Term: This is a renewable appointment. Degree and area of specialization: Bachelor's degree in Computer Science or related field preferred Minimum number of years and type of relevant work experience: Detail knowledge of computer systems, terminology, concepts and uses including broad experience with systems analysis/design methodology and techniques, Excellent communication and interpersonal skills. Ability to work with a diverse group of faculty, classified, academic staff, and students. The ability to promote a team atmosphere, Diversity in experience and knowledge of various computer application languages and associated programming principles and techniques, Knowledge of relational database structures and design requirements specific to Microsoft SQL Server and MySQL, Knowledge of development, enhancement, and maintenance of content management system Drupal, Knowledge of development, enhancement, and maintenance of MS Access forms and reporting,, Knowledge of problem solving techniques, Knowledge of team leadership, Knowledge of project management, project estimation, work plan preparation, and project change control, Knowledge of techniques used in establishing and maintaining effective working relationships with agency users of data processing, Knowledge of integration and migration techniques of existing databases to secondary platforms; experience with integrated and distributed methodology and implementation, Ability to develop project schedules, including such things as deliverables, tasks, time estimates, and critical path, Knowledge of computing and developments, Knowledge of WWW and HTML; firsthand experience with design and layout for ease of use of both technical and business applications, Ability to diagnose and solve highly complex problems with database management systems, Knowledge of project management principles, Knowledge of computer applications and systems programming principles, techniques and capabilities. Additional Information: The U.S. Department of Labor Fair Labor Standards Act (FLSA) new rules go into effect on December 1, 2016 (FLSA Threshold Rules).

It's a lot harder to recognize fake videos than you can imagine, including this President Richard ... [ ] Nixon deepfake about Apollo 11. Fifty-one years ago this week, the first moon landing took place. Two astronauts from Apollo 11 walked around on the lunar surface for a couple of hours, changing space exploration forever. Most people around the world accept this statement as truth, but there has always been an underbelly of society who (wrongly) think the moon landing in 1969 never happened. A new project shows the danger of how easy it is to spread fake news, through the power of a video related to the first moon landing.

Accuracy, precision, recall and other measures of AI efficacy are crucial but not sufficient. Will you use, trust, or make clinical decisions based on a technology that runs on "bad data" and are neither "clinically validated" nor "FDA approved"? From virtual assistants to technologies such as Apple Watch and IBM Watson, several applications of artificial intelligence (AI) have been established to augment health care systems, improve patient care, and assist care-providers. The growing involvement of technology giants such as Google, Apple, and IBM in health care technology have further enhanced the need to understand better the influence of AI on the health care industry. Many health care organizations are employing AI technologies to create new value in the industry.

While artificial intelligence capabilities have been evolving, the COVID-19 pandemic has accelerated adoption of these tools and made intelligent machines part of our new normal lives, according to a new report from Capgemini. More than half of the consumers surveyed (54%) use AI daily–compared to just 21% in 2018, the report, "The art of customer-centric artificial intelligence," finds. Sweden, Brazil, and the US have the highest daily interactions with AI. Contactless or non-touch interfaces are finding their way into numerous sectors, the report said. Over three-quarters (77%) of respondents expect to increase the use of touchless interfaces--such as voice assistants and facial recognition--to avoid direct interactions with humans or touchscreens during COVID-19, and 62% will continue to do so post-COVID.

The University of Florida and NVIDIA Tuesday unveiled a plan to build the world's fastest AI supercomputer in academia, delivering 700 petaflops of AI performance. The effort is anchored by a $50 million gift: $25 million from alumnus and NVIDIA co-founder Chris Malachowsky and $25 million in hardware, software, training and services from NVIDIA. "We've created a replicable, powerful model of public-private cooperation for everyone's benefit," said Malachowsky, who serves as an NVIDIA Fellow, in an online event featuring leaders from both the UF and NVIDIA. UF will invest an additional $20 million to create an AI-centric supercomputing and data center. The $70 million public-private partnership promises to make UF one of the leading AI universities in the country, advance academic research and help address some of the state's most complex challenges.

We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm for sampling spatial partitions that lie within a large and complex spatial state space. Our algorithm combines the advantages of evolutionary algorithms (EAs) as optimization heuristics for state space traversal and the theoretical convergence properties of Markov Chain Monte Carlo algorithms for sampling from unknown distributions. Local optimality information that is identified via a directed search by our optimization heuristic is used to adaptively update a Markov chain in a promising direction within the framework of a Multiple-Try Metropolis Markov Chain model that incorporates a generalized Metropolis-Hasting ratio. We further expand the reach of our EMCMC algorithm by harnessing the computational power afforded by massively parallel architecture through the integration of a parallel EA framework that guides Markov chains running in parallel.

Artificial intelligence (AI) has been successfully applied in numerous scientific domains including biomedicine and healthcare. Here, it has led to several breakthroughs ranging from clinical decision support systems, image analysis to whole genome sequencing. However, training an AI model on sensitive data raises also concerns about the privacy of individual participants. Adversary AIs, for example, can abuse even summary statistics of a study to determine the presence or absence of an individual in a given dataset. This has resulted in increasing restrictions to access biomedical data, which in turn is detrimental for collaborative research and impedes scientific progress. Hence there has been an explosive growth in efforts to harness the power of AI for learning from sensitive data while protecting patients' privacy. This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy, and discusses their strengths, limitations, and open problems.

In recent years, many sectors have experienced significant progress in automation, associated with the growing advances in artificial intelligence and machine learning. There are already automated robotic weapons, which are able to evaluate and engage with targets on their own, and there are already autonomous vehicles that do not need a human driver. It is argued that the use of increasingly autonomous systems (AS) should be guided by the policy of human control, according to which humans should execute a certain significant level of judgment over AS. While in the military sector there is a fear that AS could mean that humans lose control over life and death decisions, in the transportation domain, on the contrary, there is a strongly held view that autonomy could bring significant operational benefits by removing the need for a human driver. This article explores the notion of human control in the United States in the two domains of defense and transportation. The operationalization of emerging policies of human control results in the typology of direct and indirect human controls exercised over the use of AS. The typology helps to steer the debate away from the linguistic complexities of the term "autonomy." It identifies instead where human factors are undergoing important changes and ultimately informs about more detailed rules and standards formulation, which differ across domains, applications, and sectors.

Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture are usually mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University. Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. We linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use cases. ROS handles the low-level sensing and actuation, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is orchestrated and sent from ROS to the reasoning system as perceptions. These perceptions are then deliberated upon, and an action string is sent back to ROS for interpretation and driving of the necessary actuator for the action to be performed. In this paper we present our current implementation, which closes the loop on the hardware-software integration, and implements a subset of the use cases required for the full system.

In multi-label classification tasks, each problem instance is associated with multiple classes simultaneously. In such settings, the correlation between labels contains valuable information that can be used to obtain more accurate classification models. The correlation between labels can be exploited at different levels such as capturing the pair-wise correlation or exploiting the higher-order correlations. Even though the high-order approach is more capable of modeling the correlation, it is computationally more demanding and has scalability issues. This paper aims at exploiting the high-order label correlation within subsets of labels using a supervised learning classifier system (UCS). For this purpose, the label powerset (LP) strategy is employed and a prediction aggregation within the set of the relevant labels to an unseen instance is utilized to increase the prediction capability of the LP method in the presence of unseen labelsets. Exact match ratio and Hamming loss measures are considered to evaluate the rule performance and the expected fitness value of a classifier is investigated for both metrics. Also, a computational complexity analysis is provided for the proposed algorithm. The experimental results of the proposed method are compared with other well-known LP-based methods on multiple benchmark datasets and confirm the competitive performance of this method.