large language model

The Means of Prediction: How AI Really Works (And Who Benefits)

by Maximilian Kasy  · 15 Jan 2025  · 209pp  · 63,332 words

justice, and health care. In a similar vein, Timnit Gebru, a computer scientist writing during her time working at Google, warned of the dangers of large language models acting as stochastic parrots, which repeat language patterns without understanding, and in doing so replicate the biases embedded in their training data. Meredith Whittaker, currently

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perceptions of AI have oscillated from “an obscure academic niche” to the very broad “everything related to data,” and back to the narrow category of “large language models.” This book will take an intermediate stance, in between these very broad and very narrow definitions. Following the standard technical treatment of AI, this book

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measurable reward.” This definition is more specific than “anything to do with data,” but at the same time, it includes a lot more than just large language models. This definition gives us a framework to talk about the many socially consequential settings where AI is used. There are many branches of the field

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outcome as accurately as possible. Many learning problems are prediction problems: In facial recognition, an individual’s identity is predicted based on an image. In large language models, the next word is predicted based on the preceding words. In the hiring of job candidates, future performance is predicted based on candidate characteristics. In

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net), have also been central for generative AI—AI that produces text, images, or other media. This includes large language models, where the goal is to predict the most likely word to come next. (Large language models power applications such as ChatGPT.) Generative AI also includes image generation, where images are predicted based on text labels

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. Public understanding has oscillated from AI being an obscure academic niche field, to it encompassing everything related to data, to it narrowly referring exclusively to large language models. This book will take an intermediate position, following leading textbooks on the subject. According to these textbooks, the goal of AI, as a field of

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. A robot might choose which way to turn or how to move its arm. A social media algorithm might choose what ads to display. A large language model might choose what sentence to print next. A hiring algorithm might choose which job candidate to invite for an interview. There is, second, the objective

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deadly set of applications of supervised learning has led to spectacular successes of AI in language processing, computer vision, and game play in recent years: Large language models can predict the word that is most likely to come next given a sequence of words typed thus far. Algorithms for machine translation predict the

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of big data. Increasingly complex models, usually leveraging artificial neural nets, are fit on ever-larger datasets. (Examples of this development are the ever-larger large language models, which are discussed in the next chapter, where figure 6 shows the massive growth of both datasets and model complexity.) But no matter how large

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still represent the same object. For natural language processing, transformer networks, another variant of neural networks that use self-attention, form the backbone of recent large language models. Such transformer networks are based on a recognition of the fact that, in human languages, the relevant context for interpreting a word often does not

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the more general principles of AI and machine learning, which this book discusses, are likely to remain relevant in the long run. Today, virtually all large language models use the transformer architecture discussed above. The prime reason for the popularity of the transformer architecture is its practical success; its popularity is not motivated

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6 shows the number of training tokens (i.e., the size of training datasets) and the number of parameters by year of release in different large language models. The vertical axis is scaled so that each step corresponds to a tenfold increase. Some of these models have more than one trillion parameters and

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AI company DeepSeek suggest that this scale might be reduced somewhat without loss of performance.) Figure 6 The size of large language models Source: Wikipedia, “Large Language Model,” accessed October 1, 2024, https://en.wikipedia.org/wiki/Large_language_model Language modeling is one success story of deep learning; image generation is another one. The most successful algorithms for

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approach is due to its practical success. Generative AI, whether for the generation of text or of images, raises important questions of data ownership. Both large language models and image generation models are trained on data that are primarily scraped from the internet, without either the knowledge or the consent of those who

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maximizes or minimizes some objective function—such as predictive loss (i.e., prediction errors) for the next word on the internet, in the case of large language models. For generative AI, we need to modify this statement slightly: The model minimizes predictive loss for the next word in response to a prompt that

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or benefits of their actions. Externalities might be positive or negative. Pollution is a negative externality, where others are harmed. The training and deployment of large language models, for example, consumes vast amounts of energy and thereby contributes to climate change. Knowledge production by contrast creates positive externalities, where others benefit. Public funding

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an open-source license). These platforms greatly contribute to the common good. They also serve as one of the prime sources of training data for large language models, and for generative AI more broadly. All the works of art and writing that are available on the internet but that are considered someone’s

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models. In a memorable and apt metaphor, the science fiction author Ted Chiang has described large language models, such as the one that underpins ChatGPT, as a blurry compressed image of the internet. The companies that control large language models are effectively scraping and compressing much of the internet, including all its contributions from a myriad

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scalability, is the unstructured text data available on the internet. The comprehensive scraping of these data has powered much of the recent spectacular advances of large language models. Conceivably, however, limits might soon be reached in terms of the amount of text data of sufficient quality that can be made available. This suggests

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alone do not make AI. To process data, we need computers. In application domains of AI where data are abundant, such as game play or large language models, the limiting factor for AI is usually computational capacity. The heart of a typical computer is its central processing unit, or CPU. The CPU is

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and then setting the price equal to this value. Should the algorithm instead set prices to maximize consumer welfare? A chatbot algorithm based on a large language model might give answers corresponding to the most likely sequence of words that might follow a given question. The algorithm might do so by predicting responses

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, 93 enclosures, 82, 108 energy: as component of the means of prediction, 93–95; consumption of, 93; externalities generated by production and consumption of, 95; large language models’ use of, 84; scalability of use of, 95 engineers/computer scientists: and AI ethics, 97–98; capacity of, as change agents, 100–101. See also

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; competitive vs. noncompetitive, 150, 154; empirical trends in, 155–58; polarization of, 157; technology’s effects on, 149–50, 157, 161–62 Landemore, Hélène, 198 large language models: complexity of, 41; content of, 86, 87, 188; dangers of, 6; energy used by, 84; how they work, 10; as narrow definition of AI, 9

Blank Space: A Cultural History of the Twenty-First Century

by W. David Marx  · 18 Nov 2025  · 642pp  · 142,332 words

Altman, went on a full-out marketing blitz for “generative AI” with the release of the image-generation software DALL-E in early 2021 and large language model ChatGPT in late 2022. Generative AI empowered everyday users to produce stunningly humanlike creations from simple text prompts. As ChatGPT stole the cultural spotlight, the

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act of creation itself. Aspiring creators could outsource their writing, illustration, photo editing, and even songwriting to machines. While the technology was far from perfect—large language models often “hallucinated” facts with alarming confidence—it performed enough magic tricks to simultaneously delight and unsettle society. By this point, however, the public was already

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adult animation. Journalist Jason Koebler traced the oddness of the images to “prompts that are written in Hindi, Urdu, and Vietnamese, which are underrepresented in large language model training data.” He continued: “Other bizarre outcomes arise because people are using Google Translate and speech-to-text to say a prompt aloud in Hindi

The Age of Extraction: How Tech Platforms Conquered the Economy and Threaten Our Future Prosperity

by Tim Wu  · 4 Nov 2025  · 246pp  · 65,143 words

positioned to create entirely new products. And here is where we end up at the artificial intelligence technologies of the 2020s—and, in particular, the large language models. There is a direct line connecting FitzRoy, in other words, to the chatbots of the 2020s. For the AIs of the 2020s have more in

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, employing many of the top AI–neural network scientists who had not been hired by Google, including Ilya Sutskever. All this leads up to the large language models (LLMs)—the chatbots. They work like the original Perceptron—as artificial brains that have been trained on complex patterns, in this case, the patterns found

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reliant on its servers and computing capacity. After the stunning debut of ChatGPT, each of the platforms pushed hard to develop or invest in AI large language models. Facebook deployed LLaMA, Amazon invested in Anthropic (a rival to OpenAI), and Twitter developed and released Grok. The succession question will not be answered in

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become more intimate and complex the more the AI learns about the user’s personality and hobbies. Underneath the hood is a fairly advanced AI large language model, allegedly borrowed from OpenAI, that has been customized for companionship. When I met with Replika’s CEO Eugenia Kuyda, she turned out to be highly

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-directive role.” Lucy Yao and Rian Kabir, “Person-Centered Therapy (Rogerian Therapy),” February 9, 2023, in StatPearls (Treasure Island, FL: StatPearls Publishing, 2025). *2 The large language models were aided by the development of a new neural network architecture, the transformer architecture, which uses a mechanism called attention to efficiently process sequential data

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., 126 land-based economic power, 5, 125, 159 feudal system’s impact on, 126–30, 133 Lanier, Jaron, 68 lao-ban (small boss) economy, 133 large language models (LLMs), 88, 94, 99, 101. See also chatbots “Lease Easy Bundle,” 113–14 LeCun, Yann, 92–93 “Lee from America,” 68–69 Li, Fei-Fei

Fixed: Why Personal Finance is Broken and How to Make it Work for Everyone

by John Y. Campbell and Tarun Ramadorai  · 25 Jul 2025

technology. The suite of technological tools at our disposal today is already impressive and improving rapidly. The generative artificial intelligence revolution and the emergence of large language models promise to upend service provision in many areas of economic life, and personal finance appears ripe for disruption.1 Financial technology (“fintech” for short) is

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Kayak, 209 Keillor, Garrison, 271n8 Keogh accounts, 297n7 Kiyosaki, Robert, 265n6 Klarna, 182 Lake Wobegon, 137 lapsation problem, insurance and, 54–55, 61, 145–147 large language models, 180 late fees, credit card, 92, 94–95, 207, 312n58 lawsuits, approved financial products and protection from, 217 learning from others, danger of, 45–47

Gilded Rage: Elon Musk and the Radicalization of Silicon Valley

by Jacob Silverman  · 9 Oct 2025  · 312pp  · 103,645 words

industry was finding little success in monetizing its new innovation, and some analysts wondered if the promised gains would ever arrive. Some observers thought that large language models—the technology behind generative AI—had inherent limitations that would limit AI’s potential to go beyond inference and prediction to actual thinking and reasoning

Money in the Metaverse: Digital Assets, Online Identities, Spatial Computing and Why Virtual Worlds Mean Real Business

by David G. W. Birch and Victoria Richardson  · 28 Apr 2024  · 249pp  · 74,201 words

ChatGPT It seems likely, then, that the future transaction landscape will be dominated by bots, and what has been going on with ChatGPT – and other large language models (LLMs) – is a window into that future. ChatGPT is a form of generative AI, and generative AI is – let’s not beat about the bush

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International Air Transport Association ICO initial coin offering IMF International Monetary Fund IoT internet of things KYC Know Your Customer KYE Know Your Employee LLM large language model MR mixed reality MRV measurement, reporting and verification NFT non-fungible token OIDC OpenID Connect PEP policy enforcement point PII personally identifiable information SEC Securities

Extremely Hardcore: Inside Elon Musk's Twitter

by Zoë Schiffer  · 13 Feb 2024  · 343pp  · 92,693 words

was fine,” Yoel Roth wrote on the Twitter-alternative Bluesky. AI firms in particular were notorious for gobbling up huge swaths of text to train large language models (LLMs). Now that those firms were worth a lot of money, the situation was far from fine in Musk’s opinion. In November 2022, OpenAI

Artificial Intelligence: A Modern Approach

by Stuart Russell and Peter Norvig  · 14 Jul 2019  · 2,466pp  · 668,761 words

-speech tagger. In Proc. Sixth Conference on Applied Natural Language Processing. Brants, T., Popat, A. C., Xu, P., Och, F. J., and Dean, J. (2007). Large language models in machine translation. In EMNLP-CoNLL-07. Bratko, I. (2009). Prolog Programming for Artificial Intelligence (4th edition). Addison-Wesley. Bratman, M. E. (1987). Intention, Plans

Algospeak: How Social Media Is Transforming the Future of Language

by Adam Aleksic  · 15 Jul 2025  · 278pp  · 71,701 words

websites unusable.[3] That’s just from moving around tones, ignoring potential semantic substitutions, so we’re clearly quite far from a 1984-esque scenario. Large language models may get better at recognizing words in context, but people will always find creative ways to express their ideas. We’ve already seen this in

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, 52–53 L labels, 196, 202, 205. See also microlabels Lady Bountiful (Carr), 18, 18 laggards, 53–55, 54 Language and the Internet (Crystal), 10n large language models, 209 late majority, 53, 54 Latin, 5, 7, 69n Latino dance circles, 147 laughing-crying emoji , 53–54, 188 laughing emoji, 53 LDAR (lay down

Nexus: A Brief History of Information Networks From the Stone Age to AI

by Yuval Noah Harari  · 9 Sep 2024  · 566pp  · 169,013 words

Psychology: General 149, no. 4 (2020): 746–56. 22. Yue Zhang et al., “Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models” (preprint, submitted in 2023), arxiv.org/abs/2309.01219; Jordan Pearson, “Researchers Demonstrate AI ‘Supply Chain’ Disinfo Attack with ‘PoisonGPT,’ ” Vice, July 13, 2023, www

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-job-study-determines; Jules Ioannidis et al., “Gracenote.ai: Legal Generative AI for Regulatory Compliance,” SSRN, June 19, 2023, ssrn.com/abstract=4494272; Damien Charlotin, “Large Language Models and the Future of Law,” SSRN, Aug. 22, 2023, ssrn.com/abstract=4548258; Daniel Martin Katz et al., “GPT-4 Passes the Bar Exam,” SSRN

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11: THE SILICON CURTAIN: GLOBAL EMPIRE OR GLOBAL SPLIT? 1. Suleyman, Coming Wave, 12–13, 173–77, 207–13; Emily H. Soice et al., “Can Large Language Models Democratize Access to Dual-Use Biotechnology?” (preprint, submitted 2023), doi.org/10.48550/arXiv.2306.03809; Sepideh Jahangiri et al., “Viral and Non-viral Gene

On the Edge: The Art of Risking Everything

by Nate Silver  · 12 Aug 2024  · 848pp  · 227,015 words

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma

by Mustafa Suleyman  · 4 Sep 2023  · 444pp  · 117,770 words

Stories Are Weapons: Psychological Warfare and the American Mind

by Annalee Newitz  · 3 Jun 2024  · 251pp  · 68,713 words

Rise of the Robots: Technology and the Threat of a Jobless Future

by Martin Ford  · 4 May 2015  · 484pp  · 104,873 words

The Age of AI: And Our Human Future

by Henry A Kissinger, Eric Schmidt and Daniel Huttenlocher  · 2 Nov 2021  · 194pp  · 57,434 words

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future

by Orly Lobel  · 17 Oct 2022  · 370pp  · 112,809 words

Four Battlegrounds

by Paul Scharre  · 18 Jan 2023

The Singularity Is Nearer: When We Merge with AI

by Ray Kurzweil  · 25 Jun 2024

The Long History of the Future: Why Tomorrow's Technology Still Isn't Here

by Nicole Kobie  · 3 Jul 2024  · 348pp  · 119,358 words

Why Machines Learn: The Elegant Math Behind Modern AI

by Anil Ananthaswamy  · 15 Jul 2024  · 416pp  · 118,522 words

Pattern Breakers: Why Some Start-Ups Change the Future

by Mike Maples and Peter Ziebelman  · 8 Jul 2024  · 207pp  · 65,156 words

Boom: Bubbles and the End of Stagnation

by Byrne Hobart and Tobias Huber  · 29 Oct 2024  · 292pp  · 106,826 words

What If We Get It Right?: Visions of Climate Futures

by Ayana Elizabeth Johnson  · 17 Sep 2024  · 588pp  · 160,825 words

In the Plex: How Google Thinks, Works, and Shapes Our Lives

by Steven Levy  · 12 Apr 2011  · 666pp  · 181,495 words

Literary Theory for Robots: How Computers Learned to Write

by Dennis Yi Tenen  · 6 Feb 2024  · 169pp  · 41,887 words

Superbloom: How Technologies of Connection Tear Us Apart

by Nicholas Carr  · 28 Jan 2025  · 231pp  · 85,135 words

This Is for Everyone: The Captivating Memoir From the Inventor of the World Wide Web

by Tim Berners-Lee  · 8 Sep 2025  · 347pp  · 100,038 words

Everything Is Predictable: How Bayesian Statistics Explain Our World

by Tom Chivers  · 6 May 2024  · 283pp  · 102,484 words

Unit X: How the Pentagon and Silicon Valley Are Transforming the Future of War

by Raj M. Shah and Christopher Kirchhoff  · 8 Jul 2024  · 272pp  · 103,638 words

The Thinking Machine: Jensen Huang, Nvidia, and the World's Most Coveted Microchip

by Stephen Witt  · 8 Apr 2025  · 260pp  · 82,629 words

The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future

by Keach Hagey  · 19 May 2025  · 439pp  · 125,379 words

Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI

by Karen Hao  · 19 May 2025  · 660pp  · 179,531 words

These Strange New Minds: How AI Learned to Talk and What It Means

by Christopher Summerfield  · 11 Mar 2025  · 412pp  · 122,298 words

More Everything Forever: AI Overlords, Space Empires, and Silicon Valley's Crusade to Control the Fate of Humanity

by Adam Becker  · 14 Jun 2025  · 381pp  · 119,533 words

Searches: Selfhood in the Digital Age

by Vauhini Vara  · 8 Apr 2025  · 301pp  · 105,209 words

Supremacy: AI, ChatGPT, and the Race That Will Change the World

by Parmy Olson  · 284pp  · 96,087 words

AI in Museums: Reflections, Perspectives and Applications

by Sonja Thiel and Johannes C. Bernhardt  · 31 Dec 2023  · 321pp  · 113,564 words

Amateurs!: How We Built Internet Culture and Why It Matters

by Joanna Walsh  · 22 Sep 2025  · 255pp  · 80,203 words

Co-Intelligence: Living and Working With AI

by Ethan Mollick  · 2 Apr 2024  · 189pp  · 58,076 words

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

by Pedro Domingos  · 21 Sep 2015  · 396pp  · 117,149 words

The Wealth Ladder: Proven Strategies for Every Step of Your Financial Life

by Nick Maggiulli  · 22 Jul 2025

The Sirens' Call: How Attention Became the World's Most Endangered Resource

by Chris Hayes  · 28 Jan 2025  · 359pp  · 100,761 words

Code Dependent: Living in the Shadow of AI

by Madhumita Murgia  · 20 Mar 2024  · 336pp  · 91,806 words

If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All

by Eliezer Yudkowsky and Nate Soares  · 15 Sep 2025  · 215pp  · 64,699 words

Elon Musk

by Walter Isaacson  · 11 Sep 2023  · 562pp  · 201,502 words

Abundance

by Ezra Klein and Derek Thompson  · 18 Mar 2025  · 227pp  · 84,566 words

The Measure of Progress: Counting What Really Matters

by Diane Coyle  · 15 Apr 2025  · 321pp  · 112,477 words

Enshittification: Why Everything Suddenly Got Worse and What to Do About It

by Cory Doctorow  · 6 Oct 2025  · 313pp  · 94,415 words

The Big Fix: How Companies Capture Markets and Harm Canadians

by Denise Hearn and Vass Bednar  · 14 Oct 2024  · 175pp  · 46,192 words

The Economic Consequences of Mr Trump: What the Trade War Means for the World

by Philip Coggan  · 1 Jul 2025  · 96pp  · 36,083 words