The voice of reason, finally… The #iclr2023 ethics committee responded.
ETHICS
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Open Reviewing Benefits Exposed at ICLR2023 Conference
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This #ICLR2023 reviewing process has really shown the benefits of open reviewing. Bad author and reviewer behaviours have been exposed. Flawed papers have been debunked by non-reviewers. And the science is democratized, for everyone to see, from day 1.
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Digital Transformation and GXI Research Session with Equinix
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Join Us! A #data & #experience rich session today!
With @Arquelle_Shaw @Equinix @CTOAdvisor & me 🙂
Everything #DigitalTransformation + new #GXIreport #research to empower change! https://
bit.ly/EquinixLive #data #Sustainability #DEI #partner #SDGs #AI #research #ESG #Security -
Causal Theories as Inductive Biases: Benefits and Questions
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Of course, but the question is: how have causal theories to date benefited from being viewed as inductive biases?
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Important Life Advice for Technology Enthusiasts
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neither, just life advice i think is important
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Complete Data Includes Mental States and Counterfactual Questions
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That's because the data is incomplete. Complete data includes the state of the actors' minds, and allows you to correctly answer counterfactual questions (which just correspond to different states of the world).
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Human Intelligence Synergy with Artificial Intelligence
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Belonging has always been a fundamental driver of humankind. Our future will lie not in making 'artificial intelligences' more intelligent, but in making our human intelligences more intelligent, in synergy with all the other new forms …
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Time and Hard Work: Keys to Success in Tech
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We all face the same issue… All we need is time and hard work #inspiration
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Malicious Use of AI: Forecasting, Prevention, and Mitigation
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The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation Brundage and Avin et al. https://
img1.wsimg.com/blobby/go/3d82
daa4-97fe-4096-9c6b-376b92c619de/downloads/MaliciousUseofAI.pdf?ver=1553030594217
… #ArtificialIntelligence #DeepLearning #MachineLearning -
Machine Learning Model for Soldier Casualty Prediction from Shot Data
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Easy. The data is vectors of "shot/not shot" (one input variable per soldier) and "dead/alive" (output). Any learner that induces that num. shots > 0 => dead answers the question correctly.