Building trust in decisions: Optimization is AI that helps turn complexity into clarity
Mathematical optimization is the branch of AI that turns complex constraints and competing priorities into transparent, explainable decisions.
Gain a practical understanding of optimization, its place in AI, and its ability to give decision makers the confidence to act in today’s complex world that other AI approaches cannot
RegisterLeaders need to make decisions with confidence, clarity, and explainability. Technology can help with decision-making, but only when applied correctly.
Generative artificial intelligence (AI) can be brilliant, but variable. Ask twice, get two different answers. Classic machine learning can predict what’s likely to happen, but predictions alone don’t decide who gets what, when, and how.
In this webinar, we’ll explore how mathematical optimization is the branch of AI that turns complex constraints and competing priorities into transparent, explainable decisions.
First, we’ll explain how optimization models differ from LLMs and machine learning models and why they are better suited for decision making. Then, we’ll highlight features in optimization that allow executives to make decisions in the ways they need, from intermittent strategic recommendations to automated agents.
You’ll leave with a practical understanding of optimization, its place in AI, and its ability to give decision makers the confidence to act in today’s complex world that other AI approaches cannot.
Key takeaways:
- Why decision-making needs more than prediction.
- How mathematical optimization enables explainable, confident decisions.
- How optimization complements other AI approaches.
speaker
Jerome (Jerry) Yurchisin
Senior data science strategist
Gurobi
Everett Dutton
Optimization strategist
Gurobi
Sponsors
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