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Future of work glossary
Welcome to Working the Future's glossary. Here you'll find our glossary of terms relating to socio-cultural and technological shifts that are set to impact the future of work. Our feeling is that many key terms and acronyms are used interchangeably across non-specialist media, giving rise to confusion around what exactly each term means. This list, therefore, is designed to explain as succinctly as possible each term and outline what that means for day-to-day living and working.
Just like the sector more widely, this section is constantly evolving and being updated, so be sure to check back regularly.
Sometimes also known as Artificial Emotional Intelligence.
Affective computing is, according to Wikipedia, the development of systems and devices that can recognise, interpret, process, and simulate human feelings and emotions.
Oxford English Dictionary:
“The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and translation between languages.”
Merriam Webster gives two definitions:
i. a branch of computer science dealing with the simulation of intelligent behaviour in computers
ii. the capability of a machine to imitate intelligent human behaviour
“The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalise, or learn from past experience.”
According to Techopedia:
“Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world”.
The effectiveness of AI is therefore contingent on “big data” – as society progresses and becomes increasingly reliant on and interactive with websites, data about what we are interested in, what we are buying, what we like and dislike is harvested, ready to be used to predict our future behaviours and preferences. This is data analytics in situ.
Techopedia pages go on to explain: “Artificial Intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering.”
Which leads us onto….
Artificial General Intelligence or General AI is AI operating at the human level – capable of understanding and reasoning in a similar fashion to the human brain.
The challenge with this is that we still don’t know enough about how the human brain works in its entirety to be able to replicate its’ working. Humans are able to think in the abstract and come up with concepts that have no prior basis. the human brain is able to focus on several things in parallel and make decisions with very limited information (based on gut feeling), something which computers would struggle to achieve.
“Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some AI research and a common topic in science fiction and future studies. Artificial general intelligence is also referred to as "strong AI", "full AI" or as the ability of a machine to perform "general intelligent action".
Academic sources reserve "strong AI" to refer to machines capable of experiencing.” As things stand right now, AI has reached the point where its level of intelligence is the same as the average intelligence of a four-year-old. We therefore have some way to go before reaching AGI, but rest assured, progress is being made each and every day.
This is the point at which computer programmes become smarter than humans, and is a point that troubles many futurists from both an ethical and philosophical perspective as its considered to be the point at which humanity as we know it, ends.
“A superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. "Superintelligence" may also refer to a property of problem-solving systems (e.g.,
superintelligent language translators or engineering assistants) whether or not these high-level intellectual competencies are embodied in agents that act in the world. A superintelligence may or may not be created by an intelligence explosion and associated with a technological singularity.”
Automation is possibly the most overused term and it’s worth noting that its context can be quite broad. When thinking about manual tasks, such as agriculture or factory line work, we could for example say that automation has already taken place; machines or robots have replaced factory line workers in car manufacturing to bring about massive efficiencies. Equally, in agriculture, modern machinery has removed the requirement for human input. This is more precisely known as hardware automation.
Software automation, on the other hand, is where software is developed to perform tasks previously undertaken by humans. The potential for software automation in the workplace is vast – any job function that is bound by process and highly repetitive is vulnerable to automation. Think for example of software to automate tax returns or monthly management accounts.
“A general technology term that is used to describe any process being automated through the use of computers and computer software”
“Automation is the creation of technology and its application in order to control and monitor the production and delivery of various goods and services. It performs tasks that were previously performed by humans. Automation is being used in a number of areas such as manufacturing, transport, utilities, defence, facilities, operations and lately, information technology”
“Baby boomers are the demographic cohort following the Silent Generation. There are no precise dates for when this cohort starts or ends; demographers and researchers typically use starting birth years ranging from the early-to-mid 1940s and ending birth years ranging from 1960 to 1964.
The term ‘baby boomer’ is also used in a cultural context, so it is difficult to achieve broad consensus of a precise date definition. Different people, organizations, and scholars have varying opinions on who is a baby boomer, both technically and culturally. Ascribing universal attributes to such a generation is difficult, and some believe it is inherently impossible, but many have attempted to determine their cultural similarities and historical impact, and the term has thus gained widespread popular usage.
Baby boomers are associated with a rejection or redefinition of traditional values. Many commentators, however, have disputed the extent of that rejection, noting the widespread continuity of values with
older and younger generations. In Europe and North America, boomers are widely associated with privilege, as many grew up in a time of widespread government subsidies in post-war housing and education, and increasing affluence.
As a group, baby boomers were the wealthiest, most active, and most physically fit generation up to the era in which they arrived, and were amongst the first to grow up genuinely expecting the world to improve with time. They were also the generation that received peak levels of income; they could therefore reap the benefits of abundant levels of food, apparel, retirement programs, and sometimes even "midlife crisis" products. The increased consumerism for this generation has been regularly criticized as excessive.
One feature of the boomers was that they have tended to think of themselves as a special generation, very different from those that had come before. In the 1960s, as the relatively large numbers of young people became teenagers and young adults, they, and those around them, created a very specific rhetoric around their cohort, and the changes they were bringing about. This rhetoric had an important impact in the self perceptions of the boomers, as well as their tendency to define the world in terms of generations, which was a relatively new phenomenon.”
A business model emerging from Silicon Valley where a company relies on and invests in a key group of employees, while leveraging networks of on-demand talent as and when required.
"Coworking is a style of work that involves a shared working environment, often an office, and independent activity. Unlike in a typical office environment, those coworking are usually not employed by the same organisation. Typically it is attractive to work-at-home professionals, independent contractors, or people who travel frequently who end up working in relative isolation.
Coworking is a social gathering of a group of people who are still working independently, but who share values, and who are interested in the synergy that can happen from working with people who value working in the same place alongside each other.
Coworking offers a solution to the problem of isolation that many freelancers experience while working at home, while at the same time letting them escape the distractions of home.".
Also known as crowd employment.
When groups of remote workers come together, typically via an online collaborating platform, to deliver on a specific project, piece of work or problem requiring a solution.
It usually provides a secondary source of income, or, as has been in the case of Wikipedia, crowdworkers provide their input for free, in the belief that knowledge should be free to everyone.
According to Investopedia:
“Deep learning is a subset of machine learning in AI that has networks which are capable of learning unsupervised from data that is unstructured or unlabelled. Also known as 'Deep Neural Learning' or 'Deep Neural Network'."
Experts say that deep learning attempts to mimic the information processing that occurs naturally in the brain, although neuroscience and a detailed understanding of how thinking works is still, in and of itself in its infancy.
Whereas machine learning would need to receive supervised algorithms in the first instance, deep learning can work with unsupervised algorithms, becoming ever more accurate in its outputs as it moves forward.
The process by which businesses and enterprises become increasingly digital and dependent on IT for success outcomes.
The move towards digital transformation is driven increasingly by internal users and external consumers.
According to Wikipedia, this is "a reduction in the use of intermediaries between producers and consumers".
It is widely thought that technological advances will bring about a fall in the requirement of agents; for instance, Rightmove reduces the requirement for estate agents, Expedia reduces the requirement for travel agents, advances in software will negate the requirement for recruitment agents etc.
Engage for Success, the UK-based not-for-profit organisation that champions employee engagement, says:
“Employee engagement is a workplace approach resulting in the right conditions for all members of an organisation to give of their best each day, committed to their organisation’s goals and values, motivated to contribute to organisational success, with an enhanced sense of their own well-being”.
Forbes Magazine describes employee engagement as “an emotional commitment an employee has to the organisation and its goals”, suggesting that engaged employees will go above and beyond to deliver more for their employer.
The term ‘encore career’ was first coined by San Francisco-based Marc Freedman, and made popular by his book Encore: Finding Work That Matters in the Second Half of Life.
He sees an encore career as work in the second half of life that combines continued income, greater personal meaning, and social impact.
These jobs are paid positions often in public interest fields, such as education, the environment, health, the government sector, social services, and other nonprofits.
Described by Stanford HCI as teams that "structure expert crowd work to enable users to complete complex and interdependent projects (e.g., web design). Flash teams are instantiated through Foundry, a web platform that gathers workers and manages them as they follow a structured workflow defining each task and how workers interact".
Described by MIT SMR as "adaptable, flexible alternatives to traditional workflows and roles".
Wikipedia says of Gen X:
“Generation X, or Gen X, is the cohort that follows the baby boomers and precedes the millennials. There are no precise dates for when Generation X starts or ends, demographers and researchers typically use birth years ranging from the early-to-mid 1960s to the early 1980s.
Members of Generation X were children during a time of shifting societal values and as children were sometimes called the ‘latchkey generation’, due to reduced adult supervision compared to previous generations, a result of increasing divorce rates and increased maternal participation in the workforce, prior to widespread availability of childcare options outside the home. As adolescents and young adults, they were dubbed the "MTV Generation" (a reference to the music video channel of the same name). In the early 1990s they were sometimes characterized as slackers, cynical and disaffected. Some of the cultural influences on Gen X youth were the musical genres of grunge and hip hop music, and indie films. In midlife, research describes them as active, happy, and achieving a work–life balance. The cohort has been credited with entrepreneurial tendencies.”
Wikipedia says of this cohort:
“Generation Z is the demographic cohort after the millennials. Currently, there are many competing names used in connection with them in the media. There are no precise dates for when this cohort starts or ends, but demographers and researchers typically use the mid-1990s to mid-2000s as starting birth years. However, there is little consensus regarding ending birth years.
Most of Generation Z have used the Internet since a young age, and they are usually thought to be comfortable with technology and with interacting on social media. Some commentators have suggested that growing up through the Great Recession has given the generation a feeling of unsettlement and insecurity.”
This is a subset of AI.
Computer programmes are encoded as genes that then evolve using algorithms designed to establish solutions to problems. In this way, programming occurs much along the principles of Darwin’s natural selection – the computer programme works out which solutions are strongest and progresses these, ditching those options that are weaker.
The computer programme continuously evolves in a perpetual feedback loop of self-improvement.
Wired.co.uk describes this as:
“The gig economy gets its name from each piece of work being akin to an individual 'gig' – although, such work can fall under multiple names. It has previously been called the "sharing economy" — mostly in reference to platforms such as Airbnb — and the ‘collaborative economy’. However, at its core are app-based platforms that dole out work in bits and pieces — making deliveries, driving passengers or cleaning homes — leading some to prefer the term ‘platform economy’.
Not all gig economy roles are based around a technology platform. Gig economy workers can also work for more traditional companies, which have changed how their staffing system operates. Delivery drivers for Hermes, for example, also work on a piece-by-piece delivery basis, though their employer does not have the tech start-up origins often associated with this type of work.”