Regression, the task of predicting a continuous scalar target y based on some features x is one of the most fundamental tasks in machine learning and statistics. It has been observed and theoretically analyzed that the classical approach, meansquared error minimization, can lead to suboptimal results when training neural networks. In this work, we propose a new method to improve the training of these models on regression tasks, with continuous scalar targets. Our method is based on casting this task in a different fashion, using a target encoder, and a prediction decoder, inspired by approaches in classification and clustering. We showcase the performance of our method on a wide range of real-world datasets.

The translation of gender-neutral person-referring terms (e.g., the students) is often non-trivial. Translating from English into German poses an interesting case -- in German, person-referring nouns are usually gender-specific, and if the gender of the referent(s) is unknown or diverse, the generic masculine (die Studenten (m.)) is commonly used. This solution, however, reduces the visibility of other genders, such as women and non-binary people. To counteract gender discrimination, a societal movement towards using gender-fair language exists (e.g., by adopting neosystems). However, gender-fair German is currently barely supported in machine translation (MT), requiring post-editing or manual translations. We address this research gap by studying gender-fair language in English-to-German MT. Concretely, we enrich a community-created gender-fair language dictionary and sample multi-sentence test instances from encyclopedic text and parliamentary speeches. Using these novel resources, we conduct the first benchmark study involving two commercial systems and six neural MT models for translating words in isolation and natural contexts across two domains. Our findings show that most systems produce mainly masculine forms and rarely gender-neutral variants, highlighting the need for future research. We release code and data at https://github.com/g8a9/building-bridges-gender-fair-german-mt.

In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. Across academia, industry, and government bodies, a variety of endeavours are being pursued towards enhancing AI ethics. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. These different perspectives are often not understood, due in part to communication gaps.For example, AI researchers who design and develop AI models are not necessarily aware of the instability induced in consumers' lives by the compounded effects of AI decisions. Educating different stakeholders about their roles and responsibilities in the broader context becomes necessary. In this position paper, we outline some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools for surfacing different perspectives. We hope to spark interdisciplinary discussions about computational creativity broadly as a tool for enhancing AI ethics.

With over 20 years of experience as a trusted training data source, Lionbridge AI helps businesses large and small build, test and improve machine learning models. Our community of qualified contributors is located across the globe and available 24/7, providing access to a huge volume of data across all languages and file types.

With the increasing ubiquity of artificial intelligence and machine learning applications, systems are emerging that require non-ML experts to interact with machine learning at the training step, not just the final system. These users may not have the skills, time, or inclination to familiarize themselves with the way machine learning works, so training systems must be developed that can communicate the necessary information and facilitate effortless collaboration with the user. We consider how to utilize techniques from qualitative coding, a human-centered approach for manual classification, and build better user experience for ML training.

To teach artificial intelligence to responsibly execute human goals, like driverless driving and automated loan decisions, there's a need to build bridges between human thought and language computers can understand, Stephen Wolfram, founder and CEO of Wolfram Research, explains in this week's episode of Breaking Banks.

The Opening Session - Wake-up Call for Patient-Centered Radiology The SIIM 2016 Opening General Session was presented by Rasu B. Shrestha, MD, MBA, chief innovation officer, University of Pittsburgh Medical Center and President, UPMC Enterprises. The talk took the top-down approach of putting medical imaging and radiology in the context of significant macro-level changes taking place in populations, technologies, and the health care system. These changes, that UPMC is capitalizing on, highlight growing opportunities for new care models, new technologies and patient-centered care. More than ever today, this health care transformation stresses the imperative to spur changes in imaging, both incremental as well as paradigm-changing, to move toward patient-centric radiology and value-based imaging. Having lived through a century of "analog" radiology, followed by several decades of "digital" radiology, the next phase that lies ahead of medical imaging will be the phase of interoperability, analytics, and population health; one where "Context is King."