"It's not the Social Network Sites that are interesting – it is the Social Network itself. The Social Graph. The way I am connected, not the way my Web pages are connected. We can use the word Graph, now, to distinguish from Web. I called this graph the Semantic Web, but maybe it should have been Giant Global Graph!" [Tim Bernes-Lee]
The question of an intelligent Web continues to be fore and center, and not just in technology circles, especially in a couple of very important ways:
1. semantic information — for sentiment analysis, and to make behavioral predictions in social search. This matters to businesses and marketers that are looking to use the Web as a vehicle for relationships with customers, people who already own and use their product, and potentially for product innovation.
2. content delivery — algorithm and user-driven content matching is a way of peeking behind readers' shoulders and using that information and data to serve up better content. Media companies traditionally play in this space of delivering brand messages. However, branded apps are disintermediating this process.
Making media smarter is easier with technology apps that continually learn to adapt to changing conditions, and "recommend the useful, extract the essential, and automate the repetitive". Google opening up its Prediction API will make it easier to build better apps. For example:
- Recommend a new movie to a friend
- Identify most important customers
- Automatically tag posts with relevant flags
At the Google blog, they use the example from Ford, a company that does social in the way many others could follow. By integrating customer preferences and actual actions back into the way it build products and organizes the business. From the Google post:
For example, Ford Motor Co. Research is working to use the Prediction API to optimize plug-in hybrid vehicle fuel efficiency by optionally providing users with likely destinations to choose from, and soon, optimizing driving controls to conserve fuel.
Because the API is a cloud-hosted RESTful service, Ford has been able to access its computationally-intensive machine learning algorithms to find patterns that rank potential destinations based on previous driving paths. Ford will be demonstrating their work at the API’s I/O Session.
There is, as it is often the case, a dark side to this conversation about predictive technology –that we would end up finding only exactly what we are looking for and nothing further. Which would limit our knowledge base and learning.
We had that conversation here about four years ago. Will Artificial Intelligence agents also be discovery channels? I keep going back to the idea of the social graph, other people in my network discovering and sharing things I may not seek myself.
The ease of use and self-tracking I can do are not the only reasons why I still pull RSS through Google Reader. I constantly tweak my mix to insert novelty in my content discovery and follow people unlike me so I can see what they share. My technology folder, for example, has grown a great deal lately.
It is how I am also starting to see the future of work and how enterprise software will be helping change the way organizations collaborate and see what they know. Take it from someone who has been on the inside, these tools are being tested and implemented by IT groups and adopted by organizations.
Remember when the HR group promoted new employee referral programs? Together with internal conversations as communication, the intelligent Web will be helping businesses make better decisions and tap into internal networks and knowledge better.
"Recommend the useful, extract the essential, and automate the repetitive" will help businesses see the way we're connected more clearly. Will predictive technology work at the exclusion of discovery? Is there a role for human Conversation Agents? [you know my answer, I'm interested in your take]
[image by Pawel Loj]