The similarity in facial structure, size, and appearance of the actor whose face is being replaced affects the realism of the finished deepfake. It is also far easier to produce a convincing result if the person remains still, as movement can often reveal the artificial nature of the animation.

Test and evaluation is a necessary process for ensuring that engineered systems perform as intended under a variety of conditions, both expected and unexpected. In this work, we consider the unique challenges of developing a unifying test and evaluation framework for complex ensembles of cyber-physical systems with embedded artificial intelligence. We propose a framework that incorporates test and evaluation throughout not only the development life cycle, but continues into operation as the system learns and adapts in a noisy, changing, and contended environment. The framework accounts for the challenges of testing the integration of diverse systems at various hierarchical scales of composition while respecting that testing time and resources are limited. A generic use case is provided for illustrative purposes and research directions emerging as a result of exploring the use case via the framework are suggested.

The concepts of blameworthiness and wrongness are of fundamental importance in human moral life. But to what extent are humans disposed to blame artificially intelligent agents, and to what extent will they judge their actions to be morally wrong? To make progress on these questions, we adopted two novel strategies. First, we break down attributions of blame and wrongness into more basic judgments about the epistemic and conative state of the agent, and the consequences of the agent's actions. In this way, we are able to examine any differences between the way participants treat artificial agents in terms of differences in these more basic judgments. our second strategy is to compare attributions of blame and wrongness across human, artificial, and group agents (corporations). Others have compared attributions of blame and wrongness between human and artificial agents, but the addition of group agents is significant because these agents seem to provide a clear middle-ground between human agents (for whom the notions of blame and wrongness were created) and artificial agents (for whom the question remains open).

In 2011, Hibbard suggested an intelligence measure for agents who compete in an adversarial sequence prediction game. We argue that Hibbard's idea should actually be considered as two separate ideas: first, that the intelligence of such agents can be measured based on the growth rates of the runtimes of the competitors that they defeat; and second, one specific (somewhat arbitrary) method for measuring said growth rates. Whereas Hibbard's intelligence measure is based on the latter growth-rate-measuring method, we survey other methods for measuring function growth rates, and exhibit the resulting Hibbard-like intelligence measures and taxonomies. Of particular interest, we obtain intelligence taxonomies based on Big-O and Big-Theta notation systems, which taxonomies are novel in that they challenge conventional notions of what an intelligence measure should look like. We discuss how intelligence measurement of sequence predictors can indirectly serve as intelligence measurement for agents with Artificial General Intelligence (AGIs).

Digital sensing provides an unprecedented opportunity to assess and understand mobility. However, incompleteness, missing information, possible inaccuracies, and temporal heterogeneity in the geolocation data can undermine its applicability. As mobility patterns are often repeated, we propose a method to use similar trajectory patterns from the local vicinity and probabilistically ensemble them to robustly reconstruct missing or unreliable observations. We evaluate the proposed approach in comparison with traditional functional trajectory interpolation using a case of sea vessel trajectory data provided by The Automatic Identification System (AIS). By effectively leveraging the similarities in real-world trajectories, our pattern ensembling method helps to reconstruct missing trajectory segments of extended length and complex geometry. It can be used for locating mobile objects when temporary unobserved as well as for creating an evenly sampled trajectory interpolation useful for further trajectory mining.

Healthcare is a human right, however, nobody said all coverage is created equal. Artificial intelligence and machine learning systems are already making impressive inroads into the myriad fields of medicine -- from IBM's Watson: Hospital Edition and Amazon's AI-generated medical records to machine-formulated medications and AI-enabled diagnoses. But in the excerpt below from Frank Pasquale's New Laws of Robotics we can see how the promise of faster, cheaper, and more efficient medical diagnoses generated by AI/ML systems can also serve as a double-edged sword, potentially cutting off access to cutting-edge, high quality care provided by human doctors. Excerpted from New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale, published by The Belknap Press of Harvard University Press. We might once have categorized a melanoma simply as a type of skin cancer.

Silicon Valley Robotics is pleased to announce that we are a Connector organization for the E-ROBOT Prize, and other DOE competitions on the American-Made Network. There is \$2 million USD available in up to ten prizes for Phase One of the E-ROBOT Prize, and \$10 million USD available in Phase Two. Individuals or teams can sign up for the competition, as the online platform offers opportunities to connect with potential team members, as do competition events organized by Connector organizations. Please cite Silicon Valley Robotics as your Connector organization, when entering the competition. Silicon Valley Robotics will be hosting E-Robot Prize information and connection events as part of our calendar of networking and Construction Robotics Network events.

To reduce Operation and Maintenance (O&M) costs on offshore wind farms, wherein 80% of the O&M cost relates to deploying personnel, the offshore wind sector looks to robotics and Artificial Intelligence (AI) for solutions. Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational safety compliance and resilience, inhibiting the commercialization of autonomous services offshore. To address safety and resilience challenges we propose a symbiotic system; reflecting the lifecycle learning and co-evolution with knowledge sharing for mutual gain of robotic platforms and remote human operators. Our methodology enables the run-time verification of safety, reliability and resilience during autonomous missions. We synchronize digital models of the robot, environment and infrastructure and integrate front-end analytics and bidirectional communication for autonomous adaptive mission planning and situation reporting to a remote operator. A reliability ontology for the deployed robot, based on our holistic hierarchical-relational model, supports computationally efficient platform data analysis. We analyze the mission status and diagnostics of critical sub-systems within the robot to provide automatic updates to our run-time reliability ontology, enabling faults to be translated into failure modes for decision making during the mission. We demonstrate an asset inspection mission within a confined space and employ millimeter-wave sensing to enhance situational awareness to detect the presence of obscured personnel to mitigate risk. Our results demonstrate a symbiotic system provides an enhanced resilience capability to BVLOS missions. A symbiotic system addresses the operational challenges and reprioritization of autonomous mission objectives. This advances the technology required to achieve fully trustworthy autonomous systems.

The lack of explainability of a decision from an Artificial Intelligence (AI) based "black box" system/model, despite its superiority in many real-world applications, is a key stumbling block for adopting AI in many high stakes applications of different domain or industry. While many popular Explainable Artificial Intelligence (XAI) methods or approaches are available to facilitate a human-friendly explanation of the decision, each has its own merits and demerits, with a plethora of open challenges. We demonstrate popular XAI methods with a mutual case study/task (i.e., credit default prediction), analyze for competitive advantages from multiple perspectives (e.g., local, global), provide meaningful insight on quantifying explainability, and recommend paths towards responsible or human-centered AI using XAI as a medium. Practitioners can use this work as a catalog to understand, compare, and correlate competitive advantages of popular XAI methods. In addition, this survey elicits future research directions towards responsible or human-centric AI systems, which is crucial to adopt AI in high stakes applications.

A group of more than 30 democratic lawmakers led by Representatives Tom Malinowski (D-NJ) and Anna G. Eshoo (D-CA) are calling on Facebook, Twitter and YouTube to make substantive changes to their recommendation algorithms. In three separate letters addressed to the CEOs of those companies, the group makes a direct link to the January 6th US Capitol attack and the part those platforms played in radicalizing the individuals who took part in the uprising. "On Wednesday, January 6th the United States Capitol was attacked by a violent, insurrectionist mob radicalized in part in a digital echo chamber that your company designed, built and maintained," the letter addressed to Google and YouTube CEOs Sundar Pichai and Susan Wojcicki says. A letter from some Congress members to Google CEO Sundar Pichai and YouTube CEO Susan Wojcicki flexes research on how YouTube's algorithms have promoted conspiracy theories and political extremism. Citing the Capitol attacks, they request changes to its recommendations systems.