🟠🟣 Edge#132: WhyLabs, AI Observability as a Service The team believes in opening up and giving access to this technology to every practitioner
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🎙 Paroma Varma/Snorkel on programmatic approaches to data labelingand how to bridge the gap between labeling and AI application development
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✍🏽 Edge#131: Self-Supervised Learning for LanguagePlus XLM-R and Facebook's fastText
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🧠🧠🧠 The Thousand Brains Theory, A New AI Book You Must Read The Scope covers the most relevant ML papers, real-world ML use cases, cool tech releases, and $ in AI. Weekly
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📝 Guest post: Data Aggregation is Unavoidable! (And Other Big Data Lies)and the Resulting Ugly Truths!
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🧙🏻♂️ Edge#130: The ML Engineering Magic Behind OpenAI Codex10 Remarkable ML Engineering Facts About Codex
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🐹 Edge#129: Self-Supervised Learning as Non-Contrastive Learning Non-contrastive learning becomes one of the hottest topics in SSL
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🌋 The Biggest Problems in ML SafetyThe Scope covers the most relevant ML papers, real-world ML use cases, cool tech releases, and $ in AI. Weekly
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📌 Join us free for TransformX on Oct 6-7We’re excited to partner with Scale AI on TransformX Conference that explores the shift from research to reality within AI and ML. Join us free for acc…
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🤖 Edge#128: Wu Dao – the Biggest Transformer Model in History What’s New in AI, a deep dive into one of the freshest research papers or technology frameworks that is worth your attention. Our goal is to keep you u…
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🎙 Judah Phillips / Squark about No-Code Predictive AnalyticsChallenging field of predictive analytics and the future of data science
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🐼 Edge#127: How Contrastive Learning Influences Self-Supervised Learning methods Contrastive learning is considered one of the dominant forms of SSL methods
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