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The development of neural techniques has opened up new avenues for research in machine translation. Today, neural machine translation (NMT) systems can leverage highly multilingual capacities and even perform zero-shot translation, delivering promising results in terms of language coverage and quality. However, scaling quality NMT requires large volumes of parallel bilingual data, which are not equally available for the 7,000+ languages in the world 1 . Focusing on improving the translation qualities of a relatively small group of high-resource languages comes at the expense of directing research attention to low-resource languages, exacerbating digital inequities in the long run. To break this pattern, here we introduce No Language Left Behind—a single massively multilingual model that leverages transfer learning across languages. We developed a conditional computational model based on the Sparsely Gated Mixture of Experts architecture 2 – 7 , which we trained on data obtained with new mining techniques tailored for low-resource languages. Furthermore, we devised multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. We evaluated the performance of our model over 40,000 translation directions using tools created specifically for this purpose—an automatic benchmark (FLORES-200), a human evaluation metric (XSTS) and a toxicity detector that covers every language in our model. Compared with the previous state-of-the-art models, our model achieves an average of 44% improvement in translation quality as measured by BLEU. By demonstrating how to scale NMT to 200 languages and making all contributions in this effort freely available for non-commercial use, our work lays important groundwork for the development of a universal translation system. Scaling neural machine translation to 200 languages is achieved by No Language Left Behind, a single massively multilingual model that leverages transfer learning across languages.

The human reference genome is the most widely used resource in human genetics and is due for a major update. Its current structure is a linear composite of merged haplotypes from more than 20 people, with a single individual comprising most of the sequence. It contains biases and errors within a framework that does not represent global human genomic variation. A high-quality reference with global representation of common variants, including single-nucleotide variants, structural variants and functional elements, is needed. The Human Pangenome Reference Consortium aims to create a more sophisticated and complete human reference genome with a graph-based, telomere-to-telomere representation of global genomic diversity. Here we leverage innovations in technology, study design and global partnerships with the goal of constructing the highest-possible quality human pangenome reference. Our goal is to improve data representation and streamline analyses to enable routine assembly of complete diploid genomes. With attention to ethical frameworks, the human pangenome reference will contain a more accurate and diverse representation of global genomic variation, improve gene–disease association studies across populations, expand the scope of genomics research to the most repetitive and polymorphic regions of the genome, and serve as the ultimate genetic resource for future biomedical research and precision medicine. The Human Pangenome Reference Consortium aims to offer the highest quality and most complete human pangenome reference that provides diverse genomic representation across human populations.

Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good. The AI for Social Good movement aims to apply AI/ML tools to help in delivering on the United Nations’ sustainable development goals (SDGs). Here, the authors identify the challenges and propose guidelines for designing and implementing successful partnerships between AI researchers and application - domain experts.

Although most patients with chronic pain are women, the preclinical literature regarding pain processing and the pathophysiology of chronic pain has historically been derived overwhelmingly from the study of male rodents. This Review describes how the recent adoption by a number of funding agencies of policies mandating the incorporation of sex as a biological variable into preclinical research has correlated with an increase in the number of studies investigating sex differences in pain and analgesia. Trends in the field are analysed, with a focus on newly published findings of qualitative sex differences: that is, those findings that are suggestive of differential processing mechanisms in each sex. It is becoming increasingly clear that robust differences exist in the genetic, molecular, cellular and systems-level mechanisms of acute and chronic pain processing in male and female rodents and humans.Historically, preclinical pain research has been dominated by studies in male subjects. Jeffrey Mogil describes recent trends towards the inclusion of male and female subjects in research and the subsequent identification of qualitative sex differences in the mechanisms of pain processing.

Participatory action research (PAR) is an approach to research that prioritizes the value of experiential knowledge for tackling problems caused by unequal and harmful social systems, and for envisioning and implementing alternatives. PAR involves the participation and leadership of those people experiencing issues, who take action to produce emancipatory social change, through conducting systematic research to generate new knowledge. This Primer sets out key considerations for the design of a PAR project. The core of the Primer introduces six building blocks for PAR project design: building relationships; establishing working practices; establishing a common understanding of the issue; observing, gathering and generating materials; collaborative analysis; and planning and taking action. We discuss key challenges faced by PAR projects, namely, mismatches with institutional research infrastructure; risks of co-option; power inequalities; and the decentralizing of control. To counter such challenges, PAR researchers may build PAR-friendly networks of people and infrastructures; cultivate a critical community to hold them to account; use critical reflexivity; redistribute powers; and learn to trust the process. PAR’s societal contribution and methodological development, we argue, can best be advanced by engaging with contemporary social movements that demand the redressing of inequities and the recognition of situated expertise. Participatory action research (PAR) involves the participation and leadership of people experiencing issues, who take action to produce emancipatory social change, through conducting systematic research to generate new knowledge. In this Primer, Cornish et al. set out key considerations for the design of a PAR project and discuss ways to overcome the challenges faced by PAR projects.

Patient-reported outcomes are increasingly collected in clinical trials and in routine clinical practice, but strategies must be taken to include underserved groups to avoid increasing health disparities.

As big data, open data, and open science advance to increase access to complex and large datasets for innovation, discovery, and decision-making, Indigenous Peoples’ rights to control and access their data within these data environments remain limited. Operationalizing the FAIR Principles for scientific data with the CARE Principles for Indigenous Data Governance enhances machine actionability and brings people and purpose to the fore to resolve Indigenous Peoples’ rights to and interests in their data across the data lifecycle.

Integration of genomic technology into healthcare settings establishes new capabilities to predict disease susceptibility and optimize treatment regimes. Yet, Indigenous peoples remain starkly underrepresented in genetic and clinical health research and are unlikely to benefit from such efforts. To foster collaboration with Indigenous communities, we propose six principles for ethical engagement in genomic research: understand existing regulations, foster collaboration, build cultural competency, improve research transparency, support capacity building, and disseminate research findings. Inclusion of underrepresented communities in genomic research has the potential to expand our understanding of genomic influences on health and improve clinical approaches for all populations. Indigenous peoples are still underrepresented in genetic research. Here, the authors propose an ethical framework consisting of six major principles that encourages researchers and Indigenous communities to build strong and equal partnerships to increase trust, engagement and diversity in genomic studies.

Natural language computer applications are becoming increasingly sophisticated and, with the recent release of Generative Pre-trained Transformer 3, they could be deployed in healthcare-related contexts that have historically comprised human-to-human interaction. However, for GPT-3 and similar applications to be considered for use in health-related contexts, possibilities and pitfalls need thoughtful exploration. In this article, we briefly introduce some opportunities and cautions that would accompany advanced Natural Language Processing applications deployed in eHealth.

Starting with the launch of the Human Genome Project three decades ago, and continuing after its completion in 2003, genomics has progressively come to have a central and catalytic role in basic and translational research. In addition, studies increasingly demonstrate how genomic information can be effectively used in clinical care. In the future, the anticipated advances in technology development, biological insights, and clinical applications (among others) will lead to more widespread integration of genomics into almost all areas of biomedical research, the adoption of genomics into mainstream medical and public-health practices, and an increasing relevance of genomics for everyday life. On behalf of the research community, the National Human Genome Research Institute recently completed a multi-year process of strategic engagement to identify future research priorities and opportunities in human genomics, with an emphasis on health applications. Here we describe the highest-priority elements envisioned for the cutting-edge of human genomics going forward—that is, at ‘The Forefront of Genomics’. In this Perspective, authors from the National Human Genome Research Institute (NHGRI) present a vision for human genomics research for the coming decade.