BREAKING TODAY! Ardent AI Raises $2.15M to Build the First AI Data Engineer -- radicaldatascience.wordpress.com/2025/09/25/a... #AI #dataengineer

The AI Infra Summit 2025 happened earlier this month! A New Generation of AI Hardware Contenders Is Rising -- radicaldatascience.wordpress.com/2025/09/18/a... #AI #Hardware

As LLMs evolve, most companies won’t build their own models. They’ll build interfaces—tailored, vertical, context-aware. The frontier isn’t raw capability. It’s alignment with workflows. #AI #GenAI #LLM

Confidence intervals are not optional. If you're making claims without uncertainty estimates, you're storytelling—not science. Model outputs need error bars just like measurements do. #DataScience #MachineLearning #AI #RStats

Genstore, AI-native E-commerce Platform, Announces $10M Seed Round -- radicaldatascience.wordpress.com/2025/09/24/g... #AI #ecommerce

BREAKING TODAY! Hitachi agrees to acquire German data and AI services firm synvert to accelerate Agentic AI and Physical AI development, driving global expansion of HMAX business -- radicaldatascience.wordpress.com/2025/09/23/h... #AI

NEW UPDATES! Keeping a Pulse on DeepSeek -- radicaldatascience.wordpress.com/2025/06/16/k... #AI #GenAI #LLM #OpenSource #DeepSeek @deepseekai.bsky.social

What’s your personal definition of “done” in a data project? For me: — Model deployed — Results reproducible — Business decision informed Curious how others frame this across roles. #DataScience #MachineLearning #AI #RStats

Irregular Raises $80 Million to Set the Security Standards for Frontier AI -- radicaldatascience.wordpress.com/2025/09/19/i... #AI #security

I experienced my very first Waymo ride this morning. I only did it -after- consuming the company's latest research: waymo.com/research/ Great experience, although a huge city bus nearly made a right turn into the car! #AI #computervision @waymo.bsky.social

I tell students: You don’t need to memorize every ML algorithm. But you do need to understand: — loss functions — overfitting — regularization Master the “why,” not just the “how.” #DataScience #MachineLearning #Statistics #AI #RStats

ggplot2 4.0.0 is out! This is a substantial release meriting a new major version. Check out the many release details! www.tidyverse.org/blog/2025/09... #DataScience #Statistics #RStats #tidyverse

BREAKING TODAY! Intel and NVIDIA to Jointly Develop AI Infrastructure and Personal Computing Products -- radicaldatascience.wordpress.com/2025/09/18/i... #AI #hardware #GPU

Most companies don’t need deep learning. They need clean joins, honest metrics, and interpretable results. Startups often chase GPU clusters before fixing their CSVs. #DataScience #MachineLearning #AI #RStats

BREAKING TODAY! Bria Announces Series B Extension Backed by Bright Pixel Capital -- radicaldatascience.wordpress.com/2025/09/17/b... #AI

The real test of a model isn’t the test set. It’s: — deployment friction — stakeholder trust — long-term drift — decision impact Your pipeline is only as good as its weakest non-technical link. #DataScience #MachineLearning #AI #RStats

BREAKING TODAY! MongoDB Launches AI-Powered Application Modernization Platform to Reduce Technical Debt and Speed Innovation -- radicaldatascience.wordpress.com/2025/09/16/m... #AI #database #NoSQL @mongodb.bsky.social

BREAKING TODAY! Scalekit gets $5.5m as it launches authentication stack for AI agents -- radicaldatascience.wordpress.com/2025/09/16/s... #AI #AgenticAI

Working on the 2nd edition of my book reminds me: The fundamentals are still relevant. Statistics hasn’t been replaced. Linear regression still works. And clean code still beats clever code in the long run. #DataScience #MachineLearning #AI #RStats

ROUND-UP BREAKING TODAY! A New Generation of AI Hardware Contenders Is Rising -- radicaldatascience.wordpress.com/2025/09/15/a... #AI #GPU #hardware

BREAKING TODAY! The Summer of MLPerf -- radicaldatascience.wordpress.com/2025/09/11/t... #AI #LLM #GenAI #MachineLearning @mlcommons.org #MLPerf

As LLMs get bigger, so do the risks of hallucination, overconfidence, and abstraction from source truth. We're entering a phase where evaluation—not just generation—becomes the differentiator. Alignment is the product. #AI #LLM #GenAI

What’s your personal rule of thumb for calling a dataset “clean”? Mine: — no unknowns in the target — leakage ruled out — metadata validated — assumptions documented Would love to hear other practitioners’ heuristics. #DataScience #MachineLearning #AI #RStats

BREAKING TODAY! Sphinx Launches with $9.5M to Redefine How AI Works with Data -- radicaldatascience.wordpress.com/2025/09/10/s... #AI #Copilot

BREAKING TODAY! NVIDIA taps Flox to solve CUDA’s biggest pain point for open-source community -- radicaldatascience.wordpress.com/2025/09/10/n... @flox.dev #CUDA

ML success ≠ Kaggle leaderboard. The real world rewards: - Clear explanations - Thoughtful metrics - Collaboration with domain experts A 0.01 lift in F1 score won’t save you if no one understands your model. #DataSciene #MachineLearning #AI #RStats

I tell my UCLA students: “Don't just learn tools. Learn what problem the tool was designed to solve.” PCA isn’t magic. Neither is XGBoost. Context + constraints > syntax memorization. #DataScience #MachineLearning #AI #RStats

September is already HEATING UP! AI News Briefs BULLETIN BOARD for September 2025 -- radicaldatascience.wordpress.com/2025/09/01/a... #AI #DataScience #MachineLearning #LLM #GenAI

The more complex the model, the less likely it is to survive in production. Simple models win not because they’re “better,” but because they’re understandable, debuggable, and align with how humans reason. #DataScience #MachineLearning #AI

Most of your time as a data scientist won’t be spent “doing AI.” It’ll be: — chasing down missing data — clarifying goals — explaining results — fixing inputs Master these and the modeling takes care of itself. #DataScience #MachineLearning #AI #RStats

In a world flooded with dashboards and predictions, judgment is the scarce resource. The best data scientists don’t just build models—they help others make better decisions. That’s where the real value lies. #DataScience #MachineLearning #RStats

MY NEW 2nd EDITION BOOK IS OUT! It was a labor of love to get this done. Just in time for Fall Quarter at UCLA where I use it as the textbook for my Intro to Data Science class - www.amazon.com/Machine-Lear... #DataScience #MachineLearning #RStats @uclaextension

If you had to throw out every metric except one to measure your model, which would you keep—and why? Your answer says a lot about how you frame problems. I’d choose precision in high-risk domains. You? #DataScience #MachineLearning #AI #RStats

BREAKING TODAY! Uniphore Announces the Acquisitions of Orby AI and Autonom8 -- radicaldatascience.wordpress.com/2025/08/28/u... #AI

BREAKING TODAY! Salesforce AI Research Advances the Agentic Enterprise by Simulating Enterprise Environments, Measuring Agent Readiness, and Consolidating Duplicative Data -- radicaldatascience.wordpress.com/2025/08/27/s... #AI #AgenticAI

In AI, speed matters. But accuracy matters more. The companies that win aren’t just those who ship fast—they’re the ones who test assumptions, build trust, and design for change. Fast ≠ reckless. #AI #LLM #GenAI

The #1 misconception I see in beginner data science: correlation = causation. I teach it this way: “Correlation helps you find where to look. Causation tells you what to do.” Big difference. #DataScience #MachineLearning #AI #RStats

Early in my career, I focused on model performance. Now I focus on clarity. If a stakeholder can’t understand what a model does, they won’t trust it—no matter the AUC. Transparency is not a feature. It's a requirement. #DataScience #MachineLearning #AI #RStats

Most data problems are people problems in disguise: unclear definitions, conflicting goals, or lack of domain context. Before debugging your model, make sure everyone agrees on what “success” looks like. #AI #DataScience #MachineLearning #RStats

Refold AI emerges from stealth with $6.5M to kill the enterprise “integration tax” -- radicaldatascience.wordpress.com/2025/08/20/r... #AI #API

BREAKING TODAY! LambdaTest Unveils Platform to Test AI Agents: Introducing Agent-to-Agent Testing -- radicaldatascience.wordpress.com/2025/08/19/l... #AI #Testing

Exciting times in the AI world! AI News Briefs BULLETIN BOARD for August 2025 -- radicaldatascience.wordpress.com/2025/08/12/a... #AI #LLM #GenAI #DataScience #MachineLearning

I’ve worked in data science through four hype cycles—big data, Hadoop, deep learning, now generative AI. Each time, the core lesson holds: the best work is grounded in math, context, and integrity—not trends. #DataScience #MachineLearning #AI #DeepLearning #BigData

The next wave of data science won’t be about deeper models. It’ll be about better interfaces: explainability, human feedback loops, and systems that make sense to stakeholders. We don’t need more complexity. We need more clarity. #DataScience #AI #MachineLearning

“AI will replace data scientists” is a myth. Automation helps, but judgment, domain understanding, and statistical reasoning remain human strengths. Tools evolve. But critical thinking doesn’t go out of style. #AI #LLM #GenAI #DataScience #MachineLearning

NEW "Signal Over Noise" feature in AI News Briefs BULLETIN BOARD for August 2025 -- radicaldatascience.wordpress.com/2025/08/12/a... #DataScience #MachineLearning #AI #LLM #GenAI

You're reviewing a dataset. It’s clean, complete, and well-formatted—but the model still performs poorly. What do you check next? Let’s discuss the less obvious causes of underperformance. #DataScience #MachineLearning #AI #RStats

BREAKING NEWS! Bright Data Launches The Web MCP for Free to Grant AI Agents Live Web Access -- radicaldatascience.wordpress.com/2025/08/12/b... #AI @AgenticAI #MCP

When teaching at UCLA, I always start with this principle: “All models are wrong, but some are useful.” The key is knowing how a model is wrong—bias, variance, scope—and when it still adds value. #DataScience #MachineLearning #AI #RStats

I’ve reviewed hundreds of ML projects. The most common failure mode? Mismatched objectives. Optimizing AUC when the business cares about cost. Accuracy when recall matters. Align metrics with goals—or risk irrelevance. #AI #DataScience #MachineLearning