Subscribe for our latest future of work insights
Just like the sector more widely, this section is constantly evolving and being updated, so be sure to check back regularly.
According to Wikipedia:
A method of decentralised management and organisational governance developed by HolacracyOne, in which authority and decision-making are distributed throughout a holarchy of self-organising teams rather than being vested in a management hierarchy.
According to Wikipedia:
"The Internet of things (IoT) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and network connectivity, which enable these objects to connect and exchange data. Each thing is uniquely identifiable through its embedded computing system but is able to inter-operate within the existing Internet infrastructure. Experts estimate that the IoT will consist of about 30 billion objects by 2020.
The IoT allows objects to be sensed or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention. When IoT is augmented with sensors and actuators, the technology becomes an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, virtual power plants, smart homes, intelligent transportation and smart cities."
Someone operating within a corporate environment to drive innovation and the development of new products and/or services.
So-called because the role offers both the excitement of entrepreneurialism and the relative safety net of corporate life and self-employment.
“Machine Learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs.”
“The concept that a computer program can learn and adapt to new data without human interference”
“Machine learning is a type of artificial intelligence that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms can receive input data and use statistical analysis to predict an output value within an acceptable range.”
“Machine learning algorithms are often categorised as being supervised or unsupervised. Supervised algorithms require humans to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during training. Once training is complete, the algorithm will apply what was learned to new data. Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions. Unsupervised learning algorithms are used for more complex processing tasks than supervised learning systems.”
"Machine to machine refers to direct communication between devices using any communications channel, including wired and wireless. Machine to machine communication can include industrial instrumentation, enabling a sensor or meter to communicate the data it records (such as temperature, inventory level, etc.) to application software that can use it (for example, adjusting an industrial process based on temperature or placing orders to replenish inventory). Such communication was originally accomplished by having a remote network of machines relay information back to a central hub for analysis, which would then be rerouted into a system like a personal computer.
More recent machine to machine communication has changed into a system of networks that transmits data to personal appliances. The expansion of IP networks around the world has made machine to machine communication quicker and easier while using less power. These networks also allow new business opportunities for consumers and suppliers."
Someone who runs a business on a very small scale, with no more than five employees.
Wikipedia defines this cohort as follows:
“Millennials (also known as Generation Y) are the demographic cohort following Generation X. There are no precise dates for when this cohort starts or ends; demographers and researchers typically use the early 1980s as starting birth years and the mid-1990s to early 2000s as ending birth years.
Millennials are sometimes referred to as ‘Echo Boomers’ due to a major surge in birth rates in the 1980s and 1990s, and because millennials are often the children of the baby boomers. The 20th Century trend toward smaller families
in developed countries continued, however, so the relative impact of the ‘baby boom echo’ was generally less pronounced than the post-World War II baby boom.
Although millennial characteristics vary by region, depending on social and economic conditions, the generation is generally marked by an increased use and familiarity with communications, media, and digital technologies. In most parts of the world, their upbringing was marked by an increase in a liberal approach to politics and economics; the effects of this environment are disputed. The Great Recession that followed the crash of 2008 has had a major impact on this generation because it has caused historically high levels of unemployment among young people, and has led to speculation about possible long-term economic and social damage to this generation.”
Moore’s Law is a computing term which originated around 1970; the simplified version of this law states that processor speeds, or overall processing power for computers will double every two years.
Bloor Research defines this as:
A business "that is in permanent transition towards ever more effective, reliable and fast solutions to business problems"
Also known as “weak AI”, narrow AI focuses on one task at a time. In this sense, the kind of AI that we are seeing at this moment in time is narrow – a computer system is only looking at one thing and performing one specific set of actions at any one time. Computer vision, language / speech processing and recognition are still in their early stages of iteration and as such, can be considered as narrow AI.
'Weak artificial intelligence', also known as 'narrow AI', is non-sentient AI that is focused on one narrow task. Weak AI is defined in contrast to either strong AI (a machine with consciousness, sentience and mind) or artificial general intelligence (a machine with the ability to apply intelligence to any problem, rather than just one specific problem). All currently existing systems considered artificial intelligence of any sort are weak AI at most.
Siri is a good example of narrow intelligence. Siri operates within a limited pre-defined range, there is no genuine intelligence, no self-awareness, no life despite being a sophisticated example of weak AI. In Forbes (2011), Ted Greenwald wrote: "The iPhone/Siri marriage represents the arrival of hybrid AI, combining several narrow AI techniques plus access to massive data in the cloud."
Weak or "narrow" AI, in contrast, is a present-day reality. Software controls many facets of daily life and, in some cases, this control presents real issues. One example is the May 2010 "flash crash" that caused a temporary but enormous dip in the market.
— Ryan Calo, Center for Internet and Society, Stanford Law School, 30 August 2011.
The following two excerpts from Singularity Hub summarise weak-narrow AI:
When you call the bank and talk to an automated voice you are probably talking to an AI...just a very annoying one. Our world is full of these limited AI programs which we classify as "weak" or "narrow" or "applied". These programs are far from the sentient, love-seeking, angst-ridden artificial intelligences we see in science fiction, but that's temporary. All these narrow AIs are like the amino acids in the primordial ooze of the Earth.
We're slowly building a library of narrow AI talents that are becoming more impressive. Speech recognition and processing allows computers to convert sounds to text with greater accuracy. Google is using AI to caption millions of videos on YouTube. Likewise, computer vision is improving so that programs like Vitamin d Video can recognise objects, classify them, and understand how they move. Narrow AI isn't just getting better at processing its environment it's also understanding the difference between what a human says and what a human wants.
— Aaron Saenz, Singularity Hub, 10 August 2010.
A form of computer architecture onto which AI is built – they are so called because the inference is that their complexity represents the human brain.
A way of working whereby the worker delivers a specific piece of work at any time that the employer requires it.
A business demonstrating organisational modularity is one that can be separated and recombined to work more efficiently.
Also known as ubiquitous computing.
See Internet of Things.
Increasingly for some, working nine-to-five in the same place every day just doesn’t cut it. Instead, they opt for a ‘portfolio career’, splitting their time and skills between two or more part-time positions.
Irish author and philosopher, Charles Handy CBE, who specialises in organisational behaviour and management, defines the portfolio career as a kind of replacement, replacing a single, salaried job with a combination of self-directed activities: some might be for pay, some for good causes; some to exploit one’s competencies, some to learn new ones.
“In sociology and economics, the precariat is a social class formed by people suffering from precarity, which is a condition of existence without predictability or security, affecting material or psychological welfare. Unlike the proletariat class of industrial workers in the 20th century who lacked their own means of production and hence sold their labour to live, members of the precariat are only partially involved in labour and must undertake extensive "unremunerated activities that are essential if they are to retain access to jobs and to decent earnings". Specifically, it is the condition of lack of job security, including intermittent employment or underemployment and the resultant precarious existence. The emergence of this class has been ascribed to the entrenchment of neoliberal capitalism. [...] The term is a portmanteau obtained by merging precarious with proletariat.”
The Chartered Institute for Professional Development estimates that there are 1.3 million Britons employed in the gig economy, while TUC says that one-in-ten British workers are in ‘precarious work’.