14 outcomes from #AI initiatives in the enterprise >>> @Deloitte >>> @MikeQuindazzi >>> #ML #DataAnalytics #Robotics >>> Report: http://
shorturl.at/gyFP9
RESEARCH
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14 Enterprise AI Initiative Outcomes from Deloitte Report
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Google’s Mu2SLAM: Unified Model For Speech And Text Understanding
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Google’s Mu2SLAM: Toward a Single Model For All Speech and Text Understanding Tasks https://
syncedreview.com/2022/12/22/goo
gles-mu2slam-toward-a-single-model-for-all-speech-and-text-understanding-tasks/
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AGI Debate Twitter Space Montreal AI Discussion
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Maybe (Twitter Space) #AGI #AGIDebate #MontrealAI
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Data Science Accelerates Drug Approval Pipeline in Pharmaverse
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What’s the #Pharmaverse, and how do #datascience innovators use It to streamline drug approvals? Learn how @GSK did it to accelerate the drug approval pipeline in the Health and Life Sciences Data Science Innovator's Playbook. https://
domino.buzz/3VqygVb #SCE -
TikTok Audience Bias in AI Education Studies
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2) Obviously the TT audience will skew pro-tech, and these specific vids/viewers will skew toward those more familiar with AI, so it may not reflect true public perception. Larger studies should be done and should include all stakeholders in education (not just administrators).
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Opposite of Survivorship Bias: Understanding Selection Logic
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Opposite of survivorship bias tho "Survivorship bias or survival bias is the logical error of concentrating on entities that passed a selection process while overlooking those that did not. This can lead to incorrect conclusions because of incomplete data."
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Beyond Generative AI: Jay Alammar’s Foundation Model Summit Talk
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Does the term “Generative AI” really capture all that’s exciting in the world of AI these days? Join @JayAlammar as he answers this question in his presentation at the @SnorkelAI Foundation Model Virtual Summit on January 17! https://
short.cohere.ai/FMVS -
Mean Statistics Inadequate For Group Comparison Analysis
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Surely the mean is pretty much the least informative statistic for comparing those groups?
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Machine Learning Project: Low-pass Filter and PCA Analysis
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Full Machine Learning Project — Low-pass Filter & Principal Component Analysis (Part 5.1) #DataScience
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OPT-IML: Instruction-Tuned Models for Diverse Language Tasks
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For OPT-IML, we boosted performance of our OPT-175B work using instruction tuning, allowing us to adapt it for more diverse language applications (Ex. Answering Q’s, summarizing text & translation). A 30B parameter model is available today with a 175B model available soon.