Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data. This avoids heavyweight training (from scratch) using conventional methods and saves storage space by reducing the number of old data to store. But it also leads to poor performance in fairness. In this paper, we show that CIL suffers both dataset and algorithm bias problems, and existing solutions can only partially solve the problem. We propose a novel framework, CILIATE, that fixes both dataset and algorithm bias in CIL. It features a novel differential analysis guided dataset and training refinement process that identifies unique and important samples overlooked by existing CIL and enforces the model to learn from them. Through this process, CILIATE improves the fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three popular datasets and widely used ResNet models.

In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web. In this task, called WebBrain, the ultimate goal is to generate a fluent, informative, and factually-correct short article (e.g., a Wikipedia article) for a factual query unseen in Wikipedia. To enable experiments on WebBrain, we construct a large-scale dataset WebBrain-Raw by extracting English Wikipedia articles and their crawlable Wikipedia references. WebBrain-Raw is ten times larger than the previous biggest peer dataset, which can greatly benefit the research community. From WebBrain-Raw, we construct two task-specific datasets: WebBrain-R and WebBrain-G, which are used to train in-domain retriever and generator, respectively. Besides, we empirically analyze the performances of the current state-of-the-art NLP techniques on WebBrain and introduce a new framework ReGen, which enhances the generation factualness by improved evidence retrieval and task-specific pre-training for generation. Experiment results show that ReGen outperforms all baselines in both automatic and human evaluations.

Insurance contracts are 90 to 100 pages long and use complex legal and insurance-specific vocabulary for a layperson. Hence, they are a much more complex class of documents than those in traditional NLP corpora. Therefore, we introduce RISCBAC, a Realistic Insurance Synthetic Bilingual Automobile Contract dataset based on the mandatory Quebec car insurance contract. The dataset comprises 10,000 French and English unannotated insurance contracts. RISCBAC enables NLP research for unsupervised automatic summarisation, question answering, text simplification, machine translation and more. Moreover, it can be further automatically annotated as a dataset for supervised tasks such as NER.

Large Language Models (LLMs) have emerged as a cornerstone of artificial intelligence research and development, revolutionizing how machines understand and process natural language. These models, based on advanced deep learning architectures, have become increasingly sophisticated, capable of generating human-like text, answering questions, summarizing content, and performing a plethora of other tasks. The remarkable growth in the capabilities of LLMs can be attributed to advancements in computational power, the availability of large-scale datasets, and the continuous refinement of algorithmic techniques. A key element in the success of LLMs is their use of transformer-based architectures, which employ self-attention mechanisms to capture contextual information across long text sequences. Transformers have demonstrated a remarkable ability to scale, enabling the development of larger models with billions of parameters.

The advancements are already fueling disinformation and being used to stoke political divisions. Authoritarian governments have created seemingly realistic news broadcasters to advance their political goals. Last month, some people fell for images showing Pope Francis donning a puffy Balenciaga jacket and an earthquake devastating the Pacific Northwest, even though neither of those events had occurred. The images had been created using Midjourney, a popular image generator. On Tuesday, as former President Donald J. Trump turned himself in at the Manhattan district attorney's office to face criminal charges, images generated by artificial intelligence appeared on Reddit showing the actor Bill Murray as president in the White House.

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AI tools allow users to generate images, audio, and textual works in response to textual prompts. These tools "learn" how to generate this content by ingesting massive sets of preexisting, human-authored works. How and to what extent the use of AI impacts the ability to secure intellectual property (IP) rights are evolving questions in IP law. Recently, in Thaler v. Vidal2, the U.S. Federal Circuit Court analyzed AI inventorship in view of the U.S. Patent Act – ultimately concluding that the Patent Act unambiguously "requires that inventors must be natural persons; that is, human beings." In Thaler, the AI technology known as "DABUS" used general background knowledge of a technical field to conceive and recognize the utility of inventions without specific guidance from a human being.

Generative AI can seem like magic. Image generators such as Stable Diffusion, Midjourney, or DALL·E 2 can produce remarkable visuals in styles from aged photographs and water colors to pencil drawings and Pointillism. The resulting products can be fascinating -- both quality and speed of creation are elevated compared to average human performance. The Museum of Modern Art in New York hosted an AI-generated installation generated from the museum's own collection, and the Mauritshuis in The Hague hung an AI variant of Vermeer's Girl with a Pearl Earring while the original was away on loan. The capabilities of text generators are perhaps even more striking, as they write essays, poems, and summaries, and are proving adept mimics of style and form (though they can take creative license with facts).

Americans shared whether or not they believe the government should regulate Artificial Intelligence amid the technology's rapid, and ongoing, advancement. AUSTIN, Texas – The majority of Americans who spoke with Fox News said the government should stay out of regulating artificial intelligence technologies. "Keep the government out of regulating things," a Fort Worth resident told Fox News. "They regulate too many things already." Brian similarly opposed state regulation of the technology.

It is the front line of technology and causing marvel and alarm every day. Artificial Intelligence or AI may have extraordinary benefits in areas like health and education but when it's been deployed in areas like policing, it's accused of reinforcing social and racial prejudices rather than overcoming them. One artist and futurologist has been highlighting that in her new show.