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.
---------------------------
* 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
I'll be surprised if 2011 doesn't see something further happen around the wearable computing space. We need to stop tinkering with metal boxes and facilitate direct interaction with the world a bit more.
There are two social dynamics to this kind of interfacing:
1. Broadcast the display externally on walls, tables, car bonnets or bodies (not private) OR
2. Broadcast internally on glasses or hidden earpieces (i.e. privately).
I think both approaches are more favourable to the current head down into a mobile neck stretch. Mobiles are private devices and Tablets/iPads a bit less so but they are both metal objects you have to put in front of your face and carry around. The world is only there in periphery when using devices like these.
Directly communicating with others and including the web as a 'third voice' is still not an elegant flow when taken out of presentation theatres and onto buses and high streets.
Pervasive and wearable computing will see an always-on environment for audio and video. The machines will listen to you 24/7 and parse what you say. The video components will continually record and pattern match the objects around you. Forget Amazon recommends when the data you can input is your whole day! We don't need to key the data about us like monkeys with typewriters. Spines everywhere will rejoice as we lift our heads to look back at the world once more.
The demos from MIT Wearable Computing Team in 2009 still look fantastic and the prototype only cost around $300 back then.
The TED talk - Pattie Maes' lab at MIT, spearheaded by Pranav Mistry
The interface ideas
The evolution of Steve Manns private eye glass display
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.
We're building a collective digital memory with all those:
votes and ratings
comments and blogs
tags and bookmarks
We can put this data on google maps, and provide strong links between place and time as well as invent applications that use this data to create new environments. We don't even need to use the common map metaphor to see our data with IBM's wonderful tool 'Many Eyes' which allows us to analyse data in interactive graphs and visualistions. Data can be processed by simple XML allowing for automated feeds of information and graphic representation such as the example below:
And then there is some next level image-onomy or whatever new paradigm term we need to invent that Photosynth ushers in. A technology acquired by Microsoft and originally developed by Blaise Aguera y Arcas.
It allows a feed of photos to build up a map of the earth and places not just using flyover images by aeroplanes or satellite data but by using our own photographs and even illustrations. Photosynth uses public images and it doesn't matter whether these photos are taken by a £10 disposable camera or a posh SLR - it can stitch them together and produce a never ending tapestry that allows you to move around geographic areas and locations with ease.
With Photosynth you can:
Walk or fly through a scene to see photos from any angle.
Seamlessly zoom in or out of a photo whether it's megapixels or gigapixels in size.
See where pictures were taken in relation to one another.
Find similar photos to the one you're currently viewing.
Send a collection - or a particular view of one - to a friend.
Zooming in might have you moving through 10 photos using your own as a starting point. Your landscape shot of the fair ex-mining town of Cowdenbeath on your digital camera might be part of a family of 1000 photos of Cowdenbeath. Using this pool of images like stones in the middle of a pond you can step and zoom in deeper and deeper to find the Forth Road Bridge in detail when it was just a red spec on your own photo.
Photosynth takes data from everyone - from the collective memory of what the world looks like. A model emerges of the entire earth as our own photos get tagged with other peoples metadata and the mesh of linking becomes tighter and stronger. The network effect continually enriches the space and easily provides cross user and cross model experiences and information.
This is the real semantic web or 'Web3.0' along with the Social Graph developing through the use of people networks. These inferences are taking a life of their own and one can only wonder at what Web5.0 might be.
There's a great demo hosted by TED where Blaise runs through the application with jaw dropping effect.
Photosynth modestly state "Our software takes a large collection of photos of a place or an object, analyzes them for similarities, and displays them in a reconstructed three-dimensional space."
This experience is on the web to try right now but be warned Mac fans - this web experience is PC only for now.
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.
Morph is a concept demonstrating some of the possibilities nanotechnologies might enable in future communication devices. Morph can sense its environment, is energy harvesting and self cleaning. Morph is a flexible two-piece device that can adapt its shape to different use modes. Nanotechnology enables to have adaptive materials yet rigid forms on demand. It is also featured in the MoMA online exhibition "Design and the Elastic Mind". It has been a collaboration project of Nokia Research Center and Cambridge Nanoscience Center.
