AI Horizons

Bayesian optimization (BayesOpt) is a powerful tool that has found wide application in global optimization tasks, such as hyperparameter tuning, protein engineering, synthetic chemistry, robot learning, and even baking cookies. One of the main reasons BayesOpt is so useful for these problems is because they all involve optimizing black-box functions that are expensive to evaluate. A black-box function's underlying mapping from inputs (configurations of the thing we want to optimize) to outputs (a measure of performance) is unknown. However, we can attempt to understand its internal workings by evaluating the function for different combinations of inputs. Because each evaluation can be computationally expensive, we need to find the best inputs in as few evaluations as possible. BayesOpt works by repeatedly constructing a surrogate model of the black-box function and strategically evaluating the function at the most promising or informative input location, given the information obser...

 Facebook has recently announced the release of Casual Conversations v2, a new and improved dataset that aims to measure fairness and inclusivity in AI models. This consent-driven dataset is a more inclusive and diverse version of its predecessor, which was launched two years ago. With 26,467 video monologues recorded in seven countries, the dataset features 11 self-provided and annotated categories to better evaluate the fairness and robustness of certain types of AI models. One of the main challenges in evaluating fairness in AI models is the lack of diverse and inclusive datasets. With the help of Casual Conversations v2, researchers can now assess how well AI models work for different demographic groups, particularly in applications of computer vision and speech recognition. The dataset is the first open-source dataset with videos collected from multiple countries using highly accurate and detailed demographic information to help test AI models for fairness and robustness. Casu...