Ethical machine learning is about practices and strategies for creating more ethical machine learning models. There are many highly publicized/documented examples of machine learning gone awry that show the importance of the need to address ethical machine learning. Some of the first steps to prevent bias in machine learning is awareness. You should take time to identify your team goals and establish fairness criteria that should be revisited over time. This fairness criteria then can be used to establish the minimum fairness criteria allowed in production.

In March 2018, an eerie portrait created by an artificial intelligence program sold at Christie's Auction House for almost half a million dollars. A few months later, a movie written and directed by an AI algorithm was released amid much hype. And this March, a record company signed an AI artist for the first time. Artificial creativity is the subject of the second episode of the Sleepwalkers podcast, an ongoing series exploring the implications of AI. Machine-made art has flourished in recent years, thanks to advances in AI, and some examples are both impressive and unnerving.

The context: The vast majority of Facebook's moderation is now done automatically by the company's machine-learning systems, reducing the amount of harrowing content its moderators have to review. In its latest community standards enforcement report, published earlier this month, the company claimed that 98% of terrorist videos and photos are removed before anyone has the chance to see them, let alone report them. So, what are we seeing here?: The company has been training its machine-learning systems to identify and label objects in videos--from the mundane, such as vases or people--to the dangerous, such as guns or knives. Facebook's AI uses two main approaches to look for dangerous content. One is to employ neural networks that look for features and behaviors of known objects and label them with varying percentages of confidence (as we can see in the video, above.)

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There's no question that artificial intelligence and data analytics are reshaping the resources sector. These new technologies bring new challenges in the way companies consider risk, adapt to new ways of working and the skills needed for the future. These issues were the topic of conversation at a business roundtable lunch hosted by professional services firm Accenture in its new Perth Innovation Hub this week as part of the Resources Technology Showcase program of events. Invited guests, including leading policymakers and industry heavyweights, heard former SAS commander and Mettle Global managing partner Ben Pronk speak about the need to take calculated risks in the battlefield. He explained how the resources industry could adopt that philosophy to take advantage of the fourth industrial revolution.

Junning Huang* 1, Sirui Xie* 2, Jiankai Sun 4, Qiurui Ma 3, Chunxiao Liu 1, Jianping Shi 1, Dahua Lin 4, Bolei Zhou 4 Abstract -- Classical autonomous driving systems are mod-ularized as a pipeline of perception, decision, planning, and control. The driving decision plays a central role in processing the observation from the perception as well as directing the execution of downstream planning and control modules. Commonly the decision module is designed to be rule-based and is difficult to learn from data. Recently end-to-end neural control policy has been proposed to replace this pipeline, given its generalization ability. However, it remains challenging to enforce physical or logical constraints on the decision to ensure driving safety and stability. In this work, we propose a hybrid framework for learning a decision module, which is agnostic to the mechanisms of perception, planning, and control modules. By imitating the low-level control behavior, it learns the high-level driving decisions while bypasses the ambiguous annotation of high-level driving decisions. We demonstrate that the simulation agents with a learned decision module can be generalized to various complex driving scenarios where the rule-based approach fails. Furthermore, it can generate driving behaviors that are smoother and safer than end-to-end neural policies ‡ .

We present a learning-based force-torque dynamics to achieve model-based control for contact-rich peg-in-hole task using force-only inputs. Learning the force-torque dynamics is challenging because of the ambiguity of the low-dimensional 6-d force signal and the requirement of excessive training data. To tackle these problems, we propose a multi-pose force-torque state representation, based on which a dynamics model is learned with the data generated in a sample-efficient offline fashion. In addition, by training the dynamics model with peg-and-holes of various shapes, scales, and elasticities, the model could quickly transfer to new peg-and-holes after a small number of trials. Extensive experiments show that our dynamics model could adapt to unseen peg-and-holes with 70% fewer samples required compared to learning from scratch. Along with the learned dynamics, model predictive control and model-based reinforcement learning policies achieve over 80% insertion success rate. Our video is available at https://youtu.be/ZAqldpVZgm4.

Unlike previous work that mostly focused on the algorithmic design of adversarial examples in terms of improving the success rate as an attacker, in this work we show an interpretation of such patches that can prevent the state-of-the-art face detectors from detecting the real faces. W e investigate a phenomenon: patches designed to suppress real face detection appear face-like. This phenomenon holds generally across different initialization, locations, scales of patches, backbones, and state-of-the-art face detection frameworks. W e propose new optimization-based approaches to automatic design of universal adversarial patches for varying goals of the attack, including scenarios in which true positives are suppressed without introducing false positives. Our proposed algorithms perform well on real-world datasets, deceiving state-of-the-art face detectors in terms of multiple precision/recall metrics and transferring between different detection frameworks. 1. Introduction Adversarial examples are a central object of study in computer vision [33], machine learning [39, 23], security [26], and other domains [13]. In computer vision and machine learning, study of adversarial examples serves as evidences of substantial discrepancy between human vision system and machine perception mechanism [30, 25, 2, 9]. In security, adversarial examples have raised major concerns on the vulnerability of machine learning systems to malicious attacks. The problem can be stated as modifying an image, subject to some constraints, so that learning system's response is drastically altered, e.g., changing the classifier or detector output from correct to incorrect. The constraints either come in the human-imperceptible form Equal contribution.

Architecture design has become a crucial component of successful deep learning. Recent progress in automatic neural architecture search (NAS) shows a lot of promise. However, discovered architectures often fail to generalize in the final evaluation. Architectures with a higher validation accuracy during the search phase may perform worse in the evaluation. Aiming to alleviate this common issue, we introduce sequential greedy architecture search (SGAS), an efficient method for neural architecture search. By dividing the search procedure into sub-problems, SGAS chooses and prunes candidate operations in a greedy fashion. We apply SGAS to search architectures for Convolutional Neural Networks (CNN) and Graph Convolutional Networks (GCN). Extensive experiments show that SGAS is able to find state-of-the-art architectures for tasks such as image classification, point cloud classification and node classification in protein-protein interaction graphs with minimal computational cost. Please visit https://sites.google.com/kaust.edu.sa/sgas for more information about SGAS.

Automatic dialog act recognition is a task that has been widely explored over the years. In recent works, most approaches to the task explored different deep neural network architectures to combine the representations of the words in a segment and generate a segment representation that provides cues for intention. In this study, we explore means to generate more informative segment representations, not only by exploring different network architectures, but also by considering different token representations, not only at the word level, but also at the character and functional levels. At the word level, in addition to the commonly used uncontextualized embeddings, we explore the use of contextualized representations, which are able to provide information concerning word sense and segment structure. Character-level tokenization is important to capture intention-related morphological aspects that cannot be captured at the word level. Finally, the functional level provides an abstraction from words, which shifts the focus to the structure of the segment. Additionally, we explore approaches to enrich the segment representation with context information from the history of the dialog, both in terms of the classifications of the surrounding segments and the turn-taking history. This kind of information has already been proved important for the disambiguation of dialog acts in previous studies. Nevertheless, we are able to capture additional information by considering a summary of the dialog history and a wider turn-taking context. By combining the best approaches at each step, we achieve performance results that surpass the previous state-of-the-art on generic dialog act recognition on both the Switchboard Dialog Act Corpus (SwDA) and the ICSI Meeting Recorder Dialog Act Corpus (MRDA), which are two of the most widely explored corpora for the task. Furthermore, by considering both past and future context, similarly to what happens in an annotation scenario, our approach achieves a performance similar to that of a human annotator on SwDA and surpasses it on MRDA.