Inappropriate design and deployment of machine learning (ML) systems leads to negative downstream social and ethical impact -- described here as social and ethical risks -- for users, society and the environment. Despite the growing need to regulate ML systems, current processes for assessing and mitigating risks are disjointed and inconsistent. We interviewed 30 industry practitioners on their current social and ethical risk management practices, and collected their first reactions on adapting safety engineering frameworks into their practice -- namely, System Theoretic Process Analysis (STPA) and Failure Mode and Effects Analysis (FMEA). Our findings suggest STPA/FMEA can provide appropriate structure toward social and ethical risk assessment and mitigation processes. However, we also find nontrivial challenges in integrating such frameworks in the fast-paced culture of the ML industry. We call on the ML research community to strengthen existing frameworks and assess their efficacy, ensuring that ML systems are safer for all people.

Heart rate variability is an indicator of heart health. Mohammad Moshawrab's research on this topic received the best paper award at the 19th International Conference on Mobile Systems and Persuasive Computing (MobiSPC), held August 9-11 in Niagara Falls, Canada. Like the other papers accepted by MobiSPC 2022, " Cardiovascular Events Prediction using Artificial Intelligence Models and Heart Rate Variability"is published by Elsevier Science in the online open access Procedia Computer Science series. Mohammad Moshawrab is a doctoral student in engineering at the Université du Québec à Rimouski (UQAR), which welcomes about 6,700 students each year, including nearly 600 international students from more than 45 countries. His doctorate in engineering aims to train specialists capable of designing and carrying out independently a research program to advance the state of knowledge in the engineering of physical systems and industrial processes.

Assistant Professor Jia Deng has been selected for a 2018 Sloan Research Fellowship by the Alfred P. Sloan Foundation for his work in computer vision and machine learning. Prof. Deng directs the Michigan Vision & Learning Lab. His research seeks to enable computers to see and think like humans. In 2015, Prof. Deng was awarded a Google Faculty Research Award for his work on large-scale image understanding. He aimed to advance image understanding in terms of recognizing the relationships present between multiple entities in images. The development of such an image understanding system would enable image retrieval for complex or arbitrary queries, such as "is there a person standing on a red chair and fixing the light?"

This paper proposes a fully decentralized federated learning (FL) scheme for Internet of Everything (IoE) devices that are connected via multi-hop networks. Because FL algorithms hardly converge the parameters of machine learning (ML) models, this paper focuses on the convergence of ML models in function spaces. Considering that the representative loss functions of ML tasks e.g, mean squared error (MSE) and Kullback-Leibler (KL) divergence, are convex functionals, algorithms that directly update functions in function spaces could converge to the optimal solution. The key concept of this paper is to tailor a consensus-based optimization algorithm to work in the function space and achieve the global optimum in a distributed manner. This paper first analyzes the convergence of the proposed algorithm in a function space, which is referred to as a meta-algorithm, and shows that the spectral graph theory can be applied to the function space in a manner similar to that of numerical vectors. Then, consensus-based multi-hop federated distillation (CMFD) is developed for a neural network (NN) to implement the meta-algorithm. CMFD leverages knowledge distillation to realize function aggregation among adjacent devices without parameter averaging. An advantage of CMFD is that it works even with different NN models among the distributed learners. Although CMFD does not perfectly reflect the behavior of the meta-algorithm, the discussion of the meta-algorithm's convergence property promotes an intuitive understanding of CMFD, and simulation evaluations show that NN models converge using CMFD for several tasks. The simulation results also show that CMFD achieves higher accuracy than parameter aggregation for weakly connected networks, and CMFD is more stable than parameter aggregation methods.

Young scientists want machine learning revolution in Africa Kudzai Mashininga 29 September 2022 Cameroon national Loic Elnathan Tiokou Fangang concluded his masters degree in mathematical sciences at the African Institute for Mathematical Sciences (AIMS) earlier in 2022 and, as he awaits an opportunity to pursue a PhD in machine learning, he believes the dream of the institute's founders – of producing the next Einstein – has already been accomplished. AIMS is a network of six centres of excellence, which are based in South Africa, Senegal, Ghana, Cameroon, Tanzania and Rwanda. Students who join the institute get to work on driving the continent's STEM (science, technology, engineering and mathematics) agenda. The founder of AIMS, South African physicist Neil Turok, in 2008 gave a speech in which he declared his wish that the next Einstein would be from Africa. In an interview with University World News, Fangang said that, each year, AIMS is producing African Einsteins as it invests in its students – and not just by equipping them with mathematical skills.

As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group por- trait, you can easily count (and name) all of the people in the picture and even guess at their emotions from their facial appearance. Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions1 to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Palmer 1999; Livingstone 2008).

Correctly labelling training data for AI models is vital to avoid serious problems, as is using sufficiently large datasets. However, manually labelling massive amounts of data is time-consuming and laborious. Using pre-labelled datasets can be problematic, as evidenced by MIT having to pull its 80 Million Tiny Images datasets. For those unaware, the popular dataset was found to contain thousands of racist and misogynistic labels that could have been used to train AI models. AI News caught up with Devang Sachdev, VP of Marketing at Snorkel AI, to find out how the company is easing the laborious process of labelling data in a safe and effective way. AI News: How is Snorkel helping to ease the laborious process of labelling data?

In 2022, Tony Hawk is a household name, skateboarding is an olympic sport and it's possible to master digital laser flips in any number of video games on TV. Early skate screen media consisted mostly of skeptical documentaries or whimsical California dreaming-style chronicles. Things changed when, in 1983, Stacy Peralta – who managed the ragtag team of skaters that Tony Hawk was a member of – effectively invented the modern skate video. Thanks to its performative nature, skateboarding would soon form a symbiotic relationship with the technology that showcased it. Peralta claims he hoped a few hundred copies of his first video might find their way into the new VHS players that were taking the US by storm.

Knowledge graphs represent factual knowledge about the world as relationships between concepts and are critical for intelligent decision making in enterprise applications. New knowledge is inferred from the existing facts in the knowledge graphs by encoding the concepts and relations into low-dimensional feature vector representations. The most effective representations for this task, called Knowledge Graph Embeddings (KGE), are learned through neural network architectures. Due to their impressive predictive performance, they are increasingly used in high-impact domains like healthcare, finance and education. However, are the black-box KGE models adversarially robust for use in domains with high stakes? This thesis argues that state-of-the-art KGE models are vulnerable to data poisoning attacks, that is, their predictive performance can be degraded by systematically crafted perturbations to the training knowledge graph. To support this argument, two novel data poisoning attacks are proposed that craft input deletions or additions at training time to subvert the learned model's performance at inference time. These adversarial attacks target the task of predicting the missing facts in knowledge graphs using KGE models, and the evaluation shows that the simpler attacks are competitive with or outperform the computationally expensive ones. The thesis contributions not only highlight and provide an opportunity to fix the security vulnerabilities of KGE models, but also help to understand the black-box predictive behaviour of KGE models.

Language models are increasingly attracting interest from writers. However, such models lack long-range semantic coherence, limiting their usefulness for longform creative writing. We address this limitation by applying language models hierarchically, in a system we call Dramatron. By building structural context via prompt chaining, Dramatron can generate coherent scripts and screenplays complete with title, characters, story beats, location descriptions, and dialogue. We illustrate Dramatron's usefulness as an interactive co-creative system with a user study of 15 theatre and film industry professionals. Participants co-wrote theatre scripts and screenplays with Dramatron and engaged in open-ended interviews. We report critical reflections both from our interviewees and from independent reviewers who watched stagings of the works to illustrate how both Dramatron and hierarchical text generation could be useful for human-machine co-creativity. Finally, we discuss the suitability of Dramatron for co-creativity, ethical considerations -- including plagiarism and bias -- and participatory models for the design and deployment of such tools.