The 8th Annual Conference on machine Learning, Optimization and Data science (LOD) is an international conference on machine learning, computational optimization, big data and artificial intelligence. The conference includes invited talks, tutorials, special sessions, industrial tracks, demonstrations, and oral and poster presentations of refereed papers.
The LOD has established itself as a premier interdisciplinary conference in machine learning, computational optimization, data science and AI. It provides an international forum for the presentation of original interdisciplinary research results, and the exchange and dissemination of innovative and practical development experiences.
We invite submissions of papers, abstracts, posters, talks and demos on all topics related to Machine learning, Optimization and Data Science including real-world applications for the Conference Post-Proceedings – Springer-Nature Lecture Notes in Computer Science (LNCS).
The LOD Conference Manifesto
“The problem of understanding intelligence is said to be the greatest problem in science today and “the” problem for this century — as deciphering the genetic code was for the second half of the last one.
Arguably, the problem of learning represents a gateway to understanding intelligence in brains and machines, to discovering how the human brain works, and to making intelligent machines that learn from experience and improve their competences as children do.
In engineering, learning techniques would make it possible to develop software that can be quickly customized to deal with the increasing amount of information and the flood of data around us.”
Tomaso Poggio (MOD 2015 & LOD 2020 Keynote Speaker)
“Artificial Intelligence has already provided beneficial tools that are used every day by people around the world. Its continued development, guided by the Asilomar AI Principles, will offer amazing opportunities to help and empower people in the decades and centuries ahead.”
The Asilomar AI Principles have been adopted by the LOD Conference since their initial formulation (3-5 January 2017). Since then they have been an integral part of the Manifesto of the LOD Conferences (LOD 2017).