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"Mankoff, Robert"
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How about never-- is never good for you? : my life in cartoons
\"Memoir in cartoons by the longtime cartoon editor of The New YorkerPeople tell Bob Mankoff that as the cartoon editor of The New Yorker he has the best job in the world. Never one to beat around the bush, he explains to us, in the opening of this singular, delightfully eccentric book, that because he is also a cartoonist at the magazine he actually has two of the best jobs in the world. With the help of myriad images and his funniest, most beloved cartoons, he traces his love of the craft all the way back to his childhood, when he started doing funny drawings at the age of eight. After meeting his mother, we follow his unlikely stints as a high-school basketball star, draft dodger, and sociology grad student. Though Mankoff abandoned the study of psychology in the seventies to become a cartoonist, he recently realized that the field he abandoned could help him better understand the field he was in, and here he takes up the psychology of cartooning, analyzing why some cartoons make us laugh and others don't. He allows us into the hallowed halls of The New Yorker to show us the soup-to-nuts process of cartoon creation, giving us a detailed look not only at his own work, but that of the other talented cartoonists who keep us laughing week after week. For desert, he reveals the secrets to winning the magazine's caption contest. Throughout, we see his commitment to the motto \"Anything worth saying is worth saying funny.\" \"-- Provided by publisher.
Book
Impact of Socioeconomic Status on Heart Failure
Heart failure has emerged as a substantial health burden in the United States in the last few decades. This study examined the hypothesis that socioeconomic factors such as education level, social position, employment status, and poverty have a strong confounding influence on the risk for heart failure. To access relevant data, 12 published studies were retrieved from MEDLINE, Google Scholar, and Web of Science. A cross-sectional analysis of the identified studies confirmed that the four socioeconomic factors predisposed individuals to an elevated risk of heart failure-related complications. Despite their interdependencies, educational level, employment status, social position, and poverty independently confounded cardiovascular risk among individuals. Notably, individuals from households with low education were at a higher risk of these diseases. At the same time, households without employed family members were less likely to report cases of heart failure than those with low socioeconomic status. Additionally, individuals from disadvantaged backgrounds faced a greater risk for heart failure complications. The findings from this study found a strong association between socioeconomic status and heart failure risks.
Journal Article
Comic relief : a comprehensive philosophy of humor
Comic Relief: A Comprehensive Philosophy of Humor develops an inclusive theory that integrates psychological, aesthetic, and ethical issues relating to humor Offers an enlightening and accessible foray into the serious business of humor Reveals how standard theories of humor fail to explain its true nature and actually support traditional.
eBook
Who’s funny: Gender stereotypes, humor production, and memory bias
It has often been asserted, by both men and women, that men are funnier. We explored two possible explanations for such a view, first testing whether men, when instructed to be as funny as possible, write funnier cartoon captions than do women, and second examining whether there is a tendency to falsely remember funny things as having been produced by men. A total of 32 participants, half from each gender, wrote captions for 20 cartoons. Raters then indicated the humor success of these captions. Raters of both genders found the captions written by males funnier, though this preference was significantly stronger among the male raters. In the second experiment, male and female participants were presented with the funniest and least funny captions from the first experiment, along with the caption author’s gender. On a memory test, both females and males disproportionately misattributed the humorous captions to males and the nonhumorous captions to females. Men might think men are funnier because they actually find them so, but though women rated the captions written by males slightly higher, our data suggest that they may regard men as funnier more because they falsely attribute funny things to them.
Journal Article
Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning
We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 million captions, collected through crowdsourcing rating data for The New Yorker's weekly cartoon caption contest over the past eight years. This unique dataset supports the development and evaluation of multimodal large language models and preference-based fine-tuning algorithms for humorous caption generation. We propose novel benchmarks for judging the quality of model-generated captions, utilizing both GPT4 and human judgments to establish ranking-based evaluation strategies. Our experimental results highlight the limitations of current fine-tuning methods, such as RLHF and DPO, when applied to creative tasks. Furthermore, we demonstrate that even state-of-the-art models like GPT4 and Claude currently underperform top human contestants in generating humorous captions. As we conclude this extensive data collection effort, we release the entire preference dataset to the research community, fostering further advancements in AI humor generation and evaluation.
Paper
Do Androids Laugh at Electric Sheep? Humor \Understanding\ Benchmarks from The New Yorker Caption Contest
Large neural networks can now generate jokes, but do they really \"understand\" humor? We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest: matching a joke to a cartoon, identifying a winning caption, and explaining why a winning caption is funny. These tasks encapsulate progressively more sophisticated aspects of \"understanding\" a cartoon; key elements are the complex, often surprising relationships between images and captions and the frequent inclusion of indirect and playful allusions to human experience and culture. We investigate both multimodal and language-only models: the former are challenged with the cartoon images directly, while the latter are given multifaceted descriptions of the visual scene to simulate human-level visual understanding. We find that both types of models struggle at all three tasks. For example, our best multimodal models fall 30 accuracy points behind human performance on the matching task, and, even when provided ground-truth visual scene descriptors, human-authored explanations are preferred head-to-head over the best machine-authored ones (few-shot GPT-4) in more than 2/3 of cases. We release models, code, leaderboard, and corpus, which includes newly-gathered annotations describing the image's locations/entities, what's unusual in the scene, and an explanation of the joke.
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