This paper presents a new approach to evaluating the quality of answers in political question-and-answer sessions. We propose to measure an answer's quality based on the degree to which it allows us to infer the initial question accurately. This conception of answer quality inherently reflects their relevance to initial questions. Drawing parallels with semantic search, we argue that this measurement approach can be operationalized by fine-tuning a large language model on the observed corpus of questions and answers without additional labeled data. We showcase our measurement approach within the context of the Question Period in the Canadian House of Commons. Our approach yields valuable insights into the correlates of the quality of answers in the Question Period. We find that answer quality varies significantly based on the party affiliation of the members of Parliament asking the questions and uncover a meaningful correlation between answer quality and the topics of the questions.

Spatial data is a rich source of information for actuarial applications: knowledge of a risk's location could improve an insurance company's ratemaking, reserving or risk management processes. Insurance companies with high exposures in a territory typically have a competitive advantage since they may use historical losses in a region to model spatial risk non-parametrically. Relying on geographic losses is problematic for areas where past loss data is unavailable. This paper presents a method based on data (instead of smoothing historical insurance claim losses) to construct a geographic ratemaking model. In particular, we construct spatial features within a complex representation model, then use the features as inputs to a simpler predictive model (like a generalized linear model). Our approach generates predictions with smaller bias and smaller variance than other spatial interpolation models such as bivariate splines in most situations. This method also enables us to generate rates in territories with no historical experience.

Longueuil, Quebec, October 15, 2018 --The Canadian Space Agency (CSA) is positioning Canada's space community to maintain its global leadership in space robotics. Accordingly, the CSA announced today that it is investing $1.6 million in two concepts for lunar rovers that would use artificial intelligence to make their own decisions. Canadian businesses MDA, a Maxar company, and Canadensys Aerospace Corporation have each been awarded a contract worth $800 000 to develop an innovative concept for the CSA. The CSA made the announcement at the start of a three-day event to promote Canadian space capabilities to major space companies, including Blue Origin, Airbus Defense and Space and Moon Express. As part of ongoing discussions with the international space community to prepare options for Canada's participation in the next chapter of space exploration, the CSA recently signed a Memorandum of Understanding with Moon Express, a US-based company.