Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision-making must be understandable to a wide range of stakeholders. Methods to explain AI have been proposed to answer this challenge, but a lack of theory impedes the development of systematic abstractions which are necessary for cumulative knowledge gains. We propose Bayesian Teaching as a framework for unifying explainable AI (XAI) by integrating machine learning and human learning. Bayesian Teaching formalizes explanation as a communication act of an explainer to shift the beliefs of an explainee. This formalization decomposes any XAI method into four components: (1) the inference to be explained, (2) the explanatory medium, (3) the explainee model, and (4) the explainer model. The abstraction afforded by Bayesian Teaching to decompose any XAI method elucidates the invariances among them. The decomposition of XAI systems enables modular validation, as each of the first three components listed can be tested semi-independently. This decomposition also promotes generalization through recombination of components from different XAI systems, which facilitates the generation of novel variants. These new variants need not be evaluated one by one provided that each component has been validated, leading to an exponential decrease in development time. Finally, by making the goal of explanation explicit, Bayesian Teaching helps developers to assess how suitable an XAI system is for its intended real-world use case. Thus, Bayesian Teaching provides a theoretical framework that encourages systematic, scientific investigation of XAI.

Order effects occur when judgments about a hypothesis's probability given a sequence of information do not equal the probability of the same hypothesis when the information is reversed. Different experiments have been performed in the literature that supports evidence of order effects. We proposed a Bayesian update model for order effects where each question can be thought of as a mini-experiment where the respondents reflect on their beliefs. We showed that order effects appear, and they have a simple cognitive explanation: the respondent's prior belief that two questions are correlated. The proposed Bayesian model allows us to make several predictions: (1) we found certain conditions on the priors that limit the existence of order effects; (2) we show that, for our model, the QQ equality is not necessarily satisfied (due to symmetry assumptions); and (3) the proposed Bayesian model has the advantage of possessing fewer parameters than its quantum counterpart.

A technology company is bringing around 500 jobs to the Kansas City area. Torch.AI announced Friday that as part of a $27 million tax incentive program awarded by the Kansas Department of Commerce, they will create around 500 full-time jobs over five years in the Kansas City area. The company's local operations will be based in Leawood. More than 100 of those jobs will be created this year and many of those new hires' salaries will average over $100,000. Positions include entry-level engineer and data scientist positions as well as some more experienced engineering and sales positions, according to the company.

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In the meantime, a wide variety of terminologies, motivations, approaches and evaluation criteria have been developed within the scope of research on explainable artificial intelligence (XAI). Many taxonomies can be found in the literature, each with a different focus, but also showing many points of overlap. In this paper, we summarize the most cited and current taxonomies in a meta-analysis in order to highlight the essential aspects of the state-of-the-art in XAI. We also present and add terminologies as well as concepts from a large number of survey articles on the topic. Last but not least, we illustrate concepts from the higher-level taxonomy with more than 50 example methods, which we categorize accordingly, thus providing a wide-ranging overview of aspects of XAI and paving the way for use case-appropriate as well as context-specific subsequent research.

Goddard Space Center Chief Scientist Jim Garvin provides insight on'Fox New Live.' NASA released a video this week giving viewers the chance to witness the Ingenuity Mars Helicopter's historic third flight in 3D. In a release on Wednesday, the agency said that the video was meant to approximate standing on the Martian planet and witnessing the action "firsthand." "When NASA's Ingenuity Mars Helicopter took to the Martian skies on its third flight on April 25, the agency's Perseverance rover was there to capture the historic moment. Now NASA engineers have rendered the flight in 3D, lending dramatic depth to the flight as the helicopter ascends, hovers, then zooms laterally off-screen before returning for a pinpoint landing," the agency said. The Perseverance Mars rover's zoomable dual-camera Mastcam-Z instrument produced the video and other images NASA says provide "key data" for navigation and aids in scientists' efforts to locate rocket targets – and potentially ancient microbial life.

American technology companies racing to develop and adopt artificial intelligence technology should do so responsibly and safely, according to a longtime security expert who has spent years studying the issue.  In a conversation on Thursday during A.I. Week, an event produced by Scoop News Group, Yll Bajraktari, the executive director of the National Security Commission on Artificial Intelligence, urged U.S. citizens to think carefully about the ethical use of powerful new technologies. The advice comes after Bajraktari’s commission published a report advising the U.S. government on issues to consider around the use of A.I.  The commission was established in 2018 to examine America’s ability to defend against malicious automation, and understand how the U.S. might best move forward in understanding such technology. In a report published March of this year, the group warned that the U.S. is not sufficiently prepared to compete with China on the issue.  “The United States […]

Back in 2019, Ben Lorica and I wrote about deepfakes. Ben and I argued (in agreement with The Grugq and others in the infosec community) that the real danger wasn't "Deep Fakes." The real danger is cheap fakes, fakes that can be produced quickly, easily, in bulk, and at virtually no cost. Tactically, it makes little sense to spend money and time on expensive AI when people can be fooled in bulk much more cheaply. I don't know if The Grugq has changed his thinking, but there was an obvious problem with that argument.

Helping companies make sense of their data. From real-time cybercrime mapping to penetration testing, machine learning has become a crucial part of cybersecurity. Fortunately, machine learning can help solve the most common tasks, including pattern detection, prediction, regression, and classification. In an era of large amounts of data and a shortage of network security talents, machine learning seems to be an alternative to solve many problems. Indeed, through machine learning, when applied to computer security, we can sort through millions of files to discover threats.

Researchers at the University of Maryland, Baltimore County (UMBC) have developed a technique to more quickly analyze extensive data from Arctic ice sheets in order to gain insight and useful knowledge on patterns and trends. Over the years, vast amounts of data have been collected about the Arctic and Antarctic ice. These data are essential for scientists and policymakers seeking to understand climate change and the current trend of melting. Masoud Yari, research assistant professor, and Maryam Rahnemoonfar, associate professor of information systems, have utilized new AI technology to develop a fully automatic technique to analyze ice data, published in the Journal of Glaciology. This is part of the National Science Foundation's ongoing BigData project.