
Mitigating bias in algorithmic decision-making for an equitable AI-driven future
In some ML applications, algorithmic bias can creep in — and it can quickly lead to unexpected outcomes. But how does it happen, and how can we avoid this?
In some ML applications, algorithmic bias can creep in — and it can quickly lead to unexpected outcomes. But how does it happen, and how can we avoid this?
Building multilingual conversational AI is a challenging top priority for brands today. But limitations can be avoided by adopting an ensemble approach.
We don’t want to just “do Agile,” we want to “be agile.” Join us as we kick off our journey to agility and learn what it means for our Engineering Team.
Advancements have allowed ML teams to train large language models, but generative language models still has limitations.
Read this blog to learn why we built training insights and how it can enable you to deliver better customer experiences.
Ada Product Manager Muhammad Farooq explains how our new Training Optimizer feature can empower teams to improve their CX.