A judge in Colombia has caused a stir by admitting he used the artificial intelligence tool ChatGPT when deciding whether an autistic child's insurance should cover all of the costs of his medical treatment. He also used precedent from previous rulings to support his decision. Juan Manuel Padilla, a judge in the Caribbean city of Cartagena, concluded that the entirety child's medical expenses and transport costs should be paid by his medical plan as his parents could not afford them. While the judgment itself did not cause much fuss, the inclusion of Padilla's conversations with ChatGPT in the ruling has been more contentious. Among Padilla's inquiries with the chatbot, the legal documents show Padilla asked ChatGPT the precise legal matter at hand: "Is an autistic minor exonerated from paying fees for their therapies?"

Stock image and media content provider Getty Images announced that it was suing Stability AI, the developers of the text-to-image deep learning model, via a release on its website on Tuesday. Stable Diffusion makes it possible to create art and other kinds of visual media by entering text-based prompts. Getty Images noted that while it did provide licenses to tech firms that respected property rights, Stability AI did not fall under this category. "Stability AI did not seek any such license from Getty Images and instead, we believe, chose to ignore viable licensing options and long‑standing legal protections in pursuit of their stand‑alone commercial interests," said the image company. The legal proceedings were commenced in the High Court of Justice in London.

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make unclear its possible role in bringing evidence to a lawsuit. Shedding some light on this matter is the goal of the present survey, where we start by clarifying the relation between forensic applications and biometrics. To our knowledge, no previous work adopted this relation to make the point on the state of the art. Therefore, we analyzed the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discussed the current obstacles that separate 3D face reconstruction from an active role in forensic applications.

Many fishing vessels use forced labor, but identifying vessels that engage in this practice is challenging because few are regularly inspected. We developed a positive-unlabeled learning algorithm using vessel characteristics and movement patterns to estimate an upper bound of the number of positive cases of forced labor, with the goal of helping make accurate, responsible, and fair decisions. 89% of the reported cases of forced labor were correctly classified as positive (recall) while 98% of the vessels certified as having decent working conditions were correctly classified as negative. The recall was high for vessels from different regions using different gears, except for trawlers. We found that as much as ~28% of vessels may operate using forced labor, with the fraction much higher in squid jiggers and longlines. This model could inform risk-based port inspections as part of a broader monitoring, control, and surveillance regime to reduce forced labor. * Translated versions of the English title and abstract are available in five languages in S1 Text: Spanish, French, Simplified Chinese, Traditional Chinese, and Indonesian.

Open-textured terms in written rules are typically settled through interpretive argumentation. Ongoing work has attempted to catalogue the schemes used in such interpretive argumentation. But how can the use of these schemes affect the way in which people actually use and reason over the proper interpretations of open-textured terms? Using the interpretive argument-eliciting game Aporia as our framework, we carried out an empirical study to answer this question. Differing from previous work, we did not allow participants to argue for interpretations arbitrarily, but to only use arguments that fit with a given set of interpretive argument templates. Finally, we analyze the results captured by this new dataset, specifically focusing on practical implications for the development of interpretation-capable artificial reasoners.

A chatbot is perceived as more humanlike and likeable if it includes some jokes in its output. But most existing joke generators were not designed to be integrated into chatbots. This paper presents Witscript, a novel joke generation system that can improvise original, contextually relevant jokes, such as humorous responses during a conversation. The system is based on joke writing algorithms created by an expert comedy writer. Witscript employs well-known tools of natural language processing to extract keywords from a topic sentence and, using wordplay, to link those keywords and related words to create a punch line. Then a pretrained neural network language model that has been fine-tuned on a dataset of TV show monologue jokes is used to complete the joke response by filling the gap between the topic sentence and the punch line. A method of internal scoring filters out jokes that don't meet a preset standard of quality. Human evaluators judged Witscript's responses to input sentences to be jokes more than 40% of the time. This is evidence that Witscript represents an important next step toward giving a chatbot a humanlike sense of humor.

The paper presents a study on using language models to automate the construction of executable Knowledge Graph (KG) for compliance. The paper focuses on Abu Dhabi Global Market regulations and taxonomy, involves manual tagging a portion of the regulations, training BERT-based models, which are then applied to the rest of the corpus. Coreference resolution and syntax analysis were used to parse the relationships between the tagged entities and to form KG stored in a Neo4j database. The paper states that the use of machine learning models released by regulators to automate the interpretation of rules is a vital step towards compliance automation, demonstrates the concept querying with Cypher, and states that the produced sub-graphs combined with Graph Neural Networks (GNN) will achieve expandability in judgment automation systems. The graph is open sourced on GitHub to provide structured data for future advancements in the field.

We argue that all building blocks of transformer models can be expressed with a single concept: combinatorial Hopf algebra. Transformer learning emerges as a result of the subtle interplay between the algebraic and coalgebraic operations of the combinatorial Hopf algebra. Viewed through this lens, the transformer model becomes a linear time-invariant system where the attention mechanism computes a generalized convolution transform and the residual stream serves as a unit impulse. Attention-only transformers then learn by enforcing an invariant between these two paths. We call this invariant Hopf coherence. Due to this, with a degree of poetic license, one could call combinatorial Hopf algebras "tensors with a built-in loss function gradient". This loss function gradient occurs within the single layers and no backward pass is needed. This is in stark contrast to automatic differentiation which happens across the whole graph and needs a explicit backward pass. This property is the result of the fact that combinatorial Hopf algebras have the surprising property of calculating eigenvalues by repeated squaring.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Connecticut needs safeguards on state government's use of artificial intelligence including algorithms at child welfare and other agencies to prevent discrimination and increase transparency, an advisory panel to the U.S. Commission on Civil Rights said Thursday. The Connecticut Advisory Committee to the federal commission called on state lawmakers to pass laws regulating such systems, which have sparked concerns in other parts of the country. The problem, critics say, is algorithms can use flawed data that can disproportionately identify minorities, low-income families, disabled people and other groups when agencies make decisions on removing children from homes, approving health, housing and other benefits, where to concentrate law enforcement and assigning children to schools, among other uses.

House Oversight Committee Chairman James Comer told reporters Tuesday he believes Hunter Biden was "in proximity" to the classified documents found in President Biden's garage. Hunter Biden's lawyers called on federal and state prosecutors across the country to open criminal investigations into his critics on Wednesday – and in doing so, acknowledged that the notorious laptop is indeed Hunter's. Biden's attorney, Abbe Lowell, wrote letters to the Justice Department and the Delaware attorney general calling for investigations into Rudy Giuliani, Steve Bannon and John Mac Isaac, who owns the computer repair shop where Biden is said to have left his laptop. Biden's lawyers also sent cease and desist letters to others who obtained and disseminated the laptop's contents. Lowell argued in the letters that Mac Isaac and the others had no right to inspect the contents of Biden's laptop, much less make copies of it to share with the media.