Circles and Ladders with Google+ Contact Classification Paradigm

Grouping contacts is an impossible feat isn't it? We have to add Bob to Sport, Work, Musician, 'Allowed to Call after 10pm' and all those other groups we never keep up to date.

Like all user supplied up-front people classification systems Google+ Circles can quite quickly turn into hierarchy ladders when you manage your contacts using them, especially in a social context. 

Contact grouping, grading and intimacy-scoring questions arise like : "Why am I not in your Personal folder?" "Why am I only in Acquaintances?", "Why am I not in group X?"

It makes for unhappiness not to mention all the manual labour of managing those connections. 

The flat monism of classifying all your people as simply 'friends' and allowing the system (not you) to speculatively match between profiles managed by the identity owner is elegant. It causes less arguments over status and how other people classify you. 

Baboons would be relieved to have such a thing. 

Flat'ish, loosely coupled metadata overlaps between people such as : attended same school, favourite band is x, graduated in Kent, holiday in France, has photo of Mt Everest provide a more resilient model in the end for both programmatic and humanistic reasons. It also a more natural petri-dish for harmonious social groups when developing new services.

The degree to which this metadata is enhanced as you interact with 'your people' defines the living breathing classification of what they mean or meant to you. It allows for relationship management (manual and auto) between the people you already know and it also allows for the emergence of machine dialogue such as 'People You Should Know'

It's dynamic and weighted through use - it's no longer the leaden categories of 'Work', 'Home', and 'France'.

Rebuilding Iberian Motorways with Slime Mould

Wet machines and Soft Computers planning road routes organically. 

Place your 'problem' in bag and shake to get the answer!

Although done on a simple flat map/surface there is no reason why this couldn't be a 3d model with variable temperatures/variables throughout. The modelling of landscapes can be more accurate organically in order to find target map paths that are efficient from a biologic standpoint.

These types of navigational problems* were among some of the first tackled by 'hard' machines (computers like you are reading this with) when they were first developed and it's nice to see the initially parallel development of bio computing.

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* Travelling Salesman problem : What is the shortest route visiting each city exactly once and then returns to the starting city? See more classic computing problems here 

BT FON: Now this is Social Computing

The premise of BT FON by British Telecom:

You freely allow for others to share a piece of your home-hub broadband connection in return for free access to theirs and, importantly, access for free to BTs public wifi spots.

It's potentially the worlds largest Wi-Fi community in the world and
my iPad, without a telcom data card, is begging me to join and download the iPhone app to activate it.

Those Wi-Fi hotspots were mostly aimed at businessmen but this seems much more democratising....and free. Let's be frank, using mobile data is definitely the easiest approach at the moment to feed your smartphone but it does seems we are at the stage now that those large towers used by telecoms companies to throw your data signals through the air will be replaced by a million peoples home wireless hub. It's social decentralised computing and the model is good.

I notice my existing mobile data provider, O2, has capped my mobile data usage and 10 days before the end of the month I find myself with it all used up and my speed throttled. If BT FON could take some of the load off then this would help somewhat.

I wonder what the telecomms companies will make of it?

I think this is an excellent play by British Telecom. If something like this could gain momentum then it would be quite a disrupter but without the numbers the experience will be poor as I transition between Bob Smiths hub and wait another minute until I can use a little bit of Mary's down the road. If switching between free hub-pimping and mobile data is seamless then maybe the problems aren't so great.

I'd be reading the small print on the security and privacy implications but this is definitiely one to watch.

A

last.fm - The Big Biological Model gets a cold

last.fm is a service I have used and admired for a long while now and I became a paid subscriber for £3 a month (or thereabouts) a few months back which is something notable in a sea of free music services.

Their service really does manage to play music 'like the music I like' while deftly avoiding the stuff I don't like. I've trained it over the past few years like a puppy to respond to my commands of love and hate and now it does a great job in creating my personal radio station.

Their simple delta of being able to mark something as 'not liked' provided them with the extra dimension of customer modeling that the other recommendation and fuzzy logic engines sorely missed. Recommendation models without an 'unlike' are akin to physics environments where there is no 'reaction' to the 'action' - the map is too one dimensional.

I remember reading a list of the jobs that last.fm had available in 2009 and you would have been hard pressed to discern between their job descriptions for marketing and technical staff and job listings for a biotech scientist. The guys at last.fm understand nodal modelling and that the real social graph looks more like something you see in a petri dish in a microscope - constantly changing, fault tolerant, nodal, duplicated, overloaded and alive.

Some howcome last.fm are making some cardinal sins with their customer relationships at the moment?

1. Introducing subscriptions that can only be paid for by PayPal. This was moderately annoying when I was in Latin America recently and my PayPal was suspended after one too many transactions in Brazil making their fraud algorithm jumpy.

2. Removing the two key features that most subscribers pony up their cash for - streaming personal playlists and 'loved tracks'.

The on-demand streaming isn't something I use last.fm for as I use it as my personal auto-pilot radio station and secondly as my database of music likes in the cloud. The truth is I use Spotify for on-demand '' listening. The changes in service however do bother the legions that had moved from their iPods to iPhones and Android devices to have on-demand on the move.

What bothers me is the way that they are making the changes.

No push of email to subscribers to alert them of the situation and a terse statement on the site inevitably sees a raft of users angrily hitting the forums and web. The path for resolution is simply to cancel your subscription if you are unhappy about it.

Problems with licensing seems to be the battle cry with most upheavals in music services but did they really try an explore all the options they could have taken?

• Two tier subscriptions with a higher delta for on-demand streaming.
• Aborting the free model and charging a nominal fee for all users.
• Offer different services/rights to different countries

.....or even less radical - cancel the services that they were going to but run an 'ease the shock' campaign pre-warning users of the reasons why they need to do this.

We're listening to last.fm but they aren't listening back.

The Prediction Model

I've become increasingly interested in the overlap of biology and computing over the past few years. It began with the realisation that the web and stock market are really biologic in nature with their fault tolerance, nodal shape, replication of information and distributed locus of control and was further prompted by work I undertook on AI systems for Advertising and Social Computing solutions.

I’m enjoying this overlap developing into a moderate obsession and I am trying to steer my thinking on all things computing into a more ‘biologic fashion’. I’ve always been a strong believer that people involved in one discipline can offer fresh insights on other sciences and that a good set of ‘first principals’ can work well cross domain. This cross pollination was the grease that helped the machine of the Industrial Revolution into being and obliquely it’s also the reason I give for sporting sideburns like some Manchester factory owner.

This post is inspired by Jeff Hawkins who is doing work into models of the brain and attempting to derive an overarching theory of the brain which is something that, despite the reams of data we have on the brain, we are as yet unable to articulate. His talk was on the use of a Prediction Model as the primary approach to developing a theory of the brain and he got my mind racing.

After graduating from Cornell in June 1979 he read a special issue of Scientific American on the brain. In it Francis Crick lamented the lack of a grand theory explaining how the brain functions.^[3] Initially, he attempted to start a new department on the subject at his employer Intel, but was refused. He also unsuccessfully attempted to join the MIT AI Lab. He eventually decided he would try to find success in the computer industry and then try to use it to support his serious work on brains, as described in his book On Intelligence

Jeff thinks that the reason we still haven’t managed to define intelligence well is that we don’t have this overarching theory of the brain or more accurately – intelligence. Jeff postulates that the brain isn't like a powerful computer processor and that instead it’s more like a memory system that records everything we experience and helps us predict, intelligently, what will happen next.

Things like these stop me sleeping at night and last Sunday I leaned over to my girlfriend at 2am and whispered to her “I have to write some stuff”.

I slipped out of bed and knocked up the notes below. They are presented here un-edited and what you see is the first pass brain dump of some of my thoughts and concepts surrounding a Prediction Model (It's probably best to click on one and open up the set in Flickr and view from there).

If you are involved in this area at all I would love to hear from you as I intend to delve deeper. Physics has alot to add to this area with work in quantum theory and calculations surrounding boundaries of event horizons for black holes all being of relevance to the model of the brain and prediction.

Cheap Ideas for advancing Biologic Computing

One of the biggest problems in Biologic Computing today is the predictability of bacterium's movements...

How about this simple, relatively cheap project a cross functional team might be able to do at a University...

- Get feeds from the IBM public visualisation tool - specifically pictures of datasets. They can be Social Network activity feeds (or hub/colony'esque data). Here's one of many examples
- Grab the visualisations of them as well as the raw data
- Run some visual pattern matching software to compare these images against bacterium imagery - at varying scales of magnification
- Do the same pattern matching on the numbers
- See if anything interesting pops up in the pattern matching

Developing continually running real world feeds (API's/RSS or otherwise) from these types of public systems to visual biology computing resources would be potentially useful.

Their benefit is that they are
- continually updated, for free, and have simple XML descriptions of data.
- a constant public feed allow large elements of automation in such a project (bar the human analysis of 'matches' by the system)
- non-proprietary in nature and will 'out', in the end, for generating useful patterns vs custom expensive data capture

If you are up to stuff like this then I'd love to know about it. Mail me.

Recommended Reading: "Genesis Machines" by Martyn Amos

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Plea's to the Machine - a Crash Report