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Agents, Authors Question HarperCollins AI Deal
The deal is for a three-year period, and authors must opt in, per PW’s source. For those authors who do opt in, the deal provides for a $5,000 fee per book, split evenly between the author and the publisher at $2,500 each; payments will be not counted against author royalties. Crucially, agents confirmed that the deal is effectively a one-off, implemented via contract addenda, and does not seek to establish a new AI licensing right. Sources also confirmed that the unnamed AI company has agreed to several protective terms, including a commitment to limit verbatim reproduction and an agreement not to train on pirated content.
Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial | Clinical Decision Support | JAMA Network Open | JAMA Network
The clinical case vignettes were curated and summarized by human clinicians, a pragmatic and common approach to isolate the diagnostic reasoning process, but this does not capture competence in many other areas important to clinical reasoning, including patient interviewing and data collection.
Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial | Clinical Decision Support | JAMA Network Open | JAMA Network
An unexpected secondary result was that the LLM alone performed significantly better than both groups of humans, similar to a recent study with different LLM technology.31 This may be explained by the sensitivity of LLM output to prompt formulation.32 There are numerous frameworks for prompting LLMs and an emerging consensus on prompting strategies, many of which focus on providing details on the task, context, and instructions; our prompt was iteratively developed using these frameworks. Training clinicians in best prompting practices may improve physician performance with LLMs. Alternatively, organizations could invest in predefined prompting for diagnostic decision support integrated into clinical workflows and documentation, enabling synergy between the tools and clinicians. Prior studies on AI systems show disparate effects depending on the component of the diagnostic process they are used in.33,34 Given the conversational nature of chatbots, changes in how the LLM interacts with humans, for example by specifically pointing out features that do not fit the differential diagnosis, might improve diagnostic and reflective performance.35,36 More generally, we see opportunity with deliberate consideration and redesign of medical education and practice frameworks that adapt to disruptive emerging technologies and enable the best use of computer and human resources to deliver optimal medical care.
Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial | Clinical Decision Support | JAMA Network Open | JAMA Network
In the 3 runs of the LLM alone, the median score per case was 92% (IQR, 82%-97%). Comparing LLM alone with the control group found an absolute score difference of 16 percentage points (95% CI, 2-30 percentage points; P = .03) favoring the LLM alone.
Financial Statement Analysis with Large Language Models by Alex Kim, Maximilian Muhn, Valeri V. Nikolaev :: SSRN
Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes. The LLM exhibits a relative advantage over human analysts in situations when the analysts tend to struggle. Furthermore, we find that the prediction accuracy of the LLM is on par with the performance of a narrowly trained state-of-the-art ML model.
The Imperial Origins of Big Data - Yale University Press
Over the twelfth, thirteenth, and fourteenth centuries, paper emerged as the fundamental substrate which politicians, merchants, and scholars relied on to record and circulate information in governance, commerce, and learning. At the same time, governing institutions sought to preserve and control the spread of written information through the creation of archives: repositories where they collected, organized, and stored documents.
The expansion of European polities overseas from the late fifteenth century onward saw governments massively scale up their use of paper—and confront the challenge of controlling its dissemination across thousands of miles of ocean and land. These pressures were felt particularly acutely in what eventually became the largest empire in world history, the British empire. As people from the British isles from the early seventeenth century fought, traded, and settled their way to power in the Atlantic world and South Asia, administrators faced the problem of how to govern both their emigrating subjects and the non-British peoples with whom they interacted. This meant collecting information about their behavior through the technology of paper. Just as we struggle to organize, search, and control our email boxes, text messages, and app notifications, so too did these early moderns confront the attendant challenges of developing practices of collection and storage to manage the resulting information overload. And despite the best efforts of states and companies to control information, it constantly escaped their grasp, falling into the hands of their opponents and rivals who deployed it to challenge and contest ruling powers.
Opinion | Beyond the ‘Matrix’ Theory of the Human Mind - The New York Times
One is that these systems will do more to distract and entertain than to focus. Right now, the large language models tend to hallucinate information: Ask them to answer a complex question, and you will receive a convincing, erudite response in which key facts and citations are often made up. I suspect this will slow their widespread use in important industries much more than is being admitted, akin to the way driverless cars have been tough to roll out because they need to be perfectly reliable rather than just pretty good.
ChatGPT is bullshit | Ethics and Information Technology
In this paper, we argue against the view that when ChatGPT and the like produce false claims they are lying or even hallucinating, and in favour of the position that the activity they are engaged in is bullshitting, in the Frankfurtian sense (Frankfurt, 2002, 2005). Because these programs cannot themselves be concerned with truth, and because they are designed to produce text that looks truth-apt without any actual concern for truth, it seems appropriate to call their outputs bullshit.
