TY - GEN
T1 - Artificial Intelligence And Machine Learning
AU - Kataria, Deepak
AU - Walid, Anwar
AU - Daneshmand, Mahmoud
AU - Dutta, Ashutosh
AU - Enright, Michael A.
AU - Gu, Rentao
AU - Lackpour, Alex
AU - Ramachandran, Prakash
AU - Wang, Honggang
AU - Chen, Chi Ming
AU - Chng, Baw
AU - Darema, Frederica
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In the evolution of artificial Intelligence (AI) and machine learning (ML), reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects have been widely used. These features enable the creation of intelligent mechanisms for decision support to overcome the limits of human knowledge processing. In addition, ML algorithms enable applications to draw conclusions and make predictions based on existing data without human supervision, leading to quick, near-optimal solutions even in problems with high dimensionality. Hence, autonomy is a key aspect of current and future AI/ML algorithms. This chapter focuses on the development and implementation of AI/ML technologies for 5G and future networks. The objective is to illustrate how these technologies can be smoothly migrated into 5G systems to increase their performance and to decrease their cost. To that end, this chapter presents the Drivers, Needs, Challenges, Enablers, and Potential Solutions identified for the AI/ML field as applicable to future networks over three-, five-, and ten-year horizons. AI/ML applications for 5G are wide and diverse. In this document, some of the key areas are described which includes networking, securing, cloud computing and others. Over time, this white paper will evolve to encompass even more areas where AI/ML technologies can improve future network performance objectives.
AB - In the evolution of artificial Intelligence (AI) and machine learning (ML), reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects have been widely used. These features enable the creation of intelligent mechanisms for decision support to overcome the limits of human knowledge processing. In addition, ML algorithms enable applications to draw conclusions and make predictions based on existing data without human supervision, leading to quick, near-optimal solutions even in problems with high dimensionality. Hence, autonomy is a key aspect of current and future AI/ML algorithms. This chapter focuses on the development and implementation of AI/ML technologies for 5G and future networks. The objective is to illustrate how these technologies can be smoothly migrated into 5G systems to increase their performance and to decrease their cost. To that end, this chapter presents the Drivers, Needs, Challenges, Enablers, and Potential Solutions identified for the AI/ML field as applicable to future networks over three-, five-, and ten-year horizons. AI/ML applications for 5G are wide and diverse. In this document, some of the key areas are described which includes networking, securing, cloud computing and others. Over time, this white paper will evolve to encompass even more areas where AI/ML technologies can improve future network performance objectives.
KW - AI
KW - CNN
KW - Cloud Computing
KW - DL
KW - DNN
KW - GAN
KW - GPU
KW - MEC
KW - ML
KW - RNN
UR - http://www.scopus.com/inward/record.url?scp=85150307081&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85150307081&partnerID=8YFLogxK
U2 - 10.1109/FNWF55208.2022.00133
DO - 10.1109/FNWF55208.2022.00133
M3 - Conference contribution
AN - SCOPUS:85150307081
T3 - Proceedings - 2022 IEEE Future Networks World Forum, FNWF 2022
BT - Proceedings - 2022 IEEE Future Networks World Forum, FNWF 2022
T2 - 2022 IEEE Future Networks World Forum, FNWF 2022
Y2 - 12 October 2022 through 14 October 2022
ER -