Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. Traditional methods use a two-step process with separate models for entity recognition and disambiguation, which can be computationally intensive and less effective. We propose a fine-tuned model that jointly integrates entity recognition and disambiguation in a unified framework. Furthermore, our approach leverages large language models to enrich the context of entity mentions, yielding better performance in entity disambiguation. We evaluated our approach on benchmark datasets and compared with several baselines. The evaluation results show that our approach achieves state-of-the-art performance on out-of-domain datasets.

Hierarchical Merging is a technique commonly used to summarize very long texts ($>$100K tokens) by breaking down the input into smaller sections, summarizing those sections individually, and then merging or combining those summaries into a final coherent summary. Although it helps address the limitations of large language models (LLMs) with fixed input length constraints, the recursive merging process can amplify LLM hallucinations, increasing the risk of factual inaccuracies. In this paper, we seek to mitigate hallucinations by enriching hierarchical merging with context from the source document. Specifically, we propose different approaches to contextual augmentation ranging from \emph{replacing} intermediate summaries with relevant input context, to \emph{refining} them while using the context as supporting evidence, and \emph{aligning} them implicitly (via citations) to the input. Experimental results on datasets representing legal and narrative domains show that contextual augmentation consistently outperforms zero-shot and hierarchical merging baselines for the Llama 3.1 model family. Our analysis further reveals that refinement methods tend to perform best when paired with extractive summarization for identifying relevant input.

It overlaps with computational creativity as well as procedural content generation, and has roots stretching back long before digital games research had begun in the form we know it today [9]. In [6] Cook and Smith offer a critique of the field, suggesting that the history of AGD research, at the time of writing in 2015, was primarily focused on the generation of rules for games, and limited to goal-oriented games with clear objective functions for winning. They write: This mechanics-first view on games is unnecessarily limiting, stifling the creative potential for AGD and restricting the kinds of games that can be automatically designed to ones that have well-defined, simple rule systems. More than half a decade on from the publication of this work, and most of its points still hold true of AGD research today. This is not in itself a flaw in the research being done - it is still valuable, and the field is progressing and creating many new and exciting systems [1, 11]. Yet there remains a need to expand beyond this, to create the "new kinds of play experience" that Cook and Smith talk about, to expand the horizons of AGD as a research field, and most importantly to expand the scope of how AI interacts with, improves and changes games as a creative medium.

Key value databases come with a high speed and performance where we mostly cannot reach in relational databases. Herein similar to Cassandra, Redis is a fast key value store solution. In this post, we are going to adopt Redis to build an overperforming face recognition application. On the other hand, this could be adapted to NLP studies or any reverse image search case such as in Google Images. The official redis distribution is available for Linux and MacOS here.

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? Email careandfeeding@slate.com or post it in the Slate Parenting Facebook group. When my oldest was born, my father told me one of his biggest parenting regrets was not assigning regular chores to me and my siblings. I took this advice to heart with my three wonderful boys, now aged 16, 14, and 11. Since the boys were little, they've had some form of daily cleaning responsibility, starting with a "10-minute tidy" with us every evening and, as they got older and ostensibly more responsible, additional age-appropriate chores.

Artificial intelligence is among the most exciting fields of technology these days. Autonomous cars, voice assistants, facial recognition: all of them and more are an increasing part of our lives. Famous people, from Elon Musk to the late Stephen Hawking, have chimed in concerning the risks associated with machines that literally have a mind of their own. Laws are being passed, often in a hurry, to regulate issues that weren't even on anyone's radar at the start of this decade. The attention currently enjoyed by what used to be an esoteric academic field (when it wasn't the centerpiece of a sci-fi movie) likely began with Apple's release of Siri in 2011, but took a sharp uptick when AlphaGo defeated the world champion at Go, a task previously thought all but impossible.

Michael Cook, a 30-year-old senior research fellow at the University of Falmouth, has built an AI capable of imagining new video games from scratch. Cook calls the machine Angelina, a recursive acronym that stands for "A Novel Game-Evolving Labrat I've Named Angelina" (a joke that Cook says got old pretty quickly). Since its earliest form, in 2011, it has created hundreds of experimental video games, received acclaim in an international game-making competition, and had its work featured in a New York gallery exhibit. Game-making algorithms are almost as old as video games, but their use has typically been limited to generating terrain and other simple digital art. The next frontier is using increasingly sophisticated machine-learning techniques to design entirely new kinds of games that have, to date, evaded the human imagination.

A PhD student from Britain's Imperial College has launched a video game that was co-written by an artificial-intelligence (AI) machine named Angelina. PhD student Michael Cook and Fellow Simon Colton of the Imperial College's Computational Creativity Group have been working since 2010 on Angelina -- an AI machine that automates the video-game creation process. She does this by learning and borrowing code from pre-existing games, then applying that knowledge to new games under development -- at the moment, two-dimensional arcade games and side-scrolling platformers are about as complicated as she can manage, but Cook and Colton are working on expanding her abilities to be able to develop her own game concepts. So far, the team of Cook, Colton and Angelina has released one game: the Christmas-themed A Puzzling Present for Windows, Mac, Linux and Android. According to Cook, there are a few problems with a computer AI generating video games for a human audience.