With the increasing adoption of artificial intelligence (AI) applications at the workplace, the debate about the future of work, workers, and the workplace has intensified. The polarized nature of debate ranges from job losses versus new-technology job creation through performance efficiency versus performance effectiveness to liberating humans from drudgery versus being controlled by machines. While several other polarities are evident in this debate, the truth always lies somewhere in between. In addition, there are other dark-side debates in the field about ethical, legal, and moral issues in the design and implementation of AI technologies for work and society.
While popular discourse presents AI as a new phenomenon, the development of AI as an academic discipline dates back to 1956. Since then, the computational power has increased exponentially, and various new AI technologies have been developed for delivering a range of social, business, and workplace applications. The rise of expert systems in the 1980s, followed by the increasing integration of various knowledge-based systems, saw the growing adoption of intelligent agents for a range of activities. The foundational efforts employing multidisciplinary lenses from maths, science, psychology, and economics led to increased use of machine learning, deep learning, and big data, and artificial general intelligence to develop a range of developments in AI-enabled applications for the workplace.
Continue reading: https://www.forbes.com/sites/benjaminlaker/2021/11/14/embedding-artificial-intelligence-at-work-from-efficiency-gains-to-employee-experience/?sh=52ff1b16d6f8
While popular discourse presents AI as a new phenomenon, the development of AI as an academic discipline dates back to 1956. Since then, the computational power has increased exponentially, and various new AI technologies have been developed for delivering a range of social, business, and workplace applications. The rise of expert systems in the 1980s, followed by the increasing integration of various knowledge-based systems, saw the growing adoption of intelligent agents for a range of activities. The foundational efforts employing multidisciplinary lenses from maths, science, psychology, and economics led to increased use of machine learning, deep learning, and big data, and artificial general intelligence to develop a range of developments in AI-enabled applications for the workplace.
Continue reading: https://www.forbes.com/sites/benjaminlaker/2021/11/14/embedding-artificial-intelligence-at-work-from-efficiency-gains-to-employee-experience/?sh=52ff1b16d6f8
