Trends Identified
Advanced energy storage technologies
Energy storage technology can be defined as a system that absorbs energy and stores it for a period of time before releasing it on demand to supply energy or power services. Breakthroughs are needed in this technology to optimise the performance of energy systems and facilitate the integration of renewable energy resources.
2016
OECD Science, Technology and Innovation Outlook 2016
OECD
Advanced materials
Materials designed to have superior characteristics (e.g., strength, weight, conductivity) or functionality
2013
Disruptive technologies: Advances that will transform life, business, and the global economy
McKinsey
Advanced materials
Materials with significantly improved functionality, including lighter- weight, stronger, more conductive materials, e.g. nano-materials.
2017
Innovation for the Earth - Harnessing technological breakthroughs for people and the planet
PWC
Advanced materials and nanotechnology
Nanomaterials are materials manufactured and used at an infinitesimal scale, on the order of one billionth of a metre, which behave differently from their larger counterparts, for example in terms of resistance, conductivity or chemical reactivity. They encompass a wide range of organic and inorganic materials, including nanocrystals and nanocomposites. Nanotechnology is a general-purpose technology with multiple applications, which has the potential to revolutionize many industrial sectors. Its applications include: (a) Water remedation and purification, for example through nanofiltration membranes used to treat wastewater in water-scarce countries; (b) Increasing the heat resistance of materials and the Flexibility and performance of electrodes in lithium-ion batteries; (c) Precise control of the release of agrochemicals, improving seed germination and reducing toxicity in the agriculture process, increasing agricultural yields and reducing environmental impacts; (d) Nanoelectronics include devices and materials that reduce weight and power consumption of electronic devices, for example the production of small electronic circuits, enhanced memory storage and faster computer processors; and (e) Medical applications such as the use of gold nanoparticles in the detection of targeted sequences of nucleic acids, and of nanoparticles as a delivery mechanism for medications.
2018
Technology and Innovation Report 2018
UNCTAD
Advanced oil and gas exploration and recovery
Exploration and recovery techniques that make extraction of unconventional oil and gas economical
2013
Disruptive technologies: Advances that will transform life, business, and the global economy
McKinsey
Advanced robotics
Increasingly capable robots with enhanced senses, dexterity, and intelligence used to automate tasks or augment humans
2013
Disruptive technologies: Advances that will transform life, business, and the global economy
McKinsey
Advanced Robotics – The Rise of the Super Machine
Advanced robots are featured in many science fiction films and are probably 10-15 years away from mainstream, with new materials, fuel cells, motors, algorithms, sensors and designs. They would feature many aspects of true AI, like working fully autonomously, sensing the environment, recognizing and solving problems and learning from their environment and from humans. Some of the advanced robots may have humanoid appearances but the majority will probably have special functions and look more like machines or will merge into the background. Most of the advanced robots will interact with humans using voice, gesture, face/emotional recognition and neurolinks. We expect many smart robots to work collaboratively with humans and the upcoming of transhumanism, i.e. the merging of man and machine into cyborgs. Applications for advanced robots would be extensive, going beyond the first wave of automation and optimization, into an economy operated in large parts by machines and/or human-machine units. We would either have solved the ethical questions by setting up robot laws or the questions that we see around contemporary machine learning and robotics would be even more pressing, such as the borders between humans and machines: do we allow robots to control humans, how can we guard us against robot mistakes, if we will allow smart robots to design and build themselves and how we would stay in control.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Advances in Material Science
The design and manufacture of materials at the molecular level will result in ‘designer’ materials, with in-built capabilities to sense and modify their behaviour or functionality, introducing a new manufacturing paradigm. Most advances are likely to occur where material science combines with, or adopts, principles employed with other innovative disciplines including electronics, nanotechnology and biology.
2010
Global strategic trends - out to 2040
UK, Ministry of Defence
Advances in Simulation
Advances in social science, behavioural science and mathematical modelling will combine, leading to more informed decision making. Advanced processing techniques and computational power will permit a more comprehensive level of modelling, potentially enabling more effective pattern recognition. This is likely to improve the identification, representation and explanation of systems and processes. As a result, simulation will become an increasingly powerful tool to aid policy and decision makers. Simulation will also blur the line between virtual and real environments.
2010
Global strategic trends - out to 2040
UK, Ministry of Defence
Adversarial Machine Learning Becomes Key for Security & Fraud Prevention
Machine learning has had its advantage in effectively delivering rapid prediction of trends and establishing robust risk management and inference. Much investment, time and focus by organisations has been dedicated to programming and training machine learning algorithms to fulfil these functions. However, if these algorithms become compromised, they will be prone to attacks from threats and viruses. The chaotic damage that permeating cyberattacks have inflicted in algorithms can dangerously result in the misclassification/alteration of information within them; in effect an organisation’s entire system’s security can be at stake. Cybercriminals constantly seek to successfully exploit weaknesses of learning algorithms of highly valued organisations. Fraudsters are responding to the enhanced detection capabilities for transaction fraud and account fraud offered by fraud detection and prevention service providers. In some instances, they are also using machine learning algorithms to uncover weaknesses in fraud detection systems, in a type of machine learning chess match. It is here that the choice of FDP vendor becomes important, in terms of how its machine learning solution is implemented. Is a static model used, or does the vendor employ an adversarial model that adapts to changing conditions? Fraudsters will have very little knowledge of the precise algorithms being used to detect fraud. As a result, time, effort and funding must be sourced to identify weaknesses, which may then be applied or replicated across other merchants assumed to be using similar algorithms. Sectors (healthcare, industrial, advertising) where protection of huge amounts and types of sensitive data (e.g. consumer/public data) is a high priority – will be the drivers here. Spend on Fraud Detection & Prevention software in the financial sector, ie for eCommerce transactions including ticketing, money transfer and payments, will reach $10 billion by 2022. These sectors recognise the value of determining and containing susceptibilities (e.g. to detect unauthorised access points, weak security infrastructures, etc.) in machine learning approaches within adversarial circumstances. They will prioritise increasing their understanding of these vulnerabilities in machine learning algorithms. They’ll also engage with machine learning specialists to design and implement effective action steps to address these vulnerabilities. Juniper Research believes, moving forward into 2019 and beyond, that adversarial machine learning will be required by numerous industries to: · Identify weaknesses of machine learning algorithms during the learning and identification process · Enforce the protection and integrity and validity of data in these systems · Action steps in response to specific threats · Assess the potential damage of these threats · Programme algorithms to enhance their resistance to viruses · Eliminate the presence of opaque ‘black boxes’ · Grant adequate time to algorithm developers to invest into ‘breaking’ the efforts of cybercriminals to infect data Related Research: Online Payment Fraud: Emerging Threats, Segment Analysis & Market Forecasts 2018-2023.
2019
Top Tech trends 2019
Juniper Research