OR/MS Tomorrow

The rise of Artificial Intelligence (AI) and powerful generative AI (genAI) models has sparked considerable excitement, promising to revolutionize fields from supply chain optimization to customer service automation. But beneath the hype, AI’s performance varies wildly. It excels in some tasks but can spectacularly fail in others that seem equally straightforward. This “jagged technological frontier” means that some tasks are easily handled by AI, while others, despite their apparent simplicity, remain beyond its current (and perhaps future) capabilities (Dell’Acqua et al., 2023).

Artificial Intelligence (AI) is a field of science focused on building systems capable of performing tasks that typically require human intelligence or on analyzing data at a scale far beyond human capacity. Artificial Intelligence (AI) is a field of science focused on building systems capable of performing tasks comparable to or exceeding human capabilities. AI has a plethora of applications in different domains, such as face recognition, medical diagnosis, playing chess, and language generation. In this article, we unpack some of the most common myths surrounding AI: what it can do, what it can’t, and what we often misunderstand. By clarifying these misconceptions, we hope to promote a more realistic and informed conversation about the future of these intelligent systems.

Generative AI is becoming more and more commonplace across every industries, yet the same model can deliver brilliant insights one moment and blatant errors the next. This article explores the sources of Gen AI unreliability and how concepts from reliability engineering can help make these systems more stable, transparent, and dependable.

Almost 8 years after defending my PhD, I am still figuring out what is the right way to pursue one. In retrospect, I was lucky to stay in academia given how I completed mine. I have also seen other students doing the right things, certainly more often than I did only to run out of luck at critical points. But now I must tell my own students something and, more importantly, direct them in a way that would help them succeed and hedge against that luck component. So let’s start with what I think I should have done, but which I didn’t.

AIRO Young (AY) is the youth section of the Italian Association of Operations Research (AIRO), dedicated to fostering collaboration among students and early-career researchers in Operations Research (OR). Through annual workshops, networking events, and career development initiatives, AY creates opportunities for young researchers to advance their careers, expand their professional networks, and bridge the gap between academic and industry opportunities in the OR field.

A Race Against Time: Imagine a situation where a person suddenly collapses and their heart stops beating. In those first few minutes, everything depends on speed: recognizing what is happening, starting cardiopulmonary resuscitation (CPR), using a defibrillator if needed, and making the right decisions under intense pressure. This is the reality of cardiac arrest, one of the most serious medical emergencies in the world. Despite major advances in emergency medicine, survival rates after cardiac arrest are still low (Perkins et al., 2021). The reason is simple: resuscitation is not just difficult, it is a race against time.

Diffusion models such as OpenAI’s DALL·E and Stability AI’s Stable Diffusion have dazzled users with their ability to output everything from silly cat images to sophisticated diagrams of mechanical parts. Pulling back the curtain on these models reveals an elegant and sophisticated statistical framework that at its core is a combination of stochastic processes, variational inference, and score-based estimation: the same language that we as researchers in operations research speak.