Inaugural lecture of Professor Nick Pears: Very Visual Machines - A Short Tour of Computer Vision
We humans are often described as ‘very visual creatures’, able to exploit the rich information in our special visual world. Can we build machines that learn to do the same? Professor Nick Pears will describe how computer vision is impacting a wide range of sectors - from healthcare to safety and security - from sports, games and entertainment to industrial automation. Using a selection of computer vision projects that he has worked on at York, he will convey why the field is interesting, challenging and important - and what it promises for the future.

▶︎
Professor John McDermid OBE FREng: Safe, Ethical and Secure - Robots you can rely on

▶︎
Professor Ruslan Salakhutdinov, CMU, exVP of Research at Meta, Ex-Director of AI Research at Apple

▶︎
NVIDIA CEO Jensen Huang's Vision for the Future

▶︎
Inaugural lecture of Professor Steve King

▶︎
What do tech pioneers think about the AI revolution? - The Engineers, BBC World Service

▶︎
How Proctor’s texts in Karen Read lawsuit could free dangerous criminals

▶︎
Politics Chat, June 23, 2026

▶︎
How AI Cracked the Protein Folding Code and Won a Nobel Prize

▶︎
CoreWeave CEO on Nvidia, AI, and Building the Cloud | At Barron's

▶︎
Is Putin Losing His Grip on Russia? | Steve Rosenberg

▶︎
RL for Agents Workshop - Deep Dive on Training Agents with RL and Open Source

▶︎
CLAUDE CODE ADVANCED FULL COURSE (3 HOURS)

▶︎
MACSI Assembles – collaborations using mathematics and statistics

▶︎
Free Event: Power BI Beginner to Pro 2026 Edition - Full Hands-On Tutorial

▶︎
30 Years of Business Knowledge in 2hrs 26mins

▶︎
Bias in Machine Learning, Philip Winter (VRVis GmbH - Vienna Research Center for Visual Computing)

▶︎
Jensen Huang: NVIDIA - The $4 Trillion Company & the AI Revolution | Lex Fridman Podcast #494

▶︎
The Future of Computer Science Education • Panel Discussion with David R. Cheriton

▶︎
Game-changing Insights From John Doerr's 'Measure What Matters' Talk At Rice University!

▶︎
