Opis projektu
Uporanie się ze złożonością uczenia głębokiego
Głębokie sieci neuronowe, które są już obecne w niemal wszystkich branżach, stanowią awangardę, jeśli chodzi o sztuczną inteligencję. Pomimo niezwykłych możliwości, jakie oferują, mają one jednak pewne wady. Te złożone modele są mianowicie trudne do interpretacji i analizy przez człowieka, a ponadto nie są bezstronne. Finansowany przez UE projekt RRR-XAI ma na celu wyeliminowanie tych wad poprzez uczynienie głębokiego uczenia zrozumiałym. Zespół przeprowadzi analizy mające pozwolić na poznanie różnego rodzaju zjawisk, które stwarzają problemy w przypadku głębokich sieci neuronowych. W tym celu zaplanowano studia przypadków obejmujące przewidywanie wystąpienia COVID-19 na podstawie zdjęć rentgenowskich klatki piersiowej oraz wykrywanie broni w systemach alarmowych i tłumów na podstawie zdjęć.
Cel
Deep Learning (DL) is a form of machine learning (ML) that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. This hierarchy allows a DL model to learn complicated concepts by building them out of simpler ones. A graph of these hierarchies would be many layers deep, and thus its name. A Deep neural network (DNN) is based on an artificial neural network model, and its core strength is that there is no need for human assistance to formally specify all the knowledge that the model needs. This makes DNNs represent the state of the art in Artificial Intelligence (AI). Despite their top performance and ubiquity of applications (from Healthcare to autonomous cars), DNNs suffer serious shortcomings. First, DNNs are considered black box models, i.e. with complex and opaque algorithms, hard to interpret and diagnose. Second, they suffer from bias, and the testing protocols for automatic recognition are not fair, as they learn patterns in the data that are not correlated to the output; e.g. they may focus on areas outside the lung in X ray images to predict the presence of COVID-19. Although DNNs outperform many other methods, they often are not right for the right reasons (RRR). RRR-XAI tackles this mismatch and bridges this gap through a tight integration of DL and symbolic AI, with the principal objective of making DL explainable. To achieve this I will follow the rationale behind XAI under the RRR philosophy and perform analyses to understand two types of phenomena that cause trouble in DNNs. Second, I will use: 1) Domain knowledge expertise as supporting evidence to explain a particular model output; 2) Neural-Symbolic computation to communicate the explanation of such phenomena in natural language. I will study two practical use cases where supporting explanations of the model output are critical: a) COVID-19 prediction from chest X-Ray images, and b) Weapon detection in alarm systems and crowds from images.
Dziedzina nauki
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- medical and health scienceshealth sciencesinfectious diseasesRNA virusescoronaviruses
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
- humanitiesphilosophy, ethics and religionphilosophy
Słowa kluczowe
Program(-y)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Zaproszenie do składania wniosków
Zobacz inne projekty w ramach tego zaproszeniaSystem finansowania
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsKoordynator
18071 Granada
Hiszpania