  This article focuses on the application of artificial intelligence (AI) in
non-orthogonal multiple-access (NOMA), which aims to achieve automated,
adaptive, and high-efficiency multi-user communications towards next generation
multiple access (NGMA). First, the limitations of current scenario-specific
multiple-antenna NOMA schemes are discussed, and the importance of AI for NGMA
is highlighted. Then, to achieve the vision of NGMA, a novel cluster-free NOMA
framework is proposed for providing scenario-adaptive NOMA communications, and
several promising machine learning solutions are identified. To elaborate
further, novel centralized and distributed machine learning paradigms are
conceived for efficiently employing the proposed cluster-free NOMA framework in
single-cell and multi-cell networks, where numerical results are provided to
demonstrate the effectiveness. Furthermore, the interplays between the proposed
cluster-free NOMA and emerging wireless techniques are presented. Finally,
several open research issues of AI enabled NGMA are discussed.
