[rede.APPIA] White Paper Artificial Intelligence

Caros APPIAnos,
Está a decorrer uma consulta pública sobre um “White Paper on Artificial Intelligence – a European Approach” aberta a todos os agentes relacionados com IA. Deixo aqui o link caso pretendam contribuir com sugestões e divulgar por outros potenciais interessados.
ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/12270-White-Paper-on-Artificial-Intelligence-a-European-Approach/public-consultation
Alípio Jorge

[rede.APPIA] [held online] CFP: Big Data & Deep Learning in HPC (IEEE Xplore) @Porto, Portugal

Workshop on BIG DATA & DEEP LEARNING in HIGH PERFORMANCE COMPUTING (BDL2020) (sbac2020.dcc.fc.up.pt/bdl2020/)
in conjunction with the IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2020) (sbac2020.dcc.fc.up.pt/)
September 9, 2020, Porto, Portugal
—————————————————————————- ** NEW ** BDL2020 will be held online (synchronous and/or asynchronous) —————————————————————————-
We are monitoring the Coronavirus disease (COVID-19) outbreak and following the recommendations/guidelines from the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC).
The safety of all conference participants is our main priority. In this perspective, regardless of the outbreak outcomes in September, we will make BDL2020 an online (synchronous and/or asynchronous) event and we will maintain the regular publication activities, i.e., accepted papers will be eligible for publication at the IEEE Xplore.
The Workshop fee is now 200 euros.
———————————— WORKSHOP ON BIG DATA & DEEP LEARNING IN HIGH PERFORMANCE COMPUTING ————————————
The number of very large data repositories (big data) is increasing in a rapid pace. Analysis of such repositories using the “traditional” sequential implementations of ML and emerging techniques, like deep learning, that model high-level abstractions in data by using multiple processing layers, requires expensive computational resources and long running times. Parallel or distributed computing are possible approaches that can make analysis of very large repositories and exploration of high-level representations feasible. Taking advantage of a parallel or a distributed execution of a ML/statistical system may: i) increase its speed; ii) learn hidden representations; iii) search a larger space and reach a better solution or; iv) increase the range of applications where it can be used (because it can process more data, for example). Parallel and distributed computing is therefore of high importance to extract knowledge from massive amounts of data and learn hidden representations.
The workshop will be concerned with the exchange of experience among academics, researchers and the industry whose work in big data and deep learning require high performance computing to achieve goals. Participants will present recently developed algorithms/systems, on going work and applications taking advantage of such parallel or distributed environments.
———————————— LIST OF TOPICS ————————————
All novel data-intensive computing techniques, data storage and integration schemes, and algorithms for cutting-edge high performance computing architectures which targets Big Data and Deep Learning are of interest to the workshop. Examples of topics include but not limited to: – parallel algorithms for data-intensive applications; – scalable data and text mining and information retrieval; – using Hadoop, MapReduce, Spark, Storm, Streaming to analyze Big Data; – energy-efficient data-intensive computing; – deep-learning with massive-scale datasets; – querying and visualization of large network datasets; – processing large-scale datasets on clusters of multicore and manycore processors, and accelerators; – heterogeneous computing for Big Data architectures; – Big Data in the Cloud; – processing and analyzing high-resolution images using high-performance computing; – using hybrid infrastructures for Big Data analysis. – New algorithms for parallel/distributed execution of ML systems; – applications of big data and deep learning to real-life problems.
———————————— KEY DATES ————————————
Deadline for paper submission: May 25, 2020
Author notification: July 1, 2020
Camera-ready version of papers: July 25, 2020
———————————— SUBMISSION ————————————
We invite authors to submit original work to BDL. All papers will be peer reviewed and accepted papers will be published in IEEE Xplore.
Submissions must be in English, limited to 8 pages in the IEEE conference format (see www.ieee.org/conferences/publishing/templates.html)
All submissions should be made electronically through the EasyChair system: easychair.org/conferences/?conf=bdl2020
———————————— REGISTRATION ————————————
A full registration to the workshop and presentation are needed in order to have your paper included in the workshop proceedings.
The Workshop fee is 200 euros.
Registration system available in sbac2020.dcc.fc.up.pt/bdl2020/registration.html
———————————— VENUE ————————————
Department of Computer Science, Faculty of Sciences, University of Porto
Rua do Campo Alegre 1021/1055 4169-007 Porto, Portugal
The city of Porto is famous for its Port wine and beautiful scenery, architecture and cultural events.
Portugal has again been awarded the best European Tourist Destination by the World Travel Awards, the Oscars equivalent in the field of tourism.
———————————— ORGANIZATION ————————————
Carlos Ferreira (LIAAD – INESC TEC LA and Polytechnic Institute of Porto) João Gama (LIAAD – INESC TEC LA and University of Porto) Albert Bifet (Telecom ParisTech) Miguel Areias (CRACS – INESC TEC LA and University of Porto) Rui Camacho (LIAAD -INESC TEC LA and University of Porto)

Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt

[rede.APPIA] Open postdoctoral position at HUMAINT team in the field of Trustworthy Machine Learning | JRC Science Hub Communities


Open postdoctoral position at HUMAINT team in the field of Trustworthy Machine Learning

Application deadline: April 15th – EXTENDED TO MAY 17th

We are looking for a highly motivated and outstanding postdoctoral researcher with a strong background in Machine Learning. The researcher will contribute to our work on Fairness and Transparency of Machine Learning and to the development of practical methodologies for trustworthy Machine Learning systems. The outcomes of this research will contribute to the work of AI Watch to build scientific evidence for the definition of a possible regulatory framework for AI. The candidate will have to be able to work independently in the framework of the HUMAINT team and contribute to the research activities of the team and the Digital Economy Unit to support AI-related European policies.

Qualifications:

We are looking for a researcher with a strong scientific academic background, a robust professional record in research and experience in Machine Learning with:

  • PhD (doctoral Diploma) or a university degree and a minimum of five years of experience in any area of Machine Learning;
  • Research experience on fairness and transparency of Machine Learning is an asset, including the development of practical methodologies and tools. Topics: explainability, testing/evaluation, traceability, reproducibility, fairness, bias mitigation, human oversight;
  • Research track record demonstrated by scientific publications and practical developing expertise;
  • Very good writing and communication skills for scientific, policy, and the general public;
  • Very good (C1) knowledge of English;
  • Experience in working within interdisciplinary teams, following reproducibility practices and contributing to community is an advantage.

Conditions 

36 months initial contract with possible renewals up to a maximum of 6 years. 

Working place in Seville, Spain 

About HUMAINT

The project HUMAINT aims to understand the impact of machine intelligence on human behaviour, with a focus on cognitive and socio-emotional capabilities and decision-making.

Artificial Intelligence at the European Commission 

Artificial intelligence (AI) has become an area of strategic importance and a key driver of economic development. That is why the Commission set out an AI strategy – COM(2018)237 -and agreed a Coordinated Plan with the Member States to align strategies – COM(2018)795. That Plan generated the set up of AI Watch, the Commission Knowledge Service to monitor the Development, Uptake and Impact of AI.

The Commission also established a High-Level Expert Group that published Guidelines on trustworthy AI in April 2019 and in the “White Paper On Artificial Intelligence – A European approach to excellence and trust” COM(2020)65 the Commission acknowledges that as with any new technology, the use of AI brings both opportunities and risks. Citizens fear being left powerless in defending their rights and safety when facing the information asymmetries of algorithmic decision-making, and companies are concerned by legal uncertainty.

How to apply 

  1. Express your interest by applying to the JRC permanent call for researchers or the CAST permanent.  
  2. Only then you can apply to this position

More information https://recruitment.jrc.ec.europa.eu/?site=SVQ

Code: 2020-SVQ-B6-FGIV-014111 – SEVILLE
FG IV- Scientific Project Officer in the field of Trustworthy Machine Learning
Deadline: EXTENDED TO 17/05/2020 23:59 Brussels time 

[rede.APPIA] CFP: Workshop on the Future of Co-Creative Systems @ ICCC 2020


Begin forwarded message:


From: “anna.kantosalo” <anna.kantosalo@gmail.com>
Subject: [computational-creativity-forum] CFP: Workshop on the Future of Co-Creative Systems @ ICCC 2020
Date: 5 May 2020 at 08:38:05 WEST
To: Computational Creativity Forum <computational-creativity-forum@googlegroups.com>


Over the last few years, as systems become more applied and usable, co-creativity has become a key focus for computational creativity researchers. We would like to invite you to participate in planning the future of co-creative systems with us! This one-day workshop aims to bring together researchers to discuss the future of co-creativity from a range of perspectives, organised around two main topics: 
(i) what are the open questions in co-creativity research? 
(ii) what common language is needed for co-creativity researchers from a range of backgrounds to work together and progress the field?


Participants may contribute to these topics through discussions of areas including (but not limited to): studies of creative practice; creative ideation and development as a collaborative and socially structured search process; user-experience research; explainable AI; the integration of existing algorithms into co-creative applications; conversational interfaces; evaluation of co-creative systems; and co-creativity in specific application areas.


Submit your extended abstracts (~400wds, about one page) describing open questions in co-creativity research and/or designs for establishing a common language for co-creativity research. Accepted abstracts will be allocated a short presentation time and they will act as a basis for further discussion during the workshop.


Detailed information about the workshop and submission instructions can be found at:

Dates: 
Abstract submission DL: 25th of May
Workshop dates: 7-11 September, in connection with the ICCC 2020 conference