“Humans, the individual, so each particular person is quite hard to predict. But on aggregate, we’re a lot more predictable and some of our characteristics can be quite good proxies for the way we behave. And that’s essentially why my product works.”
In this episode, part 1 of a 2-part mini-series on synthetic research, we dive into the fascinating world of simulating humans using AI with Hugo Alves, the co-founder and Chief Product Officer at Synthetic Users. With a unique background spanning clinical psychology, academic research, and product management, Hugo shares his insights on how AI can simulate human behavior and decision-making to revolutionize product testing and user research.
We explore the technology behind creating diverse synthetic users with realistic personalities, biases, and emotional responses. Hugo explains how large language models (LLMs) can effectively role-play specific personas, providing valuable product feedback without real users. We discuss the challenges of overcoming AI’s tendency to please users, the importance of incorporating human biases into synthetic personas, and how different commercial AI models compare when simulating human behavior.
