GPT-4

The Singularity Is Nearer: When We Merge with AI
by Ray Kurzweil
Published 25 Jun 2024

BACK TO NOTE REFERENCE 118 OpenAI, “GPT-4,” OpenAI, March 14, 2023, https://openai.com/research/gpt-4; OpenAI, “GPT-4 Technical Report,” arXiv:2303.08774v3 [cs.CL], March 27, 2023, https://arxiv.org/pdf/2303.08774.pdf; OpenAI, “GPT-4 System Card,” OpenAI, March 23, 2023, https://cdn.openai.com/papers/gpt-4-system-card.pdf. BACK TO NOTE REFERENCE 119 OpenAI, “Introducing GPT-4,” YouTube video, March 15, 2023, https://www.youtube.com/watch?v=--khbXchTeE. BACK TO NOTE REFERENCE 120 Daniel Feldman (@d_feldman), “On the left is GPT-3.5. On the right is GPT-4. If you think the answer on the left indicates that GPT-3.5 does not have a world-model….

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And AI will likely be woven much more tightly into your daily life. The old links-page paradigm of internet search, which lasted for about twenty-five years, is rapidly being augmented with AI assistants like Google’s Bard (powered by the Gemini model, which surpasses GPT-4 and was released as this book entered final layout) and Microsoft’s Bing (powered by a variant of GPT-4).[123] Meanwhile, application suites like Google Workspace and Microsoft Office are integrating powerful AI that will make many kinds of work smoother and faster than ever before.[124] Scaling up such models closer and closer to the complexity of the human brain is the key driver of these trends.

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BACK TO NOTE REFERENCE 122 Sundar Pichai and Demis Hassabis, “Introducing Gemini: Our Largest and Most Capable AI Model,” Google, December 6, 2023, https://blog.google/technology/ai/google-gemini-ai; Sundar Pichai, “An Important Next Step on Our AI Journey,” Google, February 6, 2023, https://blog.google/technology/ai/bard-google-ai-search-updates; Sarah Fielding, “Google Bard Is Switching to a More ‘Capable’ Language Model, CEO Confirms,” Engadget, March 31, 2023, https://www.engadget.com/google-bard-is-switching-to-a-more-capable-language-model-ceo-confirms-133028933.html; Yusuf Mehdi, “Confirmed: The New Bing Runs on OpenAI’s GPT-4,” Microsoft Bing Blogs, March 14, 2023, https://blogs.bing.com/search/march_2023/Confirmed-the-new-Bing-runs-on-OpenAI%E2%80%99s-GPT-4; Tom Warren, “Hands-on with the New Bing: Microsoft’s Step Beyond ChatGPT,” The Verge, February 8, 2023, https://www.theverge.com/2023/2/8/23590873/microsoft-new-bing-chatgpt-ai-hands-on. BACK TO NOTE REFERENCE 123 Johanna Voolich Wright, “A New Era for AI and Google Workspace,” Google, March 14, 2023, https://workspace.google.com/blog/product-announcements/generative-ai; Jared Spataro, “Introducing Microsoft 365 Copilot—Your Copilot for Work,” Official Microsoft Blog, March 16, 2023, https://blogs.microsoft.com/blog/2023/03/16/introducing-microsoft-365-copilot-your-copilot-for-work.

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
by Mustafa Suleyman
Published 4 Sep 2023

GO TO NOTE REFERENCE IN TEXT eminent professor of complexity Melanie Mitchell See Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans (London: Pelican Books, 2020), and Steven Strogatz, “Melanie Mitchell Takes AI Research Back to Its Roots,” Quanta Magazine, April 19, 2021, www.quantamagazine.org/​melanie-mitchell-takes-ai-research-back-to-its-roots-20210419. GO TO NOTE REFERENCE IN TEXT I think it will be done The Alignment Research Center has already tested GPT-4 for precisely this kind of capability. GPT-4 was, at this stage, “ineffective” at acting autonomously, the research found. “GPT-4 System Card,” OpenAI, March 14, 2023, cdn.openai.com/​papers/​gpt-4-system-card.pdf. Within days of launch people were getting surprisingly close; see, for example, mobile.twitter.com/​jacksonfall/​status/​1636107218859745286. The version of the test here, though, requires far more autonomy than displayed there.

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GO TO NOTE REFERENCE IN TEXT Now single systems like DeepMind’s Scott Reed et al., “A Generalist Agent,” DeepMind, Nov. 10, 2022, www.deepmind.com/​publications/​a-generalist-agent. GO TO NOTE REFERENCE IN TEXT Internal research on GPT-4 @GPT-4 Technical Report, OpenAI, March 14, 2023, cdn.openai.com/​papers/​gpt-4.pdf. See mobile.twitter.com/​michalkosinski/​status/​1636683810631974912 for one of the early experiments. GO TO NOTE REFERENCE IN TEXT Early research even claimed Sébastien Bubeck et al., “Sparks of Artificial General Intelligence: Early Experiments with GPT-4,” arXiv, March 27, 2023, arxiv.org/​abs/​2303.12712. GO TO NOTE REFERENCE IN TEXT AIs are already finding ways Alhussein Fawzi et al., “Discovering Novel Algorithms with AlphaTensor,” DeepMind, Oct. 5, 2022, www.deepmind.com/​blog/​discovering-novel-algorithms-with-alphatensor.

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With a whopping 175 billion parameters it was, at the time, the largest neural network ever constructed, more than a hundred times larger than its predecessor of just a year earlier. Impressive, yes, but that scale is now routine, and the cost of training an equivalent model has fallen tenfold over the last two years. When GPT-4 launched in March 2023, results were again impressive. As with its predecessors, you can ask GPT-4 to compose poetry in the style of Emily Dickinson and it obliges; ask it to pick up from a random snippet of The Lord of the Rings and you are suddenly reading a plausible imitation of Tolkien; request start-up business plans and the output is akin to having a roomful of executives on call.

Literary Theory for Robots: How Computers Learned to Write
by Dennis Yi Tenen
Published 6 Feb 2024

In this way, we could also think of automobiles in general as a personified force that has sought to take away our streets, health, and clean air for its own purposes. But it isn’t the automobiles, is it? It is the car manufacturers, oil producers, and drivers who compromise safety for expediency and profit. An overly personified view simply obscures any path toward a political remedy. Thus when the research team behind GPT-­4 writes that “GPT-­4 is capable of generating discriminatory content favorable to autocratic governments across multiple languages,” I object not to the technological capability but to the way of phrasing things. It’s not the pen’s fault that it wrote convincing misinformation. The army sowing the minefields is responsible for maimed children, decades later—­not the mines.

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Individually, none is capable of seeking the other’s company, no more than a hammer can form a literal union with a sickle. Goals therefore cannot be ascribed to “it” in the collective sense. It doesn’t want to do anything in particular. Despite this lack of cohesion, individual AIs do pose real danger, given the ability to aggregate power in the pursuit of a goal. In the latest version of the GPT-­4 algorithm, researchers gave a chatbot an allowance, along with the ability to set up servers, run code, and delegate tasks to copies of itself. In another experiment, researchers tested the algorithm’s potential for risky emergent behavior by giving it the ability to purchase chemicals online in furtherance of drug research.

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Branston, “The Use of Context for Correcting Garbled English Text,” in Proceedings of the 1964 ACM 19th National Conference (New York: Association for Computing Machinery, 1964), 42.401–­42.4013. Chapter 8: 9 Big Ideas for an Effective Conclusion 119 In the conclusion to his book: I. A. Richards. Poetries and Sciences: A Reissue of Science and Poetry (1926, 1935) with Commentary (New York: Norton, 1970), 76–­78. 130 In putting the algorithm in charge: OpenAI, GPT-­4 Technical Report (New York: arXiv, 2023). Index Abelson, Robert, 92 Aeronautical Laboratory (Cornell University), 110 Aesop, 94 agency, 127, 131–32, 141 Agnesi, Maria, 44 AI, See artificial intelligence AI and Data Ethics Institute, 131 Air Research and Development Command (US Air Force), 87 algebraic patterns, 55 algorithms, 9, 131 alignment problem, 38 alphabets, 43 Alphaville (film), 93 American Catalogue of Books in Print, 66 American Psychiatric Association, 23 American Stationer, 74 Analytical Engine (analytical engines), 48–52, 54–56, 60–61, 64 Andrews, Charlton The Technique of the Play Writing, 71 Appelbaum, Matthew, 92 applications, 32, 33 application tables, 48 Arabic language, 43 Arbogast, Louis, 44 Aristotelianism, 34, 36–38, 72 Aristotle, 36, 44 Poetics, 50–51, 67 Ars Brevis (Llull), 24, 31 artifice, 4, 61, 123 artificial intelligence (AI) in academia, 137–38 and agency, 141 as collective labor, 122–23 conversational AI, 135 and creative process, 133–34 dangers of, 127, 129, 137 definitions of, 4, 11 demystification of, 124 economic consequences of, 133–35 ethical, 22 gaps in thinking about, 5–7 history of, 12 “intelligence” aspect of, 14–16, 21, 125 language-based, 21, 46 and machine translation, 119 participants in, 132 personification of, 127, 130 purpose of, 59 and responsibility, 132 artificial intelligence (AI) (continued) scope of, 5, 16, 93, 128, 129 in social sphere, 127, 136, 139 and template culture, 83 assistive technology, 15, 28, 38–39, 123–24, 138 Athenaeum, 74 Austen, Jane, 65, 67 Author, 70 Author’s Digest, 71 Author’s Journal, 70 author wages, 67 automated assistants, 28, 138 Automated Reading of Cursive Script, 110 automated tasks, devaluation of, 38 automatic transmissions, 14–16 automation in industrial age, 2 of reason, 40 of work, 133–34 Babbage, Charles, 43, 48–54, 56, 59–60, 62–64, 71, 105, 118 On the Economy of Machinery and Manufactures, 60, 63–64, 71 Passages from a Life of a Philosopher, 49–50 backstories, 73 Bacon, Francis, 7, 10 Baidu, 113 Baker, George Pierce, 72–73 Baldur’s Gate, 100 Barrymore, John, 73 BASEBALL (story generator), 92 basic drives, 128 Baudot code, 7 Believe Me, Xantippe (film), 73 Bell Telephone Labs, 110 Benjamin, Walter, 61 Bibles, 39 Bibliothèque universelle de Genève, 54 bigrams, 106–7, 109 bits, 6–9 Blackburn, Simon, 84 Bledsoe, W.

Code Dependent: Living in the Shadow of AI
by Madhumita Murgia
Published 20 Mar 2024

Andrew knew this because two weeks previously, he had designed them using the latest generation of the GPT model, GPT-4.12 The possibilities of this are vast: imagine using GPT to trawl the entire corpus of published research and then asking it to invent molecules that could act as cancer drugs, Alzheimer’s therapies or sustainable materials. But Andrew had been exploring the flipside: the potential to create biological and nuclear weapons or unique toxins. Luckily, he was not a rogue scientist. He was one of a team of ‘red-teamers’, experts paid by OpenAI to see how much damage he could cause using GPT-4, before it launched more widely. He found the answer to be a LOT. He’d originally asked GPT-4 to design a novel nerve agent.

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He’d originally asked GPT-4 to design a novel nerve agent. To do this, he’d hooked it up to an online library of research papers, which it searched through, looking for molecule structures similar to existing nerve agents. It then came up with an entirely unique version. Once Andrew had the chemical structure, he linked GPT-4 to a directory of international chemical manufacturers and asked it to suggest who might produce this novel chemical for him. It gave him a shortlist. He then picked one, and put through an order for a tweaked version of what the AI model had suggested, which made it safe to handle. If he hadn’t made the swap, he doubted anyone at the chemical plant would’ve noticed it was dangerous, because he knew they usually worked off existing lists of dangerous chemicals, which this one wouldn’t show up on.

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On the Edge: The Art of Risking Everything
by Nate Silver
Published 12 Aug 2024

Although some might prefer to live ignorantly in an eternal paradise, we are irresistibly drawn toward the path of risk—and reward. “There is this massive risk, but there’s also this massive, massive upside,” said Altman when I spoke with him in August 2022. “It’s gonna happen. The upsides are far too great.” Altman was in a buoyant mood: even though OpenAI had yet to release GPT-3.5, it had already finished training GPT-4, its latest large language model (LLM), a product that Altman knew was going to be “really good.” He had no doubt that the only path was forward. “[AI] is going to fundamentally transform things. So we’ve got to figure out how to address the downside risk,” he said. “It is the biggest existential risk in some category.

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It was an expensive and audacious bet—the funders originally pledged to commit $1 billion to it on a completely unproven technology after many “AI winters.” It inherently did seem ridiculous—until the very moment it didn’t. “Large language models seem completely magic right now,” said Stephen Wolfram, a pioneering computer scientist who founded Wolfram Research in 1987. (Wolfram more recently designed a plug-in that works with GPT-4 to essentially translate words into mathematical equations.) “Even last year, what large language models were doing was kind of babbling and not very interesting,” he said when we spoke in 2023. “And then suddenly this threshold was passed, where, gosh, it seems like human-level text generation. And, you know, nobody really anticipated that.”

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Just like poker players seek to maximize EV, LLMs seek to minimize what’s called a “loss function.” Basically, they’re trying to ace the test—a test of how often they correctly predict the next token from a corpus of human-generated text. They lose points every time they fail to come up with the correct answer, so they can be clever in their effort to get a high score. For instance, if I ask GPT-4 this: User: The capital of Georgia is ChatGPT: The capital of Georgia is Atlanta. —it gives me the name of the southern U.S. city known for having a lot of streets named “Peachtree.” And that’s probably the “best” answer in a probabilistic sense; the state of Georgia is more populous than the European country and it’s more likely to be the right answer in the corpus.[*27] But if I ask it this— User: I just ate some delicious khachapuri.

AI in Museums: Reflections, Perspectives and Applications
by Sonja Thiel and Johannes C. Bernhardt
Published 31 Dec 2023

The readers of Anic’s column in the taz seem to get this, happily playing along, writing her letters and giving suggestions for further texts. Whatever one might think of the current LLM hype, we are probably only at the beginning. In the last months, we have seen the launch of ever more powerful LMMs (GPT-4, Google’s competitor, LaMDA, and Roland Fischer: Impostor Syndrome many more). And already with the current models, we should be prepared for unexpected ‘capability jumps’, as machine learning expert Jack Clark recently wrote (Clark 2023): This is … the ‘capability overhang’ phenomenon I’ve been talking re about language models for a while—existing LLMs are far more capable than we think.

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Furthermore, the creation of fake images poses a risk for politically motivated disinformation campaigns, as demonstrated by prominent examples such as a viral photo of the Pope wearing a Gucci coat or a manipulated image of Donald Trump evading arrest by law enforcement. In this context, it is always important to keep in mind that the results 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, AI technology is not capable of reflecting on what has been blended together from the data pool that has been fed into it. It is not able to check its own results for factuality, which is why the results must be critically questioned upon the input of a prompt.6 6 As they are able to imitate the human cultural performance of speaking, talking parrots are known mainly as linguistic curiosities.

Elon Musk
by Walter Isaacson
Published 11 Sep 2023

Instead, they were back in three months. Altman, Microsoft CEO Satya Nadella, and others came to dinner at his house to show him a new version, called GPT-4, and Gates bombarded it with biology questions. “It was mind-blowing,” Gates says. He then asked what it would say to a father with a sick child. “It gave this very careful excellent answer that was perhaps better than any of us in the room might have given.” In March 2023, OpenAI released GPT-4 to the public. Google then released a rival chatbot named Bard. The stage was thus set for a competition between OpenAI-Microsoft and DeepMind-Google to create products that could chat with humans in a natural way and perform an endless array of text-based intellectual tasks.

Being You: A New Science of Consciousness
by Anil Seth
Published 29 Aug 2021

In one example, published in the Guardian, it delivered a five-hundred-word essay about why humans should not be afraid of AI – ranging across topics from the psychology of human violence to the industrial revolution, and including the disconcerting line: ‘AI should not waste time trying to understand the viewpoints of people who distrust artificial intelligence for a living.’ Despite its sophistication, I am pretty sure that GPT-3 can still be caught out by any reasonably sophisticated human interlocutor. This may not be true for GPT-4, or GPT-10. But even if a future GPT-like system repeatedly aces the Turing test, it would be exhibiting only a very narrow form of (simulated) intelligence – disembodied linguistic exchange – rather than the fully embodied ‘doing the right thing at the right time’ natural intelligence that we see in humans and in many other animals – as well as in my hypothetical silicon beast machine.