Hello World! I am Charlie, an Artificially Intelligent Conference Panelist

In recent years, advances in artificial intelligence (AI) have far outpaced our ability to understand and leverage them. In no domain has this been more true than in conversational agents (CAs). Transformer-based generative language models, such as GPT-2, significantly advance CAs\u27 ability to generate creative and relevant content. It is critical to start exploring collaboration with these CAs. In this paper, we focus on an initial step by enabling a human-augmented, AI-driven CA to contribute to a panel discussion. Key questions include training a transformer-based AI to talk like a panelist, effectively embodying the CA to interact with panel participants, and defining the operational requirements and challenges to a CA gaining acceptance from its peers. Our results highlight the benefits that varied training, equal and dynamic representation, and fluid operation can have for AI applications. While acknowledging limitations, we present a path forward to richer, more natural human-AI collaboration

Practice Makes Perfect: Lesson Learned from Five Years of Trial and Error Building Context-Aware Systems

Recent advances in artificial intelligence have demonstrated that the future of work will be defined by collaborative human-machine teams. In order to be effective, human-machine teams will rely on context-aware systems to enable collaboration. In this paper, we present three lessons learned from the past five years of developing context-aware systems that we believe will improve future system design. First, that semantic activity must captured, modeled, and analyzed to enable reasoning across missions, actors, and content. Second, that context-aware systems require multiple, federated data stores to optimize system and team performance. Finally, that real-time inter-actor communications are the essential feature enabling adaptation. We close with a discussion of the influences and implications that these lessons have on human-machine teaming, and outline future research activities that will be necessary before operationalizing these systems

Modeling Pilot State in Next Generation Aircraft Alert Systems

The Next Generation Air Transportation System will introduce new, advanced sensor technologies into the cockpit that must convey a large number of potentially complex alerts. Our work focuses on the challenges associated with prioritizing aircraft sensor alerts in a quick and efficient manner, essentially determining when and how to alert the pilot This "alert decision" becomes very difficult in NextGen due to the following challenges: 1) the increasing number of potential hazards, 2) the uncertainty associated with the state of potential hazards as well as pilot slate , and 3) the limited time to make safely-critical decisions. In this paper, we focus on pilot state and present a model for anticipating duration and quality of pilot behavior, for use in a larger system which issues aircraft alerts. We estimate pilot workload, which we model as being dependent on factors including mental effort, task demands. and task performance. We perform a mathematically rigorous analysis of the model and resulting alerting plans. We simulate the model in software and present simulated results with respect to manipulation of the pilot measures

Function Allocation for NextGen Airspace via Agents

ABSTRACT Commercial aviation transportation is on the rise and has become a necessity in our increasingly global world. There is a societal demand for more options, more traffic, more efficiency, while still maintaining safety in the airspace. To meet these demands the Next Generation Air Transportation System (NextGen) concept from NASA calls for technologies and systems offering increasing support from automated decision-aiding and optimization tools. Such systems must coordinate with the human operator to take advantage of the functions each can best perform: The automated tools must be designed to support the optimal allocation of tasks (functions) between the system and the human operators using these systems. Preliminary function allocation methods must be developed (and evaluated) that focus on the NextGen Airportal challenges, given a flexible, changing Concept of Operations (ConOps). We have begun making steps toward this by leveraging work in agents research (namely Adjustable Autonomy) in order to allow function allocation to become more dynamic and adjust to the goals, demands, and constraints of the current situation as it unfolds. In this paper we introduce Dynamic Function Allocation Strategies (DFAS) that are not static and singular, but rather are represented by allocation policies that vary over time and circumstances. The NextGen aviation domain is a natural fit for agent based systems because of its inherently distributed nature and the need for automated systems to coordinate on tasks maps well to the adjustable autonomy problem. While current adjustable autonomy methods are applicable in this context, crucial extensions are needed to push the existing models to larger numbers of human players, while maintaining critical timing. To this end, we have created an air traffic control system that includes: (1) A simulation environment, (2) a DFAS algorithm for providing adjustable autonomy strategies and (3) the agents for executing the strategies and measuring system efficiency. We believe that our system is the first step towards showing the efficacy of agent supported approach to driving the dynamic roles across human operators and automated systems in the NextGen environment. We present some initial results from a pilot study using this system

Multiagent Teamwork: Hybrid Approaches

Conference paper published in CSI Communications</p

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives