Group items tagged

The After Action Review (AAR) is a simple tool pioneered by the US army which is now widely used in organisations to capture lessons learned. It is a structured review after an activity or stage in a project, including handover or close, that analyses what happened, why it happened, and how it can be done better. The key to successful AAR's is that it helps you turn mistakes and poor performance into learning opportunities.

"The future of digital culture-yours, mine, and ours-depends on how well we learn to use the media that have infiltrated, amplified, distracted, enriched, and complicated our lives. How you employ a search engine, stream video from your phonecam, or update your Facebook status matters to you and everyone, because the ways people use new media in the first years of an emerging communication regime can influence the way those media end up being used and misused for decades to come. Instead of confining my exploration to whether or not Google is making us stupid, Facebook is commoditizing our privacy, or Twitter is chopping our attention into microslices (all good questions), I've been asking myself and others how to use social media intelligently, humanely, and above all mindfully. This book is about what I've learned."

During this innovative event, World Bank explores an internal report about 'How the Bank learns' allowing the audience to vote in real time on the top topics they want to explore using 'Poll everywhere'. We will be using this tool at the Autumn workshop.The Bank shares some thought provoking ideas on 'fast and slow thinking' and where individuals look for knowledge (video -12.15-15.15) as well as allowing participants to vote on a number of provocative statements on a five point scale, to decide choose five topics for discussion with the panel e.g. 'Drink more coffee', 'Learning from failure' and 'Taking time to reflect' (Poll results 27.30 - 30.30). Culminating in the panel discussion on the chosen topic areas including 'live questions' as the discussion develops (Panel discussion 30.30 - 1:14:00).The session is wrapped up with a final vote on the whole event (1:21:00)

Lisa Minogue-White from Willow shares their experience of creating Corporate Moocs and explores the ideas behind opening up learning, allow for the user to learn complex subjects together via a learning framework.This presentation is part of a series from the recent 'Learning Technologies' event in London. Debbie Lawley from Willow ill be joining us at the Autumn workshop to share more ideas on how to create these Learning pathways in your own organisations. 

Use of machine learning and physical modelling for adaptive vehicle control. Data-driven decision making.

In the communities that we choose to belong to (online and offline), we have to do our part in feeding it. It is only when we are generous about sharing our gifts that we build credibility to receive anything meaningful in return, build influence, thought leadership and learn.

"Some bright bulb at Microsoft Research had the clever idea of turning a machine learning program loose on Twitter yesterday to learn how humans interact with each other. Humans, predictably, interacted terribly."

"The focus of 'Mirror' is the creation of an easily used set of applications ('Mirror' apps), that enable employees to learn lessons from their own and others experiences to perform better in the future. The project facilitates learning 'on the job', at the workplace, through collaboration and reflection technologies."

IBM's Watson - the future of machine learning

Inspiring TED Talk on giving kids the opportunity to learn from failure. Just substitute 'work colleagues' for 'children'

This animation is based on the peer assist methodology as outlined in the book Learning to Fly - Practical Knowledge Management from Leading and Learning Organisations by Chris Collison and Geoff Parcell (Capstone Publishing, 2001, 2004).

A quick trip through time to examine the origins of machine learning as well as the most recent milestones.

The average piece of user-generated content (UGC) is awful. It's actually way worse than you think. It can be rife with misspellings, vulgarity or flat-out wrong information. But by identifying the best and worst UGC, machine-learning models can filter out the bad and bubble up the good without needing a real person to tag each piece of content.

"How the Quest for the Ultimate Learning Machine Will Remake Our World"

An alternative process for learning from failure

There is quite a lot of confusion about the difference between AI and machine learning. While many big companies use them interchangeably, they are not the same thing. Related, sure, but different.

Nick Temple is one of our speakers at the KIN Winter 2012 workshop

"David Damberger discusses "Learning From Failure" at TEDxYYC 2011. David is the founder of Engineers Without Borders Calgary (EWB). After building the organization in Calgary and working with them in India, David spent four years building EWB's overseas programs as the Director of Southern African Programs. In this role, David consulted for dozens of African based companies, non-profits and governments in the fields of agriculture; food processing; water and sanitation; and mobile applications for development."