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"The Top 100 Tools for Learning was compiled from the votes of over 500 learning professionals in workplace learning and education from 48 countries worldwide in the 7th Annual Survey, by Jane Hart, Centre for Learning & Peformance Technologies"

KIN has long stressed the importance of learning from failure. In this short, funny and revealing post, David D'Souza publicly shares his experience of what not to do in a TV interview. His post uses humour, it's punchy (note the bullet points) and is in the first-person. I doubt I'll ever be on TV, but everyone could immediately relate to and learn from this. Now that's real learning from failure - the antithesis of a dry 'lessons learned' report.

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions. This animated presentation explains machine learning in simple to follow graphics.

The eLearning Guild's Handbook on Synchronous e-Learning is intended for anyone who wants to produce, lead, or promote live, interactive learning events on the Web. Novices will find information on what other people are doing in this medium, which kinds of training and education are best suited to it, how to convert existing classroom content for delivery online, how to lead effective and compelling live learning events on the Web, and how to promote these events to obtain maximum participation. More experienced practitioners will find a wealth of ideas that they can use to improve or enhance their current processes. All readers will find job aids, references, examples, and information to significantly reduce the amount of time required to produce online learning events

"The problem is that too many 'lessons learned' programmes fail. Without falling into the trap of sweeping generalisations, I would suggest that more often than not the story itself is the problem; they just aren't interesting enough; they are shallow; they lack the richness that is needed to engage the intended audience; and they lack a structure that reflects the way adults learn. And that is the bottom line, all too often they just don't work in relation to the ways in which adults learn - I would argue in the vast majority of practice that there is actually little or no consideration for the the target audience of a lesson learned (the adult as a learner)."

Learning to perform a job by watching others and copying their actions is not a great technique for corporate knowledge transfer. Christopher G. Myers suggests a better approach: Coactive vicarious learning.

"Continual learning is integral to the human experience. People who can learn faster and better than others tend to do well in life. The same is true for successful organizations."

What's keeping leaders from adopting machine learning? Well, tools are still evolving, practitioners are scarce, and the technology is a bit inscrutable for comfort. But five vectors of progress are making it easier, faster, and cheaper to deploy machine learning and could bring it into the mainstream.

Many companies are seeing that the future of deep learning is here, and that it doesn't require a lot of money or resources to take advantage of this new industrial science. IBM's Watson Analytics offers a freemium service that allows you to upload up to 500MB, and enables you to explore your own real-life applications for deep learning. Inputting Google Adwords or other sales metrics into this tool can help even startup companies find relational and predictive information in their data.

Video from a series of events at British Council looking at 'How we learn' this video is one session where Sam Conniff looks at the difference in the world of learning and education. He talks about the 'sharing economy' bring the largest growing market in the works and then changes technology is having on what we teach in schools worldwide, including the fact that 70% of all on-one content is video, making the creation of content closer to the user. Highlighting the top things people learning on-line using the search 'how to...' Play in instrument, learn a language and write code.Sam also explores the notion of 'Learning how to learn', 'being connected to the internet everywhere', & 'transparency' and how these will change Education & how we will need to interact in a more ethical way (26:40 - 43:00) 

"Multi-sensory learning can be a boon to understanding and retention of information, as many studies suggest. This type of experiential learning has been shown to do better than videos when it comes to speed of learning and length of retention."

"Whether you're just learning to code or you're a seasoned machine learning practitioner, you'll find information and exercises in this resource center to help you develop your skills and advance your projects."

Google Sheets is getting smarter. After adding the machine learning-powered "Explore" feature last year, which lets you ask natural language questions about your data, it's now expanding this feature to also automatically build charts for you. This means you can now simply ask Sheets to give you a "bar chart for fidget spinner sales" and it will automatically build one for you.

How do we learn the meaning of text from data? In other words, how can a machine truly understand the phrases that human beings blab into its search fields and microphone? The researchers at Google and elsewhere have settled on an answer to that question: machine learning; specifically, a form of artificial intelligence called neural networks-self-organising systems modelled on the way the brain works.

An exclusive inside look at how artificial intelligence and machine learning work at Apple

During the past few years, the terms artificial intelligence and machine learning have begun showing up frequently in technology news and websites. Often the two are used as synonyms, but many experts argue that they have subtle but real differences.

Excellent short article on the difference between learning and knowing

Google Search itself provides one of the most familiar examples of predictive intelligence. When you enter keywords in the search box, Google predicts what you are interested in and then presents you with results that match that intent. Since it released the first version of its Prediction API in 2010, Google has made some of these methods available to developers. Adoption among developers has not been high because machine learning requires a lot of infrastructure and validation to produce accurate results. Developers have also reported discomfort with basing products on black box APIs.

"variety of approaches to individual learning, including group learning ideas."

Presentation by Ingrid Koehler of LGID on tools for digital learning. From Knowledge Hub conference 01/03/2011