Glossary of Terms (Technology)

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Artificial Intelligence

Artificial intelligence (AI) is a broad term with no single authoritative definition — and frequently, people mean different things when they use it.

However, AI commonly means:

  1. The capability of a non-human system to perform functions typically thought of as requiring human intelligence.
  2. A field of study dedicated to developing these systems.

The term AI is often imprecise. AI is sometimes used interchangeably with machine learning, but the two terms are not identical. Machine learning is one promising set of techniques used to develop AI, but others exist. We disambiguate machine learning and AI more fully below. [See sections below on Deep Learning, Machine Learning and Neural Networks from CSET Glossary.]

AI is also a moving target. The “AI effect” is a paradox in which problems thought to require AI, once largely solved, are no longer seen as requiring “intelligence.” This dynamic further contributes to ambiguity around the definition of AI. [CSET Glossary]

CyberSecurity

  • Computer Security, CyberSecurity or Information Technology Security (IT Security) is the protection of computer systems from theft or damage to their hardware, software or electronic data, as well as from disruption or misdirection of the services they provide. [Wikipedia]
    • CyberSecurity is a subset of Information Security. Information Security, sometimes shortened to InfoSec, is the practice of preventing unauthorized access, use, disclosure, disruption, modification, inspection, recording or destruction of information. [Wikipedia]
  • CyberSecurity refers to a set of techniques used to protect the integrity of networks, programs and data from attack, damage or unauthorized access. [PaloAlto Networks]
  • CyberSecurity is the practice of protecting systems, networks, and programs from digital attacks. These cyberattacks are usually aimed at accessing, changing, or destroying sensitive information; extorting money from users; or interrupting normal business processes. Implementing effective cybersecurity measures is particularly challenging today because there are more devices than people, and attackers are becoming more innovative. [Cisco]
  • CyberSecurity is strengthening the security and resilience of cyberspace.
    • CyberSecurity at the federal level includes: Combating Cyber Crime, Securing Federal Networks, Protecting Critical Infrastructure, Cyber Incident Response, Cyber Safety, Cybersecurity Governance, Cybersecurity Insurance, Cybersecurity Jobs, Cybersecurity Training & Exercises, Information Sharing, Stakeholder Engagement and Cyber Infrastructure Resilience. [U.S. Dept. of Homeland Security – CyberSecurity]
    • Cyberspace and its underlying infrastructure are vulnerable to a wide range of risk stemming from both physical and cyber threats and hazards. Sophisticated cyber actors and nation-states exploit vulnerabilities to steal information and money and are developing capabilities to disrupt, destroy, or threaten the delivery of essential services. A range of traditional crimes are now being perpetrated through cyberspace. This includes the production and distribution of child pornography and child exploitation conspiracies, banking and financial fraud, intellectual property violations, and other crimes, all of which have substantial human and economic consequences.
    • Cyberspace is particularly difficult to secure due to a number of factors: the ability of malicious actors to operate from anywhere in the world, the linkages between cyberspace and physical systems, and the difficulty of reducing vulnerabilities and consequences in complex cyber networks. Of growing concern is the cyber threat to critical infrastructure, which is increasingly subject to sophisticated cyber intrusions that pose new risks. As information technology becomes increasingly integrated with physical infrastructure operations, there is increased risk for wide scale or high-consequence events that could cause harm or disrupt services upon which our economy and the daily lives of millions of Americans depend. In light of the risk and potential consequences of cyber events, strengthening the security and resilience of cyberspace has become an important homeland security mission. [U.S. Dept. of Homeland Security – Cybersecurity and Infrastructure Security Agency]

Deep Learning

Neural networks (also known as artificial neural networks or neural nets) are one common type of machine learning algorithm. They were loosely inspired by aspects of biological brains. In the brain, signals cascade between neurons; similarly, a neural net is organized as layers of nodes that can send signals of varying strengths based on the inputs they receive. Analogous to human learning, training a neural network involves adjusting how and when the nodes in different layers activate.

Deep learning is a subfield of machine learning that has proven particularly promising over the last decade or so and is responsible for many of the well-known developments in artificial intelligence such as in computer vision and autonomous vehicles.

Deep learning is a statistical technique for fitting the parameters of deep neural networks. This process is often referred to as “training” the deep neural net. While neural networks can comprise any number of layers, deep learning uses neural networks with multiple hidden layers to process data, allowing it to recognize more complex patterns. More colloquially, any application using this approach is referred to as “deep learning.”

Deep learning has shown promise in a wide range of areas including image recognition, natural language processing, photo generation, game play, robotics, self-driving cars, drug discovery and music generation. For example, in image recognition, earlier layers (those toward the beginning of the process) may identify shapes, edges and other abstract features. Later layers may identify how those shapes come together to form ears, noses and other facial features. [CSET Glossary]

Machine Learning

Machine learning is a set of techniques by which a computer system learns how to perform a task through recognizing patterns in data and inferring decision rules, rather than through explicit instructions. Machine learning also refers to the subfield of computer science and statistics studying how to advance those techniques.

Machine learning has led to most of the recent advances in artificial intelligence. These advances have been incorporated into systems used by millions of people every day, such as Google’s search and translate tools, Amazon’s digital assistant Alexa and Netflix’s movie recommendation algorithm. It also includes specialized systems such as AlphaGo, text generators like BERT and GPT-2 and game-playing systems like OpenAI Five, AlphaStar and poker-playing Pluribus.

The essence of machine learning is a system recognizing patterns of relationships between inputs and outputs. For example, the U.S. Postal Service used machine learning to train a system to recognize handwritten zip codes on mail. The system was fed images of handwritten examples paired with the corresponding numbers as typed by humans, so it learned to identify what features were common in handwritten digits and how they varied. Once trained, the system could correctly identify previously unseen examples of handwritten digits.

Not all computing is machine learning and not all artificial intelligence systems that use computing use machine learning. Many computer programs, including most commonly used software, use rule-based systems where programmers set the actions the system should take. However, machine learning is useful for applications where it is difficult for human designers to specify the correct actions to take. For example, IBM’s Deep Blue used a rule-based, exhaustive-search approach to beat the world chess champion. Deep Blue is therefore an example of an AI system not based on machine learning. On the other hand, DeepMind used machine learning to create AlphaGo, an AI system capable of out-performing humans in Go. While it is theoretically possible to solve Go with rule-based algorithms, the search space is so large that winning against a human would have been impossible. Machine learning allowed AlphaGo to infer strategies not yet discovered by humans, leading it to beat the world champion. [CSET Glossary]

Neural Networks

Deep learning is a statistical technique that uses neural networks composed of multiple hidden layers of nodes and typically trained on large amounts of data to capture patterns and relationships in data.

Neural networks (also known as artificial neural networks or neural nets) are one common type of machine learning algorithm. They were loosely inspired by aspects of biological brains. In the brain, signals cascade between neurons; similarly, a neural net is organized as layers of nodes that can send signals of varying strengths based on the inputs they receive. Analogous to human learning, training a neural network involves adjusting how and when the nodes in different layers activate.

At its simplest, a neural network can be made up of just three layers: an input layer where data is observed, a hidden layer where data is processed and the output layer where a conclusion is communicated. When a neural network has multiple hidden layers it is called a “deep neural network.” While shallow neural networks have uses, most of the recent advances in AI have come from deep neural networks. Because of this, in contemporary usage the term neural network usually refers to a deep neural network. The term neural network is also sometimes used interchangeably with deep learning, which technically refers to the process of training the deep neural network. [CSET Glossary]

Surveillance:

Surveillance is the monitoring of behavior, activities, or information for the purpose of information gathering, influencing, managing or directing. This can include observation from a distance by means of electronic equipment, such as closed-circuit television (CCTV), or interception of electronically transmitted information, such as Internet traffic. It can also include simple technical methods, such as human intelligence gathering and postal interception. Synonym: Observation (example: this room is guarded by twenty-four hour observation). Surveillance is used by governments for intelligence gathering, prevention of crime, the protection of a process, person, group or object, or the investigation of crime. It is also used by criminal organizations to plan and commit crimes, and by businesses to gather intelligence on their competitors, suppliers or customers. Religious organisations charged with detecting heresy and heterodoxy may also carry out surveillance. Auditors carry out a form of surveillance.

Surveillance can be used by governments to unjustifiably violate people’s privacy and is often criticized by civil liberties activists. Liberal democracies may have laws that seek to restrict governmental and private use of surveillance, whereas authoritarian governments seldom have any domestic restrictions. International espionage seems to be common among all types of countries.

—Wikipedia

– Electronic Surveillance

Electronic surveillance is defined in federal law as the nonconsensual acquisition by an electronic, mechanical, or other surveillance device of the contents of any wire or electronic communication, under circumstances in which a party to the communication has a reasonable expectation of privacy. The “contents” of a communication consists of any information concerning the identity of the parties, or the existence, substance, purport, or meaning of the communication.

Examples of electronic surveillance include: wiretapping, bugging, videotaping; geolocation tracking such as via RFID, GPS, or cell-site data; data mining, social media mapping, and the monitoring of data and traffic on the Internet. Such surveillance tracks communications that falls into two general categories: wire and electronic communications. “Wire” communications involve the transfer of the contents from one point to another via a wire, cable, or similar device. Electronic communications refer to the transfer of information, data, sounds, or other contents via electronic means, such as email, VoIP, or uploading to the cloud.

—Cornell Law School, Legal Information Institute.

– Mass Surveillance

Mass surveillance is the intricate surveillance of an entire or a substantial fraction of a population in order to monitor that group of citizens.[1] The surveillance is often carried out by local and federal governments or governmental organisations, such as organizations like the NSA and the FBI, but it may also be carried out by corporations (either on behalf of governments or at their own initiative). Depending on each nation’s laws and judicial systems, the legality of and the permission required to engage in mass surveillance varies. It is the single most indicative distinguishing trait of totalitarian regimes. It is also often distinguished from targeted surveillance.

—Wikipedia

– Surveillance Capitalism

Surveillance capitalism is an economic system centred around the commodification of personal data with the core purpose of profit-making. Since personal data can be commodified it has become one of the most valuable resources on earth. The concept of surveillance capitalism, as described by Shoshana Zuboff, arose as advertising companies, led by Google’s AdWords, saw the possibilities of using personal data to target consumers more precisely.

Increased data collection may have various advantages for individuals and society such as self-optimization (Quantified Self), societal optimizations (such as by smart cities) and optimized services (including various web applications). However, collecting and processing data in the context of capitalism’s core profit-making motive might present a danger to human liberty, autonomy and wellbeing. Capitalism has become focused on expanding the proportion of social life that is open to data collection and data processing. This may come with significant implications for vulnerability and control of society as well as for privacy.

—Wikipedia

The term as used in Internet Salmagundi is as coined by and described by Shoshana Zuboff in her book Surveillance Capitalism. (The Wikipedia definition is a handy encapsulation of Zuboff’s book for my purposes here.)

 


No, this is not yet a complete listing of all terms used in this website, although from time to time I’ll make more progress to that end. For now I’m just aiming for the big-ones. More to come…

 


Resources: Dictionaries, Glossaries and Other Lexicographies

General Resources:

If you are interested, I suggest the following as good general resources:

  • FileInfo — The file extensions database. “Search over 10,000 file extensions and software programs.”
  • Free On-Line Dictionary of Computing (“FOLDOC”) — “FOLDOC is a computing dictionary. It includes definitions of acronyms, jargon, programming languages, tools, architecture, operating systems, networking, theory, standards, mathematics, telecoms, electronics, institutions and companies, projects, history, in fact any of the vocabulary you might expect to find in a computer dictionary.”
  • Information Security — True to form, Wikipedia has a thorough overview on the broad topic of InfoSec. The series on Information Security includes sections on Internet security, Cyberwarfare, Computer security, Mobile security, and Network security. It also lists a variety of threats and defenses. As with much of what’s on Wikipedia, it’s a good place to start.
  • PCMag Encyclopedia — The PCMag Encyclopedia contains definitions on common technical and computer-related terms.
  • Sideways Dictionary — Offers a different spin on defining technical terms with a sense of humor. “It’s like a dictionary, but using analogies instead of definitions. Use it as a tool for finding and sharing helpful analogies to explain technology. Because if everyone understands technology better, we can make technology work better for everyone.” (A project of The Washington Post and Jigsaw.)

Specialized Resources:

You can also reference these more specialized resources:

  • Common Vulnerabilities and Exposures (CVE) — CVE® is a list of entries—each containing an identification number, a description, and at least one public reference—for publicly known cybersecurity vulnerabilities. CVE Entries are used in numerous cybersecurity products and services from around the world, including the U.S. National Vulnerability Database (NVD). [As of this writing there are 114,074 CVE entries.]
  • Dictionary of Algorithms and Data Structures — National Institute of Standards and Technology: “This is a dictionary of algorithms, algorithmic techniques, data structures, archetypal problems, and related definitions. Algorithms include common functions, such as Ackermann’s function. Problems include traveling salesman and Byzantine generals. Some entries have links to implementations and more information. Index pages list entries by area and by type. The two-level index has a total download 1/20 as big as this page.Don’t use this site to cheat. Teachers, contact us if we can help.Currently we do not include algorithms particular to business data processing, communications, operating systems or distributed algorithms, programming languages, AI, graphics, or numerical analysis: it is tough enough covering ‘general’ algorithms and data structures.”
  • the Jargon Lexicon Glossary — From “ABEND” to “zorkmid” it’s The Jargon File glossary. (What? You’ve never heard of The Jargon File? Well then, by all means, check it out! There’s a good chance you’ll be intrigued.)
  • Machine Learning Glossary (Google Developers) – This glossary defines general machine learning terms, plus terms specific to TensorFlow.

 

Last Updated: November 28, 2020