Glossary

A

Addictive tech

 

Some applications are specifically designed to be addictive, driven by fierce competition for eyeballs and engagement. While this might be good for companies in the business of selling advertisements to audiences, there is an increasing awareness of the societal and environmental harms of addictive tech.

 

 

Adversarial machine learning

 

These are attacks on (or using) machine learning systems. Attackers may tamper with training data or identify specific inputs that a model classifies poorly to deliberately create undesired outcomes.

 

 

Affective (emotional) computing

 

A collective term for systems and devices that can recognize, interpret, process, simulate and respond to human emotions.

 

 

Agent-based simulation

 

The use of simulated independent agents, each working towards their own goals, to model a real world situation. Such simulatioms can help us understand complex phenomena such as the spread of diseases or protein folding.

 

 

AGI research

 

An Artificial General Intelligence (AGI) has broad capabilities across a range of intellectual tasks, and is often compared to human-level intelligence. This contrasts with today's "narrow" AI which can be remarkable, but only for very specific tasks.

AI agents

 

Functionality built into applications which combines the functionality of publicly available generative AI models with specific knowledge from outside the model, such as product information.

 

 

AI as a service

 

“Ready-to-go” AI solutions offered as a service on cloud platforms. They often don't require specialized AI or ML skills to be used.

 

 

AI in security

 

AI is increasingly being deployed both defensively, to respond to threats more dynamically, and offensively, to probe for weaknesses in a system.

 

 

AI marketplaces

 

Marketplaces such as AWS Marketplace, Google TensorFlow Hub and MS Azure Marketplace enable independent developers and companies to sell their models to a global market. They also allow consumers to quickly leverage those models to create value quickly.

 

 

AI safety and regulation

 

Government regulation and guidance on the use of AI, intended to ensure responsible use and consequences of AI systems. This includes monitoring, compliance and good practice.

AI-assisted software development

 

The use of AI to speed up or improve software development. Examples include code completion in IDEs, AI-created automated tests, AI that can detect bugs or even AI code generation tools.

 

 

AI-generated media

 

Images, audio or video that have been manipulated by AI. Also known as synthetic media.

 

 

AI, IoT and XR combined solutions

 

A new breed of solutions in which multiple technologies are combined and act together. Drones, robotics and autonomous vehicles are all examples of devices that require machine learning, processing streams of data and layers of intelligence to solve problems.

 

 

AI/ML on edge

 

The ability to run AI and machine learning algorithms at the edge of a network, often on resource-constrained devices.

 

 

Alternative currencies

 

Currencies other than money, such as cryptocurrencies or reputation-based currency. Increasingly, this includes vendor-specific reward-based currencies such as Starbucks Stars or Amazon Coins.

Augmented reality

 

Where the physical world is combined with the digital. A limited form of AR is now ubiquitous, delivered via Apple and Android mobile devices, capable of overlaying virtual objects to a camera view of the world. More advanced AR is delivered via a dedicated headset such as Apple Vision Pro, Microsoft’s Hololens or Meta's Quest 3.

 

 

Automated compliance

 

The use of technology to make all the data required to satisfy compliance reports, checks and balances readily available. In many cases, the automation simplifies reporting by sifting through data; however, AI is now beginning to replace manual decision making.

 

 

Automated workforce

 

The use of technology to perform repeatable or predictable workflows. Automated workforce doesn’t mean completely replacing humans; in some cases human-machine "teaming" may produce better results than either working alone.

 

 

AutoML

 

An approach to partly automate the work of data scientists and machine learning engineers by automatically selecting and training machine learning models for specific tasks.

 

 

Autonomous robots

 

Smaller and cheaper than their industrial counterparts, robots with on-board AI are able to sense their environment, navigate, learn to complete tasks and even fix themselves and other things.

 

Autonomous vehicles

 

Self-driving cars, trucks and public transport. While the headline focus may be on self-driving cars, autonomous vehicles also have high potential for specialized industrial and business applications such as mining and factory floors.

B

Brain-computer interfaces

 

A device that reads and analyzes signals from the brain and turns them into an input mechanism for a computer. The human and the device, after a period of training, work together to encode and decode human intentions.

C

Causal inference for ML

 

Techniques to draw cause and effect relationships between the input data and the outcomes of a machine learning model, which allows a model to be more generalizable and require less training data to perform effectively.

 

 

Code of ethics for software

 

A set of guidelines organizations can use to manage risk and mitigate the potential negative consequences of given technologies (such as AI bias).

 

 

Collaboration ecosystems

 

When individuals or organizations share common goals, they will probably want to work together. To do so, they need a set of tools and resources they can use to unlock value effectively — a good example is a remote environment for development teams. It allows people to solve problems together.

 

 

Consumer XR

 

Extended reality intended for consumers rather than professional or enterprise users.

D

Data clean room

 

Secure environments for organizations to share and combine data with each other without having to physically share their own data.

 

 

Data contract

 

A formal agreement between two parties – producer and consumer – to use a dataset or data product.

 

 

Data marketplaces

 

A system that enables the finding, buying, sharing and selling of data within and outside an organization.

 

 

Data mesh

 

A data platform organized around business domains where data is treated as a product, with each data product owned by a team. To enable speed and drive standardization, infrastructure teams provide tools that allow data product teams to self-serve.

 

 

Data product specification

 

A precise technical description of a data product that enables its provisioning, configuration, and governance.

Decentralized data platforms

 

Use of multiple data stores instead of singular, monolithic centralized stores. A good example is data mesh (see above).

 

 

Decentralized identity

 

Also known as self-sovereign identity, decentralized identity (DiD) is an open-standards-based identity architecture that uses self-owned and independent digital IDs and verifiable credentials to transmit trusted data. Although not dependent on blockchains, many current examples are deployed on them as well as other forms of distributed ledger technology, and private/public key cryptography, it seeks to protect the privacy of and secure online interactions.

 

Decentralized personal data stores

 

A data architecture style where individuals control their own data in a decentralized manner, allowing access on a per-usage bases (for example, Solid PODs).

 

 

Decentralized security

 

Rather than using traditional security perimeters that are a single point of failure, techniques such as zero-trust networks decentralize security checks across the network.

 

 

Decision science

 

Combines AI tools and techniques with behavioral and management sciences for the purpose of upskilling and amplifying decision making and decision makers across a variety of complex problems from scenario planning to operations research.

Developer experience platforms

 

Platforms which provide the tooling to make it as effective as possible for developers to create, test and deploy software.

 

 

DevSecOps

 

An abbreviated portmanteau for development, security and operations. This is an approach that includes security as a first-class concern, together with development and operations.

 

 

Differential privacy

 

A privacy technique that introduces noise in a dataset in such a way as to provide individual privacy while still allowing insights to be drawn or machine learning models to be built on top of the data.

 

 

Digital carbon management

 

Measuring organizational green house gas (GHG) emissions and efforts to mitigate those emissions. Establishing a carbon footprint and a program to determine it is an essential component on the journey towards net zero and is the first building block towards any sustainability strategy.

 

 

Digital ecosystems

 

Disparate participants, systems and even organizations that cooperate, collaborate and compete to create an emergent ecosystem where the whole is greater than the sum of the parts. Examples include the travel industry, online marketplaces and new “super apps” such as Gojek and WeChat.

Digital humans

 

AI-powered virtual assistants and non-playable characters that recreate human interaction within the metaverse.

 

 

Digital twin

 

A virtual model of a process, product or service that allows both simulation and data analysis. 3D visualization can be used together with live data, so you can understand what is happening to pieces of equipment you can’t actually see.

 

 

Distributed energy resources

 

A category of electrical power generation that are “behind-the-meter.” DERs generate power for the grid, and reward energy credits to the DER owner. An example is solar panels installed on a home.

E

Easing access to Generative AI

 

Making AI easier to use by lowering the barrier to entry with shared context and other data that those who aren't familiar with prompt engineering may struggle with.

 

 

Edge computing

 

Bringing data storage and processing closer to the devices where it is stored, rather than relying on a central location that may be thousands of miles away. Benefits include reduced latency for real-time systems and improved data privacy.

 

 

Encrypted computation

 

The ability to perform calculations on encrypted data, without first decrypting it. Useful to maintain data privacy while allowing data storage and manipulation to be outsourced. This includes technologies like secure multi-party computation and homomorphic encryption.

 

 

Enterprise XR

 

An umbrella term for virtual and augmented reality and related technologies which are now being used in the enterprise. Advantages can include cost reductions, efficiency or safety improvements.

 

 

Ethical frameworks

 

Decision-making frameworks that attempt to bring transparency and clarity into the way decisions are made, especially around the use of AI and potential bias in data.

Evolutionary architectures

 

In contrast to traditional up-front, heavyweight enterprise architectural designs, evolutionary architecture accepts that we cannot predict the future and instead provides a mechanism for guided, incremental change to systems architecture.

 

 

Explainable AI

 

A set of tools and approaches to understand the rationale used by an ML model to reach a conclusion. These tools generally apply to models that are otherwise opaque in their reasoning.

F

Federated learning

 

An approach that downloads a machine learning model and then computes or trains a specific, modified model using local data on another device. The approach helps multiple organizations to collaborate on model creation without explicitly exchanging protected data.

 

Fine grained data access controls

 

More granular access controls for data, such as policy-based (PBAC) or attribute-based (ABAC) that can apply more contextual elements when deciding who has access to data.

 

FinOps

 

The practice of bringing financial accountability to the variable spending model of cloud computing. It involves a collaborative approach among teams such as finance, operations and development to manage and optimize cloud costs effectively.

G

GenAI tools in IDEs

 

The integration of generative artificial intelligence (GenAI) capabilities into integrated development environments (IDEs), the software applications that programmers use to write code.

 

 

Generative AI

 

AI that creates text, image, audio and video from simple human language prompts.

 

 

Gesture recognition

Machine understanding and interpretation of human gestures such as waving, making an “up” or “down” motion, hand positioning and so on.

 

 

Green cloud

 

Data centers fed by renewable energy, running software and systems designed and optimized for efficient processing while also minimizing energy consumption.

 

 

Green software engineering

 

Choosing technologies, programming languages, algorithms and software architectures that are efficient and use less energy.

Green UX

 

Design of user interfaces and prompts that help people understand the environmental consequences of the choices they make. Examples include an airline website displaying carbon emissions for flights or a mapping tool showing the carbon output for driving a particular route.

I

Increased regulation

 

The steady increase of regulation, especially around data, privacy, security and greenhouse gas emissions.

 

 

Industrial XR

 

Using virtual environments to test and model desired physical outcomes in an industrial context.

 

 

Integrated data and AI platforms

 

Platforms designed specifically for machine learning, providing end-to-end capabilities such as data management, feature engineering, model training, model evaluation, model governance, explainability, AutoML, model versioning, promotion between environments, model serving, model deployment and model monitoring.

 

 

Intelligent machine to machine collaboration

 

Technologies enabling the direct interaction of devices and information sharing between them, usually in an autonomous fashion. This enables to decision making and action with little or no human intervention.

 

 

International law for crypto assets

 

Crypto assets are traded across the world. Similar to the move for international laws for AI, crypto assets also need international law for cross border trading. This might include costs, categories of assets and what constitutes legal trading.

K

Knowledge graphs

 

A way to represent knowledge and semantic relationships between entities using a graph data structure.

M

MLOps

 

A movement to bring DevOps practices to the field of machine learning. MLOps fosters a culture where people, regardless of title or background, work together to imagine, develop, deploy, operate, monitor and improve machine learning systems in a continuous way. Continuous Delivery for Machine Learning (CD4ML) is Thoughtworks' approach to implement MLOps end-to-end.

 

Multimodal AI

 

AI model interactions that span different modes of communication. For example, a chatbot that understands and responds in both written and spoken language.

N

Natural language processing

 

Artificial intelligence and other modern technologies that help computers understand the intent and meaning of spoken or written language. Used for everything from dictation software to analyzing documents for meaning.

 

 

Next-generation cryptography

 

Forms of cryptography created in response to technological or societal challenges. Examples include quantum-resistant encryption algorithms, confidential computing with specialized hardware secure enclaves, homomorphic encryption allowing computation to occur on the data while it is still encrypted, and energy efficient cryptography.

O

Online machine learning

 

A technique where algorithms continuously learn based on the sequential arrival of data, and can explore a problem space in real time. Contrasts with traditional machine learning where model training uses only historical data and cannot respond to dynamic or previously-unseen situations.

 

 

Operationalize AI

 

Making AI a normal part of business operations including appropriate security and governance.

P

Personal information economy

 

A business model that aims to extract business value from the possession and use of large amounts of personal information. Examples range from the primitive use of cookies to the targeted profiling of people via their online behavior. This has historically been the domain of companies or intermediate ad-based services trying to retain and target customers, but, since GDPR and similar privacy laws, we are seeing a shift towards people controlling what data they wish to expose in exchange for a service.

 

 

Personalized healthcare

 

Understanding an individual patient’s genetic profile to identify potential issues before they happen and provide more effective treatments in response to existing conditions.

 

 

Platforms as products

 

A way of creating and supporting platforms with a focus on providing customer (user) value instead of treating platform building as a time-boxed project.

 

 

Privacy first

 

Privacy first is a significant shift in business, organization and product strategy, where privacy operates as a core business value and offering. This shift moves away from the prior movement where "users are the product", into a new realm, where building trust and transparency comes first.

 

 

Privacy-aware communication

 

Communications software that directly advertises its security stance and features, such as end-to-end encryption.

Privacy-enhancing technologies (PETs)

 

A collection of technologies and techniques for preserving user privacy, such as anonymization, encrypted computing and differential privacy.

 

 

Privacy-respecting computation

 

New techniques that allow stronger guarantees for privacy, even when personal data is used in computations. Part of the broader category of privacy-enhancing technologies (PETs).

 

 

Production immune systems

 

Systems that monitor metrics across complex distributed systems and take corrective action if a problem is detected. They are often used for security, but increasingly also for resilience and recovery in the face of an outage.

Q

Quantum computing

 

The use of probabilistic states of photons, rather than binary ones and zeros, to run algorithms. Although proven to work in specific problem spaces, quantum computing has yet to scale to broadly useful applications.

 

 

Quantum machine learning

 

Machine learning algorithms adapted and executed on a quantum computing engine, generally used to analyze classical (non-quantum) data.

R

RAG (Retrieval augmented generation)

 

A method in artificial intelligence where the system enhances its response generation by fetching relevant information from a large database or knowledge source. This approach combines the creative aspects of generative AI models with the precision of data retrieval, enabling more accurate and contextually relevant responses in various business applications.

 

 

Re-decentralization

 

Systems, both human and machine, originally designed to be decentralized have become more centralized over time. Re-decentralization refers to the conscious effort of moving those systems back to a decentralized model.

 

 

Responsible tech facilitation

 

Tools and techniques are emerging that support incorporating responsible tech into software delivery processes, primarily focusing on actively seeking to incorporate under-represented perspectives; some examples include Tarot Cards of Tech, Consequence Scanning, and Agile Threat Modeling.

 

 

Retina resolution XR

 

Ultra-high resolution XR with photorealistic rendering over a wide field of view. Currently only available via extremely expensive headsets. An example is Varjo XR-3.

 

 

Robotic process automation and low code

 

Robotic process automation (RPA) aims to allow scripts or bots to interact with UIs instead of needing a human operator. Low-code seeks to democratize programming, by allowing non-programmers to create software systems.

S

Satellite networks

 

High-speed, low-latency broadband for places where traditional fiber or wireless network providers won’t spend the money to connect. Examples include Starlink from SpaceX, Kuiper from Amazon, OneWeb and Telesat.

 

 

Secure software delivery

 

Security applied to the entire process of software creation, which in modern architectures includes the delivery pipeline used to build, test and deploy applications and infrastructure.

 

 

Smart cities

 

An urban area that uses different types of IoT sensors to collect data coupled with platforms to integrate and act on the data, advising or commanding digitally enabled systems to perform some response. Insights gained from the data are used to manage assets, resources and services efficiently; in return, that data is used to improve the operations across the city.

 

 

Smart energy management systems

 

Ubiquitous availability of energy usage data via measurement equipment, APIs and tools gives a range of energy players (generators, distributors, suppliers, vendors) and customers a greater ability to understand and analyze their energy usage.

 

 

Smart homes

 

Featuring smart hubs, homes are now becoming 'smart', allowing people to control almost all household systems. Analytics can even guide or manage heat and energy supply and learn from individual habits or those in a neighbourhood.

Smart systems and ecosystems

 

Networks of networks that use AI and ML to enhance a system to become more than the sum of its parts. For example, in a smart city, networks of cars and roadside sensors help speed the flow and safety of traffic.

 

 

Software-defined vehicles

 

Automobiles where the core functionalities, features and user experience are primarily governed by software, rather than traditional mechanical and electrical systems. This approach enables increased flexibility, customization and continuous enhancement through remote updates, significantly transforming the vehicle's capabilities and, in turn, the automotive industry's business models.

 

 

Spatial audio

 

Advanced signal processing, originally from Apple, that allows sounds to be placed virtually in 3D space. Spatial audio also tracks headphones and screen position to allow for accurate sound placement.

T

Technology and sovereign power

 

Rising forces are leading to internet balkanization — the splintering of the internet — many led by nation states. Privacy legislation accelerates this process, as it enforces data rights, data sovereignty, and strongly impacts how companies deploy and distribute systems and data on the Internet.

 

 

Technology for circular economy

 

A closed economic system where raw materials and products are constantly shared so as to lose their value as little as possible. Technology that supports this includes reusable services, traceability, IoT and data mining.

 

 

Touchless interactions

 

The ability to interact with devices without touching, driven at least partially as a result of the COVID-19 pandemic. Specific technologies include hand tracking and voice and gesture recognition.

 

 

Trustworthy data

 

An emerging set of techniques to certify the provenance of data and to govern its use across an organization. This could prove transformative in the effort to track and enhance progress towards sustainability targets.

U

Ubiquitous connectivity 

 

Providing connectivity to everyone and everything, everywhere, all the time. Some predict ubiquitous connectivity will super-charge innovation in resource-limited parts of the planet, while critics see it as expensive and unnecessary.

 

 

Understandable consent

 

Most terms of service (TOS) or end-user license agreements (EULAs) are impenetrable legalese that make it difficult for people without a law background to understand. Understandable consent seeks to reverse this pattern, with easy-to-understand terms and clear descriptions of how customers' data will be used.

V

Vector databases

 

Specialized storage systems designed to efficiently handle and index high-dimensional data vectors, commonly used in machine learning and AI applications.

 

X

XR-enabled hybrid working

 

A collaboration strategy where, using XR, everyone on a hybrid local/remote team interacts with the same shared artifacts, such as whiteboards and other information radiators. This brings the remote collaborators closer to the in-person team.

Z

Zero knowledge proofs

 

A method that allows one party to prove to another that a statement is true without revealing how it knows it is true.