Different routes, same goal? Whatever the AI endgame is, #SnorkelFlow’s purpose is to find the best and quickest way for your company to get there. @Forbes digs into the different approaches that some massive corporations are taking to train AI systems:
DATA
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AI ethics review board addresses civil engineer privacy concerns
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AI raises new ethical issues for civil engineers. In this piece, @StanfordWoods senior fellow Sarah Billington discusses how @StanfordHAI
's ethics and society review board helped her team address privacy concerns in their data collection. https://
bit.ly/4hpxa7m -
LastExam: Paper, Dataset, and Code Release
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Paper, dataset, and code: http://
lastexam.ai
NYT article: -
How Businesses Can Practice Responsible AI Transformation
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How Businesses Can Practice Responsible AI Transformation
#AI #AIio #BigData #ML #NLU #Futureofwork @Kevin_Jackson @sallyeaves @schmarzo @kdnuggets @akwyz @BrettKing @TechNative @MiaD http://
ow.ly/bS1y30sJast -
Google Announces Parfait: Private Aggregation & Federated Learning
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Announcing Parfait — private aggregation & retrieval, federated, analytics, inference, & training — a GitHub organization that we’ve developed to demonstrate our state-of-the-art methods across 4 privacy pillars. Learn how it was created & is being used →
https://
goo.gle/4axVNfN -
Making Data Freely Available to LLMs for Training
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Explicitly and eagerly available to LLMs, for free, for training and RAG, and start the movement.
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Data Augmentation vs Generative Models: Key Differences Explained
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yes, a kind of flavor of it. but also not quite. in traditional data augmentation (say image/cnn contexts) you add noise to the data, rescale it etc. here, the difference is that you actually generate that data. in image/cnn land that would be using a diffusion model to generate
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CAG vs RAG vs Hybrid: Choosing the Right Data Approach
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So, Which One Should You Use?
• CAG: Perfect for smaller, static datasets where speed and simplicity matter most.
• RAG: The go-to for large, dynamic datasets where real-time retrieval is a must.
• Hybrid: Why not combine both? Use CAG for frequent queries and RAG for edge -
Context-Augmented Generation: Memory and Scalability Limitations
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That said, CAG has downsides:
• You need lots of memory as you fill the context window at every query.
• You cannot upload your whole million-rows dataset. It must be smaller than the current model's context window (e.g. 128k, 200k… tokens).
• You must host the model -
Privacy And Trust In The AI Age
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Privacy And Trust In The AI Age
#AI #AIio #BigData #ML #NLU #Futureofwork @oluskayacan @richardkimphd @MariaFariello @johnpearcenews5 @ResurgentAV @rohanmuralee @globaliqx http://
ow.ly/Om4K30sJash