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The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team. During these collaborations, there is a knowledge mismatch between AI developers, who are skilled in data science, and external stakeholders who are typically not. This difference leads to communication gaps, and the onus falls on AI developers to explain data science concepts to their collaborators. In this paper, we report on a study including analyses of both interviews with AI developers and artifacts they produced for communication. Using the analytic lens of shared mental models, we report on the types of communication gaps that AI developers face, how AI developers communicate across disciplinary and organizational boundaries, and how they simultaneously manage issues regarding trust and expectations.

Professor Michael Wooldridge has been named as the recipient of the 2021 Outstanding Educator Award by the Association for the Advancement of Artificial Intelligence (AAAI), the international membership organisation for Artificial Intelligence. The award is issued to a person (or group of people) who has made major contributions to AI education that provide long-lasting benefits to the AI community. Michael has been given the award for'outstanding global leadership in AI education and public awareness, including publishing broadly adopted books and textbooks, establishing the European Agent Systems Summer School, and inspiring public dialogue on AI andmulti-agent systems'. Michael will accept the award with a plenary talk at the AAAI conference, which this year will be virtual. The talk is entitled'Talking to the Public about AI', and will relate to his experience of public engagement with AI.

This paper elegantly shows how to automatically construct abstract representations suitable for evaluating plans composed of sequences of high-level actions in a continuous, low-level environment.

In 1964, the Civil Rights Act barred the humans who made hiring decisions from discriminating on the basis of sex or race. Now, software often contributes to those hiring decisions, helping managers screen résumés or interpret video interviews. That worries some tech experts and civil rights groups, who cite evidence that algorithms can replicate or magnify biases shown by people. In 2018, Reuters reported that Amazon scrapped a tool that filtered résumés based on past hiring patterns because it discriminated against women. Legislation proposed in the New York City Council seeks to update hiring discrimination rules for the age of algorithms.

Joining the incoming PhD class at Carnegie Mellon in the late 1980s, I was lucky to have incredible opportunities for faculty advisors and mentors in AI. Jaime Carbonell was among the more junior faculty, continuing the research that he started in his PhD combining natural language, planning, and machine learning. His thesis work addressed how people with different perspectives approach a discussion topic, through reasoning about commonalities and differences, planning how to counter previous points made by the other party, and generating dialogue utterances that took the conversation in an intended direction. I remember asking him why he did not pursue a more focused topic that may have had more impact. He argued that taking an integrated view on intelligence enables us to do better research in AI.

Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful decision-making capabilities. However, they are vulnerable to various security and reliability threats, at both hardware and software levels, that compromise their accuracy. These threats get aggravated in emerging edge ML devices that have stringent constraints in terms of resources (e.g., compute, memory, power/energy), and that therefore cannot employ costly security and reliability measures. Security, reliability, and vulnerability mitigation techniques span from network security measures to hardware protection, with an increased interest towards formal verification of trained ML models. This paper summarizes the prominent vulnerabilities of modern ML systems, highlights successful defenses and mitigation techniques against these vulnerabilities, both at the cloud (i.e., during the ML training phase) and edge (i.e., during the ML inference stage), discusses the implications of a resource-constrained design on the reliability and security of the system, identifies verification methodologies to ensure correct system behavior, and describes open research challenges for building secure and reliable ML systems at both the edge and the cloud.

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g., algorithmic discrimination. Moreover, interpretability is a desired property for deep networks to become powerful tools in other research fields, e.g., drug discovery and genomics. In this survey, we conduct a comprehensive review of the neural network interpretability research. We first clarify the definition of interpretability as it has been used in many different contexts. Then we elaborate on the importance of interpretability and propose a novel taxonomy organized along three dimensions: type of engagement (passive vs. active interpretation approaches), the type of explanation, and the focus (from local to global interpretability). This taxonomy provides a meaningful 3D view of distribution of papers from the relevant literature as two of the dimensions are not simply categorical but allow ordinal subcategories. Finally, we summarize the existing interpretability evaluation methods and suggest possible research directions inspired by our new taxonomy.

As 2020 draws to a close we look back on some of the notable research developments, awards, conferences and policy in the world of artificial intelligence. In February it was reported that MIT researchers had used a machine-learning algorithm to identify a powerful new antibiotic compound. In laboratory tests, the drug, called halicin, killed many disease-causing bacteria, including some strains that had been resistant to all existing antibiotics. Progress in the field of AI in healthcare has continued apace during 2020. A lot of this work is about providing clinicians with extra tools in their armoury.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Lin Qi, an executive producer on "Game of Thrones" creators David Benioff and D.B. Weiss' upcoming Netflix series, died at age 39 after being poisoned. Lin was the chairman and CEO of Yoozoo Group, which he founded in 2009. The company was working with the TV creators on an adaptation of a science fiction series based on "The Three-Body Problem" trilogy of novels by Chinese author Liu Cixin.