The response quality of BlenderBot is pretty bad compared to ChatGPT
Example question: What’s a covariance matrix?
BlenderBot: Principal component analysis is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data.
ChatGPT: A covariance matrix is a symmetric matrix that describes the covariance between multiple variables in a given dataset. The diagonal elements of the matrix represent the variance of each variable, and the off-diagonal elements represent the covariance between each pair of variables. The covariance matrix is used in multivariate statistics to understand the relationships between different variables and to perform statistical inference and hypothesis testing.
I tried it. It can't answer basic encyclopedia facts (e.g How many people died in World War II). It can't do basic programming (e.g write a javascript function to reverse a string). Just goes to show that everyone besides OpenAI are so terrified of these things that they can only release versions that are toys.
Do these companies fear their customers that much? Maybe that's the difference between Microsoft/OpenAI and Facebook/Google. Microsoft sells the LLM to the customer who will be more impressed by the product if it's useful. With Facebook/Google the product uses the LLM. The LLM may make them an unsuitable product if they don't click on an ad after using it.
Example question: What’s a covariance matrix?
BlenderBot: Principal component analysis is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data.
ChatGPT: A covariance matrix is a symmetric matrix that describes the covariance between multiple variables in a given dataset. The diagonal elements of the matrix represent the variance of each variable, and the off-diagonal elements represent the covariance between each pair of variables. The covariance matrix is used in multivariate statistics to understand the relationships between different variables and to perform statistical inference and hypothesis testing.