Artificial intelligence is all around us. But we often don't notice how much it's incorporated into the different aspects of our lives. This game challenges you and your kid(s) to notice. Designed by Blakeley H. Payne, a researcher at MIT, AI bingo builds on pedagogical research that shows how exposing kids to the way technology works helps develop their interest in STEM and improve their job prospects later on in life. It is also part of a broader curriculum designed for and tested by students from 9 to 14. The full curriculum can be found here.

You might think your TV is'Super Bowl ready,' but are you really prepared for the big day? Here are some tips from columnist Marc Saltzman to make sure your TV is set up perfectly. For many football fans, getting a little "action" on the Super Bowl is as commonplace as the wings and beer. Whether it is betting on the outcome of the game itself or participating in "prop bets" (such as whether the opening coin toss will be heads or tails) or "boxes" (also known as "squares," where you try to predict the correct last digit of the NFC and AFC team's score), wagers of various kinds can often be found at Super Bowl parties across the country. That's in addition to the $138.5 million legally bet through licensed sports books in Nevada.

This paper investigates three different technologies for solving a planning and scheduling problem of deploying multiple robots in a retirement home environment to assist elderly residents. The models proposed make use of standard techniques and solvers developed in AI planning and scheduling, with two primary motivations. First, to find a planning and scheduling solution that we can deploy in our real-world application. Second, to evaluate planning and scheduling technology in terms of the ``model-and-solve'' functionality that forms a major research goal in both domain-independent planning and constraint programming. Seven variations of our application are studied using the following three technologies: PDDL-based planning, time-line planning and scheduling, and constraint-based scheduling. The variations address specific aspects of the problem that we believe can impact the performance of the technologies while also representing reasonable abstractions of the real world application. We evaluate the capabilities of each technology and conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application. PDDL-based planning is able to find mostly low quality solutions while the timeline approach was unable to model the full problem without alterations to the solver code, thus moving away from the model-and-solve paradigm. It would be misleading to conclude that constraint programming is ``better'' than PDDL-based planning in a general sense, both because we have examined a single application and because the approaches make different assumptions about the knowledge one is allowed to embed in a model. Nonetheless, we believe our investigation is valuable for AI planning and scheduling researchers as it highlights these different modelling assumptions and provides insight into avenues for the application of AI planning and scheduling for similar robotics problems. In particular, as constraint programming has not been widely applied to robot planning and scheduling in the literature, our results suggest significant untapped potential in doing so.

Our research focuses on the use of Planning & Scheduling (P&S) technology for a team of robots providing daily assistance to multiple elder adults living in retirement facilities. Multi-user assistance and group-based activities require robots to plan and schedule their human-robot interaction (HRI) activities based on the specific needs, time constraints, availability and preferences of the multiple users. In this paper, we introduce and implement a novel centralized system architecture that can manage real P&S scenarios with multiple socially assistive robots, multiple users and their individual schedules, and single- and multi-person assistive activities. We describe how the main components of the proposed P&S architecture are integrated to control the robots, and to generate and monitor sequences of temporally annotated activities using off-the-shelf temporal planners. We verify that the architecture can manage realistic scenarios with three assistive robots, twenty users, and several single- and group-based activity requests during a single day.