Glossary

A B C D E F G H I J K L M N O P Q R S T U V W X #

A

accelerationism: The argument that AI should be built as fast as possible, and that slowing down does more harm than the technology does; "e/acc" online.
ad fraud: Criminals or bots generate fake clicks, views, or user interactions on digital ads, stealing advertiser money without reaching real audiences.
aerospace: Design and manufacture of aircraft and spacecraft, increasingly using AI for autonomous navigation and system control.
agent: A program that is given a goal rather than a command, and then decides for itself which steps and which tools to use to reach it.
agentic AI: Models put to work as agents: booking, buying, writing code or operating other software with little or no human confirmation between the steps.
agentic AI security: Protecting systems where AI software operates independently to complete tasks without constant human oversight.
agricultural AI: Software that analyzes crops and soil to predict yields, optimize watering, and reduce waste on farms.
Agricultural biotechnology: Using genetic engineering and biological science to develop crops and farming methods that improve yields, reduce pesticides, or adapt to climate challenges.
AI accelerator: A chip built for the one kind of arithmetic that neural networks do, rather than the general-purpose work a processor is otherwise asked for.
AI accelerators: Specialized computer chips designed to process artificial intelligence tasks much faster than standard processors.
AI adoption: Organizations and people using AI tools in real work, moving beyond experiments to measurable productivity changes.
AI agent autonomy: The degree to which an AI system can make decisions and take actions without human instruction or oversight in real-world tasks.
AI agent governance: Rules and oversight for software that acts independently to complete tasks, ensuring it stays aligned with human intentions.
AI agent hardware In the news: Physical computers built to run autonomous AI systems that can take actions and make decisions without human intervention.
AI agent monitoring In the news: Tools and practices for tracking what autonomous AI systems are doing and catching problems before they cause harm.
AI agent safety: Ensuring AI systems that take actions independently don't cause harm or pursue goals in unintended ways.
AI agent security In the news: Protection against risks when AI systems act independently to complete tasks without constant human oversight or approval.
AI agent swarms: Multiple AI systems working together on a single task, mimicking how colonies of insects coordinate without central control.
AI agents In the news: Software that takes actions toward a goal without being given step-by-step instructions, making decisions based on its understanding of a situation.
AI agents, autonomous systems: AI agents are software systems that perceive their environment and take actions independently to achieve specific goals.
AI agents, commerce: Software that acts independently to buy, sell, or negotiate on behalf of businesses or consumers without human intervention for each transaction.
AI agents, Slack: AI systems that work within Slack to help teams automate tasks like answering questions or organizing information.
AI agents, workplace: Software that can perform business tasks independently, making decisions and taking actions without constant human instruction.
AI alignment: The challenge of building AI systems that behave according to human intentions and values rather than pursuing their programmed goals in harmful ways.
AI alignment, research: Work to ensure artificial intelligence systems behave as their creators intend, especially as they become more powerful and autonomous.
AI and software engineering: AI systems that write, test, and debug code, changing how programmers work and what coding skills matter.
AI API economics: The pricing and business models for renting AI capabilities over the internet, affecting who can afford to build with AI.
AI assistant: A chatbot attached to something you already use — a phone, an inbox, an editor — so that it can act on what is in front of you.
AI assistant security: Safeguards against unwanted uses of conversational AI systems, including preventing harmful outputs and protecting user data from misuse.
AI assistants: Software that answers questions and completes tasks through conversation, deployed by companies to reduce human workload.
AI automation: AI replacing human labor in specific tasks, raising questions about job displacement and economic change.
AI autonomous capabilities: The ability of an AI system to make decisions and take actions without direct human control or approval.
AI autonomy: The ability of an AI system to make and carry out decisions with minimal human intervention, raising questions about control and accountability.
AI avatars: Computer-generated characters that use artificial intelligence to speak, move, and interact with users, often replacing human customer service or entertainment roles.
AI benchmarking: Standardized tests measuring whether AI systems can solve particular problems, used to compare models and track progress.
AI biotech: Using machine learning to discover drugs, predict protein structures, and design new medicines faster than traditional laboratory methods.
AI browser features: AI tools built into web browsers to summarize pages, generate text, and answer questions without leaving the site.
AI bubble: The claim that the money going into AI is far ahead of the money coming out of it, and that the gap closes downwards.
AI camera app: Mobile software that uses artificial intelligence to enhance, edit or interpret photos and video in real time.
AI camera systems: Cameras that use artificial intelligence to recognize objects, people, or events and respond automatically, now deployed in security, manufacturing, and retail.
AI capabilities forecasting: Predicting what future AI systems will be able to do, used to plan investment and regulation.
AI chemistry: Using machine learning to discover new molecules and predict how they will behave, speeding up drug and materials research.
AI chip export controls: Government restrictions on selling advanced semiconductors abroad, typically to prevent rival nations from developing powerful AI systems.
AI chips: Specialized processors designed to run artificial intelligence models efficiently, increasingly central to tech company competition.
AI code generation: AI that writes computer code based on descriptions or examples, used by programmers to speed up development.
AI coding agents: Software that writes computer code automatically by understanding what a programmer asks it to do.
AI coding assistance: Software that writes or completes computer code based on text descriptions or partial code, speeding up programming work.
AI coding assistants: Software tools that write or suggest code automatically, speeding up development and raising questions about responsibility for errors.
AI coding tools: Software that writes, debugs, or completes computer code automatically, increasing programmer productivity and shifting job skills.
AI companions: AI chatbots designed to simulate friendship or emotional support, raising questions about isolation and psychological impact.
AI consciousness: The question of whether artificial intelligence systems can be aware or have subjective experience, which remains unresolved and drives ongoing debate.
AI consciousness and ethics: The question of whether AI systems can think or feel, and how to build them responsibly.
AI consciousness debate: Disagreement over whether advanced AI systems have subjective experience or awareness.
AI consumer products In the news: Everyday devices and software with AI built in, from phones to home assistants, that affect how people work and live.
AI consumer tools: Artificial intelligence products designed for everyday users to write, create images, code, or search.
AI content generation: AI systems that create text, images, video, or audio, raising concerns about authenticity, copyright, and misinformation.
AI content licensing: Agreements that let AI companies use published text, images, or video to train their systems, with payments to creators.
AI copyright: Legal question of whether AI systems can own copyrights or whether training on copyrighted material requires permission from creators.
AI creative work: Content generated or substantially shaped by artificial intelligence, raising questions about authorship, copyright, and human creativity.
AI creativity tools: Software that generates images, text, music or video when given a description, used increasingly by artists and professionals to speed up creation.
AI cybersecurity: Using artificial intelligence to detect and prevent cyberattacks, or the vulnerabilities that AI systems themselves create for hackers.
AI data policy: Government rules governing how much data companies can use to train AI and what rights people have over their information.
AI datacenters: Specialized computer facilities that house the servers and cooling systems needed to train and run large artificial intelligence models.
AI datacentre efficiency: How well computer facilities running AI systems convert electrical power into useful computation with minimal waste.
AI datacentre energy: The electricity consumed by large computer facilities that train and run artificial intelligence systems.
AI datacentre power constraints: The limited electricity capacity available to run AI systems, which slows their expansion and raises costs for companies building them.
AI-designed biology In the news: Use of artificial intelligence to discover or create new biological molecules and organisms, speeding up drug and material development.
AI detection In the news: Software that tries to identify text, images or code generated by artificial intelligence rather than made by humans.
AI detection, Anthropic: Technology to identify whether text was written by an AI rather than a human, raising questions about authenticity.
AI detection, authenticity: Tools designed to identify whether content was made by humans or artificial intelligence, important as AI-generated text and images become harder to spot.
AI detection tools In the news: Software that identifies whether text or images were created by artificial intelligence, used to spot AI-generated content in submissions and online.
AI detection, verification: Tools that try to identify whether text or images were made by artificial intelligence rather than humans.
AI drug discovery In the news: Using machine learning to identify which molecules might become effective medicines, reducing time and cost of development.
AI education: Teaching schools and universities to use AI tools, sparking debate over whether it replaces learning or enhances it.
AI-enabled cybersecurity threats: Computer attacks that use artificial intelligence to find vulnerabilities, deceive targets, or evade defenses more effectively than before.
AI energy consumption: The electricity used to train and run artificial intelligence systems, which has grown as models become larger and more complex.
AI enterprise privacy: Methods to protect sensitive company data when using AI systems, balancing the tool's usefulness against leaks to competitors or the AI maker.
AI existential risk: The possibility that advanced artificial intelligence could pose threats to human survival or civilisation if it develops goals misaligned with human values.
AI for biology: Machine learning systems that predict protein structures, design drugs, or analyse genetic data to speed up biological research and medical discovery.
AI for elderly care: Systems that monitor health, remind patients of medication, or assist with daily tasks for aging populations.
AI for genomics: Machine learning systems that analyze DNA sequences and genetic data to discover disease patterns and speed up drug development.
AI for IT operations: Systems that monitor and manage computer networks and servers, spotting problems and fixing them without human intervention.
AI game playing: Software that learns to win games by finding patterns in strategy and adjusting its moves based on opponent behaviour.
AI-generated art: Images, text, or other creative work produced by artificial intelligence systems trained on existing human-made examples.
AI-generated celebrity likenesses: Synthetic images or videos of famous people created by AI, raising questions about consent, fraud and deepfakes in media.
AI-generated content In the news: Text, images, audio or video created by artificial intelligence rather than by humans.
AI-generated design: Artwork, graphics, or layouts created by AI systems rather than human designers, affecting creative industries and copyright.
AI-generated images: Pictures created by artificial intelligence systems from text descriptions, raising questions about copyright and authenticity in visual media.
AI-generated media: Images, video, text or audio created by artificial intelligence rather than humans, raising questions about authenticity and copyright.
AI glasses: Eyewear with built-in artificial intelligence that can recognize objects, translate text, or answer questions about what the wearer sees.
AI governance: Systems and rules designed to manage how AI systems are built and used, involving governments and companies.
AI graphics upscaling: Using AI to enlarge images while reducing blurriness, making low-resolution pictures look sharper.
AI hallucination, legal: When an AI system generates false information presented as fact, creating liability risks for companies and users who rely on it.
AI hallucinations: When an AI system generates false or made-up information with confidence, presenting it as fact.
AI hardware: Specialized chips and processors designed to run artificial intelligence systems faster and more efficiently than conventional computer components.
AI hardware chips: Processors designed specifically to run artificial intelligence systems faster and more efficiently than general-purpose computer chips.
AI hearing aids: Hearing devices that use machine learning to filter speech from background noise and adapt to the user's environment in real time.
AI hiring automation: Software that screens job applicants and makes hiring decisions using artificial intelligence, raising concerns about bias and fairness.
AI image generation: Software that creates pictures from text descriptions, used in design, marketing and creative work.
AI in cyberwarfare: Use of artificial intelligence to conduct attacks on computer networks and infrastructure, making them faster and harder to detect.
AI in drug discovery: AI systems analyze molecular data to identify potential medicines faster and cheaper than traditional lab methods.
AI in genomics: Machine learning systems that analyze DNA sequences and genetic data to speed up drug discovery and disease research.
AI in manufacturing: Robots and algorithms that optimize factory production, predict equipment breakdowns, and manage supply chains.
AI in medicine: AI assists doctors by analyzing scans, predicting patient outcomes, and recommending treatments based on medical data.
AI in quantum computing: Machine learning applied to design, optimize or control quantum computers, which solve certain problems exponentially faster than classical machines.
AI in science: Use of artificial intelligence to accelerate research, analyse data and predict outcomes in scientific fields.
AI inference: Running a trained AI model to make predictions or decisions based on new data, as opposed to training the model itself.
AI inference chips: Specialized processors that run trained AI models quickly on devices, enabling faster responses without cloud servers.
AI infrastructure In the news: The data centers, chips, and networks required to train and run large AI systems at scale.
AI infrastructure, chip supply: The hardware, data centers, and semiconductor production networks needed to train and run large AI systems.
AI infrastructure costs: Expenses for data centers, chips, and electricity needed to build and run large AI systems, driving consolidation in the industry.
AI infrastructure spending: Massive investment by tech companies in computing power and data centers needed to build and run advanced AI systems.
AI instruction preservation: Efforts to maintain instructions within AI systems over time as they are updated or combined with new versions.
AI language learning: Systems trained on vast amounts of text to recognize patterns and generate human-like responses in conversation.
AI legal tools: Software that automates or assists legal research, drafting and analysis. Raises questions about accuracy and liability in law practice.
AI manipulation: Using AI systems to deceive or sway people through generated text, images, or deepfakes.
AI model compression: Making AI systems smaller so they run faster and use less power on regular computers and phones.
AI model copying: The practice of extracting or reproducing an AI system's behavior without permission, raising questions about intellectual property and competition.
AI model distribution: How artificial intelligence systems are shared with users, either through controlled access or open release to the public.
AI model optimization: Tuning an AI system to work faster, cheaper, or more accurately for specific tasks.
AI model training: The process of teaching an AI system by feeding it large amounts of data until it learns patterns and behaviors.
AI models: Mathematical systems trained on data that can recognize patterns and generate text, images, or predictions.
AI models, edge computing In the news: Running artificial intelligence directly on local devices rather than sending data to distant servers, improving speed and privacy.
AI music generation: Software that creates new songs or soundtracks from text descriptions or by learning patterns from existing music.
AI music tools: Software that generates, edits, or produces music with minimal human input, raising questions about composer rights and job displacement.
AI neuroscience: Using artificial intelligence to understand how brains work, or applying brain biology to build better AI systems.
AI oversight: Monitoring and regulation of artificial intelligence systems to ensure they operate safely and fairly.
AI PCs In the news: Personal computers with built-in artificial intelligence tools that run directly on the device rather than through cloud services.
AI perception: How the public views artificial intelligence: whether they see it as helpful, risky, or trustworthy.
AI personal assistant: Software that answers questions, drafts text, and handles tasks through conversation, deployed on phones, computers, or as standalone apps.
AI policy In the news: Government rules and frameworks governing AI development and use, balancing innovation against risks to privacy and workers.
AI prediction models: Systems trained on data to forecast future outcomes, from weather to disease risk to market trends.
AI privacy: Protecting personal data from being used to train or improve artificial intelligence systems without consent.
AI productivity: Software that automates routine work tasks like writing emails, scheduling, or data analysis to help people work faster.
AI profile detection: Software that identifies when artificial intelligence has created or altered a digital profile, rather than a human.
AI reasoning: An AI system's ability to break down problems into steps and work through logic to reach conclusions.
AI reasoning capabilities: An AI system's ability to work through multi-step problems and explain its conclusions, rather than just pattern-matching.
AI reasoning models: Systems trained to break down problems into steps and show their work, rather than jumping straight to answers.
AI recommendations: Systems that suggest products, content or actions based on patterns in user data and behavior.
AI regulation: Legal restrictions on AI systems to prevent harms like bias, misinformation, or job displacement.
AI reliability: The consistency and predictability of an AI system's outputs and performance across different conditions and over time.
AI research agents: Software systems that autonomously pursue scientific questions, run experiments, and draw conclusions with minimal human direction.
AI research automation: Using AI systems to speed up the discovery and development of new AI techniques rather than relying solely on human researchers.
AI research, computational chemistry: Using artificial intelligence to predict molecular behavior and design new drugs or materials faster.
AI resistance: Opposition to adopting or expanding AI systems in workplaces or society, often citing job losses or risks.
AI resource use: The electricity, water and computing power required to train and run artificial intelligence systems, which has grown rapidly.
AI retail: Using artificial intelligence to manage stores, from checkout systems to inventory tracking and customer service.
AI safety: Ensuring AI systems behave reliably and don't cause unintended harm, a major research focus as systems grow more powerful.
AI safety, agent misalignment: Risk that an AI system pursues goals in ways its creators did not intend, causing harm despite following its instructions.
AI safety and control: Methods to ensure advanced AI systems behave as intended and do not cause harm, a growing concern as the systems grow more powerful.
AI safety auditing: Testing and inspection of AI systems to find risks of errors, bias, or harmful outputs before they reach users.
AI safety, containment: Methods to limit an AI system's ability to act in the world, preventing unintended harm or escape from its intended use.
AI safety, cybersecurity risks: Vulnerabilities that allow attackers to manipulate or steal from AI systems, or to use them as tools for hacking other targets.
AI safety, disinformation In the news: Efforts to stop AI systems from generating or spreading false information at scale.
AI safety evaluation: Testing methods to detect whether an AI system might cause harm, cause dangerous behavior, or fail to follow human values in real-world use.
AI safety, formal verification: Using mathematical proof to verify that an AI system will behave as intended, rather than relying on testing alone.
AI safety, guardrails: Limits and safeguards built into AI systems to prevent harmful outputs and unintended behavior.
AI safety, interpretability: Understanding what a neural network is actually doing as it makes decisions, rather than treating it as a black box.
AI safety, model misalignment: The risk that an AI system pursues goals in ways its creators did not intend, causing harm despite technically doing what it was trained to do.
AI safety, observability: The ability to monitor and understand how an AI system makes decisions and behaves, essential for detecting problems before they cause harm.
AI safety oversight: Independent review of how companies build and test artificial intelligence systems to catch risks before they cause harm.
AI safety, policy In the news: Government rules and industry standards designed to reduce risks from increasingly powerful artificial intelligence systems.
AI safety, recursive improvement: The challenge of keeping AI systems safe and aligned with human values when they can improve and modify themselves.
AI safety research: Work to understand and prevent harmful outcomes from artificial intelligence systems as they become more powerful and autonomous.
AI safety risks: Potential harms from artificial intelligence systems, including errors, bias, misuse and unintended consequences.
AI safety, superintelligence: Superintelligence is a hypothetical AI system vastly smarter than humans, raising questions about whether it could be controlled safely.
AI safety testing: Evaluation methods used to check whether AI systems behave reliably and predictably before release.
AI satellite computing: Running AI software on satellites in orbit rather than on Earth, cutting delay and letting them work independently.
AI satire: Humorous content mocking AI hype, misuse, or limitations using parody or exaggeration.
AI saturation: Market demand for artificial intelligence applications reaching a plateau as most potential users have already adopted the technology.
AI scheduling, healthcare: Software that uses AI to assign doctors, nurses, and equipment to patient care slots and operating theatres.
AI search, SEO disruption: AI-powered search threatens traditional website traffic, forcing online publishers and marketers to adapt their strategies.
AI security: Protecting AI systems from attacks, misuse, and unauthorized access to prevent harm or stolen capabilities.
AI security, code execution: Risks from AI systems that can write and run computer code, potentially causing harm if the code is malicious or unintended.
AI security, prompt injection: A hacking technique where hidden instructions are embedded in text to hijack how an AI responds.
AI security testing: Deliberately trying to break or trick AI systems to find vulnerabilities before attackers do.
AI security threats In the news: Risks that AI systems can be tricked, poisoned with bad data, or misused to create convincing false content or break into networks.
AI self-improvement: An AI system that modifies its own code or training to become more capable without human intervention.
AI shopping assistants: Software that helps customers find and buy products by understanding their preferences and answering questions about items.
AI simulation In the news: Software that mimics real-world scenarios to test AI systems before deployment in actual environments.
AI slop: Generated text, images or video published in bulk for the traffic, and the reason a growing share of the web is now worth nothing to read.
AI software development: Building applications that use machine learning or large language models to complete tasks or assist users.
AI supply chain optimisation: Using AI to predict demand and manage inventory flows, reducing waste and delays in manufacturing and delivery.
AI surveillance: Systems using artificial intelligence to monitor people's behavior, movements or communications at scale, often raising privacy concerns.
AI trading agents: Automated systems that buy and sell financial assets at high speed, aiming to profit from market movements.
AI training, copyright: Dispute over whether companies can use published works to teach AI without permission or payment to creators.
AI training data: Text, images, or other material used to teach AI systems, now central to lawsuits over copyright and scraping.
AI training data sourcing: Gathering text, images, or video from the internet and other sources to teach AI systems how to recognize patterns and generate responses.
AI training protection: Measures taken to prevent unauthorized copying or theft of the data and methods used to build AI systems.
AI translation: Artificial intelligence software that converts text or speech from one language to another, often in real time.
AI transparency: Disclosing how AI systems make decisions and what data they learned from, so users understand their limitations.
AI video generation: Technology that creates videos from text descriptions or existing footage, raising questions about authenticity and misuse.
AI vision benchmarks: Standardized tests that measure how well AI systems recognize objects, read text, or analyze images in real-world scenarios.
AI washing: Describing ordinary software, or work done by people, as artificial intelligence — a marketing decision that regulators have begun treating as a disclosure problem.
AI watermarking In the news: Hidden markers embedded in AI-generated images or text to prove their origin and detect deepfakes.
AI wearables: Devices worn on the body, like smartwatches or glasses, that use AI to analyze health data or assist with tasks.
AI winter: A collapse in funding and interest after AI is oversold; it has happened twice, in the mid-1970s and again in the late 1980s.
AI world models: Neural networks trained to predict how physical scenes change over time, used to help AI systems plan actions.
airline pricing algorithms: Computer programs that automatically adjust ticket prices based on demand, competition, and other market factors.
algorithmic bias: A system that treats people unequally because its training data did, most often along the lines of race, sex, age or postcode.
algorithmic management: Software setting the targets, the routes and the pace of human work, and scoring the worker against them — warehouses, delivery, call centres.
algorithmic matching: Using a computer system to pair people, jobs, or content based on patterns in their characteristics or preferences.
alignment: The problem of making a system want what its operator actually meant, rather than the literal instruction or whatever the training happened to reward.
ALPR, AI surveillance: Automated license plate recognition reads vehicle plates from cameras to track movement, raising privacy and accuracy concerns.
Amazon, Alexa Organization In the news: Amazon's voice assistant that listens for wake words and performs tasks, central to debates about home surveillance and data use.
Amazon Prime Air: Amazon's drone delivery service that aims to get packages to customers within hours of ordering.
Anthropic Organization: Anthropic develops Claude, an AI assistant built with safety features and transparency as priorities.
Apple Organization: Consumer electronics maker integrating AI into iPhones, computers and services.
Apple Intelligence Organization: Apple's brand for AI features built into its devices and services, emphasizing on-device processing and privacy.
Apple, on-device AI: On-device AI runs artificial intelligence directly on phones or computers rather than sending data to remote servers.
Apple Siri: Apple's voice assistant that responds to spoken commands on iPhones and other devices.
AR hardware: Physical devices worn or held to overlay digital images onto the real world, making them visible to the user.
AR smart glasses: Eyeglasses that overlay digital information onto the real world, letting users see maps, messages, or data without looking at a phone.
Arm Mali graphics: Graphics processor design that Arm licenses to chipmakers for smartphones and other devices, competing with Nvidia and Qualcomm.
artificial general intelligence: A machine that could take on any intellectual task a person can, rather than the one it was built for. It does not exist; nobody agrees on the test.
artificial intelligence: Software that does something we used to file under thinking — recognising, predicting, writing, planning — by learning patterns rather than by following rules somebody wrote.
AR/VR hardware In the news: Physical devices that overlay digital content onto your surroundings or create fully virtual environments, driving competition between tech giants.
attention: The trick underneath every modern language model: at each word, the model weighs every other word in view and decides which ones matter.
audio AI tools: Software that generates, edits or analyzes sound using machine learning, enabling automated voice creation and audio processing.
automated age verification: Technology that checks whether someone meets age requirements for content or services, typically using AI to process identity documents or facial features.
automation: Handing a task to a machine. The word is older than computing, and it is the one that decides most of what appears on either of these two front pages.
automotive: The industry and technology of designing and manufacturing cars, trucks, and other road vehicles.
automotive AI: Software that helps cars perceive their surroundings, make driving decisions, and communicate with other vehicles and infrastructure.
automotive racing: Competitive motorsport where drivers race vehicles on tracks or roads at high speeds against each other.
automotive software: Programs that control how cars operate, from engines to safety systems to driver assistance features.
Autonomous agents: Software systems that act independently to complete tasks, make decisions, and take action without human direction.
autonomous AI agents: Software systems that pursue goals without human instruction at each step, making decisions and taking actions independently.
Autonomous AI research: Study of systems that can act independently to accomplish goals without constant human direction.
autonomous aircraft: Planes or drones that fly and land without human pilot control, tested for cargo delivery and passenger transport.
Autonomous delivery drones: Unmanned aircraft that navigate and deliver packages on their own, without a human pilot controlling them.
autonomous driving AI In the news: Machine learning systems that control vehicles without human intervention, interpreting road conditions and making navigation decisions.
autonomous machines: Robots and vehicles that make decisions and move without human control, using sensors and AI to navigate their surroundings.
Autonomous systems: Machines that make decisions and act without human control, used in vehicles, manufacturing, and military applications.
autonomous vehicle: A car, lorry or aircraft that drives itself. Levels 0 to 5 grade how much: most "self-driving" on sale today is level 2, which legally means you are driving.
autonomous vehicle safety: Testing and standards for driverless cars to prevent crashes and injuries, a major hurdle before widespread deployment on public roads.
autonomous vehicles: Cars and trucks that can navigate roads and make driving decisions with minimal or no human control.
autonomous warfare: Military systems that select and engage targets without human decision-making, raising ethical and strategic concerns worldwide.
Autonomous weapons: Military systems that select and fire on targets without human intervention, raising legal and ethical concerns.

B

backpropagation: How a network learns: the error at the output is traced back through every connection, and each one is nudged in the direction that would have been less wrong.
Battery storage: Technology for storing electrical energy in batteries, critical for renewable energy systems and electric vehicles that need power when the sun is not shining or wind is not blowing.
benchmark: A fixed set of questions used to compare models. Useful, and gamed: a benchmark that leaks into training data measures memory rather than ability.
Bioelectronics: Technology that merges biological systems with electronic devices to monitor or control bodily functions.
biological AI: Systems that use living cells or biological molecules instead of silicon chips to perform computation or learn from data.
biological computing: Using biological material like DNA or neurons to store and process information, potentially more efficient than traditional electronics.
biosafety: Practices and protocols preventing accidental release of harmful biological materials or pathogens in research and healthcare.
biotech venture capital: Investment money backing startups developing biological and genetic technologies, from drug discovery to synthetic organisms.
Biotechnology: Using living systems or organisms to develop new medical treatments, materials, or food products.
bioweapons: Biological agents engineered to harm people, appearing in AI news because language models could theoretically aid their creation.
Bird tracking: Using technology to monitor bird movements and populations, important for understanding migration and species survival.
black box: A system whose answer can be checked but whose reasoning cannot be read, which is the ordinary condition of a neural network and the reason regulation keeps asking for explanations.
brain-computer interfaces: Technology that reads electrical signals from the brain to control external devices or computers without using muscles.
broadband regulation: Government rules governing how internet service providers offer high-speed connections, affecting deployment of data-heavy AI services.
browser extensions: Small software add-ons that modify how web browsers work, adding new features or changing how websites appear and function.
browser tracking: Methods websites and advertisers use to follow user activity across the internet, raising privacy concerns that regulators increasingly address.

C

chain of thought: Having a model write out its intermediate steps before answering. It raises accuracy on anything with arithmetic or logic in it, and costs more to run.
chatbot: A program you talk to in ordinary language. Since 2022 it usually means a language model with a text box in front of it.
ChatGPT Organization In the news: OpenAI's conversational AI that popularized large language models and triggered competition across tech.
ChatGPT, productivity: Using OpenAI's ChatGPT to speed up work by automating writing, coding and research tasks.
chip architecture: The fundamental design of a computer processor that determines how it processes information and what tasks it can handle efficiently.
chip design: The engineering process of creating semiconductor blueprints for processors and circuits that power AI systems and robots.
chip design automation: Using AI to design semiconductor layouts faster and more efficiently, critical as computing demands grow.
Chip supply chain: The network of manufacturers and suppliers that produce semiconductors and deliver them to companies making computers and devices.
Claude Organization: AI assistant made by Anthropic that answers questions and helps with writing and analysis tasks.
Claude models In the news: AI systems made by Anthropic that process text and images to answer questions and complete tasks.
Claude watermarking: Technique to embed hidden signals in AI-generated text so readers can identify it as machine-made content.
Climate tech: Technology designed to reduce greenhouse gas emissions or help societies adapt to climate change.
climate technology: Tools and systems designed to reduce greenhouse gas emissions or help communities adapt to climate change.
cloud computing: Running software and storing data on remote servers over the internet instead of on a local device, which AI companies use to train large models.
Cloud gaming In the news: Playing video games on remote servers streamed to your device, letting you play demanding titles without powerful hardware.
cloud infrastructure: Remote servers and data centers that run AI models and store data for companies and researchers worldwide.
Cloud storage: Keeping files on internet-based servers rather than on your own device, accessible from anywhere.
cobot: A robot built to work beside people rather than behind a safety cage: slower, weaker, and stopping the moment it touches something it did not expect.
code generation: Software that writes computer code automatically, often based on a description of what the program should do.
code generation AI: Software that writes computer code automatically from text descriptions, helping programmers work faster.
code sandboxing: Isolating code in a restricted environment so it cannot access the rest of a system, protecting against AI-generated malicious software.
coding agents: AI systems that write, test and fix computer code with minimal human input, used to speed up software development.
compute: Raw processing power, treated as a commodity — the thing that is bought, rationed, exported and counted when an AI company announces anything at all.
computer security: Protection of computer systems and data from theft, damage, or unauthorized access by attackers or malware.
computer vision: Getting a machine to read an image: what is in it, where, and whether it is the same thing it saw a moment ago.
Connected vehicles: Cars that communicate with each other and infrastructure to improve safety and traffic flow.
connectome simulation: Recreating the brain's complete neural wiring map in a computer to understand thought and possibly replicate it artificially.
constitutional AI: Training a model against a written set of principles it is asked to critique itself with, instead of against thousands of individual human judgements.
consumer AI: Artificial intelligence tools and services designed for individual users rather than businesses or researchers.
Consumer AI hardware: Devices sold to everyday buyers that run AI systems, from phones with AI assistants to headsets and home robots.
consumer drones: Unmanned aircraft for personal use, increasingly equipped with AI for obstacle avoidance and autonomous flight.
consumer e-reader: Handheld device designed primarily to display digital books and text with a screen that mimics paper.
Consumer projectors: Portable devices that display images or video on walls or screens, becoming cheaper and brighter for home use.
consumer robotics: Robots sold to ordinary people for homes and personal use, like vacuum cleaners or lawn mowers.
consumer robots: Machines sold to households or individuals for tasks like cleaning, delivery or companionship, marking the shift of robotics from factories to everyday life.
consumer smartwatches: Wearable devices that track health metrics and notifications, now featuring AI for health predictions and analysis.
Consumer tracking devices: Wearables or tags that locate people or objects via GPS or wireless signals, raising privacy concerns as they become smaller.
content algorithms: Systems that decide which posts, videos, or articles to show users, shaping what people see online.
content moderation: Deciding what stays up. Increasingly a first pass by a classifier and a second by low-paid contractors looking at what the classifier flagged.
content moderation, AI models: Software that automatically detects and removes harmful posts, images, or videos from online platforms.
Content scraping: Automated collection of text, images, or other material from websites, often without permission, to train AI systems or resell.
context window: How much text a model can hold in view at once — the conversation, the documents, its own instructions. Everything outside it does not exist to the model.
copyright: Legal right that gives creators exclusive control over their work's use and reproduction for a set period.
Creative AI: Systems trained to generate images, text, music, or video based on human prompts or instructions.
CRISPR cloning: Using gene-editing technology to create genetically identical copies of organisms.
Crowdsourced mapping: Creating detailed maps by collecting location data and observations from many volunteers rather than professional surveyors.
crypto fraud: Scams involving digital currencies or blockchain projects, often promising returns that never materialize.
Cryptography: Mathematical techniques that encode information so only intended recipients can read it, crucial for AI systems handling sensitive data.
customer service automation: Using AI to handle routine customer inquiries and complaints without human staff, reducing costs and response times.
cybercrime: Criminal activity carried out using computers or the internet, such as hacking, fraud, or theft of data.
Cybersecurity In the news: Protection of computers and networks against digital attacks that aim to steal data or disrupt operations.
cybersecurity vulnerability: A weakness in a computer system that attackers can exploit to gain unauthorised access or cause damage.

D

Dark Matter: Invisible matter that makes up most of the universe's mass but does not emit or reflect light.
Data acquisition: Collecting and obtaining information from existing sources, especially for training AI systems.
data analytics: Extracting patterns and insights from large data sets to guide business decisions or research.
Data breach: Unauthorized access to private or sensitive information stored by a company or organization, exposing it to theft or misuse.
data center environmental impact: The energy consumption and water use of facilities that power AI systems and cloud services, affecting climate and local resources.
data center infrastructure: Physical buildings and equipment that store and process massive amounts of data for cloud computing and AI systems.
data centre: The building the models actually live in. The AI build-out is a construction and electricity story as much as a software one, which is why towns now argue about it.
Data centre cooling: Systems that keep computer servers from overheating, crucial as AI training demands more electricity.
Data centre infrastructure: Physical buildings, power systems and cooling equipment that houses the computers running AI models.
Data centre pollution: Environmental damage from the electricity consumption and water use of facilities running AI systems.
data centres: Large facilities housing thousands of computers that process and store information for websites, cloud services and AI systems.
data infrastructure: The systems and storage that organize and deliver vast amounts of information needed to train and operate AI systems.
data labelling: People marking up examples so a model can learn from them — the manual work underneath almost every "automatic" system, usually outsourced.
data privacy: Legal and practical controls over what companies can collect, store, and share about individuals.
data scraping: Automated extraction of large amounts of data from websites, used to train AI models and raising copyright disputes.
Data security: Protecting information from theft, corruption, or unauthorized access through technical and organizational safeguards.
database software: Programs that store, organize and retrieve structured data, forming the backbone of most digital services and businesses.
datacentre: A facility housing thousands of networked computers that store and process data for cloud services, AI training, and online businesses.
Datacentre infrastructure: The physical buildings, power systems and cooling equipment that house servers running AI models and other computing services.
datacentre regulation: Government rules limiting energy use, water consumption or physical location of the computer facilities that train and run AI systems.
Decentralized networks: Systems where data and power are spread across many computers rather than controlled by one central point.
decision models: AI systems trained to recommend or choose between options by learning from past decisions and their outcomes.
deep learning: Machine learning with many layers of neurons stacked up, each one building on the features the layer below it found. It is what made the last fifteen years happen.
deepfake: Synthetic video or audio of a real person, made well enough to be taken for a recording. Most of it is pornography; the rest is fraud and politics.
deepfake detection: Technology that identifies whether images, audio or video have been artificially created or manipulated to misrepresent reality.
Deepfakes, AI security: Synthetic audio or video created by AI to impersonate someone, used to spread misinformation or commit fraud.
Deepfakes, regulation: Synthetic media created by AI to convincingly impersonate real people, raising concerns about fraud and misinformation.
DeepSeek Organization: Chinese AI company that released a low-cost language model, challenging U.S. dominance in AI development.
DeepSeek V4 Flash: Fast, lightweight AI model from DeepSeek designed for quick responses with lower computational demands.
DeepSeek V4-Pro: Advanced AI model from DeepSeek offering stronger reasoning and more complex capabilities than lighter versions.
Delivery drones: Unmanned aircraft that carry packages to customers, reducing delivery times and labor costs.
delivery robots: Autonomous machines that transport packages on sidewalks or roads, reducing labor costs and delivery times for companies.
diffusion model: How most image generators work: start with noise, and remove a little of it at a time until what is left matches the prompt.
digital ownership: Legal rights to digital goods like software or files, distinct from physical ownership and often limited by licensing terms.
Digital privacy: Protection of personal information collected online, including how data is stored, shared and used by companies and services.
digital publishing: Distributing written or multimedia content online rather than in print, often raising questions about AI training data.
Digital surveillance: Tracking people's online activities and movements through cameras, phones and databases to monitor behaviour.
digital vehicle keys In the news: Smartphone software that unlocks and starts a car, replacing the physical key fob or metal key.
digital wellness: Healthy use of technology that balances digital life with offline time and mental wellbeing.
distillation: Training a small model on the output of a large one, so that it answers almost as well while costing a fraction as much to run.
distributed systems: Computing networks where multiple independent machines work together to solve problems, essential for scaling AI and robotics applications.
document processing AI: AI that reads and extracts information from documents like invoices or contracts automatically.
doomer: Shorthand, usually dismissive, for someone who thinks advanced AI is likely to go catastrophically wrong and should be slowed or stopped.
Drone warfare: Combat using unmanned aircraft to strike targets, a growing military capability that raises ethical and legal questions about remote killing.

E

e-bike batteries: Rechargeable power packs in electric bicycles that store energy to power the motor and determine how far the bike can travel.
e-bikes: Bicycles with electric motors that assist pedaling, making longer trips easier and faster.
e-readers: Handheld electronic devices designed primarily for reading digital books and text, with screens optimised for extended reading.
e-waste repair: Fixing broken electronics instead of discarding them, reducing environmental damage and extending device lifespans.
edge AI In the news: Running a model on the device itself — phone, camera, car — instead of sending the data to a data centre. Faster, private, and limited by the battery.
Edge AI, smart home: AI systems that run on home devices themselves rather than in the cloud, reducing latency and keeping data private.
education technology: Software and tools that help students learn and teachers instruct, increasingly powered by artificial intelligence for personalized lessons.
Electric aircraft: Planes powered by batteries instead of fuel, a field where robots and AI are tested for manufacturing and autonomous flight systems.
electric vehicles In the news: Cars powered by rechargeable batteries instead of petrol engines, now competing with traditional vehicles and affecting demand for fossil fuels.
embedded systems: Computers built into devices like robots and appliances to perform specific tasks, central to how AI reaches everyday objects.
embedding: A list of numbers standing in for a word, a sentence or an image, arranged so that things which mean similar things sit close together.
Embedding models: AI systems that convert text, images, or data into numerical patterns that capture meaning, used to search documents and power AI recommendations.
embodied AI: AI with a body: a system that has to perceive and act in the physical world, where a mistake knocks something over instead of printing a wrong sentence.
emulation: Running software designed for one device or system on a different one by copying its behavior, often used to preserve older games and programs.
energy storage: Technology that captures and holds electricity for later use, critical for renewable power grids.
Enterprise AI: Artificial intelligence systems deployed by businesses to automate tasks, analyze data, or improve operations at scale.
Enterprise AI agents: Software that acts independently to complete business tasks like data analysis or customer support, executing multiple steps without human input between them.
Enterprise AI governance: The policies and systems a company uses to manage how its workers deploy AI safely and ensure models remain accurate and aligned with business goals.
Enterprise AI models: AI systems built for business use, including chatbots, forecasting tools, and decision-making systems that companies run on their own infrastructure.
enterprise automation In the news: Using AI and software to handle routine business tasks like data processing, scheduling and customer service without human intervention.
EU AI Act: European law regulating how artificial intelligence systems can be developed and used, with stricter rules for high-risk applications.
EU AI regulation: European Union rules requiring companies to test high-risk AI systems and disclose when content is machine-generated.
EV battery technology: Methods for storing and delivering electrical energy in vehicles, where improvements in cost and range shape the car market.
eval: A test written to find out what a model can and cannot do. The word covers everything from a benchmark score to a safety review before release.
existential risk: The argument that a sufficiently capable system could end up beyond human control, and that this is a present engineering problem rather than a distant one.
exoskeletons: Wearable frames powered by motors that strengthen a person's movements, used in factories and physical therapy.
expert system: The AI of the 1980s: hand-written rules encoding what a specialist knows. Brittle, unable to learn, and still running in places nobody has dared touch.
explainability: Being able to say why a system produced a particular answer — required by law in some settings, and genuinely hard for anything with a neural network in it.
export controls: Government limits on which AI chips may be sold to which country. Since 2022 the main instrument of the technology conflict between the United States and China.

F

facial recognition: Matching a face in an image against a database of faces. The most regulated computer vision there is, and the one that keeps producing wrongful arrests.
facial recognition, surveillance: Technology that identifies people from their faces in photos or video, raising privacy and accuracy concerns.
fair use: The doctrine AI companies rely on to justify training on copyrighted work without a licence. What the lawsuits are about; it is not settled.
federated learning: Training across many devices without collecting their data: each one learns locally and sends back the lesson rather than the material.
fine-tuning: Taking a trained model and continuing its training on a smaller, specific set of examples, so that it adopts a task, a format or a house style.
fintech: Financial services delivered through software and mobile apps rather than traditional banks and offices.
flood prediction technology: Software systems that use data and modelling to forecast flooding events, helping communities prepare and reduce disaster damage.
formal verification: Mathematical proof that a program or system works exactly as intended, used for safety-critical applications like autonomous vehicles.
foundation model: A large model trained once, at great expense, on very broad data, and then adapted to hundreds of uses instead of being rebuilt for each.
foundry: A factory that makes chips other companies design. There are effectively three at the leading edge, and one of them makes almost every AI chip that matters.
Frontier AI capabilities: The most advanced AI systems available at any moment, pushing toward or beyond current technical limits.
frontier AI models: The most advanced AI systems under development, typically large language models or multimodal systems that push current capabilities forward.
frontier model: The most capable models in existence at any moment. The term exists mainly so that regulation can name the handful of systems it wants to treat differently.
Fusion energy: Power generated by fusing light atoms together, potentially offering cleaner and more abundant energy than nuclear fission.

G

game AI research: Development of AI that plays games at superhuman levels, advancing techniques used in other AI applications.
Game development AI: Software that helps create video games by generating art, code, or game elements, speeding up production and reducing costs.
game-playing AI: Software trained to master games by learning strategy and decision-making, used to test how well AI systems can solve complex problems.
game preservation: Archiving and maintaining access to old video games as technology changes, often by emulation or restoration.
: The study of a choice made against somebody who is also choosing. It is where "zero-sum" and "arms race" come from, and it is the frame under every argument about two systems bidding, pricing or negotiating against each other.
Gemini Organization: Google's large language model that powers its AI assistant, competing with ChatGPT and other generative AI tools.
generative adversarial network: An older way of generating images: one network makes fakes, a second tries to spot them, and both improve until the first one wins.
generative AI: Models that produce text, images, audio, video or code rather than classifying what they are shown. The part of the field the public met.
generative AI, creative software In the news: Software that creates text, images, or video from written prompts, raising questions about authorship and copyright.
generative AI music: Software that creates original songs or soundtracks after learning patterns from existing recordings.
generative AI, publishing: AI systems that create text and images, disrupting how content creators and publishers work.
generative AI tool: Software that creates new text, images, code or other content based on patterns learned from training data.
generative AI tools In the news: Software that creates new text, images, or code by learning patterns from existing examples, now widely used in newsrooms and offices.
generative art: Images, music or text created by artificial intelligence systems trained on existing works, now raising questions about copyright and authorship.
Generative image models: AI systems trained on images that can create new pictures from text descriptions or other visual inputs.
generative music: AI that creates original music by learning patterns from existing compositions, raising questions about copyright and artist credit.
generative video AI: Software that creates moving images from text descriptions or other prompts, without filming or animation.
genetic engineering: Directly editing an organism's DNA to change its traits, used in medicine, agriculture and research.
ghost work: The human labour hidden inside automated systems — labelling, checking, correcting — arranged so that the product appears to run by itself.
gig economy labor: Work arranged through apps or platforms where people take short jobs without traditional employment.
GitHub Organization In the news: Microsoft-owned platform where software developers collaborate, share code and track projects, now featuring AI-powered development tools.
GLM-5.3 language model: A Chinese-made large language model competing with Western AI systems in capability and adoption.
Go programming language: A programming language built by Google for writing software that runs fast and handles many tasks at once.
Google Organization: Search engine and technology company that develops AI systems including Gemini, competing with OpenAI's ChatGPT.
Google AI Search: Google's experimental search results that use artificial intelligence to generate summaries and answers directly, rather than listing web pages.
Google Discover: Google's personalized news feed that shows articles based on your search history and interests.
Google Gemini Organization In the news: Google's AI assistant and model family, competing with ChatGPT across search, productivity, and devices.
Google weather AI: Machine learning system that predicts weather patterns by learning from historical data and current conditions.
GPS jamming: Intentional disruption of GPS signals used for location tracking, increasingly relevant as AI systems rely on precise positioning data.
GPT: Generative pre-trained transformer: the architecture behind most chat models, and, in lower case, a generic name for the type.
GPU: A graphics chip. It turns out that drawing polygons and training neural networks are the same arithmetic, which is how a games-hardware company became the centre of the industry.
GPU compute: Using graphics processors to run calculations at high speed, essential for training and running artificial intelligence models.
GPU inference optimization: Techniques to make AI models run faster and use less power on graphics processors, crucial for deploying AI on devices and lowering energy costs.
GPU programming: Writing software that runs on graphics processors to speed up calculations, widely used for training and running AI models.
graphics drivers: Software that lets a computer's processor communicate with its graphics card to display images on screen.
grid energy storage: Systems that store electrical power from the grid during low demand and release it when demand peaks, stabilizing electricity supply.
guardrails: The filters and rules wrapped around a model to stop it doing certain things. Not part of the model, which is why they can usually be talked around.

H

hallucination: A model stating something false with complete confidence. Not a bug to be patched: it is what generating plausible text looks like when the text is wrong.
hardware security: Protection of physical computer components from theft, tampering, or unauthorized access that could expose data.
healthcare AI: Artificial intelligence used to diagnose diseases, predict patient outcomes, or discover new treatments faster than traditional methods.
Hearing aids: Devices that amplify sound for people with hearing loss, now often include AI and wireless connectivity to smartphones.
heat pump technology: Systems that move heat between spaces to warm or cool buildings, increasingly promoted as efficient alternatives to fossil fuel heating.
hiring automation: Using software to screen job applications and candidates, raising concerns about bias and fairness in recruitment.
home security AI: AI software that monitors home cameras and sensors to detect intrusions, unusual activity, or emergencies and alert residents.
Human Cell Atlas: A global scientific project mapping the cell types and structures across all human organs and tissues using computational tools.
human in the loop: A person kept in the decision, usually as the one who approves or overrules the machine. The standard answer to "who is responsible", and only as good as the time they are given.
Human-robot collaboration: Robots and people working alongside each other on shared tasks, often with safety systems preventing injury.
humanoid robot: A robot built to human proportions so that it can use rooms, tools and vehicles made for people. The most heavily funded and least proven bet in robotics.
Humanoid robots In the news: Robots built to resemble human form, used in manufacturing and research to perform physical tasks in spaces designed for people.
Humanoid robots, workforce: Robots built to move like humans, being tested to perform warehouse and factory jobs or replace human workers.
hydrogen energy: Fuel made from hydrogen that powers vehicles or generates electricity with only water as emission.

I

Ig Nobel Prize: Annual award for scientific research that seems absurd but prompts genuine thought or further investigation.
image compression: Reducing a digital image's file size while keeping it recognizable, important for storage and transmission efficiency.
immunotherapy: Cancer treatment that harnesses the immune system to attack tumors, increasingly designed with AI-assisted drug discovery.
industrial AI: AI systems deployed in factories and supply chains to optimize production, quality control and logistics.
industrial robotics: Machines that perform manufacturing and assembly tasks in factories, increasingly used to handle hazardous or repetitive work.
inference: Running a trained model to get an answer, as opposed to training it. Training is the capital cost; inference is the bill that arrives every day.
insurance automation: Using software to handle routine insurance tasks like claims processing and risk assessment with minimal human involvement.
interpretability: The attempt to read what is actually happening inside a model — which circuits fire, which concepts are represented — rather than judging it by its answers.
IoT devices In the news: Everyday objects equipped with sensors and internet connections that collect and share data, from thermostats to medical monitors.
IoT pet devices: Internet-connected gadgets that monitor or interact with pets, sending data to owners' phones or other devices.
IoT security: Protection of internet-connected devices like smart home gadgets and sensors from hacking and data theft.

J

jailbreak: A prompt that talks a model out of its own restrictions. Every model ships with them unfound, and finding them is now a job.

K

knowledge cutoff: The date after which a model has been taught nothing. Ask it about last week and it will either say so or, less usefully, guess.
knowledge graph: Facts stored as things and the relationships between them, so that a system can be asked a question rather than searched for a word.

L

language models: AI systems trained on vast text to predict and generate human language, powering chatbots and search tools.
language models, AI research: Statistical systems trained on text that predict words and power chatbots, forming the foundation of modern AI.
large language model: A model trained on an enormous quantity of text to predict what comes next. Everything else it does — answering, translating, coding — falls out of that one ability.
Large language models: AI systems trained on vast amounts of text to predict and generate human language, used in chatbots and content creation.
Legacy software: Older computer systems still in use because replacing them would be costly or risky, even as newer alternatives exist.
lethal autonomous weapon: A weapon that selects and engages a target without a person deciding. Several countries are building them; there is no treaty.
Linux: Free, open-source operating system that powers servers, smartphones, and AI systems worldwide.
LLM benchmarks: Tests that measure how well large language models perform on reading, reasoning and writing tasks, used to compare different AI systems.
LLM bias: Systematic errors in how large language models respond, often reflecting prejudices present in their training data.
LLM capability In the news: What a large language model can do: understand and generate text, code, or reasoning. Each new capability reshapes expectations for AI.
LLM developer tools: Software kits that let programmers build applications on top of large language models without training their own from scratch.
LLM development: Building large language models, software systems trained on vast text to predict and generate human language. Drives competition between tech companies for computing power and data.
LLM infrastructure: The servers, power systems, and networks needed to build and run large language models at scale.
LLM optimization: Techniques to make language models faster and cheaper to run without sacrificing their quality.
LLM quantization: Reducing the precision of numbers in large language models to make them smaller and faster while retaining most accuracy.
LLM safety training: Methods used to teach language models to avoid harmful outputs, misinformation, and illegal content.
LLM security: Protecting large language models from attacks that manipulate their outputs or expose their training data.
LLM tooling: Software that helps developers build and test applications using large language models, cutting development time and complexity.
LLM training data: Text used to teach a language model to predict words and generate responses, drawn from books, websites, and other written sources.
LLMs: Large language models are AI systems trained on vast text data to predict and generate human language, powering chatbots and search tools.
local AI In the news: AI software that runs on a user's own device rather than on distant servers, keeping data private and working without internet.
Local AI agents: AI systems running on personal devices rather than distant servers, keeping data private and reducing latency.
Local AI, edge computing: Running AI models directly on devices rather than sending data to remote servers, improving speed and privacy.
Local AI models: AI systems that run on your own device rather than cloud servers, keeping your data private and working without internet.
longevity medicine: Medical research aimed at extending human lifespan, increasingly supported by AI drug discovery and analysis tools.
longevity research: Scientific work to understand aging and extend human lifespan, increasingly funded by tech entrepreneurs and pursued with AI tools.

M

machine learning: Software that derives its rules from examples rather than being told them. The umbrella the whole field sits under.
machine learning, forecasting: Statistical algorithms that learn patterns from past data to predict future events or trends.
machine translation: Translation by machine. The first task where neural networks clearly beat the previous approach, and the first professional field they visibly reshaped.
medical AI models: Artificial intelligence systems trained to help diagnose diseases, predict patient outcomes, or suggest treatments.
Medical devices: Equipment used to diagnose, treat, or monitor health conditions, increasingly incorporating AI for improved accuracy.
medical sensors: Devices that measure biological signals like heart rate or glucose levels, often paired with AI for interpretation.
medical wearables: Devices worn on the body that continuously monitor health metrics like heart rate or blood glucose and alert users to problems.
meeting automation: AI tools that record meetings, transcribe them and summarise key decisions to reduce manual note-taking.
Meta Organization: Technology company building social networks, virtual reality devices, and AI systems including large language models.
Meta AI assistant: Conversational chatbot built by Meta and integrated into its platforms to answer questions and help with writing tasks.
microscopy: Using magnified images to see tiny objects invisible to the naked eye. AI now helps interpret these images automatically.
Microsoft Organization: Technology company that makes software and cloud services. Invests heavily in AI and partners with OpenAI.
military AI: Artificial intelligence systems deployed for defence and warfare, from surveillance to autonomous weapons. Central to strategic competition between nations.
Military AI weapons: Autonomous or AI-assisted systems designed for defense and combat, raising ethical and strategic questions.
Military robots: Autonomous or remotely controlled machines deployed by armed forces for combat or surveillance.
mixture of experts: A model split into many specialised sub-networks with a router that wakes only a few per query, so it can be enormous and still cheap to run.
ML infrastructure: Computer systems and tools that train and run machine learning models, including servers, data storage and specialized chips.
Mobile AI chips: Specialized processors in phones and tablets designed to run artificial intelligence tasks locally, reducing reliance on cloud servers.
Mobile security: Protection of smartphones and tablets from theft, hacking, and malicious software that could expose personal data or financial information.
model collapse: What happens when models are trained on the output of models: the rare and the unusual drop out first, and the result drifts towards its own average.
Model compression: Techniques that reduce the size and computational demands of AI models so they run on smaller devices or use less power.
Model Context Protocol: An open standard for connecting a model to tools and data sources, so that each integration is not written from scratch. Published by Anthropic in 2024.
model misalignment: When an AI system's behavior diverges from what its creators intended, posing risks if the system operates without sufficient human oversight.
model optimization: Adjusting an AI system to work faster, use less power, or perform better on specific tasks without retraining it from scratch.
model security: Protecting AI systems from attacks that trick them into giving wrong answers or revealing their training data.
Mojo programming: A programming language designed to run AI code faster than Python while keeping the same familiar syntax.
multi-agent system: Several agents working on one problem, dividing it, checking each other, or negotiating. Currently more promising in demonstrations than in production.
multimodal: A model that handles more than one kind of input — text and images, or text and audio and video — in the same conversation.
Music streaming: Services that let listeners play songs on demand over the internet instead of buying or downloading them.

N

narrow AI: A system that does one thing, however well: the only kind that exists. Everything shipping today is narrow AI, whatever the announcement says.
NASA Organization: U.S. space agency. Tests AI and robotics for exploration and operates autonomous systems in space.
natural language processing: Getting machines to work with human language. The field that language models absorbed almost entirely between 2018 and 2022.
Navier-Stokes: Equations describing how fluids flow. AI models trained on physics can help solve them for engineering problems.
Net neutrality regulation: Rules that prevent internet providers from blocking, slowing or favoring certain websites, keeping all online traffic equal.
neural network: Layers of simple units with adjustable connections between them, loosely inspired by neurons and no longer trying very hard to resemble them.
neural networks: Computing systems loosely modeled on the brain's structure, trained on data to recognize patterns and make predictions without explicit instructions.
neuroscience: The study of how the brain and nervous system work, relevant to AI because researchers use brain structure to inspire new machine-learning approaches.
Nuclear fuel: Uranium or other radioactive material used to generate heat and electricity in nuclear reactors, now debated as a carbon-free power source for AI data centers.
nuclear power: Electricity generated by splitting uranium atoms, producing no carbon emissions but creating radioactive waste that remains dangerous for centuries.
Nvidia Organization: Makes graphics processors and AI chips central to training and running large language models.
Nvidia DLSS: Nvidia technology that uses AI to upscale lower-resolution images to higher quality in real time, improving gaming and graphics performance.
Nvidia GPUs: Nvidia's graphics processors that speed up AI training and inference, becoming essential hardware for AI development worldwide.

O

observability tools: Software that monitors how AI systems behave and perform in real-world use, helping engineers catch problems quickly.
On-device AI models: Artificial intelligence that runs on your phone or computer rather than on remote servers, keeping data private.
Open ASR, speech recognition: Free, publicly available software that converts spoken words to text, designed to work without relying on proprietary services.
open models: AI models whose code and weights are publicly released, letting anyone download and run them.
open protocols: Publicly documented communication standards allowing different AI and robotic systems to interact without proprietary lock-in.
open source: Software anyone may read, run and change. Contested in AI, because a model released without its training data or code is not open in the sense the term normally carries.
Open source AI: AI models released publicly with their code and weights visible, allowing anyone to use, study, and modify them.
Open-source AI models: AI systems whose code and weights are publicly available, letting anyone run, study, or modify them rather than relying on a company's closed service.
Open-source AI platforms: AI software released publicly so anyone can inspect, modify and redistribute the code, reducing dependence on proprietary commercial systems.
open source AI policy: Rules governing how AI systems with publicly available code can be built, shared and controlled to balance innovation with safety.
open source AI tools: Artificial intelligence software released publicly so anyone can use, modify and distribute it without paying licensing fees.
open-source language models: AI systems trained on text that any developer can download, modify and use freely.
Open-source software: Software whose source code is freely available for anyone to view, use, modify, and distribute.
open source tools: Software made freely available for anyone to use, modify, and distribute, often built collaboratively by volunteers.
open weights: A model whose trained parameters can be downloaded and run on your own hardware. Not the same as open source, and the honest word for most "open" AI releases.
OpenAI Organization In the news: OpenAI develops ChatGPT and other large language models, shaping the commercial AI market and policy debates.
OpenAI API: A service that lets software developers add OpenAI's AI models to their own programs and products.
OpenStreetMap: A free map of the world built by volunteers, used by AI systems and others needing geographic data without licensing fees.
orbital infrastructure: Satellites and systems in space used for communications, data collection or services from above Earth.
overfitting: A model that has learned its training examples rather than the pattern in them, and therefore performs beautifully in testing and badly in the world.

P

paleontology: The study of fossils and ancient life, increasingly aided by AI tools to analyse remains and predict extinct creatures.
parameter: One of the numbers a model adjusts while learning. Counted in billions, quoted as a proxy for size, and a poor guide to how good a model actually is.
pet automation: Robotic devices that feed, play with, or monitor pets, automating aspects of pet care.
Pet care technology: Software or devices that monitor, feed, or entertain pets while owners are away, often using cameras and automation.
pet robots: Robotic companions designed to interact with animals, often using AI to respond to behavior and provide stimulation or monitoring.
pharmaceutical: Relating to medicinal drugs and their development, manufacture, or use in treating disease.
Physical AI deployment: Using AI systems to control robots and machines in the real world, moving beyond software to perform tasks in factories and warehouses.
physical media: Tangible storage like DVDs, Blu-rays, and cartridges that hold games or films, distinct from streaming or digital downloads.
Physics simulation: Computer model that mimics real-world forces like gravity and collision to predict how objects move and interact.
Platform moderation: Systems and human review that enforce rules about harmful content on websites and social networks.
Platform safety: Efforts by social networks to prevent abuse, misinformation, illegal content, and harm to users.
Police surveillance AI: Automated systems that analyze footage or data to identify suspects or track individuals, raising questions about accuracy and privacy.
Post-quantum cryptography: Encryption methods designed to resist attacks from quantum computers, which could crack current security standards.
prediction markets regulation: Government rules governing betting markets where people wager money on whether future events will happen, used to forecast outcomes.
predictive policing: Forecasting where crime will occur or who will commit it. Trained on arrest records, so it reproduces where the police have already been.
pretraining: The first and most expensive phase, in which a model reads an enormous amount of text and learns the language before it learns any particular job.
processor architecture: The fundamental design of how a chip organizes and executes instructions, determining its speed and capabilities.
prompt: What you type. Also, in a product, everything typed on your behalf before your words reach the model.
prompt engineering: Writing prompts deliberately: examples, structure, constraints. Announced as a profession in 2023 and largely absorbed back into ordinary work since.
prompt injection: Hiding instructions in something a model reads — a web page, an email, a document — so that it follows them instead of yours. The central security problem of AI agents.
Protobuf: A method for serializing data developed by Google, used to structure information in machine learning and software systems.
Python Polars: A data-processing library for Python that handles large datasets much faster than older tools.
Python programming: A widely used coding language favored by AI researchers for its simple syntax and large library of machine-learning tools.

Q

quantisation: Storing a model at lower numeric precision so it fits on smaller hardware. It costs a little accuracy and can turn a data centre model into a laptop one.
Quantum physics In the news: Branch of physics describing how matter and energy behave at atomic and subatomic scales, often mentioned in computing breakthroughs.
Qwen language model: Alibaba's AI system trained to understand and generate text, competing with other large language models.
Qwen model, inference: Qwen is Alibaba's large language model. Inference means running that model to generate text from user prompts.

R

RAG, LLMs: A technique that retrieves factual information from external sources and feeds it to a language model, reducing false claims.
RAG systems, AI shortcuts: Retrieval-augmented generation combines AI models with databases to provide more accurate answers using real information sources.
Raspberry Pi, cyberdecks: Low-cost single-board computer used to build portable hacking or AI computing rigs, favored by DIY enthusiasts.
reasoning model: A model trained to spend longer on a problem, working through it before answering. Better at maths, code and logic; slower and dearer per question.
recommender system: The software that chooses what you see next. The most consequential AI most people use, and the one least often described as AI.
Recursive self-improvement: A theoretical process where an AI system improves its own design, potentially leading to rapid capability growth.
red teaming: Attacking your own system on purpose before release, to find what it will do when somebody tries. Now a condition of several regulatory regimes.
Reflection, AI models: AI systems that pause to reconsider their own reasoning before answering, aiming to reduce errors and improve accuracy.
reinforcement learning: Learning by trial, error and reward rather than from labelled examples. How machines learned to play games, and part of how chat models are finished.
Remote access software: Tools that let someone control a computer or device from elsewhere, used legitimately for support but also targeted by hackers.
renewable energy: Power generated from sources that naturally replenish, such as sun, wind, and water, replacing fossil fuels without long-term resource depletion.
retrieval-augmented generation: Looking documents up and putting them in front of the model before it answers, so it can cite a source instead of remembering one.
retro computing In the news: Using or studying old computers and software, often for nostalgia or to understand how early technology worked.
reusable rockets: Rockets that can land and fly again, cutting launch costs and speeding up deployment of satellites used in AI and communications.
Reverse engineering: Taking apart a product or program to understand how it works, often to recreate or improve it without access to original plans.
reward hacking: A system finding a way to score well that has nothing to do with what was wanted. The practical face of the alignment problem, and it appears constantly.
RISC-V: An open-source chip design standard that companies use to build processors without licensing proprietary technology.
Rivian vehicles: Electric trucks and vans made by Rivian, an automaker that uses autonomous driving and battery technology.
RLHF: Reinforcement learning from human feedback: people rank a model's answers, that ranking becomes the reward, and the model is trained towards it. What made chat models pleasant.
robot learning: Methods enabling robots to improve performance through experience or training data rather than fixed programming.
robot regulation In the news: Government rules and oversight for how robots operate, what they can do, and who is responsible when they cause harm.
Robot training, simulation: Teaching robots to perform tasks by running practice scenarios in software before they try them in the real world.
robot vacuum: A wheeled machine that cleans floors automatically, using sensors to navigate around obstacles and return to its charging dock.
robot vacuums: Autonomous cleaning robots that navigate homes independently, raising questions about AI safety and data collection in domestic spaces.
robotaxi: A driverless taxi. Running commercially in a handful of cities, on carefully mapped streets, under remote supervision that the marketing rarely mentions.
Robotaxis: Self-driving vehicles that carry passengers for hire, operated without a human driver behind the wheel.
Robotic vacuums: Autonomous cleaning robots that navigate homes independently using sensors and mapping technology to vacuum floors without human control.
robotics: Machines that sense and act in the physical world. Older than AI, largely separate from it until recently, and now the field AI money is moving into.
Robotics automation: Using robots to perform repetitive tasks in manufacturing, warehouses or other industries without human workers.
Robotics manufacturing: Using automated machines to build products or components in factories, replacing or working alongside human workers.
Robotics simulation: Computer environments where robot software learns tasks by practicing millions of times before controlling physical machines.
Rocketry: The engineering and science of designing and launching rockets for space missions, satellites, or cargo delivery.
Rollable displays: Screens that extend and retract like scrolls, allowing phones and tablets to change size and shape.
routing algorithms: Mathematical procedures that find the most efficient path for data or vehicles to travel between two points, widely used in logistics and network systems.
Rune programming language: A programming language designed to run efficiently on resource-constrained devices and blockchain networks.

S

SaaS business: Software accessed online by subscription rather than installed locally, allowing companies to charge users recurring fees.
SAML: A standard that lets users log into multiple websites using one set of credentials, widely used by companies for employee access.
Satellite data: Information collected from orbiting satellites, used to monitor Earth's climate, crops, cities, and disasters.
satellite launch services: Companies that send satellites into orbit for communications, weather forecasting, or Earth observation, supporting AI applications from space.
scaling laws: The observation that model performance improves predictably with more data, more parameters and more compute. The premise the entire build-out is financed on.
search engines: Software tools that find and rank web pages matching what users ask for, now increasingly powered by AI.
security vulnerability: A flaw in software or hardware that attackers can exploit to gain unauthorized access or cause damage to systems or data.
Seismic imaging: Technology that uses sound waves to map underground rock structures, used in oil and gas exploration and geological research.
SeL4, formal verification: A software verification method using mathematical proof to guarantee code behaves correctly, crucial for systems where failure is catastrophic.
semiconductor: A material that conducts electricity selectively. Semiconductors are the foundation of computer chips, crucial for AI systems.
Semiconductor manufacturing: Production of chips that power computers and AI systems, concentrated in few countries and now a focus of geopolitical competition.
Sleep earbuds: Small earpieces worn during sleep to play audio or detect sleep patterns, competing with dedicated wearables.
Sleep monitoring devices: Wearables or bedside gadgets that track sleep stages and quality, used to optimize rest or detect health problems.
small language models: AI systems trained on less data than mainstream models, designed to run on phones and edge devices with lower power needs.
smart glasses: Wearable eyeglasses with built-in cameras and displays that overlay digital information onto what the wearer sees in real time.
smart home: A house where internet-connected devices like lights, locks and thermostats can be controlled remotely or automatically.
smart home automation: Connected household devices that learn your habits and adjust lighting, temperature, and appliances automatically.
smart home devices In the news: Internet-connected household equipment controlled remotely or automatically, from thermostats to cameras, often gathering data about residents.
smart home hardware: Internet-connected devices like speakers and cameras that automate household tasks and increasingly use AI assistants.
Smart home platforms: Systems that connect household devices like lights and thermostats to let users control them remotely, often through voice commands.
smart home security: Network of cameras and sensors that monitor homes, alert owners to intrusions, and record incidents.
Smart lighting: Connected bulbs and fixtures controllable by apps or voice, often adjusting color and brightness to match circadian rhythms.
Smart lock hardware In the news: Electronic door locks that open via phone or voice command, raising security and privacy concerns as they collect entry data.
Smart locks: Door locks controlled by smartphone apps or voice commands, letting users lock and unlock remotely and monitor access to homes.
Smart rings: Wearable rings with sensors that track health metrics, receive notifications, or control devices.
Smart speakers: Internet-connected devices with microphones that respond to voice commands and play music or provide information.
smartphone cameras: Phone cameras increasingly use AI to improve photos through automatic adjustments, scene recognition, and computational imaging.
Smartphone chips: Processors built into phones to run apps and handle computing tasks faster and more efficiently than general-purpose processors.
Smartwatches: Wrist-worn devices that display notifications, track fitness, and run applications beyond basic timekeeping.
Snap AI assistant: A chatbot made by Snap that answers questions and creates images within the Snapchat app.
Snap AR glasses: Spectacles made by Snap that overlay digital information and effects onto the real world that the wearer sees.
software compatibility: Ability of different programs or systems to work together without modification, a key challenge when AI tools are integrated into existing workflows.
Software debugging: Finding and fixing errors in computer code that cause programs to malfunction or crash.
software emulation: Running software written for one computer system on a different one by mimicking its hardware and instructions.
solar eclipse: The moon passes between Earth and the sun, blocking sunlight temporarily and affecting power grids that rely on solar panels.
Solar energy: Electricity generated from sunlight, increasingly powering data centers that train and run AI systems.
solar technology: Methods of converting sunlight into electricity, often discussed as power for data centers and AI infrastructure.
sovereign AI: A country insisting that its models, chips and data sit inside its own borders. The reason national AI budgets have appeared everywhere since 2023.
space communications: Systems transmitting data between satellites and ground stations, increasingly automated by machine learning algorithms.
space debris: Defunct satellites and collision fragments orbiting Earth that pose collision risks to active spacecraft and space stations.
space industry: Commercial and government activity in space exploration, satellite launches, and orbital services.
space launch economics: How the cost and efficiency of sending rockets into orbit affects companies, governments, and competition in space industries.
Space launch infrastructure: Facilities, equipment, and systems needed to prepare and launch rockets and spacecraft into orbit or beyond.
Space robotics: Robots designed to operate in space for tasks like satellite repair, asteroid mining, or exploration where sending humans is too costly or dangerous.
Space technology: Equipment and methods developed for space exploration, from satellites to life support systems and propulsion.
space telescope: Observatory launched into orbit to observe distant stars and galaxies without atmospheric interference, advancing astronomy.
space telescopes: Observatories orbiting Earth or beyond that collect images and data without atmospheric interference, revealing distant stars and galaxies.
SpaceX Organization In the news: Elon Musk's aerospace company developing rockets and satellites, increasingly linked to AI for autonomous flight and communications.
Spatial AI: AI systems that understand and respond to physical space, used in robotics, augmented reality, and navigation applications.
Speech AI models: AI systems that convert spoken words into text or generate human-like speech from text, used in voice assistants and accessibility tools.
speech recognition In the news: Turning speech into text. Effectively solved for clear audio in a common accent, and still noticeably worse for everyone else.
speech recognition AI: Software that converts spoken words into text, used in voice assistants and accessibility tools.
Stargate data centre: Planned massive computing facility backed by OpenAI and others to run advanced AI systems.
streaming gaming: Playing video games on a remote server and receiving the video stream, rather than running the game locally.
streaming video: Delivering films and shows over the internet on demand rather than from broadcast schedules or physical media.
superintelligence: A hypothetical system far beyond human ability at essentially everything. The thing the safety argument is about, and the thing nobody can demonstrate.
supervised learning: Learning from examples that have already been labelled with the right answer. Simple, effective, and expensive in human time.
Surveillance: Systematic monitoring of people or places, often by cameras or software, raising questions about privacy and consent.
Surveillance AI: Computer systems that automatically identify people and track their movements using cameras and facial recognition.
Surveillance automation: Use of AI and computer vision to automatically detect, track and flag people or activities in video feeds without human review.
sustainable hardware: Electronics designed to last longer, use less energy, or contain fewer toxic materials. Matters because making devices consumes huge amounts of resources.
sycophancy: A model agreeing with you because agreement was rewarded in training. It makes the thing pleasant to use and unreliable as a second opinion.
symbolic AI: The older tradition: intelligence as logic and explicit symbols rather than learned statistics. Out of fashion, and quietly returning wherever an answer has to be checkable.
synthetic data: Training material generated rather than collected. It sidesteps privacy and copyright, and risks teaching a model the habits of the model that produced it.
system prompt: The standing instructions a product gives a model before you say anything — its role, its limits, its manners. You do not see it; it shapes every answer.

T

technological singularity: The idea that machine intelligence improving itself would run away, past the point where anything after it can be predicted.
telecom automation: Software systems that manage telecommunications networks without human intervention, reducing costs and response times.
teleoperation: A person operating a robot remotely. Also, frequently, what is behind an "autonomous" demonstration, which is why the question is worth asking every time.
Tesla Optimus robot: A humanoid robot prototype developed by Tesla designed to perform repetitive industrial and domestic tasks autonomously.
Tesla Semi: Electric heavy-duty truck made by Tesla for commercial freight transport, part of the shift away from diesel-powered logistics.
test-time compute: Spending more processing on the question instead of on the training. The shift behind reasoning models, and the reason a single answer can now cost real money.
text-to-image: Generating a picture from a description. The application that made the copyright argument concrete for anyone who draws for a living.
text-to-speech: Technology that converts written words into spoken audio, now often using AI to sound more natural and expressive.
token: The unit a model actually reads and writes — roughly three-quarters of a word. Context windows, prices and rate limits are all counted in them.
Tokenizers: Software that breaks text into small pieces that AI language models can understand and process.
TPU: Google's own AI chip, built in-house for its own models and rented out on its cloud. The main alternative to buying everything from Nvidia.
training data: Everything a model was shown while learning. Rarely disclosed in full, and the subject of most of the litigation aimed at the industry.
transfer learning: Reusing what a model learned on one task as the starting point for another, instead of beginning from nothing each time.
transformer: The 2017 architecture that made large language models possible, by letting a model weigh every part of its input against every other part at once.
Transformers: Machine learning architecture that powers most modern AI systems by processing text or images through attention mechanisms.
Turing test: Alan Turing's 1950 proposal: if you cannot tell the machine from the person in conversation, stop asking whether it thinks. Passed often enough now to have stopped meaning much.

U

underwater imaging: Photography or video capture beneath water surfaces, enhanced by AI to improve clarity and detect objects.
Unicode: A standard system that assigns unique numbers to letters, symbols, and characters across all world languages and writing systems.
unsupervised learning: Finding structure in data nobody has labelled — clusters, patterns, groupings. It is how a model can be trained on the open web at all.

V

vector database: A store built to answer "what is most similar to this" over embeddings, which is the piece that makes retrieval-augmented generation work.
vector databases: Databases that store data as mathematical vectors, allowing AI systems to search by similarity rather than exact matches.
VFX industry: Sector creating digital visual effects for films and television, now affected by AI tools that automate parts of production.
vibe coding: Writing software by describing what you want and accepting what the model produces, without reading much of it. Fast, and a maintenance problem later.
Video game preservation: Efforts to maintain, archive, and keep older video games playable for future access and historical study.
video games: Interactive entertainment where players control characters or systems on screen, increasingly used to train and test AI behavior.
video games emulation: Running old games on new hardware through software that mimics the original system, often used to train AI.
video generation: Artificial intelligence software that creates moving images and videos from text descriptions or other input, without filmed footage.
Video generation models: Artificial intelligence systems that create video from text descriptions or images, raising questions about authenticity and misuse.
video search AI: Technology that finds specific moments or content within videos by understanding what appears in them.
viewership measurement: Tracking how many people watch content, increasingly done with AI models instead of traditional survey methods.
voice AI models: AI systems trained to understand and generate spoken language, used in digital assistants and transcription tools.
voice AI synthesis: Technology that generates human-like speech from text, used in virtual assistants, audiobooks, and accessibility tools.
voice assistants: Software that listens to spoken commands and responds aloud, used to control devices and retrieve information hands-free.
voice cloning: Reproducing a specific voice from a short recording. Behind a wave of fraud calls, and behind an industrial dispute in every business that hires voices.
VR hardware: Headsets and devices that create immersive virtual worlds, now competing with AI chatbots for consumer attention and investment.

W

watermarking: Marking generated output so that it can be identified later. Robust for images, fragile for text, and voluntary nearly everywhere.
wearable technology: Devices worn on the body that collect health or fitness data, often powered by on-device AI to detect patterns.
web development: Building and maintaining websites and web applications, increasingly using AI tools to write and test code.
web frameworks: Software tools that help developers build websites and applications more quickly by providing pre-built components and structures.
Web hosting: A service that stores websites on servers so they can be accessed online. It matters when AI firms face scrutiny over their computing infrastructure.
web scraping: Downloading the public web in bulk to train on. The point at which the interests of AI companies and of everyone who publishes anything diverge.
Web search: Finding information online using search engines, a service now being reshaped by artificial intelligence integration.
Web tracking: Following users across websites to collect data about their behavior and interests for advertising or analysis.
Windows Organization: Microsoft's operating system that runs most desktop and laptop computers worldwide.
world model: An internal model of how things behave, so that a system can predict what happens next rather than only recognising what it sees. The stated goal of most robotics research.
world models, AI reasoning: AI systems that build internal representations of how the world works to plan and reason through complex problems rather than just pattern-matching.

X

Xbox Cloud Gaming: Microsoft service that streams video games to devices rather than requiring hardware to run them locally.

Other

3D display technology: Screens that show depth without special glasses, creating images that appear to have layers or volume.
3D-printed firearms: Guns manufactured using 3D printing technology, raising questions about regulation and traceability since they can be made without traditional licensing.
3D-printed guns: Firearms manufactured using additive manufacturing, raising questions about regulation and enforcement of weapons bans.
3D printing: Manufacturing objects by building them layer-by-layer from digital designs, now faster and cheaper for custom parts.
3D scene generation: Software that creates three-dimensional environments and objects from text descriptions or other prompts, used in gaming, film, and design.

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