A session on Future of ICT and on promoting innovation saw 6 discussants from different parts of the world and with different experiences outline their vision.

As a brief summary there was a consensus on the fact that we are going to see innovation in the second part of this decade as result of the cross fertilisation deriving from various areas, including studies on the brain and the new science of  ”connectomics”.
A particular emphases was given to the advances in 3D printing that will result in a significant change in manufacturing and in the progressive embedding of ICT in any objects leading to more flexibility and to a push of autonomic/autonomous systems.

Innovation will be ubiquitous in terms of “bright ideas” but those Countries that can offer local conducive environment to the deployment of innovation are going to benefit most.

There was also consensus on the importance to create a culture of entrepreneurship and stimulate young people to learn from mistake and try again!

Here is a summary of the points I touched upon:

• The most significant innovations in Information Technology have been made possible by the progress in electronic performances (both in terms of processing and storage). Big Data and huge processing capacity has made possible statistical approaches in areas like image and speech recognition, language translation, signal processing, AI, human like interfaces….
• The evolution in communications, in turns, is fueling “parallel processing” and the complexities of networks are pushing towards autonomic systems ….
• So the real foundation for ICT evolution is electronics (Moore’s law) but in turns the ICT evolution is fueling evolutions in many other “basic” areas like nanotechnology and biotechnology.
• What we can expect in the near term is a feedback from these evolving areas into ICT thus accelerating further ICT and global evolution. We can expect the application of new understanding of bio, and of brains, to fuel a tremendous advance in ICT.
• What is most striking, however, is the amazing decrease of transaction cost, in ICT and in all related areas where ICT is used as a tool. This is lowering the thresholds of capital investment in creating innovation, thus innovation is no longer solely the turf of big companies but is becoming more and more the playing field for small companies, even for single persons.
• This lowering cost of innovation has multiplied the number of players but to affect the millions innovations have to affect processes, often need a different regulatory framework, and require significant capital intensive infrastructures of various sort. Apple, Google are providing these kinds of infrastructures.
• Therefore we are confronted with a sort of paradox: innovation happens at the micro scale (hundreds of thousands of apps) but can only be sustained through some de facto monopolies.
• Europe has invested a huge amount of money to increase its competitiveness funding research on ICT but in spite of the billion dollars spent over more than 30 years it is still lagging behind US, as it was the case in the 1980ies and now is lagging behind South Korea as well. What went wrong?
• Several studies point out that the problem is not in the low quality of research results, nor in lacking of bright minds. The problems appear to be in the transmission chain that moves research results to the market, what we usually call innovation, reserving the word invention to a research result.
• It was also noticed that one of the reason for the failure in the transmission chain is the lower level of entrepreneurship in technology sector in Europe (in other sectors, on the contrary, that is not the case) and University, but possibly also college education, is among the culprit.
• The European market is au pair with the US and surely bigger than the ones of Korea and Japan but is very much fragmented with very little circulation of “brains”. This is another reason that is often indicated for not leading the world innovation in technology.
• Because of this Europe has set up the EIT, the European Institute of Innovation and Technology, more specifically in the area focussing on ICT, eit.ictilabs.eu. It is founded on the three pillars of education, research and business. It is also “funded” and the funds are directed to create innovation out of invention. Hence the funding is on top of research funding already invested. Education aims at creating entrepreneurs and provide students with an environment rich in research and business. In other words Europe is investing, through a selected number of partners of excellence, to create an ecosystem that is conducive to innovation to the whole of Europe.
• The program is not closed, actually it is soliciting participation of foreign students. We have now over 50% of students in our master coming from other continents.
• Innovation in the future will be more and more happening through cross fertilization across different disciplines and across different geographical areas. There is a political responsibility in ensuring that the innovation can be exploited in the local environment and territory. Although global in reach, innovation is local in its effect and its success is strongly dependent on local conditions.
• Open cultural environment, diversity, a strong attention to people well being as well as to the preservation of basic rights, like the ones of privacy and ownership, are sometimes considered a drawback when we look at the efficiency in innovating but on the other hand they are the ultimate conditions for successful innovation in the longer term. This is the challenge that Europe wants to win.

We know that the behaviour of electrons in an electrical network can be described in terms of random walks; amazingly this local behaviour allows the network, as a whole system, to solve a complex optimization problem, which is minimizing heat dissipation for a given level of current flow. One might be tempted to extend this metaphor to communication networks.

Well, apparently, it’s even better: while the rules governing physical systems are fixed, for future communication networks we may be able, in principle, to engineer microscopic “autonomic rules” so as to achieve the desired macroscopic consequences at the network level. Specifically, as future communication networks will dramatically grow in size, I think that phase-transitions and meta-stability will be a highly interesting issues for the implications that these phenomena might have in network management and control.

As an example, in the project Univerself (http://www.univerself-project.eu/), I’m working on a microscopic description of a communication network as a Markov process with a large number of locally interacting components. Even if the number of possible states of each component is finite, there is the possibility of having meta-stable, i.e., persistent, states on the time scale of practical interest. This possibility is not just theoretical mathematics, but it may have a number of nice practical implications for management and control of network flows. For example, have a look at this paper:

http://www.statslab.cam.ac.uk/~frank/PAPERS/kmbk1.pdf

it shows how the integration of streaming traffic and file transfers can have a stabilizing effect on the variability of the number of flows in a network. Other examples show the impact of flow admission control strategies, or dynamic routing on the instability regions of a network.  A solid mathematical modelling of future networks will allow designing the said “autonomic rules” without compromising network stability.

Look at this picture:

Neurons networks in a fruit fly

I took it from an article on Technology Review. It shows the illuminated neurons of a fruit fly. It really makes for an amazing image!

How did they do it? Well, apparently with genetic engineering it has become possible to attach a fluorescent dye to proteins used in the nerve cells and bio-engineers have now a very good map of the DNA of fruit flies, so good that they can start building on it instruction to create new structures like adding a fluorescent dye to a specific molecule. The genetically engineered fly will then glow and it is possible to photograph its neurons network. The technology has been developing over the last 3 years and has got a name: Brainbow, an appropriate name indeed!

This technology opens up the study to how these network may work and this is indeed interesting on two aspects. First, by learning how structures are tied to functionalities we can learn something about our own brain. Second, by understanding how the fly brain processes information we can mimic its techniques for better robots and control system. As an example, the fly has an incredible capacity of controlling fly stability and handling and this with a control systems having just a few thousands neurons. Being able to mimic that for the control of a fighter jet would be great!

To me, however, these results are interesting because they are providing information on networks and our telecommunications networks may get some interesting hints out of that.

Particularly the network of the future, autonomic networks, that need to be able to interface with a wide variety of devices, themselves part of the network, and readjust to match new contexts.

I really feel that we are on the starting line of a new era, no longer building top down but bottom up. Our growing understanding of Nature, of genetics and the control of nanotechnologies are going to change the rules of the game by the end of this decade. We have really just begun our “evolution”.

I’m attending the kick-off meeting of the Integrated Project Univerself (a three years project funded by European Commission in the FP 7 Call 5 “Future Networks”). Overall goal is developing and assessing solutions for embedding autonomic capabilities into future network equipments. Consortium represents 17 Partners coming from Industry (among them three Vendors and two Network Providers), Academia and Research Centres.

Today, we’ve had to elaborate a use-case enough representative for hooking together all project activities. I’ve put forward the vision of future pervasive networks as a field of (processing, storage and networking) nodes to be dynamically allocated and managed in a flexible way to serve Users’ and applications requests. This is an even more challenging network evolution when considering that several smart objects (with embedded communications and increasing processing and storage capabilities) will be offered to the market; at the end of the day, future networks will composed of nodes, Users’ terminals and smart objects (morphing into nodes) highly interconnected with each other.

In this direction, there is an interesting metaphor (that I’ve already mentioned in a previous post). We’ve seen how in computer developments, virtualization has allowed, in the past, different OSs (e.g. Window, Linux, MAC) running on a common, simple and stable h/w. This has introduced programmability, strong isolation whilst letting at the same time competition to flourish above (on applications). Taking this example also for network evolution, a resource virtualization layer (covering all resources) might provide a clean separation between a common hardware substrate (physical infrastructure) and an open programmable environment on top of it (increasing dramatically flexibility and providing also isolation).

It looks like an “abstract” network which is independent from the physical resources (as the platonic idea of mind which has “life” independent from the “physical body”) that can move, adapt and (re-)configure the body (i.e. the resources) which it controls and manages. This network is not fixed, bounded to any resources and it can be (virally) installed as a distributed operating system. Autonomic resource management is part of this picture.

By the way, this has been also elaborated also at ICT2010 Networking Session

http://ec.europa.eu/information_society/events/cf/ict2010/item-display.cfm?logoff=true&id=3025

If this open environment really will take the shape of a distributed network operating system, then probably new kind of ecosystems and value networks will emerge and flourish (encompassing physical infrastructure providers, infrastructure-as-a-service providers, service and application providers, application developers/communities, vendors, application store owners).

McMaster and IBM have launched a research project to investigate how to create a car that can predict vehicle failures before they happen, redirect drivers to less congested routes and help reduce traffic accidents.

http://www.physorg.com/news193338148.html

Actually, cars generate huge amounts of data that could be used both for the local management of the car and for interacting, exchanging information with other cars and/or systems (for example kiosks). It is easy imaging millions of cars embedding sensors, processing and storage power and wireless communications interacting with each other and accessing the web. By the way, energy consumption in a car is a less limiting factor than in other pervasive context of Internet of Things.

Moreover, autonomic capabilities, designed to mimic the human body’s nervous system (that acts and reacts to stimuli independent of the individual’s conscious input), could enhance Vehicular Ad-Hoc Networks with (local and global) self-organising capabilities while remaining invisible to the drivers.

Consider the potential savings that could be achieved by reducing car accidents thanks to elaborating data gathered from embedded sensors and car-to-car or car-to-X communications. Even other services could be easily imagined, such as data forwarding/dowloading (from distributed kiosks), car traffic engineering, air quality management or emergency communications.

http://www.physorg.com/news188501045.html

Communications, storage and computing services are becoming more and more pervasive and, in the future, will be embedded in all everyday objects and in the physical environment. This will determine an increasing complexity of future networks and the consequent challenge of managing large amounts of devices and nodes, moreover based on heterogeneous technologies. This cannot be handled by a traditional centralized approach, but there will be the need to evolve towards distributed approaches. In particular, concerning the traditional areas of resource management (such as fault, configuration and performance), a promising approach is the distribution and automation of the decision-making control-loops and the actuation processes (thus hiding complexities and reducing human efforts and mistakes). Even more, these control loops may include also sophisticated cognitive functions (such as planning and learning).

In order to reach such objective, it is necessary to embed in future objects/devices/nodes autonomic components (with self-* capabilities) virtualising physical resources and interacting with each other to realize services and applications. Architectures based on such distributed autonomic components are intrinsically robust, can dynamically self-configure, evolve their plans and self-organize even to adapt to unexpected situations. Eventually future autonomic systems and networks will consist of different nested and linked control loops exhibiting learning and reasoning capabilities to support a sort of cognitive cooperation based on a shared (network) knowledge.

The key requirement leading to such robust, scalable and adaptable behaviours is coordination and cooperation between components. This is posing an even more critical question: how representing the information shared by (and necessary to) the components? How information is updated and transformed into knowledge? How selecting a relevant part of the global information/knowledge for certain goals?

A possible way to approach this issue for distributed architectures is to consider a sort of information/knowledge field (adopting metaphor of field in Physics), spatially distributed across components and as such resources. Information/knowledge field looks like a sort of gravitational field emitted by each component itself. This information/knowledge field could be locally created and accessible by all autonomic components depending on their location, thus providing them with a local context of the global situation of the network. By following the local shape of this field, a component itself can intrinsically perform plan evolution, decision-making and actuation processes. In other words components sense the information/knowledge field and act accordingly to evolutionary behavioural patterns or plans.

Information/knowledge field models can potentially be implemented, as an overlay network, on any middleware, providing basic support for data storing, communication and event-notification. What is required is to provide simple storage mechanisms (to store field values), communication protocols and primitives (to propagate field values to peers), and basic pub-sub mechanisms. In this context it seems promising also to consider the application of bio-inspired mechanisms and algorithms (i.e. gossiping, reaction-diffusion, gradient, self-organization) for enabling creation and development of information/knowledge field across all (communication, storage, computing) pervasive resources.

Neuroscientists argue that brain networks show small world and exponentially truncated power law characteristics. These are considered, by the way, the best structural basis for coexistence of both informational processing segregation (in specialized regions) and integration (by coherent oscillations in wider regions).

Most of neuroscience investigations, as far as I know, are currently based on the assumption that thoughts, feelings, consciousness emerge from the electrical and chemical communications between brain cells. On one side there are experimental studies, using different anatomical technologies (e.g. magnetic resonance imaging) to observe and elaborate about brain patterns. On the other side, there are studies and simulations on various computational models of complex networks neurons: for example how neurons fire electrically in response to inputs of other neurons, or release neurotransmitters to communicate with each other.

Anyway, how such network of networks of cells can produce the subtlety of mind, or can perform the body autonomic control, is still a big open question.

An interesting approach [1] proposes alternatively to focus on the relevance of abstractions (and their relationships to the natural evolution, or the human design).  Being an electronic engineer (fond of Physics) let me resume the example of the Ohm’s law, V = IR, where V, I and R denote abstract entities respectively known as voltage across a resistor, current through it and resistance. So, the movement of electrons, in a physical resistor, results in the realisation of an abstract scheme satisfying the Ohm’s law. Engineers make use of such (and many others) abstract schemes to design electronic systems performing particular functions (but, basically ignoring the laws behind the movement of electrons). The same ideas probably apply to bio-systems. Bio-systems contain several components of various kinds, conforming to some scheme of abstractions. On the other hand, they differ from machines in that the entities concerned often lack a formal definition, their properties being inferred from investigations (at different levels of observation) of several instances encountered in nature.

 
This reasoning equally applies to autonomic systems functioning.  The human autonomic nervous system, in its environment, is probably characterised by a hierarchy of systems conforming to a range of abstract schemes; typically these schemes relate to particular neural circuits or biological sub-systems and their adaptive behaviour in a given environment.

Natural design and evolution are driving (indirectly) the human autonomic nervous system to conform to such abstract schemes, through development and cognition (learning, at least). I think this is an important point.

Following this reasoning, to engineer autonomic systems requires defining, not only control-loop circuitry (at the proper level), but also hierarchies of abstract schemes and the processes of evolutionary development and cognition in dynamic environments.

In a next post, we’ll elaborate about how cognition may control phase-transitions in autonomic networks.

[1] http://www.tcm.phy.cam.ac.uk/~bdj10/papers/messina2001.html

I’ve recently read the paper Converging Cognitive Enhancements by Anders Sandberg and Nick Bostrom. Authors argued that there are few resources more useful than cognitive ability. Cognition can be defined as the processes an organism uses to capture and organize information. This includes acquiring information (perception), selecting (attention), representing (understanding), retaining (memory) and using it to optimize behaviors (reasoning and coordination). Then cognitive enhancement is the amplification or extension of these core cognitive capacities through improvement or augmentation of internal or external information processing systems.

Paper captured even more my attention when they mentioned that increasing links between environment and Users is an excellent example of cognitive enhancement. Imagine many pieces of software helping people in acquiring more and better selected information, representing, organizing, correlating it, keeping multiple items in memory, performing everyday routine tasks, etc.

We see that this cognitive enhancement can be achieved through embedding communications in sensors, clothes, food packaging and other everyday objects; so, following this trend, future communications will not focus on people only, but on any networking of “people, machine and things” and, quoting Sandberg, this “increased communication will be the key to increasing social intelligence”.

On the other hand, given the growing availability of computing, communication and storage capabilities at low costs, what appears more crucial (and probably strategic) is the ability to link together these objects and pieces of information into usable concepts and associations. This might be a new “missing link” to reach Customers and thus to create new potential opportunities.

Anyway, what appears amazing to me, from another perspective, is that also scientific community (e.g. see FP7 Vth Call, Strategic Objective 1.1, closed yesterday) and many standardization bodies are recognizing the need (and the potential) of introducing (or enhancing) cognitive capabilities into future networks!

Currently, most efforts focus on cognitive radios (research in this domain is well ahead compared to cognitive networking which is just taking off). Main idea behind cognitive networking is that future networks (in order to face their growing complexity) have to embed cognitive capabilities making them self-aware of their status, capable of taking decisions (according to determined plans, policies, end-to-end goals) and acting accordingly. These autonomic decisions (with limited human intervention) will encompass self-management, self-control and cross-layer (and even cross-node and cross-domain) self-optimization of real and virtual resources. Again, cognitive networking is based on acquiring data/information (this time from the network), selecting, representing, retaining it (e.g., with a combination of statistical and semantic models) and using it (e.g., machine learning, large scale reasoning) to optimize network behaviors (e.g., optimization algorithms).

Indeed we’re talking about converging cognitive enhancements.

(paper is available at http://www.nickbostrom.com/papers/converging.pdf).