In this paper--as a case study--we present a systematic study of gender bias in machine translation with Google Translate. We translated sentences containing names of occupations from Hungarian, a language with gender-neutral pronouns, into English. Our aim was to present a fair measure for bias by comparing the translations to an optimal non-biased translator. When assessing bias, we used the following reference points: (1) the distribution of men and women among occupations in both the source and the target language countries, as well as (2) the results of a Hungarian survey that examined if certain jobs are generally perceived as feminine or masculine. We also studied how expanding sentences with adjectives referring to occupations effect the gender of the translated pronouns. As a result, we found bias against both genders, but biased results against women are much more frequent. Translations are closer to our perception of occupations than to objective occupational statistics. Finally, occupations have a greater effect on translation than adjectives.

Anomaly detection consists in identifying, within a dataset, those samples that significantly differ from the majority of the data, representing the normal class. It has many practical applications, e.g. ranging from defective product detection in industrial systems to medical imaging. This paper focuses on image anomaly detection using a deep neural network with multiple pyramid levels to analyze the image features at different scales. We propose a network based on encoding-decoding scheme, using a standard convolutional autoencoders, trained on normal data only in order to build a model of normality. Anomalies can be detected by the inability of the network to reconstruct its input. Experimental results show a good accuracy on MNIST, FMNIST and the recent MVTec Anomaly Detection dataset

The company is committed to providing best-in-class RCM processes for compliance officers, risk officers, counsel, and regulators alike. This includes an expansion into G-20 regulatory coverage, enhanced workflows, advanced reporting, and expanding its partnerships with the industry's leading GRC's to facilitate accessibility. "Our platform is growing quickly, both in terms of the capabilities we offer, and our client base of compliance and risk professionals," said Kayvan Alikhani, CEO and co-founder of Compliance.ai. "The Series-A funding will accelerate expansion into international jurisdictions and expedite our mission of transforming compliance processes." Compliance.ai helps compliance professionals navigate the mounting and complex regulations.

A polar bear on melting ice: It's a favorite image of nature documentaries and charity ads alike, never failing to put you in the emotional dumps for a simple reason--it forces you to grapple with a changing world, a darker future. But that emotion is often temporary, replaced quickly by others, because its effects are not immediately or directly felt, explained Peter Schlosser, the vice president and vice provost of global futures at Arizona State University. Footage of houses on fire in California, Oregon, and Australia alarms us, but falls short of making us understand that our own home may be next. These "delusions of escape," in the words of science fiction author Kim Stanley Robinson, or "failures of imagination," in the words of Future Tense academic director Ed Finn, placate us into reactive, piecemeal, short-sighted decision-making. But storytelling lights the path forward, agreed Robinson, Finn, Schlosser, Future Tense fellow Alexandra Zapata Hojel, and Malka Older, also a sci-fi author.

Helping address these problems, however, is a wealth of data sets--containing atmospheric, environmental, oceanographic, and other information--that are mostly open and publicly available. This fortuitous combination of pressing challenges and plentiful data is leading to the increased use of data-driven approaches, including machine learning (ML) models, to solve Earth science problems. Machine learning, a type of artificial intelligence (AI) in which computers learn from data, has been applied in many domains of Earth science (Figure 1). In traditional Earth science modeling, researchers use a top-down approach based on our understanding of the physical world and the laws that govern it. This approach allows us to interpret model outputs, yet it can be limited by the sheer amount of computing power required to solve large problems and by the difficulty of finding patterns where we don't expect them.

As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely impacted by predicted outcomes (e.g., an applicant denied a loan) with "recourse" ---i.e., a description of how they can change their features to obtain a positive outcome. We propose a novel algorithm that leverages adversarial training and PAC confidence sets to learn models that theoretically guarantee recourse to affected individuals with high probability without sacrificing accuracy. To the best of our knowledge, our approach is the first to learn models for which recourses are guaranteed with high probability. Extensive experimentation with real world datasets spanning various applications including recidivism prediction, bail outcomes, and lending demonstrate the efficacy of the proposed framework.

Conversational Intelligence requires that a person engage on informational, personal and relational levels. Advances in Natural Language Understanding have helped recent chatbots succeed at dialog on the informational level. However, current techniques still lag for conversing with humans on a personal level and fully relating to them. The University of Michigan's submission to the Alexa Prize Grand Challenge 3, Audrey, is an open-domain conversational chat-bot that aims to engage customers on these levels through interest driven conversations guided by customers' personalities and emotions. Audrey is built from socially-aware models such as Emotion Detection and a Personal Understanding Module to grasp a deeper understanding of users' interests and desires. Our architecture interacts with customers using a hybrid approach balanced between knowledge-driven response generators and context-driven neural response generators to cater to all three levels of conversations. During the semi-finals period, we achieved an average cumulative rating of 3.25 on a 1-5 Likert scale.

Artificial intelligence, or AI, is a computer system which learns from the experiences it encounters. It can adjust on its own to new inputs, allowing it to perform tasks in a way that is similar to what a human would do. How we have defined AI over the years has changed, as have the tasks we've had these machines complete. As a term, artificial intelligence was defined in 1956. With increasing levels of data being processed, improved storage capabilities, and the development of advanced algorithms, AI can now mimic human reasoning.

This data/information is entered into one of my published books, “A Few Tech Definitions From A to Z…” I thought I might help as many as I can to understand what all those “techie speak” words, abbreviations, and symbols imply. To the student(s) who

eBrevia automates the contract review process by using machine learning technology. Our products, which leverage artificial intelligence research from Columbia University, are used by law firms and corporate legal departments to more efficiently, accurately, and cost effectively extract and summariz