1,079 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Digital agriculture: research, development and innovation in production chains.
Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil
Control and Archaism
The presentation will delve into the relationship between control society and archaism. Deleuze’s conceptualization of control implies the reconfiguration of
former spaces of discipline. While the Foucauldian model of discipline was characterized by enclosed spaces (such as prisons, armies, and churches),
Deleuze’s notion of control highlights a continuous network where individuals are no longer molded but modulated. This prompts us to ponder the shift in the
temporal structure that occurs during the transition from a disciplinary society to one governed by control. Specifically, this presentation aims to explore the
disparities in our historical perspectives when viewed from disciplinary and control paradigms. In this context, I will explore Deleuze and Guattari's concept of
‘archaism’. According to Deleuze and Guattari, archaism is an inherent aspect of capitalism, its continual endeavor to reconstruct territoriality and replicate
antiquated coding patterns. Capitalism necessitates archaism due to its lack of inherent belief structures. In essence, the system, which the duo name the
‘age of cynicism’, requires the revival of old codes to sustain its systems of subjugation and dominance. As my presentation will demonstrate, one can
discern a transformation in the evolution of archaism as society shifts from discipline to control. By comparing the fascist archaism of the thirties in
Germany and the archaism of contemporary alt-right movements, I will show that a disciplinary society presupposes a more centralized form of archaism,
which is highly susceptible to state control and deeply ingrained in the institutional fabric of social life. Conversely, a control society implies a
diversification and creativity in archaic attitudes, hinting at its potential for emancipation—a viewpoint emphasized by Deleuze and Guattari themselves
in ’Anti-Oedipus’
Discovering Causal Relations and Equations from Data
Physics is a field of science that has traditionally used the scientific
method to answer questions about why natural phenomena occur and to make
testable models that explain the phenomena. Discovering equations, laws and
principles that are invariant, robust and causal explanations of the world has
been fundamental in physical sciences throughout the centuries. Discoveries
emerge from observing the world and, when possible, performing interventional
studies in the system under study. With the advent of big data and the use of
data-driven methods, causal and equation discovery fields have grown and made
progress in computer science, physics, statistics, philosophy, and many applied
fields. All these domains are intertwined and can be used to discover causal
relations, physical laws, and equations from observational data. This paper
reviews the concepts, methods, and relevant works on causal and equation
discovery in the broad field of Physics and outlines the most important
challenges and promising future lines of research. We also provide a taxonomy
for observational causal and equation discovery, point out connections, and
showcase a complete set of case studies in Earth and climate sciences, fluid
dynamics and mechanics, and the neurosciences. This review demonstrates that
discovering fundamental laws and causal relations by observing natural
phenomena is being revolutionised with the efficient exploitation of
observational data, modern machine learning algorithms and the interaction with
domain knowledge. Exciting times are ahead with many challenges and
opportunities to improve our understanding of complex systems.Comment: 137 page
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)
(b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!
Towards a human-centric data economy
Spurred by widespread adoption of artificial intelligence and machine learning, “data” is becoming
a key production factor, comparable in importance to capital, land, or labour in an increasingly
digital economy. In spite of an ever-growing demand for third-party data in the B2B
market, firms are generally reluctant to share their information. This is due to the unique characteristics
of “data” as an economic good (a freely replicable, non-depletable asset holding a highly
combinatorial and context-specific value), which moves digital companies to hoard and protect
their “valuable” data assets, and to integrate across the whole value chain seeking to monopolise
the provision of innovative services built upon them. As a result, most of those valuable assets
still remain unexploited in corporate silos nowadays.
This situation is shaping the so-called data economy around a number of champions, and it is
hampering the benefits of a global data exchange on a large scale. Some analysts have estimated
the potential value of the data economy in US$2.5 trillion globally by 2025. Not surprisingly, unlocking
the value of data has become a central policy of the European Union, which also estimated
the size of the data economy in 827C billion for the EU27 in the same period. Within the scope of
the European Data Strategy, the European Commission is also steering relevant initiatives aimed
to identify relevant cross-industry use cases involving different verticals, and to enable sovereign
data exchanges to realise them.
Among individuals, the massive collection and exploitation of personal data by digital firms
in exchange of services, often with little or no consent, has raised a general concern about privacy
and data protection. Apart from spurring recent legislative developments in this direction,
this concern has raised some voices warning against the unsustainability of the existing digital
economics (few digital champions, potential negative impact on employment, growing inequality),
some of which propose that people are paid for their data in a sort of worldwide data labour
market as a potential solution to this dilemma [114, 115, 155].
From a technical perspective, we are far from having the required technology and algorithms
that will enable such a human-centric data economy. Even its scope is still blurry, and the question
about the value of data, at least, controversial. Research works from different disciplines have
studied the data value chain, different approaches to the value of data, how to price data assets,
and novel data marketplace designs. At the same time, complex legal and ethical issues with
respect to the data economy have risen around privacy, data protection, and ethical AI practices. In this dissertation, we start by exploring the data value chain and how entities trade data assets
over the Internet. We carry out what is, to the best of our understanding, the most thorough survey
of commercial data marketplaces. In this work, we have catalogued and characterised ten different
business models, including those of personal information management systems, companies born
in the wake of recent data protection regulations and aiming at empowering end users to take
control of their data. We have also identified the challenges faced by different types of entities,
and what kind of solutions and technology they are using to provide their services.
Then we present a first of its kind measurement study that sheds light on the prices of data
in the market using a novel methodology. We study how ten commercial data marketplaces categorise
and classify data assets, and which categories of data command higher prices. We also
develop classifiers for comparing data products across different marketplaces, and we study the
characteristics of the most valuable data assets and the features that specific vendors use to set
the price of their data products. Based on this information and adding data products offered by
other 33 data providers, we develop a regression analysis for revealing features that correlate with
prices of data products. As a result, we also implement the basic building blocks of a novel data
pricing tool capable of providing a hint of the market price of a new data product using as inputs
just its metadata. This tool would provide more transparency on the prices of data products in
the market, which will help in pricing data assets and in avoiding the inherent price fluctuation of
nascent markets.
Next we turn to topics related to data marketplace design. Particularly, we study how buyers
can select and purchase suitable data for their tasks without requiring a priori access to such
data in order to make a purchase decision, and how marketplaces can distribute payoffs for a
data transaction combining data of different sources among the corresponding providers, be they
individuals or firms. The difficulty of both problems is further exacerbated in a human-centric
data economy where buyers have to choose among data of thousands of individuals, and where
marketplaces have to distribute payoffs to thousands of people contributing personal data to a
specific transaction.
Regarding the selection process, we compare different purchase strategies depending on the
level of information available to data buyers at the time of making decisions. A first methodological
contribution of our work is proposing a data evaluation stage prior to datasets being selected
and purchased by buyers in a marketplace. We show that buyers can significantly improve the
performance of the purchasing process just by being provided with a measurement of the performance
of their models when trained by the marketplace with individual eligible datasets. We
design purchase strategies that exploit such functionality and we call the resulting algorithm Try
Before You Buy, and our work demonstrates over synthetic and real datasets that it can lead to
near-optimal data purchasing with only O(N) instead of the exponential execution time - O(2N)
- needed to calculate the optimal purchase. With regards to the payoff distribution problem, we focus on computing the relative value
of spatio-temporal datasets combined in marketplaces for predicting transportation demand and
travel time in metropolitan areas. Using large datasets of taxi rides from Chicago, Porto and
New York we show that the value of data is different for each individual, and cannot be approximated
by its volume. Our results reveal that even more complex approaches based on the
“leave-one-out” value, are inaccurate. Instead, more complex and acknowledged notions of value
from economics and game theory, such as the Shapley value, need to be employed if one wishes
to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms.
However, the Shapley value entails serious computational challenges. Its exact calculation
requires repetitively training and evaluating every combination of data sources and hence O(N!)
or O(2N) computational time, which is unfeasible for complex models or thousands of individuals.
Moreover, our work paves the way to new methods of measuring the value of spatio-temporal
data. We identify heuristics such as entropy or similarity to the average that show a significant
correlation with the Shapley value and therefore can be used to overcome the significant computational
challenges posed by Shapley approximation algorithms in this specific context.
We conclude with a number of open issues and propose further research directions that leverage
the contributions and findings of this dissertation. These include monitoring data transactions
to better measure data markets, and complementing market data with actual transaction prices
to build a more accurate data pricing tool. A human-centric data economy would also require
that the contributions of thousands of individuals to machine learning tasks are calculated daily.
For that to be feasible, we need to further optimise the efficiency of data purchasing and payoff
calculation processes in data marketplaces. In that direction, we also point to some alternatives
to repetitively training and evaluating a model to select data based on Try Before You Buy and
approximate the Shapley value. Finally, we discuss the challenges and potential technologies that
help with building a federation of standardised data marketplaces.
The data economy will develop fast in the upcoming years, and researchers from different
disciplines will work together to unlock the value of data and make the most out of it. Maybe
the proposal of getting paid for our data and our contribution to the data economy finally flies,
or maybe it is other proposals such as the robot tax that are finally used to balance the power
between individuals and tech firms in the digital economy. Still, we hope our work sheds light on
the value of data, and contributes to making the price of data more transparent and, eventually, to
moving towards a human-centric data economy.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Georgios Smaragdakis.- Secretario: Ángel Cuevas Rumín.- Vocal: Pablo Rodríguez Rodrígue
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Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project ‘Sonic Palimpsest’1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include women’s voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)
This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023
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