Feel smarter, retweet scholarly articles you haven’t read! https://
x.com/emollick/statu
/emollick/status/1589006910480842752
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RESEARCH
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Sharing Scholarly Articles Without Reading Them Undermines Credibility
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MIT Researcher Explains How AI Image Generators Work
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MIT researcher explains AI image generators: https://
bit.ly/3E47BYO -
AI Definition Debate: Cognilytica Explores Artificial Intelligence Meaning
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In this @Cognilytica #AIToday AI Glossary Series #podcast 'Artificial Intelligence' hosts @rschmelzer & @kath0134 share how there still is no commonly accepted definition for #artificialintelligence and Cognilytica’s definition for #AI. Full episode: https://
cognilytica.com/2022/11/04/ai-
today-podcast-ai-glossary-series-artificial-intelligence/?utm_source=dlvr.it&utm_medium=twitter
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Bay Area Robotics Symposium 2022 Video Now Available Online
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is now available from Friday's Bay Area Robotics Symposium (#BARS2022) with over 300 attendees, 20 faculty talks (6 mins each), 50 poster spotlights (1 min each). Major thanks to Mark Mueller of @UCBerkeley and @DorsaSadigh @Stanford for organizing: https://
youtu.be/rQDx_QSnoOw?t=
4810
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Best Books Learning Machine Learning Zero Hero
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The Best Books To Learn #MachineLearning From Zero To Hero http://
bit.ly/3cxlTDo #AI #DeepLearning #NeuralNetworks #TensorFlow #DataScience #Python #javascript #iot #Rstats #linux #ReactJS #serverless #BigData #Analytics #ML #100DaysofCode #womenwhocode #DigitalTransformation -
Large Language Models Demonstrate Self-Improvement Capabilities
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Large Language Models Can Self-Improve Huang et al.: https://
arxiv.org/abs/2210.11610 #ArtificialIntelligence #DeepLearning #MachineLearning -
Academic Review Process Rigor and Accountability Critique
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I learned more about the potential of overlap from your tweets than this metareview. That shows how lazy the metareview is. If the AC's gonna wield significant power to overturn three reviewers, they have to do more work than this. Read the paper and point out the exact overlap
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Team structure and ethical considerations in AI dataset creation
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I would say: smaller teams of full time people in a single org, simpler performance goals, more paid compute, no specific goals on ethical sourcing or social impact of the dataset creation process