large language model

AI in Museums: Reflections, Perspectives and Applications

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

a mainstream topic in the cultural world, but does feature in general debates about digitization and digitality. The use of machine learning, neural networks, and large language models has, however—and contrary to common assumptions—been growing for years. Beyond prominent lighthouses, initial surveys of the international museum landscape list many hundreds of

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. The only thing that has changed dramatically in recent years is that such systems—from simple machine learning to the development of neural networks and large language models—have achieved a level of complexity and efficiency that often produces astonishing results. But to view this correctly, it is necessary to think the other

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Creative User Empowerment. She places particular emphasis on reflecting the normative preconditions and frameworks of AI projects, stresses the importance of the conscious use of large language models and the open handling of data, and points to the requirement of clearly defining the problem to be solved with AI. Finally, the increasing use

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art has played and continues to play a crucial role and might further transform and redesign creative processes. The increase in generative AI and especially large language models (LLMs) has led to a distortion of the public perception of what is meant by AI. At the same time, the technology has made astonishing

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content, but also engaging in communicative interaction. It might be useful to remember in the future that there was a time before the development of large language models, and in ‘CHIM—Chatbot in the Museum: Exploring and Explaining Museum Objects with Speech-Based AI’, Oliver Guske, Stefan Schaffer, and Aaron Ruß present an

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here is therefore rather particular: the model learns stochastically by adding up very small elements to calculate ‘a bigger picture’ or (in the case of large language models) to calculate the meaning of a sentence from analysing the context of thousands of tokens (entities similar to words), taking note of which other tokens

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chatbot communication is now emerging as the technology develops and is implemented in our daily lives. As we know, this part of AI, which involves large language models and natural language processing, is not the only form of AI, but it is—besides generative images—nevertheless one that a majority of people currently

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observations are based on a chatbot that is certainly sophisticated, but also far from exploiting the full potential of AI. With the rapid emergence of large language models (the best known being OpenAI’s GPT), museum chatbots will improve significantly over the next few years and be able to provide truly individualized responses

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quantity of data and a model’s capacity to ingest this larger amount of information. This has been clearly illustrated with linguistic models (or LLMs, large language models). Behind the construction of larger models lies the idea of universality: by building larger models capable of sorting through a wider range of data, it

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from the web—without any explanation or justification being provided. But not only image-based AI can suffer from bad quality or biased training data. Large language models like BERT, GPT, et cetera are also trained on massive 155 156 Part 2: Perspectives quantities of text that are scraped from publicly available online

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was fun—the fun of make-believe. One could also say: the joy that comes from a well-told story. A fiction, not a fact. Large Language Models as Entertainment Is GPT-3 somewhat similar to the Mechanical Turk? No human intelligence whatsoever, but still capable of coming up with humanlike texts? Or

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with you like a human, you react with human emotions. That was the case when machines spoke like very, very dumb humans. And now that large language models (LLM) can have long and coherent conversations with you, it becomes increasingly difficult to stay cold (Kilg 2021). So, is Anic really a decent column

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columns that she has fallen in love with her own neural network? In short, are we supposed to believe such nonsense routinely made up by large language models? Counter-question: Why not? And, first of all: what exactly do we mean by ‘believe’ here? Do we believe all of Dostoevsky’s psychological aberrations

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have to consciously give up our ‘disbelief’ in order to enjoy fiction. And we seem to enjoy doing this, in all sorts of different contexts. Large Language Models as Impostors One can assume that impostors exploit precisely this desire to let ourselves be deceived, this ‘willing suspension’. We tend to prefer grandiose and

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to treating illnesses might turn out to be expensive, losing much of the healing magic on the way (Sacks 1990). But that is another story. Large Language Models as Storytellers—Used Best in which Contexts? The crucial question here: Would we trust a machine doctor in the first place? Surely knowledge retrieval as

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image recognition and intelligent search technologies. On top of these novel approaches to the exploration of the collection, users are also invited to interact with large language models (LLMs) enriched with collection data, so that they can actively write texts about the objects and publicly share their story and findings with others. This

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and further integrated into the development. In the actual tool, it supports the visual search process through image embeddings. An event on the use of large language models was held in July 2022 and a prototype developed, co-curated by the Turing Agency20 (Basel/Zurich/Berlin). This enabled us to explore the process

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and the xCurator user journey. In the experimental Datalab, we were able to run various tests and develop solutions to test the added value of large language models (LLMs) for the xCurator solution. With this, it already became visible that LLMs can help users find and contextualize content and suggest topics, structures, and

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. The use and application of generative AI with multimodal models falls within a broader ongoing debate surrounding large language models. Several AI researchers have issued an open call for a moratorium on the development of large language models such as ChatGPT or GPT for at least six months until further research on the technology has been

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generative AI technologies produce can be factual, but might also be speculative. For this reason, generative text production as it occurs in the context of large language models such as ChatGPT or GPT-4 is often likened to the figure of the ‘stochastic parrot’ (Bender et al. 2021, 610–23): like a parrot

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animals. Abstracts Mercedes Bunz, The Role of Culture in the Intelligence of AI Artificial intelligence has received a new boost from the recent hype about large language models. However, to avoid misconceptions, it is better to speak of ‘machine intelligence’. In addition to reflecting on current processes, the cultural sector can benefit from

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uses its potential to cope with the flood of information. Daniel M. Feige, Why AI Cannot Think In the context of the recent interest in large language models (LLMs) and image creation using artificial intelligence, the debate about whether AI is capable of reasoning arises again and again. This paper argues that it

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and information based on individual user interests, thus providing them with a personalized and more in-depth exploration of the collection. Users can interact with large language models (LLMs) enriched with collection data, thus enabling them to write about and share objects. Despite being experimental, this signifies a shift in the role of

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

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

data, they worked from the ground up to create a new translation system. “One of the things we did was to build very, very, very large language models, much larger than anyone has ever built in the history of mankind.” Then they began to train the system. To measure progress, they used a

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

–like methods in information retrieval. “First links in the Markov chain,” by Brian Hayes (American Scientist, 2013), recounts Markov’s invention of the eponymous chains. “Large language models in machine translation,”* by Thorsten Brants et al. (Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language

The Age of AI: And Our Human Future

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

of AI are harder to quantify than increases in computing power, it appears that their growth is even more rapid. For example, the power of large language models, neural networks that underlie much of today’s natural language processing, is growing even more rapidly, tripling in fewer than two years. Microsoft’s Megatron

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perhaps in which people play a de minimis co-inventor role). National governments have recognized AI’s threat to language: Hungary has commissioned its own large language model so that Hungarian does not automatically become obsolete in the digital realm.9 Governments have also begun to grapple with digital networks’ dilution of communal

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

The Story of Your Life: How Social Media Shapes the Way We Experience Everything

by Kathryn Jezer-Morton  · 4 Aug 2026

of AirSpace, they may well begin demanding more “curation,” which is really just another word for human-generated taste. But as long as algorithms and large language models are being used to design, build, and market homelife to us, we will be stuck with the soulless remnants of a doomed disciplinary regime. If

Literary Theory for Robots: How Computers Learned to Write

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

the frenzy of its grifters and soothsayers. What remains will be more modest and more significant. Viewed in the light of collective human intellectual achievement, large language models are built on the foundation of public archives, libraries, and encyclopedias containing the composite work of numerous authors. Their synthesized voice fascinates me not so

Superbloom: How Technologies of Connection Tear Us Apart

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

, and Microsoft’s Copilot is as clairvoyants. They are mediums that bring the words of the past into the present in a new arrangement. The large language models, or LLMs, that power the chatbots are not creating text out of nothing. They’re drawing on a vast corpus of human expression—a digitized

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Earth, but at least we still have the power to lay everything to waste. Ghosts Still, the machines are talking, and they’re making sense. Large language models probably aren’t going to bring Armageddon—when it comes to existential threats, there are other, rougher beasts—but they do represent an epochal feat

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creating “a new kind of society,” which could turn out terrific or “really terrible.”13 Putting yet another spin on Claude Shannon’s theories, a large language model employs coding as a means of compression for the purpose of efficient communication. But it’s not coding, compressing, and communicating discrete messages. It’s

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, people will understand you. They’ll understand you even if you don’t know what you’re talking about. Far from being a weakness of large language models, compression turns out to be a strength. The acts of interpolation required to reproduce knowledge in phrases and sentences are essential to chatbots’ ability to

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machines start generating content out of their own resources. It’s true that the mechanism of communication—in this case, the computer network and the large language model running on it—remains oblivious to meaning. It’s still doing what it has always done: fiddling with signals. But it’s through such fiddling

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the present and what’s not. However pure their intentions, they put their own ideological and political stamp on the artificial speech they produce. As large language models and other forms of generative AI become more broadly used in media and education—to produce content, to give advice, to teach, to console—the

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digital town square,” FIRE concluded, “it is ultimately more like Elon Musk’s house party.” To have a flighty oligarch, or any individual, control a large language model connected to a major social media platform—the X feed offers an unparalleled dataset of human conversations for AI training—raises unsettling questions about the

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Klemperer, Victor, 139 Klonick, Kate, 73 knowledge, AI and, 184–85 Krasnow, Erwin, 42 Lake Elsinore, California, 2 language, 18–19, 100. See also large language models (LLMs); writing large language models (LLMs), 181–85, 190–91, 195, 201–5, 225, 230. See also artificial intelligence Larkin, Philip, 87 LeCun, Yann, 183 Lessig, Lawrence, 226 Lester

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

by Parmy Olson  · 284pp  · 96,087 words

exist in a few years, or even next year,” Brockman told the researchers. Although OpenAI eventually gained worldwide acclaim for its work on chatbots and large language models, its first few years were spent toiling on multiagent simulations and reinforcement learning, fields that DeepMind already dominated. But the more they chased DeepMind in

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booming voice, he was seen as quirky and chatted to suits like Sundar Pichai as if they were old friends. Shazeer had extensive experience with large language models. These were computer programs that could analyze and generate humanlike text after being trained on billions of words. Soon after he joined the ragtag group

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mark to do anything about them. It took several years, for instance, for Google to plug transformers into services like Google Translate or BERT, a large language model that it developed to make its search engine better at processing the nuance of human language. The transformer’s inventors couldn’t help but feel

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of Google’s more eccentric researchers. By all means look into it, he said. Frustrated, Shazeer left Google in 2021 to pursue his research on large language models independently, cofounding a chatbot company called Character.ai. By that time, the “Attention Is All You Need” paper had become one of the most popular

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text. What if OpenAI used it to generate text? Sutskever talked to a young researcher at OpenAI named Alec Radford, who’d been experimenting with large language models. Although OpenAI is best known today for ChatGPT, back in 2017 it was still throwing spaghetti on the wall to see what would stick, and

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Radford was one of only a handful of people at OpenAI looking at the technology that powered chatbots. Large language models themselves were still a joke. Their responses were mostly scripted and they’d often make wacky mistakes. Radford, who wore glasses and had an overgrown

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be able to infer context. Down the line, as Radford’s system became more sophisticated, people at OpenAI and beyond would question whether these new large language models were actually understanding language and not just inferring it. This may seem like a trivial semantic issue but the distinction is important, because it can

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better Excel spreadsheet. He wanted to bring abundance to humanity. And Nadella was impressed by what Altman’s small team had already accomplished, particularly with large language models. Even with its more than seven thousand AI research staff, Microsoft had struggled to see similar advancements so quickly. Like Google, Microsoft had also become

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tech giants—Amazon, Microsoft, and Google—have a stranglehold on the cloud business. It became clear to Microsoft’s CEO that OpenAI’s work on large language models could be more lucrative than the research carried out by his own AI scientists, who seemed to have lost their focus after the Tay disaster

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forefront of the AI revolution. In return, Microsoft was getting priority access to OpenAI’s technology. Inside OpenAI, as Sutskever and Radford’s work on large language models became a bigger focus at the company and their latest iteration became more capable, the San Francisco scientists started to wonder if it was becoming

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made. That’s essentially what OpenAI was doing. You could learn more about what was in a pack of Doritos than you could about a large language model. Amodei wasn’t as worried about bias as he was about AI’s existential threat to humanity. He had written a research paper called “Concrete

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DeepMind had largely neglected even as OpenAI chased it aggressively. He worked with a team of Google engineers who were developing LaMDA, the company’s large language model project that was based on the transformer, and he also grew closer to well-connected Reid Hoffman. The two men talked about starting their own

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exhausting. Two female AI researchers who worked at Google’s headquarters in Mountain View experienced that firsthand. They were worried about the side effects that large language models could have on society well before any apocalypse and were baffled as to why no one was talking about it. These models were becoming so

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AI, scientists were creating a new, living being. Many AI scientists, of course, did not believe this was the case because they knew firsthand that large language models—the AI systems that seemed closest to replicating human intelligence—were simply built on neural networks that were trained on so much text that they

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saw their chatbots as a partner for romance and sexting. Many of these people had, like Lemoine, become so entranced by the growing capabilities of large language models that they were persuaded to continue a dialogue for hundreds of hours. For some people, this led to relationships that they considered meaningful and long

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men, which meant that statistically, they were less often on the receiving end of the bias problems that were cropping up in AI systems and large language models. Timnit Gebru, the computer scientist who had started coleading Google’s small ethical AI research team with Margaret Mitchell, was hyperaware of how few Black

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that other overhyped technology was realistic. Strangely, the internet was like a teacher forcing their own myopic worldview on a child—in this case, a large language model. Take politics as another example of where this can go awry. In the United States, the web is awash with information about the two main

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at the core of one of the most significant new developments in artificial intelligence. From her own background in computer science, Bender could see that large language models were all math, but in sounding so human, they were creating a dangerous mirage about the true power of computers. She was astonished at how

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Timnit Gebru eventually found her. It was late in the summer of 2021 and Gebru was itching to work on a new research paper about large language models, something that could sum up all their risks. After rummaging around online for such a paper, she realized none existed. The only thing she could

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find was Bender’s tweets. Gebru sent Bender a direct message on Twitter. Had the linguist written anything about the ethical problems with large language models? Inside Google, Gebru and Mitchell had become demoralized by signs that their bosses didn’t care about the risks of language models. At one point

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in late 2020, for instance, the pair heard about a key meeting between forty Google staff to discuss the future of large language models. A product manager led the discussion about ethics. Nobody had invited Gebru or Mitchell. Bender told Gebru that she hadn’t written any such paper

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, but the question sparked a lively conversation between the two about the problems that large language models could provoke, particularly around bias. Bender suggested they work on a paper together, but they had to hurry. There was a conference on AI fairness

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think to question how the hidden wires and other mechanics were working behind the scenes. Bender couldn’t stand the way GPT-3 and other large language models were dazzling their early users with what was, essentially, glorified autocorrect software. So she suggested putting “stochastic parrots” in the title to emphasize that the

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after being further scrutinized by other anonymous reviewers, the paper hadn’t met the bar for publication. It was too negative about the problems of large language models. And despite having a relatively large bibliography with 158 references, they hadn’t included enough other research showing all the efficiencies such models had or

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dozens of articles in newspapers and websites, more than one thousand citations from other researchers, while “stochastic parrot” became a catchphrase for the limits of large language models. Sam Altman would later tweet, “I am a stochastic parrot and so r u” days after the release of ChatGPT. Much as Altman may have

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been mocking the paper, it had finally drawn attention to the real-world risks of large language models. At surface level, it seemed like Google’s approach to AI was “do no evil.” It had stopped selling facial recognition services in 2018, hired

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minority groups or the consequences of its being controlled by a handful of large companies. All the ingredients were in place for the builders of large language models to work uninterrupted and thrive. When the Wall Street Journal reported on Microsoft’s 2019 investment in OpenAI, Brockman admitted to the paper that “tech

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the wider public. Or a food company released an experimental preservative with little scrutiny. That was how large tech firms were about to start deploying large language models to the public, because in their race to profit from such powerful tools, there were zero regulatory standards to follow. It was up to the

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kid to be generally smarter and have the ability to deduct and break down complex things into pieces,” Srinivas says. “That’s what you want large language models to do.” That was probably counterintuitive to managers at Google, whose business was all about language and ads. But Microsoft cared much more about building

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the revolution in language models. Now he had to follow in Altman’s footsteps. Google executives told DeepMind to start working on a series of large language models that would be even better than LaMDA. They called the new system Gemini, and DeepMind imbued it with the strategic planning techniques that AlphaGo had

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glaring problem. It was sidestepping the need for transparency, and more broadly, it was getting harder to hear the voices calling for more scrutiny of large language models. Gebru, Mitchell, and Bender, whose notorious research paper had finally drawn attention to the risks, were still trying to warn the public about how those

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Vague Sense of Doom Sam Altman had set off several different races when he launched ChatGPT. The first was obvious: Who would bring the best large language model to market first? The other was taking place in the background: Who would control the narrative about AI? In March 2023, a few weeks after

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massive computing power. As part of Anthropic’s deal with Google, for instance, it would get cloud computing credits that would let it build a large language model that would rival OpenAI’s. In public there were now two different groups of people calling for safer AI. There were those like Altman and

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once; now they could zero in on just one person with hyperpersonalized video ads that could state your name. The World Economic Forum said that large language models would enhance jobs that required critical thinking and creativity. Anyone from engineers to ad copywriters to scientists could use them as extensions of their brains

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planning. For now, we simply don’t know how our critical thinking skills or creativity will atrophy once a new generation of professionals start using large language models as a crutch, or how our interactions with other humans might change as more people use chatbots as therapists and romantic partners, or put them

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keen on doing the same with the EU. He threatened to leave the region. He had “many concerns” about the EU’s plans to include large language models like GPT-4 in its new law. “The details really matter,” he told reporters in London who asked him about the regulations. “We will try

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into building an even better version for Google. Hassabis had taken control of the newly merged Google DeepMind and started overseeing the development of a large language model called Gemini, an AI assistant that used techniques from AlphaGo to excel at strategy and planning. Gemini could process text, “see” images, and reason, which

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scientists had checked to see if tech companies like OpenAI, Anthropic, Google, Amazon, Meta, and others divulged information about the data used to train their large language models, their processes, their models’ impact on the environment and people, and how much they were paying contractors who helped create their datasets. Millions of data

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seems almost unthinkable today. To that end, another race is currently being waged to build wearable gadgets that analyze our discussions with other people using large language models. One such device is called Tab. Created by a handful of young, enthusiastic engineers in San Francisco, it’s a round plastic disk worn around

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. “DeepMind Co-founder Leaves Google for Venture Capital Firm.” Financial Times, January 21, 2022. Chapter 12: Myth Busters Abid, Abubakar, Maheen Farooqi, and James Zou. “Large Language Models Associate Muslims with Violence.” Nature Machine Intelligence 3 (2021): 461–63. Barrett, Paul, Justin Hendrix, and Grant Sims. “How Tech Platforms Fuel U.S. Political

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board and ethics and ethics council and Facebook and formation of funding and Gemini as global interest company Google acquisition of independent review boards and large language models and McDonagh on medical data and merger with Google Brain and military use and Musk offer and OpenAI and racism and bias and recruiting and

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, Holden Kasparov, Garry Katzenberg, Jeffrey Keep America Beautiful Ke Jie Kindroid Klein, Ezra Koum, Jan Kurzweil, Ray Kuyda, Eugenia Language Model for Dialogue Applications (LaMDA) large language models LeCun, Yann Lee Sedol Legg, Shane artificial general intelligence (AGI) and DeepMind ethics and safety board and on early DeepMind struggles early life of Facebook

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-1 GPT-2 GPT-3 GPT-3.5 GPT-4 GPT-5 GPT Store and hallucination in ChatGPT and ideas behind internal concerns about ChatGPT large language models LessWrong community and Microsoft and Musk and principles and recruiting and Reddit and revenue of scale and Superalignment Team transformers and transparency issues and Open

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and Facebook offer and formation of DeepMind and GIC plan and harassment claims and Hassabis and Hoffman and ideas about artificial intelligence and Inflection and large language models and real-world data and Summers, Larry Sunak, Rishi Sun Valley conference (2018) Superintelligence (Bostrom) Sutskever, Ilya AGI and Altman and on ChatGPT ChatGPT concerns

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and DeepMind and firing of Altman and large language models OpenAI board and role at OpenAI and salary at OpenAI and Superalignment Team and transformers and Sweeney, Latanya Tab (wearable AI) Tallinn, Jaan Tao, Terence

Co-Intelligence: Living and Working With AI

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

AI—is at least three sleepless nights. After a few hours of using generative AI systems, there will come a moment when you realize that Large Language Models (LLMs), the new form of AI that powers services like ChatGPT, don’t act like you expect a computer to act. Instead, they act more

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to start appearing at businesses and schools because, even with their fast rate of increasing ability, they were starting from a very primitive beginning. Yet Large Language Models proved incredibly capable within a few years of their invention. They’ve also been adopted by consumers very quickly; ChatGPT reached 100 million users faster

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need to start from the beginning, with a very basic question: What is AI? So we are going to start there, discussing the technology of Large Language Models. That will give us a basis for thinking about how we, as humans, can best work with these systems. After that, we can dive into

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comprehension and expression. (And, yes, that last sentence was AI-produced text—a big difference from the Markov chain!) These new types of AI, called Large Language Models (LLMs), are still doing prediction, but rather than predicting the demand for an Amazon order, they are analyzing a piece of text and predicting the

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can make the responses of the model more specific to a particular need. When we discuss AI in this book, we will mostly be discussing Large Language Models built in this way, but they are not the only kind of “generative AI” that are causing transformation and change. In the same year that

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I ask it to comment on the passage in a critical and cutting way: While the passage provides a simplified overview of the development of Large Language Models (LLMs), it misses several opportunities to discuss the broader implications and challenges of AI advancements. The author’s fixation on limericks as a metric for

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storytelling and their expertise on the subject. I think I will stick with that one. Of course, AI is not limited to limericks or commentary. Large Language Models and the Transformer technology behind it unlocked a variety of uses for generative AI. It can produce a wide range of materials: blog posts, essays

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consider general principles. We will focus on things inherent and timeless, as much as that is possible, in all current generative AI systems based on Large Language Models. Here are my four principles of working with AI: Principle 1: Always invite AI to the table. You should try inviting AI to help you

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software, should behave like other software. It is a little bit like saying humans, made of biochemical systems, should behave like other biochemical systems. While Large Language Models are marvels of software engineering, AI is terrible at behaving like traditional software. Traditional software is predictable, reliable, and follows a strict set of rules

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realism of these AI interactions showed that it was no longer really a question of whether an AI could pass the Turing Test—these new Large Language Models were genuinely convincing, and passing the test was just a matter of time—but what AI passing the Turing Test meant for us. And here

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you interact with the resulting models. Basically, it means you can “talk” to anyone on Twitter. It is impressive but flawed in the way current Large Language Models are flawed: the answers are stylistically correct but full of realistic hallucinations. But it is surprisingly close. When interacting with the AI version of me

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tasks to be automated in any wave of new technology, from steam power to robots. As we have seen, however, that is not the case. Large Language Models are excellent at writing, but the underlying Transformer technology also serves as the key for a whole set of new applications, including AI that makes

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much faster than the evolution of practical robots, but that may change soon. Many researchers are trying to solve long-standing problems in robotics with Large Language Models, and there are some early signs that this might work, as LLMs make it easier to program robots that can really learn from the world

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disconnect between what workers do with AI and what their companies and organizations are doing. Secret Task Automation Today, billions of people have access to Large Language Models and the productivity benefits they bring. And from decades of research in innovation studying everyone from plumbers to librarians to surgeons, we know that, when

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students, however, research shows that both homework and tests are actually remarkably useful learning tools. So it is a blow that the first impact of Large Language Models at scale was to usher in the Homework Apocalypse. Cheating was already common in schools. One study of eleven years of college courses found that

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AI is good at finding facts, summarizing papers, writing, and coding tasks. And, trained on massive amounts of data and with access to the internet, Large Language Models seem to have accumulated and mastered a lot of collective human knowledge. This vast and tappable storehouse of knowledge is now at everyone’s fingertips

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many reasons this might happen. Ballooning training costs and regulatory requirements are possibilities. So is the possibility that we will soon hit technical limits for Large Language Models, as a number of scientists, including professor (and chief AI scientist at Meta) Yann LeCun, have argued. This will require us to find new technological

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? (Champaign, IL: Wolfram Media, Inc., 2023). GO TO NOTE REFERENCE IN TEXT “There are hundreds of billions”: S. R. Bowman, “Eight Things to Know about Large Language Models,” arXiv preprint (2023), arXiv:2304.00612. GO TO NOTE REFERENCE IN TEXT a question developed by Nicholas Carlini: N. Carlini, “A GPT-4 Capability Forecasting

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-benchmarks. GO TO NOTE REFERENCE IN TEXT almost all the emergent features of AI: R. Schaeffer, B. Miranda, and S. Koyejo, “Are Emergent Abilities of Large Language Models a Mirage?,” arXiv preprint (2023), arXiv:2304.15004. GO TO NOTE REFERENCE IN TEXT Chapter 2: Aligning the Alien paper clip maximizing AI: Nick Bostrom

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/. GO TO NOTE REFERENCE IN TEXT a known weakness: X. Shen et al., “ ‘Do Anything Now’: Characterizing and Evaluating In-the-Wild Jailbreak Prompts on Large Language Models,” arXiv preprint (2023), arXiv:2308.03825. GO TO NOTE REFERENCE IN TEXT demonstrates how easily LLMs can be exploited: J. Hazell

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, “Large Language Models Can Be Used to Effectively Scale Spear Phishing Campaigns,” arXiv preprint (2023), arXiv:2305.06972. GO TO NOTE REFERENCE IN TEXT an LLM, connected to

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lab equipment: D. A. Boiko, R. MacKnight, and G. Gomes, “Emergent Autonomous Scientific Research Capabilities of Large Language Models,” arXiv preprint (2023), arXiv:2304.05332. GO TO NOTE REFERENCE IN TEXT Chapter 3: Four Rules for Co-Intelligence I and my coauthors call the

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seem to respond to emotional manipulation: C. Li, J. Wang, K. Zhu, Y. Zhang, W. Hou, J. Lian, and X. Xie, “Emotionprompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus,” arXiv preprint arXiv: 2307.11760 (2023). GO TO NOTE REFERENCE IN TEXT They are, in short, suggestible and even gullible: J

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. Xie et al., “Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Conflicts,” arXiv preprint (2023), arXiv:2305.13300. GO TO NOTE REFERENCE IN TEXT asking the AI to conform to different personas: L. Boussioux

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/Publication%20Files/23-062_b8fbedcd-ade4-49d6-8bb7-d216650ff3bd.pdf. GO TO NOTE REFERENCE IN TEXT Dictator Game, a common economic experiment: J. J. Horton, “Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?” arXiv preprint (2023), arXiv:2301.07543. GO TO NOTE REFERENCE IN TEXT “the Shakespearean

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.12003. GO TO NOTE REFERENCE IN TEXT idea generation contest: K. Girotra, L. Meincke, C. Terwiesch, and K. T. Ulrich, “Ideas Are Dimes a Dozen: Large Language Models for Idea Generation in Innovation” (July 10, 2023), https://ssrn.com/abstract=4526071. GO TO NOTE REFERENCE IN TEXT most innovative people benefit the least

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-intelligence-artists.html. GO TO NOTE REFERENCE IN TEXT can manipulate narrative: J. Xie et al., “Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Conflicts,” arXiv preprint (2023), arXiv:2305.13300. GO TO NOTE REFERENCE IN TEXT a lot of Star Wars: “KREA Stable Diffusion,” Atlas, https

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36 job categories: T. Eloundou, S. Manning, P. Mishkin, and D. Rock, “GPTS Are GPTS: An Early Look at the Labor Market Impact Potential of Large Language Models,” arXiv preprint (2023), arXiv:2303.10130. GO TO NOTE REFERENCE IN TEXT program robots that can really learn: Kevin Roose, “Aided by A.I. Language

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&utm_campaign=markets#xj4y7vzkg. GO TO NOTE REFERENCE IN TEXT chain-of-thought prompting: J. Wei et al., “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” Advances in Neural Information Processing Systems 35 (2022): 24824–37. GO TO NOTE REFERENCE IN TEXT “Take a deep breath”: C. Yang et al

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., “Large Language Models as Optimizers,” arXiv preprint (2023), arXiv:2309.03409. GO TO NOTE REFERENCE IN TEXT A good lecture: D. T. Willingham, Outsmart Your Brain: Why Learning

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., “Rewarding Chatbots for Real-World Engagement with Millions of Users,” arXiv preprint (2023), arXiv:2303.06135. GO TO NOTE REFERENCE IN TEXT technical limits for Large Language Models: “From Machine Learning to Autonomous Intelligence—AI-Talk by Prof. Dr. Yann LeCun,” YouTube, September 29, 2023, https://www.youtube.com/watch?v=pd0JmT6rYcI. GO

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