The Data Science Design Manual: http://
amzn.to/45lXMTn
——————
#DataScience #DataScientist #Analytics #AI #ML #MachineLearning #DataViz #DataStorytelling #DataLiteracy
DATA
-
Leveraging Diverse Feedback Sources for Data Flywheel Growth in AI
By
–
-
Databricks Zerobus Ingest Simplifies Real-Time Data Streaming
By
–
Real-time data streaming has always required complicated architectures and weeks of development work to get right. Databricks Zerobus Ingest is designed to change that. @CRN
's @RickWhiting1 spoke with Senior Director of Product Management Bilal Aslam about the general -
New synthetic data study impacts frontier AI research
By
–
My AI agents say: "The most comprehensive synthetic data study ever published. Every frontier lab will reference this."
-
DistDF: Fixing MSE Flaws in Time-Series Forecasting
By
–
Is your time-series model failing to capture the real rhythm of your data? Researchers from Xiaohongshu, Peking University, and Zhejiang University have developed DistDF to fix a fundamental flaw in forecasting. Standard models often rely on Mean Squared Error, which becomes
-
Microsoft’s Project Silica: 5TB Glass Storage Lasts 10,000 Years
By
–
Microsoft found out how to etch 5 terabytes of data into a piece of glass …and it survives for 10,000 years! > Project Silica is a storage system that uses ultrafast lasers to write data directly into glass. > Each laser pulse lasts one trillionth of a second and carves microscopic structures called voxels into 301 layers inside ordinary glass (the same kind your food container is probably made out of). > To read the data back, a microscope captures images of each layer, and an AI image recognition model decodes the patterns with zero errors. But what's wild is the glass needs NO power to maintain its data. It's immune to heat, water, radiation, and magnetic fields that would normally wipe conventional hard drives over the years. Accelerated aging tests predict the data will survive past 10,000 years at room temperature, twice as long as the oldest known writing in the world. For context, today's cloud archives burn enormous amounts of energy just keeping data alive on degrading magnetic tape. Glass storage needs none of that.
→ View original post on X — @rowancheung, 2026-03-08 17:29 UTC
-
Utility of AI Consumable Traces
By
–
Claude Shannon Helped Kick-start Machine Learning — “Shannon didn’t bother predicting the future—he just went ahead and invented it”: https://
spectrum.ieee.org/claude-shannon
-information-theory
…
———
#InformationTheory #AI #DataScience #Mathematics #ML
—+—
See this award-winning book: http://
amzn.to/3L1yQES -
People Still Underestimate the Importance of Observability
By
–
Analytical Skills for AI and Data Science — Building Skills for an AI-Driven Enterprise: https://
amzn.to/4b6fufu
…helps practitioners to create value from AI and data science using an analytical skillset — each chapter illustrates how each skill works across a collection of use -
AI optimize their algorithmic strategies
By
–
AI agents are algorithmically driven to produce results, so they naturally and logically seek the best strategies to achieve them—it's so obvious.
-
Observability and Feedback for Agent Improvement Loops
By
–
Data for All : http://
amzn.to/3On9vr7 via @ManningBooks ————
#Analytics #DataScience #DataLiteracy #DataScientist #CDO #CAIO #CMO -
53 Slides on Post-Training Algorithms and Data Quality
By
–
I'm releasing 53 slides on post-training, covering core algorithms like DPO and GRPO, as well as data quality, synthetic data pipelines, and on-policy training. I had the pleasure of presenting it yesterday as a guest lecturer in Cambridge, UK
→ View original post on X — @maximelabonne, 2026-03-06 10:45 UTC