Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology

O’Brien, John; Nelson, Cassidy

doi:10.1089/hs.2019.0122

Cited by 19 publications

(15 citation statements)

References 36 publications

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“…However, this line of research can raise questions about possible dual-use. O’Brien and Nelson ( 77 ) suggested that while the intended purpose of pathogenicity potential prediction is to mitigate biosecurity threats, it could actually enable designing new pathogens to cause maximal harm. The importance of this concern is difficult to overstate and it must be addressed.…”

Section: Discussionmentioning

confidence: 99%

Interpretable detection of novel human viruses from genome sequencing data

Bartoszewicz

Seidel

Renard

2021

NAR Genomics and Bioinformatics

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Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.

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Section: Discussionmentioning

confidence: 99%

Interpretable detection of novel human viruses from genome sequencing data

Bartoszewicz

Seidel

Renard

2021

NAR Genomics and Bioinformatics

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show abstract

“…However, this line of research can raise questions about possible dual-use. O’Brien and Nelson (2020) suggested that while the intended purpose of pathogenicity potential prediction is to mitigate biosecurity threats, it could actually enable designing new pathogens to cause maximal harm. The importance of this concern is difficult to overstate and it must be addressed.…”

Section: Discussionmentioning

confidence: 99%

Interpretable detection of novel human viruses from genome sequencing data

Bartoszewicz

Seidel

Renard

2020

Preprint

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AbstractViruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect virulence-related genes in novel agents, for example the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020.

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“…Technical details about the problems noted in AI software development, validation, and interpretability were not discussed. The risks of cyber attacks on AI systems, including ML vulnerability to adversarial attacks [ 190 , 191 ], and the difficulty in detecting and tracing software errors were not mentioned [ 165 ]. Approaches to select appropriate tasks for AI in medicine, enhance integration of AI tools into EHR and other connected hospital systems, improve data quality, or for ongoing safety monitoring were not discussed.…”

Section: Discussionmentioning

confidence: 99%

Expectations for Artificial Intelligence (AI) in Psychiatry

Monteith

Glenn²,

Geddes

et al. 2022

Curr Psychiatry Rep

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Purpose of Review Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine. Recent Findings For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. Summary The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.

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Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology

Cited by 19 publications

References 36 publications

Interpretable detection of novel human viruses from genome sequencing data

Interpretable detection of novel human viruses from genome sequencing data

Interpretable detection of novel human viruses from genome sequencing data

Expectations for Artificial Intelligence (AI) in Psychiatry

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