With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. While we feel that the methods are adaptable to other regions of the world, we focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population. We present a quantitative analysis of deep learning domain adaptation methods for wheat farm identification based on curated remote sensing data from France. We model climate change impacts on crop yields using the existing crop yield modeling tool WOFOST and we identify key drivers of crop simulation error using a longitudinal penalized functional regression. A description of a system dynamics model of the food distribution system in India is also presented, along with results of food insecurity identification based on seeding this model with the predicted crop yields.

SleepioTM is a digital mobile phone and web platform that uses techniques from cognitive behavioural therapy (CBT) to improve sleep in people with sleep difficulty. As part of this process, Sleepio captures data about the sleep behaviour of the users that have consented to such data being processed. For neural networks, the scale of the data is an opportunity to train meaningful models translatable to actual clinical practice. In collaboration with Big Health, the therapeutics company that created and utilizes Sleepio, we have analysed data from a random sample of 401,174 sleep diaries and built a neural network to model sleep behaviour and sleep quality of each individual in a personalised manner. We demonstrate that this neural network is more accurate than standard statistical methods in predicting the sleep quality of an individual based on his/her behaviour from the last 10 days. We compare model performance in a wide range of hyperparameter settings representing various scenarios. We further show that the neural network can be used to produce personalised recommendations of what sleep habits users should follow to maximise sleep quality, and show that these recommendations are substantially better than the ones generated by standard methods. We finally show that the neural network can explain the recommendation given to each participant and calculate confidence intervals for each prediction, all of which are essential for clinicians to be able to adopt such a tool in clinical practice.

We propose a categorization of solution-centric evaluation metrics for a class of domain-independent AI challenge tasks known as MacGyver problems. Our definitions formally describe different classes of novel solutions for general creative problem solving tasks described in the MacGyver framework. Furthermore, inspired by existing theories of creativity, we extend the MacGyver problem formalism to incorporate subjective observers of problem solving tasks. By doing this, we explicitly model solutions to creative problem solving tasks as subjective evaluations based on the varying domain knowledge of observing agents. As an application of our extended formalism, we then illustrate how previous work on goal-driven conceptual blending represents a powerful form of human creativity whose creative solutions can be more formally described through our classes of novel solutions. Additionally, we conclude by highlighting strong connections between observer-oriented creative problem solving as described here and personalized procedural content generation in games.

This paper describes a new candidate method of computing autonomous "self confidence." We describe how to analyze a plan for possible but unexpected break down cases and how to adapt the plan to circumvent those conditions. We view the result plan as more stable than the original one. The ability of achieving such plan stability is the core of how we propose to compute a system’s self confidence in its decisions and plans. This paper summarizes this approach and presents a preliminary evaluation that shows our approach is promising.

Communication is social by nature, and reveals psycho-social dimensions about an actor’s perceptions of themself and others. While grammar and spell-check can help polish the presentation of communication, it does not reflect the way that a message will be received in a particular social space. A means to analyze the communication for actor beliefs can help the author and others understand the underlying social climate and message that is being transmitted. NASA has identified the need to monitor individual behavioral health and team dynamics as crucial to ensuring high performance and mission success. We describe an application that integrates theories from sociolinguistics with natural language processing techniques to successfully detect individual moods, attitudes, and team dynamics relevant to long duration exploration class missions. The methods were used to analyze data gathered from human subject experiments at three diverse analog studies, with results showing high correlation with subject self-reports and third party observations. We discuss preliminary results and implications for the tool’s potential wide-spread use.