Highlights from the Artificial Intelligence Conference in London 2018

Watch highlights from expert talks covering artificial intelligence, machine learning, automation, and more.

By Mac Slocum
October 10, 2018
London skyline - AI UK '18 London skyline - AI UK '18

People from across the AI world came together in London for the Artificial Intelligence Conference. Below you’ll find links to highlights from the event.

The state of automation technologies

Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.

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AI in production: The droids you’re looking for

Jonathan Ballon explains why Intel’s AI and computer vision edge technology will drive advances in machine learning and natural language processing.

AI and machine learning at Amazon

Ian Massingham discusses the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding.

Why we built a self-writing Wikipedia

Amy Heineike explains how Primer created a self-updating knowledge base that can track factual claims in unstructured text.

Trust and transparency of AI for the enterprise

Ruchir Puri explains why trust and transparency are essential to AI adoption.

AI for a better world

Ashok Srivastava draws upon his cross-industry experience to paint an encouraging picture of how AI can solve big problems.

Rethinking software engineering in the AI era

Yangqing Jia talks about what makes AI software unique and its connections to conventional computer science wisdom.

Bringing AI into the enterprise: A functional approach to the technologies of intelligence

Kristian Hammond maps out simple rules, useful metrics, and where AI should live in the org chart.

Fireside chat with Marc Warner and Louis Barson

Marc Warner and Louis Barson discuss the internal and external uses of AI in the UK government.

Building artificial people: Endless possibilities and the dark side

Supasorn Suwajanakorn discusses the possibilities and the dark side of building artificial people.

Deep learning at scale: A field manual

Jason Knight offers an overview of the state of the field for scaling training and inference across distributed systems.

The missing piece

Cassie Kozyrkov shares machine learning lessons learned at Google and explains what they mean for applied data science.

Notes from the frontier: Making AI work

Drawing on the McKinsey Global Institute’s research, Michael Chui explores commonly asked questions about AI and its impact on work.

Post topics: AI & ML

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