We examine the ability of large language models (LLMs) to generate salient (interesting) negative statements about real-world entities; an emerging research topic of the last few years. We probe the LLMs using zero- and k-shot unconstrained probes, and compare with traditional methods for negation generation, i.e., pattern-based textual extractions and knowledge-graph-based inferences, as well as crowdsourced gold statements. We measure the correctness and salience of the generated lists about subjects from different domains. Our evaluation shows that guided probes do in fact improve the quality of generated negatives, compared to the zero-shot variant. Nevertheless, using both prompts, LLMs still struggle with the notion of factuality of negatives, frequently generating many ambiguous statements, or statements with negative keywords but a positive meaning.

The lawsuit is the latest salvo in the ongoing debate over how AI tools should be trained and whether the companies behind them owe anything to the original creators of the training data. Large language models are generally trained on billions of sentences of text pulled from the internet, including news stories, Wikipedia and comments on social media sites. OpenAI and other AI companies such as Google and Microsoft do not say specifically what data they use, but AI critics have long suspected that it includes well-known collections of pirated books that have circulated online for years.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The suit was organized by the Authors Guild and also includes David Baldacci, Sylvia Day, Jonathan Franzen and Elin Hilderbrand among others. "It is imperative that we stop this theft in its tracks or we will destroy our incredible literary culture, which feeds many other creative industries in the U.S.," Authors Guild CEO Mary Rasenberger said in a statement. "Great books are generally written by those who spend their careers and, indeed, their lives, learning and perfecting their crafts. To preserve our literature, authors must have the ability to control if and how their works are used by generative AI."

WE HUMANS are in trouble. We have let loose a new evolutionary process that we don't understand and can't control. The latest leaps forward in artificial intelligence, with its large language models and deepfakes, are rightly causing anxiety. Yet people are responding as though AI is just one more scary new technology, like electricity or cars once were. We invented it, the argument goes, so we should be able to regulate and manage it for our own benefit.

OpenAI has announced Dall-E 3, its latest AI art tool. It uses OpenAI's smash-hit chatbot, ChatGPT, to help create more complex and carefully composed works of art by automatically expanding on a prompt in a way that gives the generator more detailed and coherent instruction. What's new with Dall-E 3 is how it removes some of the complexity required with refining the text that is fed to the program--what's known as "prompt engineering"--and how it allows users to make refinements through ChatGPT's conversational interface. The new tool could help lower the bar for generating sophisticated AI artwork, and it could help OpenAI stay ahead of the competition thanks to the superior abilities of its chatbot. Take this image of the potato king, for example. This kind of quirky AI-generated art has become commonplace on social media thanks to a number of tools that turn a text prompt into a visual composition.

When Amazon launched the Alexa virtual assistant nine years ago, its ability to decode voice commands to set a timer or play a song seemed almost magical. Today, the bar for impressive language skills is much higher, thanks to OpenAI's ChatGPT. Amazon is giving its voice assistant a reboot that takes advantage of the technology behind the new wave of chatbots that can engage in remarkably lifelike conversation. Amazon announced the upgrade to Alexa at an event held at its second headquarters in Arlington, Virginia. The assistant will answer much more complex questions and engage in more flowing, open-ended conversation, dropping the need for users to say "Alexa …" at each turn.

The news: Google DeepMind says it's trained an artificial intelligence system that can predict which DNA variations in our genomes are likely to cause disease--predictions that could speed diagnosis of rare disorders and possibly yield clues for drug development. The background: Back in 2021, DeepMind announced that its program AlphaFold was able to accurately predict the shape of proteins, a problem considered a "grand challenge" in biology. Now the company says it has fine-tuned that protein model to predict which misspellings found in human DNA are safe to ignore and which are likely to cause disease. Why it matters: Although not intended to directly make diagnoses, computer predictions are already used by doctors to help locate the genetic causes of mysterious syndromes. But critics say the real test of modern artificial intelligence is whether it can lead to new cures, something that still hasn't happened.

We present Kosmos-2.5, a multimodal literate model for machine reading of text-intensive images. Pre-trained on large-scale text-intensive images, Kosmos-2.5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware text blocks, where each block of text is assigned its spatial coordinates within the image, and (2) producing structured text output that captures styles and structures into the markdown format. This unified multimodal literate capability is achieved through a shared Transformer architecture, task-specific prompts, and flexible text representations. We evaluate Kosmos-2.5 on end-to-end document-level text recognition and image-to-markdown text generation. Furthermore, the model can be readily adapted for any text-intensive image understanding task with different prompts through supervised fine-tuning, making it a general-purpose tool for real-world applications involving text-rich images. This work also paves the way for the future scaling of multimodal large language models.

We discuss the emerging new opportunity for building feedback-rich computational models of social systems using generative artificial intelligence. Referred to as Generative Agent-Based Models (GABMs), such individual-level models utilize large language models such as ChatGPT to represent human decision-making in social settings. We provide a GABM case in which human behavior can be incorporated in simulation models by coupling a mechanistic model of human interactions with a pre-trained large language model. This is achieved by introducing a simple GABM of social norm diffusion in an organization. For educational purposes, the model is intentionally kept simple. We examine a wide range of scenarios and the sensitivity of the results to several changes in the prompt. We hope the article and the model serve as a guide for building useful diffusion models that include realistic human reasoning and decision-making.

This paper presents the overview of the AuTexTification shared task as part of the IberLEF 2023 Workshop in Iberian Languages Evaluation Forum, within the framework of the SEPLN 2023 conference. AuTexTification consists of two subtasks: for Subtask 1, participants had to determine whether a text is human-authored or has been generated by a large language model. For Subtask 2, participants had to attribute a machine-generated text to one of six different text generation models. Our AuTexTification 2023 dataset contains more than 160.000 texts across two languages (English and Spanish) and five domains (tweets, reviews, news, legal, and how-to articles). A total of 114 teams signed up to participate, of which 36 sent 175 runs, and 20 of them sent their working notes. In this overview, we present the AuTexTification dataset and task, the submitted participating systems, and the results.