Two orthopedic surgeons and one four hour surgery later, the young feline is on the mend. The surgery to heal the injured wildcat took four hours. Breakthroughs, discoveries, and DIY tips sent six days a week. In February, Tracie Young, director of the Raven Ridge Wildlife Center in Pennsylvania, received an unforgettable phone call. A game warden asked if the center in southeastern Pennsylvania had room for a bobcat that had been hit and dragged by a car.

Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker. CAT methods adaptively select the next most informative question/item for each student given their responses to previous questions, effectively reducing test length. Existing CAT methods use item response theory (IRT) models to relate student ability to their responses to questions and static question selection algorithms designed to reduce the ability estimation error as quickly as possible; therefore, these algorithms cannot improve by learning from large-scale student response data. In this paper, we propose BOBCAT, a Bilevel Optimization-Based framework for CAT to directly learn a data-driven question selection algorithm from training data. BOBCAT is agnostic to the underlying student response model and is computationally efficient during the adaptive testing process. Through extensive experiments on five real-world student response datasets, we show that BOBCAT outperforms existing CAT methods (sometimes significantly) at reducing test length.

Modelling deontic notions through preferences [12] has the advantage of linking deontic notions to the manifold research on preferences, in multiple disciplines, such as philosophy, mathematics, economics and politics. In recent years, preferences have also been addressed within AI [15,8,18] and applications can be found in multi-agent systems [19] and recommender systems [17]. We shall model deontic notions through ceteris-paribus preferences, namely, conditional preferences for a state of affairs over another state of affairs, all the rest being equal. In particular, we shall focus on the ceteris-paribus preference for a proposition over its complement. The idea of ceteris-paribus preferences was originally introduced by the philosopher and logician Georg von Wright [22].

Artificial intelligence was on the tip of the tongue this week at CES, the annual technology extravaganza formerly known as the Consumer Electronics Show. From Samsung's Neon avatars and LG's smart washing machine, to Intel's Tiger Lake processors and the gun-detecting PATSCAN, AI seemed to be everywhere. Samsung's research subsidiary, STAR Labs, unveiled its latest AI project, called Neon. Similar to a chatbot, Neon generates a photo-realistic digital avatar that interacts with people in real time. The South Korean technology giant plans to weave the Neons into people's day-to-day lives, where the avatars will play the role of doctors, personal trainers, and TV anchors giving you the evening news.