I recently followed a “lunch box lecture”, organised by the University of Sydney. In the talk, Professor Zdenka Kuncic explored the very topical issue of artificial intelligence.
The world is infatuated with artificial intelligence (AI), and understandably so, given its super-human ability to find patterns in big data as we all notice when using Google, Facebook, Amazon, eBay and so on. But the so-called “general intelligence” that humans possess remains elusive for AI.
Interestingly, Professor Kuncic approached this topic from a physics perspective. By viewing the brain’s neural network as a physical hardware system, rather than the algorithm-based software as for example AI-based research used in social media.
Her approach reveals clues that suggest the underlying nature of intelligence is physical.
Basically, what this means is that a software-based system will require ongoing input from software specialists to make updates based on new developments. Her approach, however, is to look at a physical system based on nanotechnology and use these networks as self-learning systems, where human intervention is no longer required.
Imagine the implications of the communications technologies that are on the horizon, where basically billions of sensors and devices will be connected to networks.
The data from these devices need to be processed in real-time and dynamic decisions will have to be made without human intervention. The driverless car is, of course, a classic example of such an application.
The technology needed to make such a system work will have to be based on edge technology in the device out there in the field. It is not going to work – in any scaled-up situation – if the data from these devices will first have to be sent to the cloud for processing.
Nano networks are a possible solution for such situations. A nanonetwork or nanoscale network is a set of interconnected nanomachines (devices a few hundred nanometers or a few micrometres at most in size), which at the moment can perform only very simple tasks such as computing, data storing, sensing and actuation.
However, Professor Kuncik expects that new developments will see expanded capabilities of single nanomachines both in terms of complexity and range of operation by allowing them to coordinate, share and fuse information.
Professor Kuncik concentrates, in her work, on electromagnetics for communication in the nanoscale.
This is commonly defined as the ‘transmission and reception of electromagnetic radiation from components based on novel nanomaterials’.
Professor Kuncik mentioned this technology was still in its infancy. She was very upbeat about the future, based on the results of recent research and international collaboration. Advancements in carbon and molecular electronics have opened the door to a new generation of electronic nanoscale components such as nanobatteries, nanoscale energy harvesting systems, nano-memories, logical circuitry in the nanoscale and even nano-antennas.
From a communication perspective, the unique properties observed in nanomaterials will decide on the specific bandwidths for the emission of electromagnetic radiation, the time lag of the emission, or the magnitude of the emitted power for input energy.
The researchers are looking at the output of these nanonetworks rather than the input. The process is analogue rather than digital. In other words, the potential output provides a range of possible choices, rather than one (digital) outcome.
The trick is to understand what choices are made in a nanonetwork and why.
There are two main alternatives for electromagnetic communication in the nanoscale – the one as pursued by Professor Kuncik – the other one being based on molecular communication.
Nanotechnology could have an enormous impact on for example the future of 5G. If nanotechnology can be included in the various Internet of Things (IoT) sensors and devices than this will open an enormous amount of new applications.
It has been experimentally demonstrated that is possible to receive and demodulate an electromagnetic wave by means of a nano radio.
Second, graphene-based nano-antennas have been analysed as potential electromagnetic radiators in the terahertz band.
Once these technologies are further developed and commercialised, we can see a revolution in edge-computing.