What's one dataset that taught you more than any textbook ever could? For me: — messy healthcare billing data — multiple date formats — unclear labels It was a masterclass in defining the problem. #DataScience #MachineLearning #AI #RStats

I teach my students: Don’t confuse fitting a model with solving a problem. A good model tells you something useful. A great model tells you something actionable. The rest is just math. #DataScience #MachineLearning #AI #RStats

I’ll say it again: #RStats is still the best language for statistical modeling, especially in applied settings. Why? — Native support for inference — Elegant formulas — Visual diagnostics baked into the workflow #DataScience #MachineLearning

We’re now beyond GenAI as a novelty. The frontier: — Alignment with real tasks — Memory + context integration — Traceability The next wave isn’t smarter LLMs—it’s smarter interfaces. #AI #GenAI #LLM

Classiq and BQP Demonstrate Hybrid Quantum-Classical Breakthrough for Scalable Digital Twin and CFD Simulation with NVIDIA -- radicaldatascience.wordpress.com/2025/11/21/c... #quantum

You don’t need deep learning for every problem. You need: — Clean data — Clear framing — Honest evaluation Simplicity scales better than complexity that no one trusts. #DataScience #MachineLearning #AI #RStats

Editing my book has reminded me: We don't teach enough about model degradation over time. Even a perfect model will fade without maintenance. Model performance is not a destination—it’s a decaying asset. #AI #DataScience #MachineLearning #RStats

Don't miss all the latest new!! AI News Briefs BULLETIN BOARD for November 2025 -- radicaldatascience.wordpress.com/2025/11/17/a... #AI #DataScience #MachineLearning #GenAI #LLM

WOW! Bystro AI Launches “ChatGPT for your DNA” -- radicaldatascience.wordpress.com/2025/11/20/b... #AI #GenAI #LLM #DNA #genome

Students often ask: “Is this model good enough?” My reply: “For what?” AUC, precision, F1—none of them matter unless you know what decision you're informing. Always tie metrics to action. #DataScience #MachineLearning #AI #RStats

Generating some serious signal at #SC25! Building Huge, Affordable Vector Databases -- pliops.com/achieving-hu... #AI #LightningAI #VectorSearch #LLMinference #VectorDB #AIInfrastructure #Pliops

Is the Pliops × Zilliz Collaboration Compelling for the AI Industry? -- medium.com/@ddgutierrez... #AI #GenAI #LLM #RAG

The GenAI ecosystem is becoming modular: — Model providers — Vector DBs — Prompt routers — Toolchains In 2026, the competitive edge won’t be in building the model—it’ll be in owning the workflow. #AI #GenAI #LLM

Creating buzz at #SC25! Redefining KV-Cache Offload: How to enable cost-effective scaling for LLMs - pliops.com/how-pliops-l... #AI #GenAI #RAG #LLM

What’s the most underused function in base R that you still reach for? For me: table() — fast, simple, and always reveals something. #RStats #DataScience #MachineLearning #AI

BREAKING TODAY! Pliops Accelerates GenAI Data Access at SC25 and Announces Collaboration with Zilliz -- radicaldatascience.wordpress.com/2025/11/17/p... @zilliz-universe.bsky.social #AI #RAG #vectordatabase #SC25 #GenAI

Prime Data Centers and Lambda Partner to Power the Next Era of Superintelligence with AI-Optimized Infrastructure in Southern California -- radicaldatascience.wordpress.com/2025/11/14/p... #AI #DataCenters

I still use R when I care about: — statistical rigor — quick EDA — publication-ready graphics Its design reflects how statisticians think, not just how coders write. #DataScience #MachineLearning #AI #RStats

Great new title written by University of Michigan analyst Christopher Gardner makes complex data architecture accessible for students and early-career IT professionals. Reads like a toolkit. Explains “why” and “how” the of logical data management wrt today’s AI use cases. #AI @oreilly.bsky.social

GenAI makes it easy to generate. It doesn’t make it easy to be right. Fluency isn’t accuracy. Reliability comes from grounding, feedback loops, and deep system integration—not temperature settings. #AI #LLM #GenAI

Most “data science” jobs don’t fail at the modeling stage. They fail at the: — unclear objectives — ambiguous metrics — political friction Data doesn’t speak for itself. It needs a translator. #DataScience #MachineLearning #AI #RStats

BREAKING TODAY! Aryaka Announces Unified SASE as a Service 2.0, Evolving Platform for Era of AI and Hybrid Workforce -- radicaldatascience.wordpress.com/2025/11/12/a... #AI #Security #SASE

While editing the new edition of my book, I kept coming back to this: The real value isn’t in the model’s accuracy. It’s in the clarity of its reasoning, and the trustworthiness of its assumptions. #DataScience #MachineLearning #AI #RStats

BREAKING TODAY! Memgraph to Offer a Unique Toolkit for Non-Graph Users to Jumpstart Their Journey to Full GraphRAG AI Capability -- radicaldatascience.wordpress.com/2025/11/11/m... #AI #RAG #graphdatabase

Most ML failures aren’t technical. They’re: — Versioning issues — Poor stakeholder alignment — Lack of monitoring Data science ≠ Kaggle. It’s shipping, integrating, maintaining. #DataScience #MachineLearning #AI #RStats

November is well under way with news galore! AI News Briefs BULLETIN BOARD for November 2025 -- radicaldatascience.wordpress.com/2025/11/07/a... #AI #GenAI #LLM #DataScience #MachineLearning

aiOla unveils Drax, an open-source speech model with state-of-the-art accuracy and up to 5× faster than models from OpenAI and Alibaba -- radicaldatascience.wordpress.com/2025/11/07/a... #AI #OpenSource

LLMs don’t “understand” text. They predict the next token. The illusion of fluency can conceal failure modes: — Calibration — Attribution — Security Understanding how they work is critical to knowing when they fail. #AI #GenAI #LLM

What’s one model, concept, or technique you once struggled with—until one key example unlocked it? For me: — PCA (until I mapped it to variance in rotation) #DataScience #MachineLearning #RStats #SignalOverNoise

Anyscale Collaborates with Microsoft to Deliver AI-Native Computing on Azure -- radicaldatascience.wordpress.com/2025/11/06/a... @microsoft.com #AI #cloud #azure

I’ve taught dozens of intro data science courses. The hardest concept isn’t regression or classification. It’s thinking conditionally. — "Given X, then Y." — "Controlling for Z…" Statistical reasoning is harder than syntax. #DataScience #MachineLearning #Statistics #AI

BREAKING TODAY! Hitachi Vantara Unveils Hitachi iQ Studio to Simplify and Accelerate Agentic AI Development -- radicaldatascience.wordpress.com/2025/11/05/h... #AI @hitachivantara.bsky.social

The GenAI gold rush is shifting. It’s no longer about training frontier models—it's about: — Specialized inference stacks — Efficient fine-tuning — Enterprise guardrails Winning isn’t scale alone. It’s control. #AI #LLM #GenAI

BREAKING TODAY! Snowflake Intelligence Brings Agentic AI to the Enterprise -- radicaldatascience.wordpress.com/2025/11/04/s... #AI #AgenticAI

BREAKING TODAY! mimic robotics raises $16 million to deploy frontier physical AI across industries -- radicaldatascience.wordpress.com/2025/11/03/m... #AI #Robotics #PhysicalAI

R is still the most productive environment for exploratory data analysis. — Fast loading — Strong stat packages — Concise, readable syntax For slicing truth from noise in tabular data, #RStats remains unmatched. #AI #DataScience #MachineLearning

AND THAT'S A WRAP for AI News Briefs BULLETIN BOARD for October 2025 -- radicaldatascience.wordpress.com/2025/10/30/a... #AI #LLM #GenAI #DataScience #MachineLearning

While editing the 2nd edition of my book, I kept trimming code and adding more explanation. Good code is useful. But understanding why it works—and when it fails—is what creates good data scientists. #SignalOverNoise #DataScience #MachineLearning #RStats

App Orchid Launches New Conversational Analytics Agent With Continuous Semantic Enrichment for Benchmark-Breaking Accuracy -- radicaldatascience.wordpress.com/2025/10/30/a... #AI

The scariest thing about GenAI isn’t the hallucination rate. It’s the overconfidence in the outputs. A wrong answer said fluently is more dangerous than silence. Trust layers and human-in-the-loop systems are now table stakes. #AI #LLM #GenAI

BREAKING TODAY! Token Security Launches Token Research to Deliver Actionable Insights into Agentic AI Security Risks -- radicaldatascience.wordpress.com/2025/10/29/t... #AI #Security

What's one tool you love in R but struggle to replicate in Python? For me: ggplot2 still sets the gold standard for layered, expressive graphics. Even after years of using matplotlib + seaborn. #RStats #DataViz #DataScience #MachineLearning

Everyone’s watching LLM capabilities. But the real action? AI infrastructure. Foundry tools, vector DBs, fine-tuning stacks, and orchestration layers will define the next phase. The model is just the engine. The system wins the race. #AI #LLM #GenAI

Crusoe, the AI factory company, raising $1.375 billion at a valuation above $10 billion to power the future of AI infrastructure - radicaldatascience.wordpress.com/2025/10/28/c... @crusoeai.bsky.social #AI #cloud

When teaching regression, I always emphasize this: The model is only as honest as your assumptions. Check: — Linearity — Independence — Homoscedasticity — Normality Violating these? You’re interpreting fiction. #RStats #DataScience #MachineLearning #AI

Chipmind Launches from Stealth with $2.5m for Its AI Agents to Speed Chip Making -- radicaldatascience.wordpress.com/2025/10/27/c... #AI #AgenticAI

GenAI isn't replacing data scientists. It’s exposing which parts of our workflows were mechanical all along. Your future value? Critical thinking, domain insight, and narrative translation of data into decisions. That’s not automatable. #AI #LLm #GenAI #DataScience

NextSilicon Reveals New Processor Chip in Challenge to Intel, AMD -- radicaldatascience.wordpress.com/2025/10/24/n... @nextsilicon.com #AI #Hardware

GREAT to walk into my classroom to see all the copies of my book's recent new 2nd Edition in so many of my student's hands! -- amazon.com/Machine-Lear... #DataScience #MachineLearning #RStats @uclaextension.bsky.social

I still use R for many of my statistical workflows. Why? — Transparent model behavior — Robust packages like caret, ggplot2, dplyr — Clean inferential workflows #RStats is underrated for projects where explainability matters most. #DataScience #MachineLearning #AI