Artificial intelligence (AI) and machine learning (ML) are still viewed with skepticism by many in IT, despite a decades-long long history, continuing advances within academia and industry, and numerous successful applications. But it’s not hard to understand why: The very concept of an algorithm running on a digital computer being able to duplicate and even improve upon the knowledge and judgement of a highly-experienced professional – and, via machine learning, improve these results over time – still sounds at the very least a bit off in the future. And yet, thanks to advances in AI/ML algorithms and significant gains in processor and storage performance and especially the price/performance of solutions available today, AI and ML are already hard at work in network operations, as we’ll explore below.
The primary motivations for the adoption of AI and ML in day-to-day operations include the increasing complexity of network solutions, most notably on the wireless side; lack of a sufficient number of network professionals to handle the increasing scope and scale of network operations; ever-present requirements to minimize labor-intensive operating expenses; and continuing efforts to increase end-user productivity and assure the network capacity essential to growing numbers of end users with multiple mobile devices simultaneously in use, especially running time-bounded applications.
Essential limitations on human performance is another factor; it’s increasingly unreasonable to assume that even the best operations professionals can simultaneously consider the number of variables present in today’s networks, especially while keeping up with new technologies and products. As a result, embodying smarts in AI/ML-based products and cloud-based services is rapidly becoming a front-burner interest for even the skeptical.
Defining artificial intelligence and machine learning
AI and ML, while still continuing to evolve, are in fact relatively mature technologies, with production deployments going back to the 1980s. Simply put, AI is the emulation of human knowledge captured and engineered into algorithms and operational solutions often called expert systems. ML is the ability of these algorithms to improve their performance, based on operational experience, but without manual intervention or conventional reprogramming (but, of course, often via feedback from human operations professionals). Such technologies as neural networks and deep learning are often applied; consider IBM’s Watson solution, which has demonstrated benefits across multiple applications.