The Transfer Elastic Net is an estimation method for linear regression models that combines $\ell_1$ and $\ell_2$ norm penalties to facilitate knowledge transfer. In this study, we derive a non-asymptotic $\ell_2$ norm estimation error bound for the estimator and discuss scenarios where the Transfer Elastic Net effectively works. Furthermore, we examine situations where it exhibits the grouping effect, which states that the estimates corresponding to highly correlated predictors have a small difference.

Explainability in deep networks has gained increased importance in recent years. We argue herein that an AI must be tasked not just with a task but also with an explanation of why said task was accomplished as such. We present a basic framework--Task and Explanation Network (TENet)--which fully integrates task completion and its explanation. We believe that the field of AI as a whole should insist--quite emphatically--on explainability. With the meteoric rise of AI over the past decade, and in particular deep learning, an issue that has been gaining more and more traction is that of explainability.

These notions vary across conceptualization Intersectionality is a critical framework that, through inquiry and (e.g., group, individual fairness [8]) and operationalization (e.g., praxis, allows us to examine how social inequalities persist through pre/in/post-processing [2]) [54]; nevertheless, the literature generally domains of structure and discipline. Given AI fairness' raison d'être agrees on the goal of minimizing negative outcomes across of "fairness," we argue that adopting intersectionality as an analytical demographic groups, including groups associated with multiple, framework is pivotal to effectively operationalizing fairness. "intersectional" demographic attributes (e.g., Black women) [92]. Through a critical review of how intersectionality is discussed in However, Kong [66] observes that AI fairness papers often narrowly 30 papers from the AI fairness literature, we deductively and inductively: interpret intersectional subgroup fairness as intersectionality, the 1) map how intersectionality tenets operate within the critical framework from which the term originates [29, 67]. This AI fairness paradigm and 2) uncover gaps between the conceptualization myopic conceptualization of intersectionality has non-trivial consequences and operationalization of intersectionality. We find that for just AI design and epistemology (i.e., ways of knowing).

In the final chapter of Plagiarism in Higher Education: Tackling Tough Topics in Academic Integrity (2021) I contemplate the future of plagiarism and academic integrity. I introduced the idea of life in a postplagiarism world; thinking about the impact of artificial intelligence on writing. Here, I expand on those ideas. Hybrid writing, co-created by human and artificial intelligence together is becoming prevalent. Soon it will be the norm.

In this paper, we tackle the challenging problem of Few-shot Object Detection. Existing FSOD pipelines (i) use average-pooled representations that result in information loss; and/or (ii) discard position information that can help detect object instances. Consequently, such pipelines are sensitive to large intra-class appearance and geometric variations between support and query images. To address these drawbacks, we propose a Time-rEversed diffusioN tEnsor Transformer (TENET), which i) forms high-order tensor representations that capture multi-way feature occurrences that are highly discriminative, and ii) uses a transformer that dynamically extracts correlations between the query image and the entire support set, instead of a single average-pooled support embedding. We also propose a Transformer Relation Head (TRH), equipped with higher-order representations, which encodes correlations between query regions and the entire support set, while being sensitive to the positional variability of object instances. Our model achieves state-of-the-art results on PASCAL VOC, FSOD, and COCO.

In early December, Cogito published some new research designed to capture consumers' understanding of artificial intelligence (AI), their overall perception and utilization of it, and any apprehensions they had with their utilization of it related to data privacy and regulation. While the study found that most consumers don't think that AI is a threat to jobs and can help make the lives of employees easier, they expressed a lingering mistrust surrounding brands' use of their data, privacy and the overall use of AI. In fact, of the consumers surveyed, 72% said that they had concerns about data privacy and what AI-enabled tools are tracking. That number represents a significant trust gap. But, what should companies be doing in the face of that level of concern?

Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this work, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENET uses temporal convolutional neural networks with an integrated attention mechanism to detect anomalous attack patterns. TENET is able to achieve an improvement of 32.70% in False Negative Rate, 19.14% in the Mathews Correlation Coefficient, and 17.25% in the ROC-AUC metric, with 94.62% fewer model parameters, 86.95% decrease in memory footprint, and 48.14% lower inference time when compared to the best performing prior work on automotive anomaly detection.

The Joint Artificial Intelligence Center will lead implementation of responsible AI across the Defense Department, according to a new directive. In a departmentwide memo signed last week, Deputy Defense Secretary Kathleen Hicks enumerated foundational tenets for responsible AI, reaffirmed the ethical AI principles the department adopted last year, and mandated the JAIC director start work on four activities for developing a responsible AI ecosystem. "As the DoD embraces artificial intelligence (AI), it is imperative that we adopt responsible behavior, processes, and outcomes in a manner that reflects the Department's commitment to its ethical principles, including the protection of privacy and civil liberties," Hicks said in the memo, which was announced June 1. "A trusted ecosystem not only enhances our military capabilities, but also builds confidence with end-users, warfighters, and the American public." Hicks assigned the JAIC director to coordinate responsible AI through a working council, which must in turn hammer out a strategy and implementation pathway, create a talent management framework, and report on how responsible AI can be integrated into acquisitions.

Time travel has been a beloved science-fiction idea at least since H.G. Wells wrote The Time Machine in 1895. The concept continues to fascinate and fictional approaches keep coming, prodding us to wonder whether time travel is physically possible and, for that matter, makes logical sense in the face of its inscrutable paradoxes. Remarkably, last year saw both a science-fiction film that illuminates these questions, and a real scientific result, spelled out in the journal, Classical and Quantum Gravity,1 that may point to answers. The film is writer-director Christopher Nolan's attention-getting Tenet. Like other time travel stories, Tenet uses a time machine.

As machine learning disrupts more and more industries, it has demonstrated its potential to reduce time spent by employees on manual tasks. However, training machine learning models can take months to achieve, creating excessive costs. With this in mind, AWS vice-president of machine learning, Swami Sivasubramanian used his keynote speech at AWS re:Invent to announce new tools that aim to speed up operations and save costs. Sivasubramanian went through five tenets for machine learning that AWS observes, which acted as vessels for further explanations of use cases for the new tools. Firstly, Sivasubramanian explained the importance of providing firm foundations, vital for freedom of creativity.