optimization

the goal is to search out the most effective (maximum or minimum) value of an objective function by choosing real variables that satisfy a set of equality and inequality constraints. A general constrained optimization...

Introduction that always operates with surprising inefficiency: manual processes, piles of paperwork, legal complexities. Many corporations still run on paper or Excel and don’t even collect data on their shipments. But what if an organization...

This text was written in collaboration with César Ortega, whose insights and discussions helped shape the ideas presented here. the best data product starts with sitting down with business partners to know day-to-day workflows,...

which have pervaded nearly every facet of our day by day lives are autoregressive decoder models. These models apply compute-heavy kernel operations to churn out tokens one after the other in a way...

and operating AI products involves making trade-offs. For instance, a higher-quality product may take more time and resources to construct, while complex inference calls could also be slower and costlier. These trade-offs are...

on Real-World Problems is Hard Reinforcement learning looks straightforward in controlled settings: well-defined states, dense rewards, stationary dynamics, unlimited simulation. Most benchmark results are produced under those assumptions. Observations are partial and noisy, rewards...

Never miss a brand new edition of , our weekly newsletter featuring a top-notch number of editors’ picks, deep dives, community news, and more. Most of the issues practitioners encountered when LLMs first burst onto the...

Optimizing Multimodal Agents Multimodal AI agents, those who can process text and pictures (or other media), are rapidly entering real-world domains like autonomous driving, healthcare, and robotics. In these settings, we now have traditionally used...