Existing full text datasets of U.S. public domain newspapers do not recognize the often complex layouts of newspaper scans, and as a result the digitized content scrambles texts from articles, headlines, captions, advertisements, and other layout regions. OCR quality can also be low. This study develops a novel, deep learning pipeline for extracting full article texts from newspaper images and applies it to the nearly 20 million scans in Library of Congress's public domain Chronicling America collection. The pipeline includes layout detection, legibility classification, custom OCR, and association of article texts spanning multiple bounding boxes. To achieve high scalability, it is built with efficient architectures designed for mobile phones. The resulting American Stories dataset provides high quality data that could be used for pre-training a large language model to achieve better understanding of historical English and historical world knowledge. The dataset could also be added to the external database of a retrieval-augmented language model to make historical information - ranging from interpretations of political events to minutiae about the lives of people's ancestors - more widely accessible. Furthermore, structured article texts facilitate using transformer-based methods for popular social science applications like topic classification, detection of reproduced content, and news story clustering. Finally, American Stories provides a massive silver quality dataset for innovating multimodal layout analysis models and other multimodal applications.

A diversity of tasks use language models trained on semantic similarity data. While there are a variety of datasets that capture semantic similarity, they are either constructed from modern web data or are relatively small datasets created in the past decade by human annotators. This study utilizes a novel source, newly digitized articles from off-copyright, local U.S. newspapers, to assemble a massive-scale semantic similarity dataset spanning 70 years from 1920 to 1989 and containing nearly 400M positive semantic similarity pairs. Historically, around half of articles in U.S. local newspapers came from newswires like the Associated Press. While local papers reproduced articles from the newswire, they wrote their own headlines, which form abstractive summaries of the associated articles. We associate articles and their headlines by exploiting document layouts and language understanding. We then use deep neural methods to detect which articles are from the same underlying source, in the presence of substantial noise and abridgement. The headlines of reproduced articles form positive semantic similarity pairs. The resulting publicly available HEADLINES dataset is significantly larger than most existing semantic similarity datasets and covers a much longer span of time. It will facilitate the application of contrastively trained semantic similarity models to a variety of tasks, including the study of semantic change across space and time.

Now that generative AI models can produce photorealistic, fake images of child sexual abuse, regulators and child safety advocates are worried that an already-abhorrent practice will spiral further out of control. But lost in this fear is an uncomfortable possibility--that AI-generated child pornography could actually benefit society in the long run by providing a less harmful alternative to the already-massive market for images of child sexual abuse. The growing consensus among scientists is that pedophilia is biological in nature, and that keeping pedophilic urges at bay can be incredibly difficult. "What turns us on sexually, we don't decide that--we discover that," said psychiatrist Dr. Fred Berlin, director of the Johns Hopkins Sex and Gender Clinic and an expert on paraphilic disorders. "It's not because [pedophiles have] chosen to have these kinds of urges or attractions. They've discovered through no fault of their own that this is the nature of what they're afflicted with in terms of their own sexual makeup … We're talking about not giving into a craving, a craving that is rooted in biology, not unlike somebody who's having a craving for heroin."

The Friday decision follows losses for Thaler on bids for U.S. patents covering inventions he said were created by DABUS, short for Device for the Autonomous Bootstrapping of Unified Sentience.

Masks created with photos from social media that can penetrate a system protected by face ID. They sound like the stuff of science fiction, but these techniques are already available to criminals preying on everyday consumers. The proliferation of scam technology has alarmed regulators, police and people at the highest levels of the financial industry. Artificial intelligence in particular is being used to "turbocharge" fraud, U.S. Federal Trade Commission Chair Lina Khan warned in June, calling for increased vigilance from law enforcement. Even before AI broke loose and became available to anyone with an internet connection, the world was struggling to contain an explosion in financial fraud.

We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain conversations. We demonstrate a long-range open-domain conversation through iterative "memorization-retrieval-response" cycles. This requires us to carefully design tailored tuning instructions for each distinct stage. The instructions are reconstructed from a collection of public datasets to teach the LLMs to memorize and retrieve past dialogues with structured memos, leading to enhanced consistency when participating in future conversations. We invite experts to manually annotate a test set designed to evaluate the consistency of long-range conversations questions. Experiments on three testing scenarios involving both open-source and API-accessible chatbots at scale verify the efficacy of MemoChat, which outperforms strong baselines. Our codes, data and models are available here: https://github.com/LuJunru/MemoChat.

Current research on bias in machine learning often focuses on fairness, while overlooking the roots or causes of bias. However, bias was originally defined as a "systematic error," often caused by humans at different stages of the research process. This article aims to bridge the gap between past literature on bias in research by providing taxonomy for potential sources of bias and errors in data and models. The paper focus on bias in machine learning pipelines. Survey analyses over forty potential sources of bias in the machine learning (ML) pipeline, providing clear examples for each. By understanding the sources and consequences of bias in machine learning, better methods can be developed for its detecting and mitigating, leading to fairer, more transparent, and more accurate ML models.

Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often generate images that do not align well with human preferences, such as awkward combinations of limbs and facial expressions. To address this issue, we collect a dataset of human choices on generated images from the Stable Foundation Discord channel. Our experiments demonstrate that current evaluation metrics for generative models do not correlate well with human choices. Thus, we train a human preference classifier with the collected dataset and derive a Human Preference Score (HPS) based on the classifier. Using HPS, we propose a simple yet effective method to adapt Stable Diffusion to better align with human preferences. Our experiments show that HPS outperforms CLIP in predicting human choices and has good generalization capability toward images generated from other models. By tuning Stable Diffusion with the guidance of HPS, the adapted model is able to generate images that are more preferred by human users. The project page is available here: https://tgxs002.github.io/align_sd_web/ .

The USCO agreed that the work was generated by an AI model that Thaler calls the Creativity Machine. He claimed that the USCO's "human authorship" requirement was unconstitutional. However, Howell indicated that Thaler's case wasn't an especially complex one, since he admitted that he wasn't involved in the creation of A Recent Entrance to Paradise. "In the absence of any human involvement in the creation of the work, the clear and straightforward answer is the one given by the [Federal] Register: No," Howell ruled. Thaler plans to appeal the decision.

Jared Genser in many ways fits a certain Washington, D.C., type. He wears navy suits and keeps his hair cut short. He graduated from a top law school, joined a large firm, and made partner at 40. Eventually, he became disenchanted with big law and started his own boutique practice with offices off--where else--Dupont Circle. What distinguishes Genser from the city's other 50-something lawyers is his unusual clientele: He represents high-value political prisoners.