: 69–82. doi:10.1016/j.strueco.2016.03.004. ISSN   0954-349X.
  18. Hilbert, Martin (2020). "Information Theory for Human and Social Processes". Entropy. 23 (1): 9. Bibcode:2020Entrp..23....9H. doi: 10.3390/e23010009 . ISSN   1099-4300. PMC   7822471 . PMID   33374607.
  19. Hilbert, Martin; Darmon, David (2020). "How Complexity and Uncertainty Grew with Algorithmic Trading". Entropy. 22 (5): 499. Bibcode:2020Entrp..22..499H. doi: 10.3390/e22050499 . ISSN   1099-4300. PMC   7516984 . PMID   33286272.
  20. Hilbert, Martin (2012). "Toward a synthesis of cognitive biases: How noisy information processing can bias human decision making". Psychological Bulletin. 138 (2): 211–237. doi:10.1037/a0025940. ISSN   1939-1455. PMID   22122235.
  21. Hilbert, Martin (1 November 2011). "Digital gender divide or technologically empowered women in developing countries? A typical case of lies, damned lies, and statistics". Women's Studies International Forum. 34 (6): 479–489. doi:10.1016/j.wsif.2011.07.001. ISSN   0277-5395.
  22. Hilbert, Martin; López, Priscila (2011). "The World's Technological Capacity to Store, Communicate, and Compute Information". Science. 332 (6025): 60–65. Bibcode:2011Sci...332...60H. doi:10.1126/science.1200970. ISSN   0036-8075. PMID   21310967.
  23. "Martin Hilbert, gurú del Big Data: "La democracia no está preparada para la era digital y está siendo destruida"". BBC News Mundo (in Spanish). Retrieved 12 July 2024.
  24. "El gurú del 'big data': "Facebook sabe más de ti con 250 likes que tú mismo"". ELMUNDO (in Spanish). 27 November 2017. Retrieved 12 July 2024.
  25. "Digital Technology and Social Change". Coursera. Retrieved 12 July 2024.
  26. "Computational Social Science". Coursera. Retrieved 12 July 2024.
  27. "The Hidden Digital Divide". www.informationisbeautifulawards.com. Retrieved 12 July 2024.
  28. "Martin Hilbert, experto en redes digitales: Obama y Trump usaron el Big Data para lavar cerebros”; The Clinic (2017); Daniel Hopenhayn (winner of Chilean Journalism of Excellence award). https://www.theclinic.cl/2018/05/15/periodista-the-clinic-daniel-hopenhayn-gano-premio-periodismo-excelencia-la-uah-mejor-entrevista-escrita/
  29. "Big Data and Its Impact on Democracy". Library of Congress, Washington, D.C. 20540 USA. Retrieved 12 July 2024.
  30. University of California Computational Social Science, Coursera Online Specialization, https://www.classcentral.com/report/review-ucdavis-comp-social-science/
  31. "CV & bio". MartinHilbert.net. Retrieved 12 July 2024.