Offline Reinforcement Learning with Implicit Q-Learning Kostrikov et al.: https://
arxiv.org/abs/2110.06169 #ArtificialIntelligence #DeepLearning #ReinforcementLearning
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Offline Reinforcement Learning with Implicit Q-Learning
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RPA Implementation: On-Premise vs Cloud-Native Efficiency
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Robotic Process Automation (RPA) software can be structured and implemented in various environments, including on-premise and cloud-native solutions. Let's see how these diverse configurations influence the overall efficiency of the system's operation. Microblog @antgrasso
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Google’s Med-PaLM 2 Advances Medical AI Technology
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Google’s Med-PaLM 2 to Be Most Advanced Medical AI: Google, one of the world’s leading technology companies, is taking a bold step into the realm of… #DataAnalytics #DataScience #DataDriven #BigData #Infrastructure #Blockchain #ArtificialIntelligence #IoT https://
analyticsvidhya.com/blog/2023/07/g
oogle-med-palm2-to-be-most-advanced-medical-ai/?utm_source=dlvr.it&utm_medium=twitter
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Technology adoption and business creation before public markets
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So, pre-1999 when RHT went public? Because as soon as there was adoption in sufficient quantity there has always been business.
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Code Interpreter Cheat Sheet for AI Development
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7. Code interpreter cheat sheet (by @aakashgupta
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Leadership Training for Employee Well-Being and Mental Health
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The Role of #Leadership: How Managers and Supervisors Can Promote Employee Well-Being Through Training https://
bit.ly/42N02zu #MentalHealth -
Starbucks Ordered to Rehire Memphis Workers Labor Dispute
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Starbucks Required to Rehire Memphis Workers #HRTech #SHRM #BigData https://
shrm.org/resourcesandto
ols/legal-and-compliance/employment-law/pages/starbucks-memphis-seven.aspx?utm_source=dlvr.it&utm_medium=twitter
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LLMs Unreliability Demands Regulatory Framework
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LLMs are wildly unreliable; that’s part of why they need to be regulated.
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Data Augmentation Techniques for Texture-based Model Training
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Since it's a domain-specific dataset, and in particular textures, you can do many augmentations to get more out of it… Training at 256×256, that's approximately 27,6k unique patches and with flip/rot augmentations about 221k! (Training random patches works even better.)
