Papers with Code is a free community-driven resource for machine learning (ML) papers and code that joined Facebook AI in December. Follow what is trending this week from the global research community. WIRED is where tomorrow is realized. If you’d like to learn more about this issue or have any comments for Gollnick or me, visit our show page to listen to the full podcast and join the discussion. You’ve … Hosted by: Yolanda Gil (Computer Science) and Neda Jahanshad (Neurology) Academic Health Leadership Training – Now Accepting Applications 2020-2021 Cohort, Ted Rogers Centre 2020 Heart Failure Symposium, COVID-19: Investigating a Viral Phenomenon. “Scientific progress depends on the ability of researchers to scrutinize the results of a study and reproduce the main finding to learn from,” says Dr. Benjamin Haibe-Kains, who is jointly appointed as Associate Professor in Medical Biophysics at the University of Toronto and affiliate at the Vector Institute for Artificial Intelligence. When Facebook attempted to replicate AlphaGo, the system developed by Alphabet’s DeepMind to master the ancient game of Go, the researchers appeared exhausted by the task. “Machine Learning: Living in the Age of AI,” examines the extraordinary ways in which people are interacting with AI today. “Researchers are more incentivized to publish their finding rather than spend time and resources ensuring their study can be replicated,” explains Haibe-Kains. “The foundation of the scientific method is that research results must be testable by others. The vast computational requirements—millions of experiments running on thousands of devices over days—combined with unavailable code, made the system “very difficult, if not impossible, to reproduce, study, improve upon, and extend,” they wrote in a paper published in May. Researchers are not able to learn how the model works and replicate it in a thoughtful way. Many researchers would still feel pressure to use more computers to stay at the cutting edge, and then tackle efficiency later. The authors voice their concern about the lack of transparency and reproducibility in AI research after “International Evaluation of an AI System for Breast Cancer Screening,” a study by Google Health’s Scott Mayer McKinney et al., published in Nature in January 2020, claimed an AI … In the article titled Transparency and reproducibility in artificial intelligence, the authors offer numerous frameworks and platforms that allow safe and effective sharing to uphold the three pillars of open science to make AI research more transparent and reproducible: sharing data, sharing computer code and sharing predictive models. The WIRED conversation illuminates how technology is changing every aspect of our lives—from culture to business, science to design. It’s time to address the reproducibility crisis in AI Recently I interviewed Clare Gollnick, CTO of Terbium Labs, on the reproducibility crisis in science and its implications for data scientists. Request PDF | On Oct 15, 2020, Benjamin Haibe-Kains and others published Transparency and reproducibility in artificial intelligence | Find, read and cite all the research you need on ResearchGate Artificial Intelligence Confronts a 'Reproducibility' Crisis Machine-learning systems are black boxes even to the researchers that build them. The breakthroughs and innovations that we uncover lead to new ways of thinking, new connections, and new industries. They call it “Show Your Work.”. A closer examination raised some concerns: the study lacked a sufficient description of the methods used, including their code and models. She is determined to nip study is beautiful,” says Haibe-Kains, “But if we can't learn from it then it has little to no scientific value.”. 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