  The popularity of Artificial Intelligence (AI) -- and of Machine Learning
(ML) as an approach to AI, has dramatically increased in the last few years,
due to its outstanding performance in various domains, notably in image, audio,
and natural language processing. In these domains, AI success-stories are
boosting the applied field. When it comes to AI/ML for data communication
Networks (AI4NETS), and despite the many attempts to turn networks into
learning agents, the successful application of AI/ML in networking is limited.
There is a strong resistance against AI/ML-based solutions, and a striking gap
between the extensive academic research and the actual deployments of such
AI/ML-based systems in operational environments. The truth is, there are still
many unsolved complex challenges associated to the analysis of networking data
through AI/ML, which hinders its acceptability and adoption in the practice. In
this positioning paper I elaborate on the most important show-stoppers in
AI4NETS, and present a research agenda to tackle some of these challenges,
enabling a natural adoption of AI/ML for networking. In particular, I focus the
future research in AI4NETS around three major pillars: (i) to make AI/ML
immediately applicable in networking problems through the concepts of effective
learning, turning it into a useful and reliable way to deal with complex
data-driven networking problems; (ii) to boost the adoption of AI/ML at the
large scale by learning from the Internet-paradigm itself, conceiving novel
distributed and hierarchical learning approaches mimicking the distributed
topological principles and operation of the Internet itself; and (iii) to
exploit the softwarization and distribution of networks to conceive
AI/ML-defined Networks (AIDN), relying on the distributed generation and
re-usage of knowledge through novel Knowledge Delivery Networks (KDNs).
