- Добавил: literator
- Дата: 6-04-2023, 02:49
- Комментариев: 0
Название: Human-Centered Artificial Intelligence: Advanced Lectures
Автор: Mohamed Chetouani, Virginia Dignum, Paul Lukowicz, Carles Sierra
Издательство: Springer
Серия: Lecture Notes in Artificial Intelligence
Год: 2023
Страниц: 434
Язык: английский
Формат: pdf (true)
Размер: 34.6 MB
As a discipline, human-centered AI (HCAI) aims to create Artificial Intelligence (AI) systems that collaborate with humans, enhancing human capabilities and empowering humans to achieve their goals. That is, the focus amplify and augment rather than displace human abilities. HCAI seeks to preserve human control in a way that ensures artificial intelligence meets our needs while also operating transparently, delivering equitable outcomes, and respecting human rights and ethical standards. Design methods that enable representation of and adherence to values such as privacy protection, autonomy (human in control), and non-discrimination are core to HCAI. These are themes closely connected to some of the most fundamental challenges of AI. Artificial neural networks provide a distributed computing technology that can be trained to approximate any computable function, and have enabled substantial advances in areas such as computer vision, robotics, speech recognition and natural language processing. The future of AI lies in enabling people to collaborate with machines to solve complex problems. This requires good communication, trust, clarity, and understanding, like any efficient collaboration. Explainable AI (XAI) addresses such challenges, and for years different AI communities have studied such topics, leading to different definitions, evaluation protocols, motivations, and results.
Автор: Mohamed Chetouani, Virginia Dignum, Paul Lukowicz, Carles Sierra
Издательство: Springer
Серия: Lecture Notes in Artificial Intelligence
Год: 2023
Страниц: 434
Язык: английский
Формат: pdf (true)
Размер: 34.6 MB
As a discipline, human-centered AI (HCAI) aims to create Artificial Intelligence (AI) systems that collaborate with humans, enhancing human capabilities and empowering humans to achieve their goals. That is, the focus amplify and augment rather than displace human abilities. HCAI seeks to preserve human control in a way that ensures artificial intelligence meets our needs while also operating transparently, delivering equitable outcomes, and respecting human rights and ethical standards. Design methods that enable representation of and adherence to values such as privacy protection, autonomy (human in control), and non-discrimination are core to HCAI. These are themes closely connected to some of the most fundamental challenges of AI. Artificial neural networks provide a distributed computing technology that can be trained to approximate any computable function, and have enabled substantial advances in areas such as computer vision, robotics, speech recognition and natural language processing. The future of AI lies in enabling people to collaborate with machines to solve complex problems. This requires good communication, trust, clarity, and understanding, like any efficient collaboration. Explainable AI (XAI) addresses such challenges, and for years different AI communities have studied such topics, leading to different definitions, evaluation protocols, motivations, and results.