Machine Learning Times
EXCLUSIVE HIGHLIGHTS
AGI Is Infeasible. Instead, Pursue Superhuman Adaptable Intelligence
  Originally published in Forbes On a recent episode of the...
Artifact-Driven Development: Making It Possible to Query Large Analytics and AI Projects
 A practical introduction to making complex project structure explicit...
Incoherent AGI Hype Spurs An Industrywide Pivot To Hybrid AI
  Originally published in Forbes Recently on The Dr. Data Show,...
The AI Paradox: More Humanlike Means Less Autonomous
  Originally published in Forbes The AI executives are at...

Industry News

Why AI hasn’t replaced software engineers, and won’t

 Originally published on AI as normal Technology, June 11, 2026. There is great anxiety and uncertainty about AI replacing jobs. How can we move past vague warnings and bombastic predictions and bring data to bear on this question? One good way is to look at the profession where AI capabilities are furthest along and adoption has

A reality check on the AI jobs hysteria

 Originally published on MIT Technology Review, May 26, 2026. What do the numbers really say about the impact of artificial intelligence on the labor market? The answer might surprise you. Haven’t you heard? White-collar jobs are going away,...

Apocalypse No

  Originally published on Prof G Media, May 8, 2026. Few brands have fallen further faster in the past 18 months than America and AI. Last week, I wrote about the reckoning I see coming for America. This...

There will be no AI jobpocalypse.

  Originally published on Deeplearning.AI, May 8, 2026. The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment...

Tech CEOs suddenly love blaming AI for mass job cuts. Why?

 Originally published on BBC, March 30, 2026. Sweeping job cuts at Big Tech companies have become an annual tradition. How executives explain those decisions, however, has changed. Out are buzzwords like efficiency, over-hiring, and too many management layers....

Escaping the Prototype Mirage: Why Enterprise AI Stalls

 Originally published on Towards Data Science, March 4, 2026. Too many prototypes, too few products Software development has fundamentally changed in the GenAI era. With the ubiquity of vibe coding tools and agent-first IDEs like Google’s Antigravity, developing...

How Instacart Uses Machine Learning to Suggest Replacements for Out-of-Stock Products

  Originally published in tech-at-instacart Key contributors: Sharath Rao Karikurve, Jagannath Putrevu, Haixun Wang, Allan Stewart and Weian Sheng Imagine this: You’re at home, preparing to rely on Instacart for your grocery delivery. You’ve carefully chosen each item,...

How to Build a Recommendation System at Scale: Insights from Instacart

 Originally published in Data Tinkerer A Senior ML Engineer’s perspective on production constraints, rules vs ML and the workflow behind large-scale recommender systems Following on from previous posts talking to people in the field, today we will be...

Government by AI? Trump Administration Plans to Write Regulations Using Artificial Intelligence

 Originally published on ProPublica, January 26, 2026. The Transportation Department, which oversees the safety of airplanes, cars and pipelines, plans to use Google Gemini to draft new regulations. “We don’t need the perfect rule,” said DOT’s top lawyer....

From Text To Tables: Why Structured Data Is AI’s Next $600 Billion Frontier

 Originally published on Forbes, January 15, 2026. In the current wave of generative AI innovation, industries that live in documents and text — legal, healthcare, customer support, sales, marketing — have been riding the crest. The technology transformed...

Page 1 of 87 1 2 3 4 5 6 87