scholarly journals Data-Driven Journalistic Operation: Reshaping the Idea of News Values with Algorithms, Artificial Intelligence and Increased Personalization

2020 ◽  
Vol 16 (3) ◽  
pp. 458-475
Author(s):  
Márcio Carneiro Dos Santos
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Media ◽  
Social Engagement ◽  
Current Level ◽  
Data Driven ◽  
News Values ◽  
News Organizations ◽  
Media Ecosystem

This article discusses the strategic reconfiguration of data in news organizations through the use of algorithms and artificial intelligence, more specifically machine learning, to increase the idea of value around information, which has suffered from fragmented audiences, a massive increase in the number of broadcasters, indirect competition from large technology companies, and changes to the digital media ecosystem. We propose to identify patterns of interest, predict social engagement, and allocate resources for new coverage as forms for increasing the current level of personalization offered to news consumers.Discute-se a reconfiguração estratégica da utilização de dados dentro das organizações jornalísticas, através de algoritmos e soluções de inteligência artificial, entre elas especificamente o aprendizado de máquina, como alternativa para elevação da percepção de valor sobre o produto informativo, reduzida pela fragmentação das audiências, explosão de emissores, concorrência indireta das grandes empresas de tecnologia e transformações do ecossistema de meios digitais. Tal abordagem propõe a identificação de padrões de interesse, a predição de engajamento social e a alocação de recursos para coberturas como formas de expandir o atual nível de personalização oferecido aos consumidores de notíciaSe discute la reconfiguración estratégica del uso de datos en las organizaciones periodísticas a través de algoritmos y soluciones de inteligencia artificial, específicamente, el aprendizaje automático como una alternativa para aumentar la percepción de valor sobre el producto de información, el cual se reduce por la fragmentación de las audiencias, el aumento en la cantidad de emisores, la competencia indirecta de grandes compañías tecnológicas y las transformaciones del ecosistema de medios digitales. Tal enfoque propone la identificación de patrones de interés, la predicción del compromiso social y la asignación de recursos para la cobertura como formas de expandir el nivel actual de personalización ofrecido a los consumidores de noticias.

scholarly journals Before and beyond trust: reliance in medical AI

2021 ◽  
pp. medethics-2020-107095
Author(s):  
Charalampia (Xaroula) Kerasidou ◽  
Angeliki Kerasidou ◽  
Monika Buscher ◽  
Stephen Wilkinson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
New Technology ◽  
Public Trust ◽  
Learning Technologies ◽  
Data Driven ◽  
Policy Makers ◽  
The Public ◽  
Regulatory Frameworks ◽  
Practice Of Medicine

Artificial intelligence (AI) is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed.

scholarly journals Reports of the 2013 AAAI Spring Symposium Series

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 93-98 ◽  
Author(s):  
Vita Markman ◽  
Georgi Stojanov ◽  
Bipin Indurkhya ◽  
Takashi Kido ◽  
Keiki Takadama ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Development ◽  
Behavior Change ◽  
Autonomous Systems ◽  
Data Driven ◽  
Intelligent Robots ◽  
Symposium Series ◽  
Development Data ◽  
Weakly Supervised

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

Geophysics Bright Spots

2020 ◽  
Vol 39 (7) ◽  
pp. 518-519
Author(s):  
Jyoti Behura
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Driven ◽  
Daily Routines ◽  
Theoretical Approaches ◽  
Bright Spots ◽  
Catch Up ◽  
A Minor ◽  
Silver Lining

Welcome to the latest installment of Geophysics Bright Spots. For most of us, the pandemic has upended our daily routines. Personally, a minor silver lining in all of this chaos has been the gain of a few extra hours every week. I have used this time to catch up on the fascinating work being done in the field of artificial intelligence and its applications to numerous disciplines. An emerging trend is physics-informed machine learning, which will help us bridge the gap between traditional theoretical approaches and more recent data-driven methodologies, leading to physically plausible and meaningful results. To follow is a list of research that the editors found interesting in the latest issue of Geophysics. I sincerely hope these articles enlighten you and take your mind off some of the chaos surrounding us.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

Media Technologies in Communication and Critical Cultural Studies

2018 ◽  
Author(s):  
Ned O'Gorman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Studies ◽  
Digital Media ◽  
Media Studies ◽  
21St Century ◽  
Social Life ◽  
Media Technologies ◽  
Cultural Processes ◽  
The Relationship

Media technologies are at the heart of media studies in communication and critical cultural studies. They have been studied in too many ways to count and from a wide variety of perspectives. Yet fundamental questions about media technologies—their nature, their scope, their power, and their place within larger social, historical, and cultural processes—are often approached by communication and critical cultural scholars only indirectly. A survey of 20th- and 21st-century approaches to media technologies shows communication and critical cultural scholars working from, for, or against “deterministic” accounts of the relationship between media technologies and social life through “social constructivist” understandings to “networked” accounts where media technologies are seen embedding and embedded within socio-material structures, practices, and processes. Recent work on algorithms, machine learning, artificial intelligence, and platforms, together with their manifestations in the products and services of monopolistic corporations like Facebook and Google, has led to new concerns about the totalizing power of digital media over culture and society.

Technology Focus: Field Development (September 2021)

2021 ◽  
Vol 73 (09) ◽  
pp. 43-43
Author(s):  
Reza Garmeh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Development Strategies ◽  
Digital Transformation ◽  
Data Driven ◽  
Development Work ◽  
Field Development

The digital transformation that began several years ago continues to grow and evolve. With new advancements in data analytics and machine-learning algorithms, field developers today see more benefits to upgrading their traditional development work flows to automated artificial-intelligence work flows. The transformation has helped develop more-efficient and truly integrated development approaches. Many development scenarios can be automatically generated, examined, and updated very quickly. These approaches become more valuable when coupled with physics-based integrated asset models that are kept close to actual field performance to reduce uncertainty for reactive decision making. In unconventional basins with enormous completion and production databases, data-driven decisions powered by machine-learning techniques are increasing in popularity to solve field development challenges and optimize cube development. Finding a trend within massive amounts of data requires an augmented artificial intelligence where machine learning and human expertise are coupled. With slowed activity and uncertainty in the oil and gas industry from the COVID-19 pandemic and growing pressure for cleaner energy and environmental regulations, operators had to shift economic modeling for environmental considerations, predicting operational hazards and planning mitigations. This has enlightened the value of field development optimization, shifting from traditional workflow iterations on data assimilation and sequential decision making to deep reinforcement learning algorithms to find the best well placement and well type for the next producer or injector. Operators are trying to adapt with the new environment and enhance their capabilities to efficiently plan, execute, and operate field development plans. Collaboration between different disciplines and integrated analyses are key to the success of optimized development strategies. These selected papers and the suggested additional reading provide a good view of what is evolving with field development work flows using data analytics and machine learning in the era of digital transformation. Recommended additional reading at OnePetro: www.onepetro.org. SPE 203073 - Data-Driven and AI Methods To Enhance Collaborative Well Planning and Drilling-Risk Prediction by Richard Mohan, ADNOC, et al. SPE 200895 - Novel Approach To Enhance the Field Development Planning Process and Reservoir Management To Maximize the Recovery Factor of Gas Condensate Reservoirs Through Integrated Asset Modeling by Oswaldo Espinola Gonzalez, Schlumberger, et al. SPE 202373 - Efficient Optimization and Uncertainty Analysis of Field Development Strategies by Incorporating Economic Decisions in Reservoir Simulation Models by James Browning, Texas Tech University, et al.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine (Preprint)

2019 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

UNSTRUCTURED Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound, with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

Contemporary Imagetics and Post-Images in Digital Media Art

2020 ◽  
pp. 1-24
Author(s):  
Jose Alberto Raposo Pinheiro
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Media ◽  
Digital Information ◽  
Media Art ◽  
Biometric Data ◽  
New Developments

Scientific studies have contributed, over the last years, to an expansion of the Image concept, in articulation with new developments in Computational Media, based in a stratification around technical digital properties, which frame its existence to the form of digital information – extending it beyond a visual surface idea. Biometric data, artificial intelligence, bitcoin, glitches or machine learning are examples of instantiation tools used by artists to explore elements of mediation included in Post-images. This chapter addresses today's perspectives in Contemporary Imagetics emerging from the field of Digital Media Art (DMA), curating contributions from classic postproduction techniques to computational media instantiations and contextualizing imagery creation practice in DMA.

Artificial Intelligence in Practice

2020 ◽  
pp. 98-123
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Practical Application ◽  
Learning Techniques ◽  
The Cost

In this chapter, the authors discuss machine learning techniques and artificial intelligence applications, their role in business, and present a practical application of it. They try to highlight how important machine learning can be in data-driven organisations, where the cost and/or the advantages to implement such tools are far greater than having a human—or a team of humans—doing it.

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