At the PGA Championship, the real story was not just “AI at an event.” The real story was what AI requires to be useful at enterprise scale. AI depends on data. Data depends on the network. And under pressure, the question is not: “Can the model find a pattern?” It
@ronald_vanloon
-
Leaders’ common mistakes about artificial intelligence
By
–
What leaders still get wrong about #AI
by Beth Stackpole @MITSloan Learn more: https://
bit.ly/4dyeLVB #ArtificialIntelligence #MachineLearning #ML -
AI cheating tools multiply as detectors fail to keep pace
By
–
#AI tools that help students cheat are multiplying, and the detectors can’t keep up
by Pranob Mehrotra @DigitalTrends Learn more: https://
bit.ly/44hpuzs #GenerativeAI #ArtificialIntelligence #MachineLearning #ML -
Turning AI Hype Into Measurable Business Outcomes: CEO Playbook
By
–
Turning #AI Hype Into Measurable Business Outcomes: A CEO's Playbook
by Sasi Kiran Malladi @Forbes Learn more: https://
bit.ly/42DVEEH #ArtificialIntelligence #MachineLearning #ML -
SHIVAA AI Robot Transforms Strawberry Harvesting
By
–
SHIVAA: The #AI-Powered #Robot Transforming Strawberry Harvesting
— Ronald van Loon (@Ronald_vanLoon) 24 juin 2026
via @WevolverApp#AgriTech #Innovation #TechForGood #EmergingTech #Technology pic.twitter.com/lPs8h9z6cuSHIVAA: The #AI-Powered #Robot Transforming Strawberry Harvesting
via @WevolverApp #AgriTech #Innovation #TechForGood #EmergingTech #Technology -
Drones use onboard AI to squeeze through narrow gaps
By
–
#Drones learn to squeeze through narrow gaps using onboard #AI control
by Ingrid Fadelli @TechXplore_com Learn more: https://
bit.ly/44kpJK1 #ArtificialIntelligence #MachineLearning #ML -
Aligning AI with Long-Term Skill Development for Sustainable Growth
By
–
The focus is on aligning AI with long-term skill development rather than short-term profit. This is a chance to redefine how we integrate AI into education and workforce training. Organizations that prioritize sustainable growth will lead the way.
-
Centralized AI processing limits responsiveness for time-sensitive use cases
By
–
Most AI systems still rely on centralized processing: → Data is sent → Processed → Returned That model works, but for time-sensitive use cases, responsiveness and control become critical.
-
AI Limitations: Latency and Decision Location Drive Edge
By
–
AI isn’t failing because it lacks intelligence.
— Ronald van Loon (@Ronald_vanLoon) 23 juin 2026
In many cases, it’s limited by latency and where decisions happen.
For time-sensitive environments, that’s a critical constraint.
Here’s why AI is moving to the edge, and what it unlocks…
@TMobileBusiness Partner pic.twitter.com/XmoiFQQscvAI isn’t failing because it lacks intelligence. In many cases, it’s limited by latency and where decisions happen.
For time-sensitive environments, that’s a critical constraint. Here’s why AI is moving to the edge, and what it unlocks… @TMobileBusiness Partner -
Reinforcement learning for broadly persistent beneficial models
By
–
Reinforcement learning towards broadly and persistently beneficial models
by Akshay V. Jagadeesh Rahul K. Arora @OpenAI Learn more: https://
bit.ly/4eAqbsm #AI #GenerativeAI #ArtificialIntelligence #MachineLearning