New technology is arriving at a faster rate and holds the promise of solving many of the world’s pressing challenges. Lighter, cheaper and flexible electronics made from organic materials have found endless practical applications and drugs are being delivered via nanotechnology at the molecular level, at the moment just in medical labs.
However, outdated government regulations, inadequate existing funding models for research and uninformed public opinion are the greatest challenges in effectively moving emerging technologies from the research labs to people’s lives.
1) Robotics 2.0
A new generation of robotics takes machines away from just automating the most manual manufacturing assembly line tasks and orchestrates them to collaborate in creating more advanced assemblies, subassemblies and complete products. Collaborative robotics can accelerate time-to-market, improve production accuracy and reduce rework. By using GPS technology that is commonly available in smartphones, robots can be used in precision agriculture for weed control and harvesting.
We’ve seen robots that can walk like an ape and run like a cheetah, robots that can mix a perfect martini, help the disabled, or drive you to the store. Robots could replace soldiers on the battlefield. In Japan, robots are being tested in nursing roles: they help patients out of bed and support stroke victims in regaining control of their limbs.
Artificial Intelligence, machine learning and computer vision are constantly developing and perfecting new technologies that “enable the machine” to perceive and respond to its ever changing environment.
2) Neuromorphic Engineering
Neuromorphic engineering, also known as neuromorphic computing started as a concept developed by Carver Mead in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analogue circuits to mimic neurobiological architectures present in the nervous system.
A key aspect of neuromorphic engineering is understanding how the morphology of individual neurones, circuits and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change. Neuromorphic Computing is next stage in machine learning.
IBM’s million “neurones” TrueNorth chip, revealed in prototype in August 2014, has a power efficiency for certain tasks that is hundreds of times superior to conventional CPU’s and comparable for the first time to the human cortex.
3) Intelligent Nanobots – Drones
Again, Emergent AI and Computer Vision will provide drones with human like capabilities allowing them to complete tasks too dangerous or remote for humans to do like checking electric power lines or delivering medical supplies in an emergency for example.
Autonomous drones will improve agricultural yields by collecting and processing vast amounts of visual data from the air, allowing precise and efficient use of inputs such as fertiliser and irrigation.
4) 3D Printing
Imagine a future in which a device connected to a computer can print a solid object. A future in which we can have tangible goods as well as intangible services delivered to our desktops or high-street shops over the Internet.
And a future in which the everyday “atomisation” of virtual objects into hard reality has turned the mass pre-production and stock-holding of a wide range of goods and spare parts into no more than an historical legacy.
Emerging Technologies in 3D printing that eventually lead to lighter, more efficient plane parts that could save fuel on your flights, replacement body parts, from printed knickers to 3D printed pills and from synthetic hearts to 3D printed homes on Mars and other planers.
5) Precision Medicine
From heart disease to cancer, all have a genetic component. Cancer is best described as a disease of the genome. The ability to sequence a patient’s whole genome is close to entering the clinic in cancer hospitals.
We’ve come a long way in technology since we sequenced the first genome, and as it gets faster and cheaper we will be able to both personalise treatments and learn more about the enormous number of genetic changes that lead to each type and subtype of cancer. With digitisation, doctors will be able to make decisions about a patient’s cancer treatment informed by a tumour’s genetic make-up.
This new knowledge is also making precision medicine a reality by enabling the development of highly targeted therapies that offer the potential for improved treatment outcomes, especially for patients battling cancer and finally by combining whole-genome sequencing (or transcriptome sequencing) with advancing sequence-based drug technology the future really looks amazing!