Can I read The Next Frontier: AI We Can Really Trust on EtoBox?

The Next Frontier: AI We Can Really Trust by Michael Kamp; Irena Koprinska; Adrien Bibal; Tassadit Bouadi; Benoît Frénay; Luis Galárraga; José Oramas; Linara Adilova; Yamuna Krishnamurthy; Bo Kang; Christine Largeron; Jefrey Lijffijt; Tiphaine Viard; Pascal Welke; Massimiliano Ruocco; Erlend Aune; Claudio Gallicchio; Gregor Schiele; Franz Pernkopf; Michaela Blott; Holger Fröning; Günther Schindler; Riccardo Guidotti; Anna Monreale; Salvatore Rinzivillo; Przemyslaw Biecek; Eirini Ntoutsi; Mykola Pechenizkiy; Bodo Rosenhahn; Christopher Buckley; Daniela Cialfi; Pablo Lanillos; Maxwell Ramstead; Tim Verbelen; Pedro M. Ferreira; Giuseppina Andresini; Donato Malerba; Ibéria Medeiros; Philippe Fournier-Viger; M. Saqib Nawaz; Sebastian Ventura; Meng Sun; Min Zhou; Valerio Bitetta; Ilaria Bordino; Andrea Ferretti; Francesco Gullo; Giovanni Ponti; Lorenzo Severini; Rita Ribeiro; João Gama; Ricard Gavaldà; Lee Cooper; Naghmeh Ghazaleh; Jonas Richiardi; Damian Roqueiro; Diego Saldana Miranda; Konstantinos Sechidis; Guilherme Graça is a book available to read on EtoBox.

What is The Next Frontier: AI We Can Really Trust about?

Enormous advances in the domain of statistical machine learning, the availability of large amounts of training data, and increasing computing power have made Artificial Intelligence (AI) very successful. For certain tasks, algorithms can even achieve performance beyond the human level. Unfortunately, the most powerful methods suffer from the fact that it is difficult to explain why a certain result was achieved on the one hand, and that they lack robustness on the other. Our most powerful machine learning models are very sensitive to even small changes. Perturbations in the input data can have a dramatic impact on the output and lead to entirely different results. This is of great importance in virtually all critical domains where we suffer from low data quality, i.e. we do not have the expected i.i.d. data. Therefore, the use of AI in domains that impact human life (agriculture, climate, health, ...) has led to an increased demand for trustworthy AI. Explainability is now even mandatory due to regulatory requirements in sensitive domains such as medicine, which requires traceability, transparency and interpretability capabilities. One possible step to make AI more robust is to com

Author

Michael Kamp; Irena Koprinska; Adrien Bibal; Tassadit Bouadi; Benoît Frénay; Luis Galárraga; José Oramas; Linara Adilova; Yamuna Krishnamurthy; Bo Kang; Christine Largeron; Jefrey Lijffijt; Tiphaine Viard; Pascal Welke; Massimiliano Ruocco; Erlend Aune; Claudio Gallicchio; Gregor Schiele; Franz Pernkopf; Michaela Blott; Holger Fröning; Günther Schindler; Riccardo Guidotti; Anna Monreale; Salvatore Rinzivillo; Przemyslaw Biecek; Eirini Ntoutsi; Mykola Pechenizkiy; Bodo Rosenhahn; Christopher Buckley; Daniela Cialfi; Pablo Lanillos; Maxwell Ramstead; Tim Verbelen; Pedro M. Ferreira; Giuseppina Andresini; Donato Malerba; Ibéria Medeiros; Philippe Fournier-Viger; M. Saqib Nawaz; Sebastian Ventura; Meng Sun; Min Zhou; Valerio Bitetta; Ilaria Bordino; Andrea Ferretti; Francesco Gullo; Giovanni Ponti; Lorenzo Severini; Rita Ribeiro; João Gama; Ricard Gavaldà; Lee Cooper; Naghmeh Ghazaleh; Jonas Richiardi; Damian Roqueiro; Diego Saldana Miranda; Konstantinos Sechidis; Guilherme Graça

Publisher

Springer International Publishing : Imprint: Springer

Published

2021

Language

EN

ISBN

9783030937324

Subjects

Engineering, Mathematics, Computer Science