The current excitement about artificial intelligence (AI), particularly machine learning (ML), is palpable and contagious. The expectation that AI is poised to “revolutionize,” perhaps even take over, humanity has elicited prophetic visions and concerns from some luminaries. There is also a great deal of interest in the commercial potential of AI, which is attracting significant sums of venture capital and state‐sponsored investment globally. But, as with earlier AI breakthroughs, such as expert systems in the 1980s and neural networks in the 1990s, there is also considerable hype and a tendency to overestimate the promise of these advances.
It is apparent that chemical engineering is at an important crossroads. The discipline is undergoing an unprecedented transition—one that presents significant challenges and opportunities in modeling and automated decision‐making. This has been driven by the convergence of cheap and powerful computing and communications platforms, tremendous progress in molecular engineering, the ever‐increasing automation of globally integrated operations, tightening environmental constraints, and business demands for speedier delivery of goods and services to market. One important outcome from this convergence is the generation, use, and management of massive amounts of diverse data, information, and knowledge, and this is where AI, particularly ML, would play an important role.
Prof. Venkatasubramanian's article is aimed broadly at chemical engineers who are interested in the prospects for AI in chemical engineering, as well as at researchers new to this area. Highlighting past efforts, and drawing on these lessons to identify promising current and future opportunities for AI in chemical engineering, this article provides a much needed perspective to the promise of AI in chemical engineering. The AIChE persepective article is now available online here.