Possibility: By 2025, LinkedIn’s Economic Graph will be its most important commercial product and widely seen by policy-makers and pundits as the most insightful economic, education and social policy tool in the world as it helps us confront challenges of unemployment, skills gaps and creating a positive culture of an engaged workforce. Read more
A piece that’s still relevant, maybe more now then when it was first published. Reading this piece allows for some context on the foresight Garry shared at the time of writing.
5 minute Summary: I just backed a micro fuel cell project on Kickstarter that is on track to raise $1 million! Why? There is a very seductive vision based on a new distribution model for portable fuels and micro power plants. This post highlights a vision for 2030 when any person in the world can buy clean hydrogen-rich molecule fuels and mini power plants (micro fuel cells) on any local retail shelf. Read more
Summary: OYOD or Own Your Own Data is a simple, radical (likely unattainable) idea based on new tools, behaviors and policies that allow people to control their data and grant access to third parties. The early stage reality of an OYOD-ish future will likely be messy and imperfect — but still better than what we see today. Read more
The International Energy Agency (IEA) is the world’s most recognized energy information and forecasting organization. The IEA is the starting point for understanding the complex interplay of government policies, private sector markets and the dyanmics of end-user demand.
Here are my take aways from the event as well as the IEA World Energy Outlook from 2015 presentation shown at the event.
Soon after 2020, service and knowledge workers might begin to see their own personal Intelligent Assistant as the key to their workplace success.
The age of the Intelligent Assistant is close but will take years and decades to unfold. Rather then envision Hal from 2001:A Space Odesssy or dream of Scarlett Johannsen’s voice “Samantha’ from the movie Her — focus on understanding the implications of systems like IBM Watson or Kensho’s Warren at work.
I had a brief Twitter stream exchange with Christoper Mims, Chris Nelder, and John Licata (all brilliant people; respect!) where I threw out some alternative assumptions on two future directions of electric vehicles via plug-in battery versus hydrogen fuel cells. Friends and colleagues have asked why I am so skeptical of battery-powered EVs. Read more
Media Image Released by European Space Agency
Philosopher Alan Watts once said Rocks are Not Dead and that they are ‘just as much a part of us as our fingernails’. In the universe there are three types of rocks – comets, asteroids and meteroids. In the next few months our understanding of comets will (hopefully) grow in interesting ways thanks to a rendezvous that just occured nearly 4 billion miles from Earth. Read more
Solar power receives more media attention and seems to capture our imagination around the ‘future of energy’, but fuel cell energy appliances represent a more compelling distributed power strategy for utilities looking to develop solutions for commercial customers who demand more resiliency, always-on power and a reduced carbon footprint.
Today we sense high expectations for the future of solar, and either complete confusion, indifference or disdain to the future of fuel cells. Fuel cells are often over-sold by evangelists or dismissed by skeptics but within a decade, energy pundits may speak of fuel cells as being a more radical (and practical) energy innovation platform than solar energy. With fuel cells you can imagine putting a power plant in every hand, home or community.
Google Knowledge Graph
First the web was a platform to help manage networks of documents and webpage files. Then the web evolved into a platform to help us manage our social networks. Today we see the web maturing into a serious platform for lifelong learning to help us better understand the world around us.
Among the more radical (and plausible) scenarios for the future of lifelong learning is that our daily life experiences become reshaped by a learning graph that serves as visualized data record of what we know, how well we know it, where and who we learned it from and what we are trying to learn more about.
News organization could adapt their content with our learning graph to make us more informed. Celebrity chefs might access our learning graph and deliver new experiences to teach us more about food. Libraries might make book and course recommendations based on subjects that motivate us to learn more. Work training programs might tap into our learning graphs to make sure our skills stay relevant and can be tied to real world business challenges.
The most idealized, techno-optimistic vision is a world where people become less focused on consuming and more demanding for experiences that support a lifetime of learning. On the flip side, it does not take too much effort to imagine – or focus only on a dystopian future of learning graph abuse.
Today our social graphs shape our daily interactions with people, organizations and information flows. The next phase of our web-influenced culture will be teaching people the risks, rewards and responsibilities of managing a personal learning graph.
Bringing this vision of a learning graph influenced world will require us to go through the same transition as we saw with social norms confronting the era of social graphs. At first people will likely be confused, indifferent or threatened by the very idea of knowing more about what they know. Then we’ll enter a phase where people can say ‘how did we ever live without it’.
When Social Seemed Confusing and Creepy
Think way back to 2004 when the idea of a ‘social graph’ impacting our daily lives seemed like a confusing and creepy concept. The early social graphs attempted to understand who I know and how I know them?
The information was structured in graph databases that are visualized by circles and lines. Circle nodes (e.g. people, concepts, places, resources) and lines that reveal relationships or types (e.g. is a friend, is a co-worker, ‘likes’). Unlike traditional ‘row and column’ databases, graph databases are perfect for data+information that is rich in connections and relationships.
Ten years ago, nobody was begging for a social graph. No organization thought about packaging its content to be compatible with social graph databases. There were no algorithmic ‘bots’ designed to scrape social network data for sentiment and behavior.
Today, hundreds of millions of people across the world cannot get through a day without tapping their social graphs in Facebook, Twitter, LinkedIn, Match.com or some other equal.
Social graphs, for better and worse, influence the thoughts and stories we share and the content we receive. Social graph databases are behind everything from crowd funding Kickstarter projects, Twitter ‘hash tag’ activism, to helping us find a new job or life partner.
All of this is possible because the social data is structured in graph databases made up of circles (nodes) and lines (edges) that make it easier to find connections, understand relationships and identify the right pathways to reaching a goal.
How about the learning graph?
Welcome to the (very) early days of the Learning Graph.
The learning graph is a visualized data record of what we know (which concepts and domains have we explored), how well we know it (where are we along the path of mastery), and where and who we learned it from (connection to people and resource collections). It might also include hints of what we are trying to learn more about.
Today the learning graph is only an idea. It is an idea with several names (e.g. Learning Record Store; Adaptive Learning Platforms) and potential ally technical specifications (e.g. ExperienceAPI, Mozilla Open Badges)
Yet it is a logical and plausible evolution based on today’s current direction of change in this networked society.
Here is a short list of assumptions to explore and learn more about.
#1 Graph Databases Play a Big Role
Learning is about making connections between things. It is about understanding relationships and abstractions. How we structure data-information will matter.
The category of ‘graph databases’ (e.g. Neo4j, Titan) offers the most appealing foundation to integrating web content into a world of personal learning graphs. The more content is structured around relationships, the more likely they will fit into the ecosystem of learning graphs.
Graph data-what?!
Think circles and lines! Circle nodes (e.g. people, concepts, places, resources) and lines that reveal relationships or types.
The circle (node) of Pablo Picasso would have a line connected (type of art movement) to another circle node of Cubism Art Movement. From the Cubism Art Movement circle you would find many other lines to other artists from the movement.
The future of learning has more to do with our we structure our data than any devices that we use to learn.
Instead of ‘writing books’ or developing learning material content in simple text or video form, we might see all content providers structuring information in graph format so that they have more seamless integration with other connected database frameworks.
#2 Learning Graphs will Stumble if We Limit Implementation to ‘Schools’
Technology-led solutions for school-based learning always seem to run into challenges and constraints that limit the effectiveness of the tool.
A more desirable path for the learning graph might be found in lifelong learning that occurs via self-direct experiences, social learning communities or with civic institutions like libraries, museums and arts organizations.
Instead imagine focusing on lifelong learning. Empowering likely early adopters like young people with learning graph experiences that simply capture their passion for learning – not taking a high stakes test.
Imagine a teenager inspired to learn more about street art. Their learning graph might contain location of where they encountered a particular artist. The graph might include the names of well-known artists and the connections between them in terms or style or cities. The graph might include links to widely read books, blogs or Twitter feeds. The graph might include connections of street artists to other art genres and artists that proceeded this movement. The graph might include connections to social, cultural or political themes explored by artists.
In this visualized experience the teenager can jump from concept to concept – and at surface level realize the relationships between concepts, people and resources. They can see at a glance how much there is to learn vs the concepts that they have attempted to learn about.
#3 Privacy & Data Ownership will be an Issue
Digital Data. Privacy.
We know this story well. Our digital lives have turned individuals into personal data factories. Organizations (public-private sector) see enormous value in knowing more about our lives through access to personal data. The notion of ‘privacy’ is being challenged at all levels.
Social norms and legal frameworks have not caught up. This has not stopped the mainstream embrace of social graph experiences that go far beyond Facebook.
The way forward is to anticipate challenges and work to develop solutions.
Individuals working in the world of learning data and analytics are hoping to avoid a repeat of this missed opportunity to bring people and institutions up to speed on the risks, rewards and responsibilites of managing personal data that drives our lifelong learning.
There will likely be a push for ‘Own Your Own Data‘ policies around lifelong learning. There will also likely be a push for companies to ‘own your data’. There will be learning graph data leaks and break-ins.
It is impossible to predict how it will unfold. We cannot resolve this emerging issue. People will have to stand up for their right to own their learning data. Keep calm, Carry on.
Learn More….
Folks talking about learning graph – learning map related visions:
Danny Hillis – best known for a visual learning map (a concept likely built on top of a learning graph)
OSCON 2012
Danny Hillis, Applied Minds – 2012 talk
Jon Bischke (Twitter; LinkedIn; CEO of Entelo) –
The Learning Graph & Reputation Graph (He references an earlier post by Kirstin Winkler)