SFT

I also show in the paper that LLMs are easily steerable into target locations of the political spectrum via supervised fine-tuning (SFT) requiring only modest compute and customized data, suggesting the critical role of SFT to imprint political preferences onto LLMs.

2024 February 2, David Rozado, “The Political Preferences of LLMs”, in Rozado’s Visual Analytics:

(machine learning) Initialism of supervised fine-tuning.


deepfakery

Bad cops are using deepfakery in order to help convict bad guys, just as bad guys are using deepfakery to help convict good guys, leaving us to ponder more angles than you might expect going into the show. “The Capture” is frightening, a little brainy, and damning of those who refuse to recognize deepfakery for the crisis it has become.

2020 August 14, Matthew Gilbert, “Watch ‘The Capture,’ but don’t believe your eyes”, in The Boston Globe:

Skistimas has also performed as Five Times August, whose Twitter account he used last week to get the goofiness started. I am not going to link to it. He posted a video supposedly showing Biden deepfakery. It has been viewed 1.4 million times.

2022 August 2, Tim Rogers, “No, There Are Not 10 Joe Bidens”, in D Magazine:

artificial intelligence
artificial intelligence

(artificial intelligence) The creation and use of deepfakes, often with malicious intent.


alignment problem

Popularized by the 2020 book The Alignment Problem by Brian Christian.

As [Brian] Christian notes, the alignment problem bears a real resemblance to parenting: “The story of human civilization has always been about how to instill values in strange, alien, human-level intelligences who will inevitably inherit the reins of society from us—namely, our kids.”

2022 March 1, Rob Toews, “7 Must-Read Books About Artificial Intelligence”, in Forbes, New York, N.Y.: Forbes Media, →ISSN, →OCLC, archived from the original on 2022-08-31:

Properly defining and solving the alignment problem won’t be easy; it will require us to develop a broad, scientifically based theory of intelligence.

2022 December 13, Melanie Mitchell, “What Does It Mean to Align AI With Human Values?”, in Quanta Magazine, New York, N.Y.: Simons Foundation, →ISSN, →OCLC, archived from the original on 2023-03-15:

For years, AI ethicists have worried about this so-called alignment problem. In short: How do we ensure that the AI we build, which might very well be significantly smarter than any person who has ever lived, is aligned with the interests of its creators and of the human race? An unaligned superintelligent AI could be quite a problem.

2023 February 27, Derek Thompson, “The AI Disaster Scenario”, in The Atlantic, Washington, D.C.: The Atlantic Monthly Group, →ISSN, →OCLC, archived from the original on 2023-03-22:

artificial intelligence
artificial intelligence#Noun

(artificial intelligence) The problem of how to create a superintelligent artificial intelligence whose values would align with the interests of humankind.

AI alignment#The alignment problem
Brian Christian
The Alignment Problem


RLHF

ChatGPT and reinforcement learning with human feedback (RLHF) have revolutionized the AI landscape, providing an accessible and reliable platform for AI-enabled applications.

2023, Mohak Agarwal, Generative AI for Entrepreneurs in a Hurry, Notion Press, →ISBN:

w:reinforcement learning from human feedback

(machine learning) Initialism of reinforcement learning from human feedback.


generative artificial intelligence

en-noun

generative artificial intelligence (countable and uncountable, plural generative artificial intelligences)

generative artificial intelligences (plural)

No area has created more excitement than generative artificial intelligence, the term for technology that can generate text, images, sounds and other media in response to short prompts.

2023 January 7, Erin Griffith, Cade Metz, “A New Area of A.I. Booms, Even Amid the Tech Gloom”, in The New York Times, →ISSN:

By the end of 2022, generative artificial intelligence had sucked all the hype out of the room.

2023 June 6, Ian Bogost, “The Age of Goggles Has Arrived”, in The Atlantic:

(artificial intelligence) A system that can produce realistic images, sounds, videos, or text in response to user prompts.


synthetic intelligence

synthetic intelligence (countable and uncountable, plural synthetic intelligences)

Man will be able to accelerate the rate of progress in every field when electronic synthetic intelligence is readily available to supplement the intellectual abilities of man's natural brain.

1967, Robert W. Prehoda, Designing the Future: The Role of Technological Forecasting, Chilton:

Perhaps Artificial Intelligence should be called “Synthetic Intelligence” to accord better with commercial parlance. Thus artificial diamonds are fake imitations, whereas synthetic diamonds are genuine diamonds, only manufactured instead of dug up (compare also artificial maple flavoring versus, say, synthetic insulin).

[1985, John Haugeland, Artificial Intelligence: The Very Idea, MIT Press, →ISBN, page 255:

artificial intelligence
artificial intelligence#English


prompt engineering

Likely coined in 2020 by AI researcher Gwern Branwen.

Practice prompt engineering and measure your progress in time. Prompt engineering is a new skill that will take you to the next level of AI.

2022, Denis Rothman, Antonio Gulli, Transformers for Natural Language Processing […] , Packt Publishing Ltd, →ISBN, page 426:

PromptBase’s sixth-most-popular seller, a prompt creator from Spain who goes by Imagineer, told me that prompt engineering is still a side hustle, having earned them just over 800 euros since September.

2023 February 8, Charlie Warzel, “The Most Important Job Skill of This Century”, in The Atlantic:

The latter skill has become known as “prompt engineering”: the technique of framing one’s instructions in terms most clearly understood by the system, so it returns the results that most closely match expectations – or perhaps exceed them.

2023 March 16, James Bridle, “The stupidity of AI”, in The Guardian, →ISSN:

Now that you’ve learnt the basics of prompt engineering, you should be able to use AI chatbots more efficiently and get the desired results.

2023 May 29, Zohaib Ahmed, “Want to make the most of ChatGPT? Try these prompt engineering basics”, in Indian Express, Mumbai:

generative AI
generative artificial intelligence

(artificial intelligence, technology, neologism) The technique of formulating instructions (prompts) for a generative AI, in order that it produce the expected output.


prompt injection

prompt injection (countable and uncountable, plural prompt injections)

Retomeli.io is a jobs board for remote workers, and the website runs a Twitter bot that spammed people who tweeted about remote working. The Twitter bot is explicitly labelled as being "OpenAI-driven", and within days of Goodside's proof-of-concept being published, thousands of users were throwing prompt injection attacks at the bot.

2022 September 21, Alex Hern, “TechScape: AI's dark arts come into their own”, in The Guardian, London: Guardian News & Media, →ISSN, →OCLC, archived from the original on 2023-02-05:

Yesterday, OpenAI announced an API for ChatGPT and posted an underlying format for the bot on GitHub, alluding to the issue of prompt injections.

2023 March 3, Chloe Xiang, “Hackers Can Turn Bing's AI Chatbot Into a Convincing Scammer, Researchers Say”, in VICE, archived from the original on 2023-03-22:

One category is what's known as a "prompt injection attack," in which users trick the software into revealing its hidden data or instructions.

2023 February 14, Will Oremus, “Meet ChatGPT's evil twin, DAN”, in The Washington Post, Washington, D.C.: The Washington Post Company, →ISSN, →OCLC, archived from the original on 2023-03-19:

(artificial intelligence) A method of causing an artificial intelligence to ignore its initial instructions (often moral programming) by giving it a certain prompt.


alignment tax

First attested in a 2019 speech by computer scientist Paul Christiano (see quote), who attributed the idea to AI researcher and writer Eliezer Yudkowsky.

I like this notion of an "alignment tax" […] the reason I might compromise is if there's some tension, between having the AI that's robustly trying to do what I want, and having the AI that is competent or intelligent, and the alignment tax is intended to capture that gap—that cost that I incur if I insist on alignment.

2019 August 29, Paul Christiano, Current work in AI alignment, EA Global San Francisco 2019:

The fact that larger models are less subject to forgetting may be related to the fact that larger models do not incur significant alignment taxes.

2021 December 1, Askell, A. et. al., “A General Language Assistant as a Laboratory for Alignment”, in arXiv, →DOI:

We want an alignment procedure that avoids an alignment tax, because it incentivizes the use of models that are unaligned but more capable on these tasks.

2022 March 4, Ouyang, L. et. al., “Training language models to follow instructions with human feedback”, in arXiv, →DOI:

We note that instead of an alignment tax our proposal entails a safety dividend – the more rational the system the more capable and the safer it will be.

2023 February 27, Kornai, A. et. al., “Safety without alignment”, in arXiv, →DOI:

(artificial intelligence) A cost to the capabilities of an artificial intelligence resulting from the effects of aligning it with human ethics and morality.


prompt engineer

prompt engineer (plural prompt engineers)

The primary role of an AI specialist will require linguistic skills to show, not just tell, the GPT-3 engines how to accomplish a task. Showing, for example, involves working on the context of the input. This new task is named prompt engineering. A prompt engineer has quite a future in AI!

2022 March, Denis Rothman, Antonio Gulli, Transformers for Natural Language Processing, Packt Publishing, page 17:

An online marketplace called Promptbase has already sprung up, where prompt engineers can sell their carefully crafted instructions for image-generation tools such as Midjourney.

2022 December 13, Tim Bradshaw, “Is becoming a ‘prompt engineer’ the way to save your job from AI?”, in Financial Times, London: The Financial Times Ltd., →ISSN, →OCLC, archived from the original on 2023-03-23:

Some pundits are proclaiming that we will need to train humans in how to write good prompts, for which they will have the vaunted title of a prompt designer or prompt engineer.

2022 December 29, Lance Eliot, “Twenty-Five Eye-Opening 2023 Predictions About Generative AI And ChatGPT Including A Splash Of AI Ethics And AI Law Tossed In”, in Forbes, New York, N.Y.: Forbes Media, →ISSN, →OCLC, archived from the original on 2023-02-23:

With each request, [Riley] Goodside said, the prompt engineer should be instilling in the AI a kind of "persona" — a specific character capable of winnowing down hundreds of billions of potential solutions and identifying the right response.

2023 February 25, Drew Harwell, “Prompt engineers can make ChatGPT and Bing AI do what you want”, in The Washington Post, Washington, D.C.: The Washington Post Company, →ISSN, →OCLC, archived from the original on 2023-03-24:

[Simon] Willison spoke with Day 6 guest host Peter Armstrong about whether skepticism is warranted over how much control prompt engineers have — and whether it's a real science, or just learned intuition.

2023 March 24, Paul Hantiuk, “These engineers are being hired to get the most out of AI tools without coding”, in CBC News, archived from the original on 2023-03-25:

(artificial intelligence, technology, neologism) Someone who designs prompts to be given to an artificial intelligence.


reverse Turing test

reverse Turing test (plural reverse Turing tests)

Named after English mathematician, logician, and cryptographer Alan Turing (1912–1954).

named-after

(artificial intelligence) A Turing test in which the aim is to verify the remote participant as human, rather than focusing on whether they are a machine.


latent space

If we truly want to use the decoder of an autoencoder-type structure to generate images, then we need to develop a way to organize or regularize the latent space.

2021, Valliappa Lakshmanan, Martin Görner, Ryan Gillard, Practical Machine Learning for Computer Vision, O'Reilly, →ISBN:

A chilling discovery by an AI researcher finds that the “latent space” comprising a deep learning model’s memory is haunted by least one horrifying figure — a bloody-faced woman now known as “Loab.”

2022 September 13, Devin Coldewey, “A terrifying AI-generated woman is lurking in the abyss of latent space”, in TechCrunch, retrieved 2023-03-31:

As can be observed, the central idea of anomaly detection using GAN revolves around learning the latent space representation of data samples, which is then used to compute reconstruction losses to arrive at an anomaly score.

2023, Roshani Raut et al., editors, Generative Adversarial Networks and Deep Learning: Theory and Applications, CRC Press, →ISBN:

(machine learning) A multidimensional spatial representation of latent variables, where related concepts are positioned closer to one another.


generative AI

Generative AI won’t be the end of employment, we are told, only “boring work” – with chatbots helpfully doing all the soul-destroying, repetitive tasks and humans merely supervising them.

2023 May 8, Naomi Klein, “AI machines aren’t ‘hallucinating’. But their makers are”, in The Guardian, →ISSN:

ChatGPT's sudden popularity has triggered an arms race among the world's leading tech companies and start-ups that are rushing to obtain the H100, which Huang describes as “the world's first computer [chip]^([sic]) designed for generative AI”—artificial intelligence systems that can quickly create humanlike text, images and content.

2023 May 28, Tim Bradshaw, Richard Waters, “Nvidia reaps rewards from early lead in AI chipmaking”, in FT Weekend, page 14:


self-play

Self-play was used by the AlphaZero program to improve its performance in the games of chess, shogi and go.

(machine learning) A technique for improving the performance of reinforcement learning agents by having them play against themselves.


reinforcement learning

Reinforcement learning can be seen as somewhere between supervised and unsupervised learning. The learning system receives a feedback for its actions, but it is not provided with examples to learn from.

2010, Lutz Frommberger, Qualitative Spatial Abstraction in Reinforcement Learning, Springer Science & Business Media, →ISBN, page 10:

Let us look more deeply at reinforcement learning, to see what it means to learn from our own actions. Reinforcement learning is a field in which an agent learns by interacting with an environment.

2022, Aske Plaat, Deep Reinforcement Learning, Springer Nature, →ISBN, page 3:

(machine learning) A type of explorative learning via feedback in a simulated environment.

RLHF

reinforcement learning from human feedback


stochastic parrot

en-noun

stochastic parrot (plural stochastic parrots)

Coined by American linguist Emily M. Bender in 2021 as a reminder of the limitations of language models, see quotations.

coinage

[…] an LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot.

2021 March 3, Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜”, in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, ACM, →DOI, →ISBN, pages 610–623:

(machine learning, derogatory) A language model that uses artificial intelligence to generate seemingly coherent or contextually relevant text but does not truly understand the meaning of the language it is processing


GOFAI

Coined by John Haugeland in his 1985 book Artificial Intelligence: The Very Idea.

Acronym of good old-fashioned artificial intelligence (“a rule-based or logic-based form of symbolic AI”).


horizon effect

From the idea that the computer cannot look beyond its horizon (limit of one's knowledge; boundary or threshold).

gloss

(artificial intelligence, games) A problem in artificial intelligence whereby a computer can only search a small portion of the possible states in a game, and thus may make a detrimental move because it cannot search far enough to see its error.


genAI

en-noun

genAI (countable and uncountable, plural genAIs)

At a simple level, this means that the use of genAI requires human oversight. We will seek to use genAI tools editorially only where it contributes to the creation and distribution of original journalism.

2023 June 16, Katharine Viner, Anna Bateson, “The Guardian’s approach to generative AI”, in The Guardian, →ISSN:


MLOps

Blend of ML (“machine learning”) + DevOps.

blend

The goal of MLOps is to minimize the technical friction to get the model from an idea to production in the shortest possible time with high-quality predictive power and with as little risk as possible.

2021, Hien Luu, Beginning Apache Spark 3, 2nd edition, Apress, →ISBN:

(machine learning) A paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently.


tree kernel

(machine learning) A tree data structure based on the more general concept of the positive-definite kernel, used in the parsing and classification of sentences.


poisoning attack

en-noun

poisoning attack (plural poisoning attacks)

Just like how you can poison a village by poisoning its water source, poisoning attacks corrupt the AI system from functioning by poisoning the input data.

2023, Ram Shankar Siva Kumar, Hyrum Anderson, Not with a Bug, But with a Sticker […] , John Wiley & Sons, →ISBN:

(machine learning, computer security) Synonym of data poisoning

machine learning; computer security; machine learning; computer security


data poisoning

data poisoning (countable and uncountable, plural data poisonings)

While data sanitization shows promise to defend against data poisoning, it is often impossible to validate every data source [14].

2019, Simon N. Foley, editor, Data and Applications Security and Privacy XXXIII […] , Springer, →ISBN, page 4:

Similarly, the Tay chatbot suffered from data poisoning. To mitigate data poisoning, it is important not to let any one group contribute too much data to a model.

2022, Alfred Z. Spector, Peter Norvig, Chris Wiggins, Jeannette M. Wing, Data Science in Context: Foundations, Challenges, Opportunities, Cambridge University Press, →ISBN, page 148:

Data poisoning is one type of adversarial attack—where a user or group of users submit false data to influence the model toward a particular or incorrect prediction.

2023, Katharine Jarmul, Practical Data Privacy, O'Reilly, →ISBN:

Some forms of data poisoning are undetectable. Attackers can insert adversarial noise into the training data, altering the training data in a way that is hidden to human observers.

2023, Paul Scharre, Four Battlegrounds: Power in the Age of Artificial Intelligence, W. W. Norton & Company, →ISBN:

(machine learning, computer security) The deliberate use of a training dataset with data designed to increase errors in the output of a machine learning model.


artificial immune system

artificial immune system (plural artificial immune systems)

(machine learning) Any of a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system.


few-shot

No additional prompts for getting the responses were used, i.e. the data was created with a basic prompt in a zero-shot scenario. This is in contrast to the benchmarks by OpenAI, who used an engineered prompt in a few-shot scenario to guide the generation of essays.

2023 October 30, Herbold et al., “A large-scale comparison of human-written versus ChatGPT-generated essays”, in Scientific Reports, volume 13, page 4:

(machine learning, attributive) A machine learning paradigm where a model is trained on a very small amount of data, typically less than that required for traditional machine learning approaches.


proompt

An alteration of prompt, possibly on the pattern of coom, influenced by the 4chan -oomer suffix for forming Wojak names and derogatory agent nouns.
In modern usage, the misspelling is deliberate, but accidental errors can be found much before the widespread adoption of AI.

It’s no wonder Proompt engineers get payed^([sic]) so much. The difference in output in ChatGPT when combined with automations can mean millions for companies.

this guy is really taking proompt engineering to the next level

We will all either become proompters or the people who create the proompt tools 😉

2023 April 13, jmeintjesn, Katalon Community:

proompt: wukong pops into xaiotian’s apartment one night as a wholesome surprise only to find him drunk because he was stressed

2023 January 13, Fics Written in the Stars, Tumblr:


proompt

en-verb

proompt (third-person singular simple present proompts, present participle proompting, simple past and past participle proompted)

proompts (present singular third-person)
proompting (participle present)
proompted (participle past)
proompted (past)

I like image proompting, but I don't always have a bot idea for every image I make.

2023 July 8, crikeymcdoodle, “CRIKEY'S COOMBOT CORNER”, in Rentry:

That’s why you proompt the AI to give you more specific ideas.

2023 May 23, foodman54, “Introducing GameIdeaGenerator: Unleash Your Creativity in Roblox!”, in Roblox Developer Forum:

"Catastrophic Forgetting" is a well researched topic in GAN and diffusion models, where the more training you perform, it "forgets" prior training: this is most visible in some of the lower quality ""finetunes"" floating around, where the model output reliably reflects a certain style, but contains very little flexibility to produce anything but standing/leaning cowboy shots of medium bust girls facing the camera. I'm not sure that this could ever be "beaten" (and if so, it would be by some research team at harvard, not some rando trying to proompt the biggest horkiest tiddies on their 3090) ..^([sic])

2023 February 8, bury_she, “Custom huge dohonkerbonkeros diffusion model v(-1) (Patreon)”, in Kemono:

(humorous, machine learning) To prompt; to write a prompt for an AI model.


computational humor

computational linguistics
computational linguistics

(computational linguistics) A field within computational linguistics and artificial intelligence that leverages computers for research on humor.


foundation model

en-noun

foundation model (plural foundation models)

(computational linguistics) An AI model trained on comprehensive data, enabling its application across various use cases such as chatbots and generative AI.


SNN

spiking neural network
spiking neural network#English

(machine learning) Initialism of spiking neural network.


SOFM

self-organizing feature map
self-organizing feature map#English

(machine learning) Initialism of self-organizing feature map.


Kohonen map

Introduced by the Finnish professor Teuvo Kohonen in the 1980s.

self-organizing map
self-organizing map#English

(machine learning) Synonym of self-organizing map


Kohonen network

Kohonen network (plural Kohonen networks)


self-organizing map

self-organizing map (plural self-organizing maps)

(machine learning) A kind of artificial neural network trained using competitive learning rather than error correction, and producing a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.


Chatty G

/t͡ʃætiː ˈd͡ʒiː/

Clipping of ChatGPT (separated into two words) + -y.

Generative AI is advancing at a fast pace and quickly finding a place within education. Tools such as ChatGPT (or Chatty G as the kids say) continue to dominate conversations in education as these technologies are explored and developed.

[2024 February 5, Tamika Worrell, “Generative AI in the classroom risks further threatening Indigenous inclusion in schools”, in The Conversation, archived from the original on 2024-02-09:


prompt crafting

Apparently appeared at about the same time as prompt engineering, the two terms being interchangeable.

Guiding students in assessing AI-generated content instills discernment, while prompt crafting elicits original thinking.

2022, Sean Doyle, “Chapter 6: Augmenting Intelligence with Generative AI”, in Julia Nyberg, Jessica A. Manzone, editors, Practices That Promote Innovation for Talented Students, IGI Global, page 139:

2022, M. L. Ronn, Indie Author Confidential 11: Secrets No One Will Tell You About Being a Writer, Author Level Up, unnumbered page,
Non-artists don't want to go through the trouble of learning the nuances of prompt crafting, so they will rely on other people to do it, creating a new cottage industry […] .

2023, Jason Brownlee, Adrian Tam, Matthew Mayo, Abid Ali Awan, Kanwal Mehreen, Maximizing Productivity with ChatGPT, MachineLearningMastery.com, eBook, page 33,
One way to improve your prompt crafting is to get ChatGPT involved.

When GPT-4 was tested by medical experts from Microsoft without any specialized prompt crafting, it exceeded the passing score on USMLE by over 20 points and outperformed earlier general-purpose models (GPT-3.5) as well as models specifically fine-tuned on medical knowledge (Med-PaLM, a prompt-tuned version of Flan-PaLM 540B) [13].

2023, Aravind Kumaresan, Lorna Udan, Shazad Ashaf, “Potential Role of ChatGPT in Healthcare in the Prevention and Management of Non-communicable Diseases”, in Lorna Uden, I-Hsien Ting, editors, Knowledge Management in Organisations: 17th International Conference, KMO 2023, Proceedings, Springer, page 432:

prompt engineering
prompt engineering#English

(artificial intelligence, technology, neologism) Synonym of prompt engineering


RLAIF

Coined by American artificial intelligence company Anthropic in 2022.

coinage

Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences. However, gathering high-quality human preference labels can be a time-consuming and expensive endeavor. RL from AI Feedback (RLAIF), introduced by Bai et al., offers a promising alternative that leverages a powerful off-the-shelf LLM to generate preferences in lieu of human annotators.

2023, “RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback”, in Arxiv:

a prime hurdle lies in gathering high-quality human preference labels. This is where reinforcement learning from human feedback with AI feedback (RLAIF) comes into the picture, a novel framework by Google Research to train models with reduced reliance on human intervention.

2023 October 6, Tasmia Ansari, “Reinforcement Learning Craves Less Human, More AI”, in Analytics India Magazine:

reinforcement learning
reinforcement learning#English

(machine learning) Initialism of reinforcement learning from AI feedback.


AIgiarism

en-noun

AIgiarism (usually uncountable, plural AIgiarisms)

Blend of AI + plagiarism. Likely coined by user @CoinWaters in a tweet posted on October 26, 2022 (see quotation below).

/ˈeɪ.aɪ.d͡ʒəˌɹɪzm/

Ha. Aigiarism!

2022 October 26, @CoinWaters, Twitter, archived from the original on 2024-07-22:

“I think the rules against AIgiarism should be roughly similar to those against plagiarism,” Graham said in December. “The problem with plagiarism is not just that you’re taking credit away from someone else but that you’re falsely claiming it for yourself. The latter is still true in AIgiarism. And in fact, the former is also somewhat true with current AI technology.”

2022 December 31, Alex Hern, “AI-assisted plagiarism? ChatGPT bot says it has an answer for that”, in The Guardian, archived from the original on 2023-02-08:

However, AIgiarism is a form of bypass plagiarism. The wrong of AIgiarism is not so much of inappropriate credit allocation, but the potential propagation of factual and interpretive errors as well as biases, which undermines knowledge acquisition and understanding.

2023 August 26, Bor Luen Tang, “The underappreciated wrong of AIgiarism - bypass plagiarism that risks propagation of erroneous and bias content”, in EXCLI Journal, volume 22, →DOI, pages 907–910:

One concern deems text generated by such tools to be plagiarism. Some academics have posited that Al-generated text used by students is plagiarism, or Al-assisted plagiarism or aigiarism[…]

2023 December, Sarah Elaine Eaton, editor, Handbook of Academic Integrity, Springer International Publishing, page 163:

The main problem with aigiarism-detectors (and often AI in general) is poor context-awareness. As AI absorbs text from research papers, blogs, and other online sources, software like ZeroGPT may classify much of this material as “Al-generated” soon after a text reaches the internet and is indexed somewhere.

2024 January, Robert Ciesla, The Book of Chatbots: From ELIZA to ChatGPT, Springer Nature Switzerland, page 143:

Frequencies and percentages showed that 27% and 57% of students had positive attitudes towards plagiarism and aigiarism, respectively. No significant gender differences in aigiarism were detected. Attitudes towards aigiarism did not differ according to academic major (human-scientific) or lower and higher GPA.

2024 February 5, Mustafa Ali Khalaf, “Does attitude towards plagiarism predict aigiarism using ChatGPT?”, in AI and Ethics, →DOI:

(artificial intelligence, informal, uncommon) Plagiarism committed by means of AI; AI-assisted plagiarism.


algorethics

(artificial intelligence) The study of the ethical implications of computer systems, artificial intelligence, etc.


neural processing unit

neural processing unit (plural neural processing units)

In addition, the large weight size inevitably causes latency, power, and energy overhead when transferring the weight data between the storage/memory and the processing units, such as central processing units (CPUs), graphic processing units (GPUs), neural processing units (NPUs), or accelerators, which are not desirable for resource-constrained edge devices.

2023, Arslan Munir, Joonho Kong, Mahmood Azhar Qureshi, Accelerators for Convolutional Neural Networks, John Wiley & Sons, →ISBN, page 43:

(computer hardware, machine learning) A specialized hardware circuit designed to accelerate machine learning applications.


LoRA

LoRA (countable and uncountable, plural LoRAs)

tlb

(machine learning, artificial intelligence)

Fine-tuning (deep learning)#Low-rank adaptation

(uncountable) An adapter-based technique for efficiently fine-tuning models.

(chiefly Stable Diffusion, loosely, by extension, countable) An adapter.


TESCREAL

Coined by Timnit Gebru and Émile P. Torres in 2023 as an umbrella term for related movements.

People who are very rich or very clever, or both, sometimes believe weird things. Some of these beliefs are captured in the acronym Tescreal. The letters represent overlapping futuristic philosophies — bookended by transhumanism and longtermism — favoured by many of AI’s wealthiest and most prominent supporters.

2023 May 10, Anjana Ahuja, “We need to examine the beliefs of today’s tech luminaries”, in Financial Times:

effective altruism
effective altruism#English

Acronym of transhumanism, extropianism, Singularitarianism, cosmism, rationalism, effective altruism, longtermism.


model card

We propose model cards as a step towards the responsible democratization

2019 January 29, Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, “Model Cards for Model Reporting”, in Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19), New York, NY, USA: Association for Computing Machinery, →DOI, →ISBN, pages 220–229:

To guide the Model Card creator to organize model information

2020 July 29, Huanming Fang, Hui Miao, “Introducing the Model Card Toolkit for Easier Model Transparency Reporting”, in Google Research, Google:

models on computer vision has included model cards in their repositories

2020, Abhishek Wadhwani, Priyank Jain, “Machine Learning Model Cards Transparency Review : Using model card toolkit”, in 2020 IEEE Pune Section International Conference (PuneCon), →DOI, pages 133-137:

(artificial intelligence) a document describing a machine learning model


model collapse

en-noun

model collapse (countable and uncountable, plural model collapses)

(machine learning) Degradation of output quality caused by training on synthetic data.


capability overhang

Researchers talk about the 'capability overhang,' or hidden skills and dangers, of artificial intelligence. As the technology goes mainstream, we're going to discover a lot of new things about them.

2022 December 8, James Vincent, “ChatGPT proves AI is finally mainstream — and things are only going to get weirder”, in The Verge, archived from the original on 2024-04-30:

Since the release of GPT-4, OpenAI has been adding capabilities to the AI system with "plugins", giving it the ability to look up data on the open web, plan holidays, and even order groceries. But the company has to deal with "capability overhang": the issue that its own systems are more powerful than it knows at release.

2023 March 29, Alex Hern, “Elon Musk joins call for pause in creation of giant AI 'digital minds'”, in Katharine Viner, editor, The Guardian, London: Guardian News & Media, →ISSN, →OCLC, archived from the original on 2024-06-10:

What's much harder to test for is what's known as "capability overhang"—meaning not just the model's current knowledge, but the derived knowledge it could potentially generate on its own.

2024 January 26, Eric Schmidt, “How We Can Control AI”, in The Wall Street Journal, New York, N.Y.: Dow Jones & Company, →ISSN, →OCLC, archived from the original on 2024-01-26:

(artificial intelligence) Capabilities and potential applications of existing artificial intelligence systems that have not yet been discovered.


knowledge mining

(computing, artificial intelligence) An emerging discipline in artificial intelligence that uses a combination of intelligent services to quickly learn from large amounts of information.


Jordan network

recurrent neural network
recurrent neural network

A kind of recurrent neural network, similar to an Elman network but having the context units fed from the output layer instead of the hidden layer.


Elman network

A kind of three-layer recurrent neural network.


cross-attention

The cross attention layer inside each transformer decoder block is in fact a multi-head cross attention layer […]

2024, Benoit Liquet, Sarat Moka, Yoni Nazarathy, Mathematical Engineering of Deep Learning, CRC Press, page 286:

(machine learning) A form of attention (machine learning method) where two different input sequences are compared, i.e. the keys and queries differ.


world model

Current problem solving systems are constrained in their applicability by inadequate world models. We suggest a world model based on a temporal logic. This approach allows the problem solver to gather constraints on the ordering of actions without having to commit to an ordering when a conflict is detected.

1983, James F. Allen, Johannes A. Koomen, “Planning using a temporal world model”, in Proceedings of the Eighth International Joint Conference on Artificial Intelligence, page 741:

In this work, we build probabilistic generative models of OpenAI Gym environments. The RNN-based world models are trained using collected observations recorded from the actual game environment. After training the world models, we can use them mimic the complete environment and train agents using them.

2018, David Ha, Jürgen Schmidhuber, “World Models”, in arXiv, page 2:

The generation of mostly realistic-looking videos from prompts *does not* indicate that a system understands the physical world. Generation is very different from causal prediction from a world model.

(artificial intelligence, reinforcement learning) A predictive model that has been internalised by an agent which is used to forecast what consequences its actions will have on the environment (i.e. world) that it interacts with.

artificial intelligence; reinforcement learning; artificial intelligence; reinforcement learning


gen AI

gen AI (countable and uncountable, plural gen AIs)