Trends Identified

#twinning: Farming's digital doubles will help feed a growing population using less resources.
Imagine a planet where instant access to critical data on the world’s farmland could be provided to anyone that needs it. In the next five years, this will become reality when a digital twin of the world’s agricultural resources is readily available.
2019
5 in 5 - Research predicts five innovations that will change our lives within five years.
IBM Research
Spoiler alert: Blockchain will prevent more food from going to waste.
Within five years, we’ll eliminate many of the costly unknowns in the food supply chain. From farmers to grocery suppliers, each participant in the food ecosystem will know exactly how much to plant, order, and ship. Food loss will diminish greatly and the produce that ends up in our carts will be fresher—when blockchain technology, IoT devices, and AI algorithms join forces.
2019
5 in 5 - Research predicts five innovations that will change our lives within five years.
IBM Research
Mapping the microbiome will protect us from bad bacteria.
Within five years, food safety inspectors around the world will gain a new superpower: the ability to understand how millions of microbes coexist within the food supply chain. These microbes—some healthy for human consumption, others not—are everywhere –in foods at farms, factories, and grocery stores. The ability to constantly and cheaply monitor the behaviors of microbes at every stage of the supply chain represents a huge leap in food safety.
2019
5 in 5 - Research predicts five innovations that will change our lives within five years.
IBM Research
Dinner plate detectives: AI sensors will detect foodborne pathogens at home.
Within five years, the world’s farmers, food processors, and grocers—along with its billions of home cooks—will be able to detect dangerous contaminants effortlessly in their food. All they’ll need is a cell phone or a countertop with AI sensors.
2019
5 in 5 - Research predicts five innovations that will change our lives within five years.
IBM Research
A radical new recycling process will breathe new life into old plastic.
In five years, the disposal of trash and the creation of new plastics will be completely transformed. Everything from milk cartons and cookie containers to grocery bags and clothing will be recyclable, and polyester manufacturing companies will be able to take in refuse and turn it into something useful again.
2019
5 in 5 - Research predicts five innovations that will change our lives within five years.
IBM Research
The gender gap in labour force participation remains large
The much lower labour force participation rate of women, which stood at 48 per cent in 2018, com-pared with 75 per cent for men, means that around three in five of the 3.5 billion people in the global labour force in 2018 were men. After a period of rapid improvement that lasted until 2003, subsequent progress on closing the gender gap in participation rates has stalled. The sizeable gap of 27 percentage points registered in 2018 should motivate policy action aimed at both improving gender equality in global labour markets and maximizing human capabilities. Overall, labour force participation rates among adults have been declining for the past 25 years; the decline is even more pronounced among young people aged 15–24. This downward trend is projected to continue in the future. Some of the factors behind it – such as increased educational enrolment, greater retirement opportunities and higher life expectancy – are of course positive. Yet, the rise in the dependency ratio (i.e. the proportion of economically inactive people relative to the active) poses new challenges in terms of the organization of work and the distribution of resources in society.
2019
World Employment and Social Outlook
International Labour Organization (ILO)
Decent work deficits are widespread
A majority of the 3.3 billion people employed globally in 2018 experienced a lack of material well-being, economic security, equal opportunities or scope for human development. Being in employment does not always guarantee a decent living. Many workers find themselves having to take up unattractive jobs that tend to be informal and are characterized by low pay and little or no access to social protection and rights at work. Significantly, 360 million people in 2018 were contributing family workers and 1.1 billion worked on their own account, often in subsistence activities that are pursued because of an absence of job opportunities in the formal sector and/or the lack of a social protection system. Overall, 2 billion workers were in informal employment in 2016, accounting for 61 per cent of the world’s workforce. The poor quality of many jobs also manifests itself in the fact that, in 2018, more than one quarter of workers in low- and middle-income countries were living in extreme or moderate poverty. On a positive note, the incidence of working poverty has decreased greatly over the past three decades, especially in middle-income countries. In low-income countries, however, the pace of poverty reduction is not expected to keep up with employment growth, so that the actual number of working poor in these countries is projected to rise.
2019
World Employment and Social Outlook
International Labour Organization (ILO)
More than 170 million people are unemployed despitethe continued decrease in the global unemployment rate
An estimated 172 million people worldwide were unemployed in 2018, which corresponds to an un-employment rate of 5.0 per cent. It is remarkable that, whereas it took only one year for the global un-employment rate to jump from 5.0 per cent in 2008 to 5.6 per cent in 2009, the recovery to the levels that prevailed before the global financial crisis has taken a full nine years. The current outlook is un-certain. Assuming stable economic conditions, the unemployment rate in many countries is projected to decline further. However, macroeconomic risks have increased and are already having a negative impact on the labour market in a number of countries. On balance, the global unemployment rate should remain at roughly the same level during 2019 and 2020. The number of people unemployed is projected to increase by 1 million per year to reach 174 million by 2020 as a result of the expanding labour force.
2019
World Employment and Social Outlook
International Labour Organization (ILO)
Labour underutilization is more prevalent among women
Apart from the unemployed, a further 140 million people were in the “potential labour force” in 2018, which means that they have to be classified as underutilized labour. This group of people who are looking for a job but are not available to take up employment, or who are available but are not looking for a job, includes far more women (85 million) than men (55 million). The corresponding rate of labour underutilization is consequently much higher for women, at 11.0 per cent, than for men, at 7.1 per cent. In addition, women are much more likely to work part time and a significant proportion say they would prefer more hours of employment.
2019
World Employment and Social Outlook
International Labour Organization (ILO)
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