Advertisement
| character ai language model: Deep Learning for Natural Language Processing Jason Brownlee, 2017-11-21 Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects. |
| character ai language model: Supremacy Parmy Olson, 2024-09-10 Shortlisted for the 2024 Financial Times & Schroders Business Book of the Year In November of 2022, a webpage was posted online with a simple text box. It was an AI chatbot called ChatGPT, and was unlike any app people had used before. It was more human than a customer service agent, more convenient than a Google search. Behind the scenes, battles for control and prestige between the world’s two leading AI firms, OpenAI and DeepMind, who now steers Google's AI efforts, has remained elusive - until now. In Supremacy, Olson, tech writer at Bloomberg, tells the astonishing story of the battle between these two AI firms, their struggles to use their tech for good, and the hazardous direction they could go as they serve two tech Goliaths whose power is unprecedented in history. The story focuses on the continuing rivalry of two key CEOs at the center of it all, who cultivated a religion around their mission to build god-like super intelligent machines: Sam Altman, CEO of OpenAI, and Demis Hassabis, the CEO of DeepMind. Supremacy sharply alerts readers to the real threat of artificial intelligence that its top creators are ignoring: the profit-driven spread of flawed and biased technology into industries, education, media and more. With exclusive access to a network of high-ranking sources, Parmy Olson uses her 13 years of experience covering technology to bring to light the exploitation of the greatest invention in human history, and how it will impact us all. |
| character ai language model: Pythonic AI Arindam Banerjee, 2023-10-31 Unlock the power of AI with Python: Your Journey from Novice to Neural Nets KEY FEATURES ● Learn to code in Python and use Google Colab's hardware accelerators (GPU and TPU) to train and deploy AI models efficiently. ● Develop Convolutional Neural Networks (CNNs) using the TensorFlow 2 library for computer vision tasks. ● Develop sequence, attention-based, and Transformer models using the TensorFlow 2 library for Natural Language Processing (NLP) tasks. DESCRIPTION “Pythonic AI” is a book that teaches you how to build AI models using Python. It also includes practical projects in different domains so you can see how AI is used in the real world. Besides teaching how to build AI models, the book also teaches how to understand and explore the opportunities that AI presents. It includes several hands-on projects that walk you through successful AI applications, explaining concepts like neural networks, computer vision, natural language processing (NLP), and generative models. Each project in the book also reiterates and reinforces the important aspects of Python scripting. You'll learn Python coding and how it can be used to build cutting-edge AI applications. The author explains each essential line of Python code in detail, taking into account the importance and difficulty of understanding. By the end of the book, you will learn how to develop a portfolio of AI projects that will help you land your dream job in AI. WHAT YOU WILL LEARN ● Create neural network models using the TensorFlow 2 library. ● Develop Convolutional Neural Networks (CNNs) for computer vision tasks. ● Develop Sequence models for Natural Language Processing (NLP) tasks. ● Create Attention-based and Transformer models. ● Learn how to create Generative Adversarial Networks (GANs). WHO THIS BOOK IS FOR This book is for everyone who wants to learn how to build AI applications in Python, regardless of their experience level. Whether you're a student, a tech professional, a non-techie, or a technology enthusiast, this book will teach you the fundamentals of Python and AI, and show you how to apply them to real-world problems. TABLE OF CONTENTS 1. Python Kickstart: Concepts, Libraries, and Coding 2. Setting up AI Lab 3. Design My First Neural Network Model 4. Explore Designing CNN with TensorFlow 5. Develop CNN-based Image Classifier Apps 6. Train and Deploy Object Detection Models 7. Create a Text and Image Reader 8. Explore NLP for Advanced Text Analysis 9. Up and Running with Sequence Models 10. Using Sequence Models for Automated Text Classification 11. Create Attention and Transformer Models 12. Generating Captions for Images 13. Learn to Build GAN Models 14. Generate Artificial Faces Using GAN |
| character ai language model: Generative AI with Python and TensorFlow 2 Joseph Babcock, Raghav Bali, 2021-04-30 Fun and exciting projects to learn what artificial minds can create Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook Description Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation. What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning. |
| character ai language model: Meet ChatGPT: The Revolutionary AI Language Model Taking the World by Storm! Shu Chen Hou, Introducing Meet ChatGPT: The Revolutionary AI Language Model Taking the World by Storm! - the definitive guide to understanding and leveraging the power of ChatGPT, the groundbreaking AI language model that is transforming the way we communicate, learn, and create. In this comprehensive ebook, you will discover the history and evolution of AI language models, the creation and architecture of ChatGPT, and the numerous applications and potential of this groundbreaking technology. From customer service and language translation to content creation and creative writing, ChatGPT has the potential to enhance efficiency, inspire creativity, and generate new forms of art and expression. But that's not all - this ebook also explores the ethical considerations and limitations of ChatGPT, the challenges of improving it, and its impact on society, including the future of work and the creative industries. You will learn about the potential risks of ChatGPT in misinformation and fake news, as well as the role of ChatGPT in improving accessibility for people with disabilities. Moreover, this ebook provides insights on how freelancers can make money with ChatGPT, including automated content creation, personalized services, and book writing. You will also discover examples of how ChatGPT is being used in real-life scenarios and its impact on various industries. Whether you are a writer, marketer, entrepreneur, or simply curious about the future of AI technology, this ebook is a must-read. Get your copy of Meet ChatGPT: The Revolutionary AI Language Model Taking the World by Storm! and unlock the full potential of this groundbreaking technology. |
| character ai language model: Applications, Challenges, and the Future of ChatGPT Sharma, Priyanka, Jyotiyana, Monika, Kumar, A.V. Senthil, 2024-05-28 The rapid progress of artificial intelligence (AI) technologies has resulted in a complicated landscape for researchers and practitioners. Understanding and navigating the complexities of AI applications, particularly in the context of ChatGPT and its interactions with other AI tools, can be challenging. Researchers and academics need guidance to keep up with these technologies' evolving trends and implications, which leads to gaps in knowledge and implementation strategies. Additionally, the ethical and societal impacts of integrating AI into various domains remain a significant concern, requiring a comprehensive approach to address. Applications, Challenges, and the Future of ChatGPT provide a comprehensive solution to these issues by offering a detailed analysis of the current research trends in AI, focusing on ChatGPT and its interactions with other AI tools. The book delves into how we can effectively utilize ChatGPT and other AI tools to address complex problems by exploring AI applications' collaborative potentials and emerging paradigms. By identifying research gaps and suggesting future directions, this book equips researchers and practitioners with the knowledge and tools necessary to navigate the evolving landscape of AI. |
| character ai language model: Character Theology Tom Steffen, Ray Neu, 2024-02-21 Character Theology provides a natural, universal way for the world to engage God through his chosen cast of characters. As the media eras continue to change (oral to print to digital-virtual), too many Bible scholars, and consequently pastors and Bible teachers in the West and beyond, lack capability to effectively communicate Scripture to Millennials, Gen Z, and Gen Alpha. These generations find little if any relevance in the Christianity promoted by those stuck in modernity’s sticky abstract systematic theology. Character Theology relates, sticks, and transforms these generations. Why? Because people grasp and engage God most naturally and precisely through his interaction with biblical characters and their interaction with each other! Characters communicate the Creator’s characteristics. The roadmap to the recovery and expansion of Christianity in the twenty-first century will be through Bible characters. |
| character ai language model: Next Generation AI Language Models in Research Kashif Naseer Qureshi, Gwanggil Jeon, 2024-11-13 In this comprehensive and cutting-edge volume, Qureshi and Jeon bring together experts from around the world to explore the potential of artificial intelligence models in research and discuss the potential benefits and the concerns and challenges that the rapid development of this field has raised. The international chapter contributor group provides a wealth of technical information on different aspects of AI, including key aspects of AI, deep learning and machine learning models for AI, natural language processing and computer vision, reinforcement learning, ethics and responsibilities, security, practical implementation, and future directions. The contents are balanced in terms of theory, methodologies, and technical aspects, and contributors provide case studies to clearly illustrate the concepts and technical discussions throughout. Readers will gain valuable insights into how AI can revolutionize their work in fields including data analytics and pattern identification, healthcare research, social science research, and more, and improve their technical skills, problem-solving abilities, and evidence-based decision-making. Additionally, they will be cognizant of the limitations and challenges, the ethical implications, and security concerns related to language models, which will enable them to make more informed choices regarding their implementation. This book is an invaluable resource for undergraduate and graduate students who want to understand AI models, recent trends in the area, and technical and ethical aspects of AI. Companies involved in AI development or implementing AI in various fields will also benefit from the book’s discussions on both the technical and ethical aspects of this rapidly growing field. |
| character ai language model: The Developer's Playbook for Large Language Model Security Steve Wilson, 2024-09-03 Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models. Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. You'll learn: Why LLMs present unique security challenges How to navigate the many risk conditions associated with using LLM technology The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained How to identify the top risks and vulnerabilities associated with LLMs Methods for deploying defenses to protect against attacks on top vulnerabilities Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization |
| character ai language model: Research and Development in Intelligent Systems XXIX Max Bramer, Miltos Petridis, 2012-10-30 The papers in this volume are the refereed papers presented at AI-2012, the Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2012 in both the technical and the application streams. They present new and innovative developments and applications, divided into technical stream sections on Data Mining, Data Mining and Machine Learning, Planning and Optimisation, and Knowledge Management and Prediction, followed by application stream sections on Language and Classification, Recommendation, Practical Applications and Systems, and Data Mining and Machine Learning. The volume also includes the text of short papers presented as posters at the conference. This is the twenty-ninth volume in the Research and Development in Intelligent Systems series, which also incorporates the twentieth volume in the Applications and Innovations in Intelligent Systems series. These series are essential reading for those who wish to keep up to date with developments in this important field. |
| character ai language model: Proceedings of 4th International Conference on Artificial Intelligence and Smart Energy S. Manoharan, |
| character ai language model: Transformers for Natural Language Processing and Computer Vision Denis Rothman, 2024-02-29 The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal Generative AI, risks, and implementations with ChatGPT Plus with GPT-4, Hugging Face, and Vertex AI Key Features Compare and contrast 20+ models (including GPT-4, BERT, and Llama 2) and multiple platforms and libraries to find the right solution for your project Apply RAG with LLMs using customized texts and embeddings Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases Purchase of the print or Kindle book includes a free eBook in PDF format Book DescriptionTransformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You’ll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.What you will learn Breakdown and understand the architectures of the Original Transformer, BERT, GPT models, T5, PaLM, ViT, CLIP, and DALL-E Fine-tune BERT, GPT, and PaLM 2 models Learn about different tokenizers and the best practices for preprocessing language data Pretrain a RoBERTa model from scratch Implement retrieval augmented generation and rules bases to mitigate hallucinations Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V Who this book is for This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field. Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution. |
| character ai language model: MultiMedia Modeling Duc-Tien Dang-Nguyen, Cathal Gurrin, Martha Larson, Alan F. Smeaton, Stevan Rudinac, Minh-Son Dao, Christoph Trattner, Phoebe Chen, 2023-03-30 The two-volume set LNCS 13833 and LNCS 13834 constitutes the proceedings of the 29th International Conference on MultiMedia Modeling, MMM 2023, which took place in Bergen, Norway, during January 9-12, 2023. The 86 papers presented in these proceedings were carefully reviewed and selected from a total of 267 submissions. They focus on topics related to multimedia content analysis; multimedia signal processing and communications; and multimedia applications and services. |
| character ai language model: Interactive Storytelling Lissa Holloway-Attaway, John T. Murray, 2023-12-01 This two-volume set LNCS 14383 and LNCS 14384 constitutes the refereed proceedings of the 16th International Conference on Interactive Digital Storytelling, ICIDS 2023, held in Kobe, Japan, during November 11–15, 2023. The 30 full papers presented in this book together with 11 short papers were carefully reviewed and selected from 101 submissions. Additionally, the proceedings includes 22 Late Breaking Works. The papers focus on topics such as: theory, history and foundations; social and cultural contexts; tools and systems; interactive narrative design; virtual worlds, performance, games and play; applications and case studies; and late breaking works. |
| character ai language model: Artificial Intelligence Dr. Kavyashree .N, Mrs. B. Geetha, Dr. R. Karthikeyan, |
| character ai language model: Introduction to Shazam! Fury of the Gods Gilad James, PhD, The sequel to the 2019 film Shazam! is titled Shazam! Fury of the Gods. Directed by David F. Sandberg and written by Henry Gayden, the sequel continues the story of a teenage boy named Billy Batson who transforms into a superhero named Shazam by uttering the magical word Shazam. In this new installment, Shazam and his family of superheroes will face off against a new villain, Hespera, and her sister Kalypso, the daughters of Atlas. The first film received positive reviews and impressed audiences with its lighthearted tone and humor. Shazam! Fury of the Gods promises to continue this trend, with reports suggesting that the sequel will add more magic and mythology to the story. The film features a star-studded cast, with returning actors Zachary Levi, Asher Angel, Jack Dylan Grazer, and Marta Milans reprising their roles, alongside newcomers Rachel Zegler, Lucy Liu, and Helen Mirren. Shazam! Fury of the Gods is set to release on June 2, 2023, and fans of the first film are eagerly anticipating its arrival. |
| character ai language model: Advances in Artificial Intelligence Marie-Jean Meurs, Frank Rudzicz, 2019-05-21 This book constitutes the refereed proceedings of the 32nd Canadian Conference on Artificial Intelligence, Canadian AI 2019, held in Kingston, ON, Canada, in May 2019. The 27 regular papers and 34 short papers presented together with 8 Graduate Student Symposium papers and 4 Industry Track papers were carefully reviewed and selected from 132 submissions. The focus of the conference was on artificial intelligence research and advanced information and communications technology. |
| character ai language model: Artificial Intelligence Utku Kose, M. Umut Demirezen, 2024-11-29 This book provides an examination of cutting-edge research and developments in the field of artificial intelligence. It seeks to extend the view in both technical and societal evaluations to ensure a well-defined balance for societal outcomes. It explores hot topics such as generative artificial intelligence, artificial intelligence in law, education, and climate change. Artificial Intelligence: Technical and Societal Advancements seeks to bridge the gap between theory and practical applications of AI by giving readers insight into recent advancements. It offers readers a deep dive into the transformative power of AI for the present and future world. As artificial intelligence continues to revolutionize various sectors, the book discusses applications from healthcare to finance and from entertainment to industrial areas. It discusses the technical aspects of intelligent systems and the effects of these aspects on humans. To this point, this book considers technical advancements while discussing the societal pros and cons in terms of human-machine interaction in critical applications. The authors also stress the importance of deriving policies and predictions about how to make future intelligent systems compatible with humans through a necessary level of human management. Finally, this book provides the opinions and views of researchers and experts (from public/private sector) including educators, lawyers, policymakers, managers, and business-related representatives. The target readers of this book include academicians; researchers; experts; policymakers; educators; and B.S., M.S., and Ph.D. students in the context of target problem fields. It can be used accordingly as a reference source and even supportive material for artificial intelligence-oriented courses. |
| character ai language model: Creativity and Rationale John M. Carroll, 2012-07-26 Creativity and rationale comprise an essential tension in design. They are two sides of the coin; contrary, complementary, but perhaps also interdependent. Designs always serve purposes. They always have an internal logic. They can be queried, explained, and evaluated. These characteristics are what design rationale is about. But at the same time designs always provoke experiences and insights. They open up possibilities, raise questions, and engage human sense making. Design is always about creativity. Creativity and Rationale: Enhancing Human Experience by Design comprises 19 complementary chapters by leading experts in the areas of human-computer interaction design, sociotechnical systems design, requirements engineering, information systems, and artificial intelligence. Researchers, research students and practitioners in human-computer interaction and software design will find this state of the art volume invaluable. |
| character ai language model: HCI International 2024 Posters Constantine Stephanidis, |
| character ai language model: VR Developer Gems William R. Sherman, 2019-06-07 This book takes the practicality of other Gems series such as Graphics Gems and Game Programming Gems and provide a quick reference for novice and expert programmers alike to swiftly track down a solution to a task needed for their VR project. Reading the book from cover to cover is not the expected use case, but being familiar with the territory from the Introduction and then jumping to the needed explanations is how the book will mostly be used. Each chapter (other than Introduction) will contain between 5 to 10 tips, each of which is a self-contained explanation with implementation detail generally demonstrated as pseudo code, or in cases where it makes sense, actual code. Key Features Sections written by veteran virtual reality researchers and developers Usable code snipits that readers can put to immediate use in their own projects. Tips of value both to readers entering the field as well as those looking for solutions that expand their repertoire. |
| character ai language model: Neural Information Processing Biao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li, 2023-11-14 The six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. |
| character ai language model: Library of Congress Subject Headings Library of Congress, Library of Congress. Subject Cataloging Division, Library of Congress. Office for Subject Cataloging Policy, 2013 |
| character ai language model: Introduction to Creed III Gilad James, PhD, Creed III is an upcoming American sports drama film directed by Michael B. Jordan. The movie will serve as a sequel to Creed II and is the third installment in the Creed film series. Written by Zach Baylin, the film is expected to hit theaters on November 23, 2022. Creed III follows the life of Adonis Creed, portrayed by Michael B. Jordan, who has now become the world heavyweight boxing champion. As he tries to balance his personal life and professional career, he faces new challenges in the form of a dangerous opponent. The movie also explores Adonis’ relationship with his father Apollo Creed and his mentor, Rocky Balboa. The film is expected to be an emotionally charged portrayal of Adonis’ journey and is anticipated to be a box office success. |
| character ai language model: Introduction to Pedro Pascal Gilad James, PhD, Pedro Pascal is a Chilean-American actor who has gained fame for his roles in television shows and movies. He was born on April 2, 1975, in Santiago, Chile. Pascal grew up in Orange County, California after his family fled the political turmoil in Chile. He attended the Orange County School of the Arts and later studied at New York University's Tisch School of the Arts, where he trained in acting. Pascal's breakthrough role was in the hit Netflix series Narcos, where he played the role of DEA agent Javier Peña. He has also had notable roles in television shows such as Game of Thrones, where he played Prince Oberyn Martell, and The Mandalorian, where he played the titular character. Pascal has also appeared in movies such as Kingsman: The Golden Circle, The Great Wall, and Wonder Woman 1984. Pascal has received critical acclaim for his performances and has been nominated for several awards, including a Primetime Emmy Award for his role in The Mandalorian. |
| character ai language model: Machine Translation and Transliteration involving Related, Low-resource Languages Anoop Kunchukuttan, Pushpak Bhattacharyya, 2021-08-12 Machine Translation and Transliteration involving Related, Low-resource Languages discusses an important aspect of natural language processing that has received lesser attention: translation and transliteration involving related languages in a low-resource setting. This is a very relevant real-world scenario for people living in neighbouring states/provinces/countries who speak similar languages and need to communicate with each other, but training data to build supporting MT systems is limited. The book discusses different characteristics of related languages with rich examples and draws connections between two problems: translation for related languages and transliteration. It shows how linguistic similarities can be utilized to learn MT systems for related languages with limited data. It comprehensively discusses the use of subword-level models and multilinguality to utilize these linguistic similarities. The second part of the book explores methods for machine transliteration involving related languages based on multilingual and unsupervised approaches. Through extensive experiments over a wide variety of languages, the efficacy of these methods is established. Features Novel methods for machine translation and transliteration between related languages, supported with experiments on a wide variety of languages. An overview of past literature on machine translation for related languages. A case study about machine translation for related languages between 10 major languages from India, which is one of the most linguistically diverse country in the world. The book presents important concepts and methods for machine translation involving related languages. In general, it serves as a good reference to NLP for related languages. It is intended for students, researchers and professionals interested in Machine Translation, Translation Studies, Multilingual Computing Machine and Natural Language Processing. It can be used as reference reading for courses in NLP and machine translation. Anoop Kunchukuttan is a Senior Applied Researcher at Microsoft India. His research spans various areas on multilingual and low-resource NLP. Pushpak Bhattacharyya is a Professor at the Department of Computer Science, IIT Bombay. His research areas are Natural Language Processing, Machine Learning and AI (NLP-ML-AI). Prof. Bhattacharyya has published more than 350 research papers in various areas of NLP. |
| character ai language model: Generative AI and Education B. Mairéad Pratschke, |
| character ai language model: Artificial Intelligence with Uncertainty Deyi Li, Yi Du, 2007-09-27 The information deluge currently assaulting us in the 21st century is having a profound impact on our lifestyles and how we work. We must constantly separate trustworthy and required information from the massive amount of data we encounter each day. Through mathematical theories, models, and experimental computations, Artificial Intelligence with U |
| character ai language model: c't Working with AI c't-Redaktion, 2024-01-24 The special issue of c't KI-Praxis provides tests and practical instructions for working with chatbots. It explains why language models make mistakes and how they can be minimised. This not only helps when you send questions and orders to one of the chatbots offered online. If you do not want to or are not allowed to use the cloud services for data protection reasons, for example, you can also set up your own voice AI. The c't editorial team explains where to find a suitable voice model, how to host it locally and which service providers can host it. The fact that generative AI is becoming increasingly productive harbours both opportunities and risks. Suitable rules for the use of AI in schools, training and at work help to exploit opportunities and minimise risks. |
| character ai language model: PRICAI 2008: Trends in Artificial Intelligence Tu-Bao Ho, Zhi-Hua Zhou, 2008-11-24 This book constitutes the refereed proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008, held in Hanoi, Vietnam, in December 2008. The 49 revised long papers, 33 revised regular papers, and 32 poster papers presented together with 1 keynote talk and 3 invited lectures were carefully reviewed and selected from 234 submissions. The papers address all current issues of modern AI research with topics such as AI foundations, knowledge representation, knowledge acquisition and ontologies, evolutionary computation, etc. as well as various exciting and innovative applications of AI to many different areas. Particular importance is attached to the areas of machine learning and data mining, intelligent agents, language and speech processing, information retrieval and extraction. |
| character ai language model: Supervised Machine Learning for Text Analysis in R Emil Hvitfeldt, Julia Silge, 2021-10-22 Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are. |
| character ai language model: Real-World Natural Language Processing Masato Hagiwara, 2021-12-14 Voice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you''ll explore the core tools and techniques required to build a huge range of powerful NLP apps. about the technology Natural language processing is the part of AI dedicated to understanding and generating human text and speech. NLP covers a wide range of algorithms and tasks, from classic functions such as spell checkers, machine translation, and search engines to emerging innovations like chatbots, voice assistants, and automatic text summarization. Wherever there is text, NLP can be useful for extracting meaning and bridging the gap between humans and machines. about the book Real-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq. In this practical guide, you''ll begin by creating a complete sentiment analyzer, then dive deep into each component to unlock the building blocks you''ll use in all different kinds of NLP programs. By the time you''re done, you''ll have the skills to create named entity taggers, machine translation systems, spelling correctors, and language generation systems. what''s inside Design, develop, and deploy basic NLP applications NLP libraries such as AllenNLP and Fairseq Advanced NLP concepts such as attention and transfer learning about the reader Aimed at intermediate Python programmers. No mathematical or machine learning knowledge required. about the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009, focusing on Natural Language Processing and machine learning. He has interned at Google and Microsoft Research, and worked at Baidu Japan, Duolingo, and Rakuten Institute of Technology. He now runs his own consultancy business advising clients, including startups and research institutions. |
| character ai language model: Neural Information Processing Jun Wang, Laiwan Chan, DeLiang Wang, 2006-10-03 The three volume set LNCS 4232, LNCS 4233, and LNCS 4234 constitutes the refereed proceedings of the 13th International Conference on Neural Information Processing, ICONIP 2006, held in Hong Kong, China in October 2006. The 386 revised full papers presented were carefully reviewed and selected from 1175 submissions. |
| character ai language model: Artificial Intelligence: Theories, Models and Applications Ilias Maglogiannis, Vassilis Plagianakos, Ioannis Vlahavas, 2012-05-26 This book constitutes the proceedings of the 7th Hellenic Conference on Artificial Intelligence, SETN 2012, held in Lamia, Greece, in May 2012. The 47 contributions included in this volume were carefully reviewed and selected from 81 submissions. They deal with emergent topics of artificial intelligence and come from the SETN main conference as well as from the following special sessions on advancing translational biological research through the incorporation of artificial intelligence methodologies; artificial intelligence in bioinformatics; intelligent annotation of digital content; intelligent, affective, and natural interfaces; and unified multimedia knowledge representation and processing. |
| character ai language model: Introduction to Scream (2022 film) Gilad James, PhD, Introduction to Scream is an upcoming horror movie directed by Matt Bettinelli-Olpin and Tyler Gillett. This movie is planned to be the fifth installment in the Scream franchise, which began in 1996. Neve Campbell, Courteney Cox, and David Arquette will reprise their roles as Sidney Prescott, Gale Weathers, and Dewey Riley, respectively. Along with the original cast members, the movie will also feature new characters played by Jack Quaid, Melissa Barrera, and Jenna Ortega, among others. The plot of Introduction to Scream revolves around a series of murders in a small town, which bear a striking resemblance to the killings depicted in the original Scream movies. Sidney, Gale, and Dewey return to their hometown to investigate the murders, and they are soon joined by a new group of teenagers who become targets of the killers. The movie promises to be a modern take on the classic slasher genre, with meta-humor, suspense, and some scares. Fans of the franchise are eagerly anticipating the release of Introduction to Scream, which is set to hit theaters in January 2022. |
| character ai language model: Integrating Cognitive Architectures into Virtual Character Design Turner, Jeremy Owen, Nixon, Michael, Bernardet, Ulysses, DiPaola, Steve, 2016-06-06 Cognitive architectures represent an umbrella term to describe ways in which the flow of thought can be engineered towards cerebral and behavioral outcomes. Cognitive Architectures are meant to provide top-down guidance, a knowledge base, interactive heuristics and concrete or fuzzy policies for which the virtual character can utilize for intelligent interaction with his/her/its situated virtual environment. Integrating Cognitive Architectures into Virtual Character Design presents emerging research on virtual character artificial intelligence systems and procedures and the integration of cognitive architectures. Emphasizing innovative methodologies for intelligent virtual character integration and design, this publication is an ideal reference source for graduate-level students, researchers, and professionals in the fields of artificial intelligence, gaming, and computer science. |
| character ai language model: Introduction to The Last of Us (TV series) Gilad James, PhD, The Last of Us is an upcoming TV series adaptation of the popular video game, developed by Naughty Dog and published by Sony Computer Entertainment. The game was released in 2013 and quickly gained a strong following for its narrative, characters, and gameplay. It takes place in a post-apocalyptic world where humanity has been decimated by a fungal outbreak that turns people into zombie-like creatures. The story follows Joel, a smuggler, and Ellie, a teenage girl, as they journey across the United States in search of safety. The TV series will be produced by HBO, with Neil Druckmann, who was the writer and creative director of the game, serving as one of the executive producers. Craig Mazin, who wrote and produced the critically acclaimed Chernobyl, will be the showrunner. The series is highly anticipated by fans of the game, who are eager to see how the story and characters will be adapted for television. So far, there is no release date for the series, but it is expected to premiere sometime in 2022. |
| character ai language model: Information Retrieval Technology Sung Hyon Myaeng, Ming Zhou, Kam-Fai Wong, Hong-Jiang Zhang, 2007-05-25 TheAsiaInformationRetrievalSymposium(AIRS)wasestablishedbytheAsian information retrieval community after the successful series of Information - trieval with Asian Languages (IRAL) workshops held in six di?erent locations in Asia, starting from 1996. While the IRAL workshops had their focus on inf- mation retrieval problems involving Asian languages, AIRS covers a wider scope of applications, systems, technologies and theory aspects of information retrieval in text, audio, image, video and multimedia data. This extension of the scope re?ects and fosters increasing research activities in information retrieval in this region and the growing need for collaborations across subdisciplines. We are very pleased to report that we saw a sharp increase in the number of submissions and their quality, compared to the IRAL workshops. We received 106papersfromninecountriesinAsiaandNorthAmerica,fromwhich28papers (26%) were presented in oral sessions and 38 papers in poster sessions (36%). It was a great challenge for the Program Committee to select the best among the excellent papers. The low acceptance rates witness the success of this year’s conference. After a long discussion between the AIRS 2004 Steering Committee and Springer, the publisher agreed to publish our proceedings in the Lecture Notes in Computer Science (LNCS) series, which is SCI-indexed. We feel that this strongly attests to the excellent quality of the papers. |
| character ai language model: KI 2018: Advances in Artificial Intelligence Frank Trollmann, Anni-Yasmin Turhan, 2018-09-17 This book constitutes the refereed proceedings of the 41st German Conference on Artificial Intelligence, KI 2018, held in Berlin, Germany, in September 2018. The 20 full and 14 short papers presented in this volume were carefully reviewed and selected from 65 submissions. The book also contains one keynote talk in full paper length. The papers were organized in topical sections named: reasoning; multi-agent systems; robotics; learning; planning; neural networks; search; belief revision; context aware systems; and cognitive approach. |
| character ai language model: Advances in Artificial Intelligence -- IBERAMIA 2012 Juan Pavón, Néstor D. Duque-Méndez, Rubén Fuentes Fernández, 2012-11-15 This book constitutes the refereed proceedings of the 13th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2012, held in Cartagena de Indias, Colombia, in November 2012. The 75 papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on knowledge representation and reasoning, information and knowledge processing, knowledge discovery and data mining, machine learning, bio-inspired computing, fuzzy systems, modelling and simulation, ambient intelligence, multi-agent systems, human-computer interaction, natural language processing, computer vision and robotics, planning and scheduling, AI in education, and knowledge engineering and applications. |
Evaluating Language Model Character Traits - arXiv.org
We formalise a behaviourist view of LM character traits: qualities such as truth-fulness, sycophancy, or coherent beliefs and intentions, which may manifest as consistent patterns of …
STYLEFUSION DAPTIVE MULTI TYLE GENERATION IN …
Adapter, a novel approach to enhance style awareness and consistency in character-level language models, addressing a critical gap in the current landscape of natural language …
Character-Aware Neural Language Models - Harvard University
CNNs in NLP typically involve temporal (rather than spatial) convolutions over words. 1. Apply a convolution between C and H to obtain a vector. where hA; Bi = Tr(ABT ) is the Frobenius …
CharBERT: Character-aware Pre-trained Language Model
In this paper, we propose a character-aware pre-trained lan-guage model named CharBERT improving on the previous methods (such as BERT, RoBERTa) to tackle these problems.
Character-Aware Neural Language Models - web.stanford.edu
• Derive a powerful, robust language model effective across a variety of languages. • Encode subword relatedness: eventful, eventfully, uneventful… • Address rare-word problem of prior …
CharacterBench: Benchmarking Character Customization of …
To address these issues, we propose CHARACTERBENCH, the largest bilingual generative benchmark, with 22,859 human-annotated samples covering 3,956 characters from 25 …
Abstract - arXiv.org
l needs. On top of CharacterGLM, we can customize various AI characters or social agents by configuring their attributes (identities, interests, viewpoints, experiences, achievements, social …
UCM - The first steps Universal Character Model The first steps
Sep 22, 2022 · In the Universal Character Model (UCM) white paper, we outlined a direction to advance immersive narratives and evolve interactive characters through the combination of …
Better Character Language Modeling through Morphology
We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition im-proves bits-per-character (BPC) performance …
Handwritten Text Recognition using Deep Learning - Stanford …
We used two main approaches to accomplish this task: classifying words directly and character segmenta-tion. For the former, we use Convolutional Neural Network (CNN) with various …
A Character-Based Model for Pragmatic Caption Generation
We extend previous work by making use of a character LSTM in our neural model, which allows for improved prag-matic captioning. We use Visual Genome for data, which allows us to obtain …
Character-Level Language Modeling with Deeper Self …
In this paper, we show that a deep (64-layer) transformer model (Vaswani et al. 2017) with fixed context outperforms RNN variants by a large margin, achieving state of the art on two popular …
Character-Level Language Modeling with Deeper Self-Attention
In this paper, we show that a non-recurrent model can achieve strong results on character-level language modeling. Specifically, we use a network of transformer self-attention layers …
Evaluating Language Model Character Traits - ACL Anthology
We formalise a behaviourist view of LM character traits: qualities such as truth- fulness, sycophancy, or coherent beliefs and intentions, which may manifest as consistent patterns of …
arXiv:2407.11484v9 [cs.AI] 10 Jan 2025
role-playing with language models, focusing on their development from early persona-based models to advanced character-driven simula-tions facilitated by Large Language Models …
Character-LLM: A Trainable Agent for Role-Playing - arXiv.org
In this work, we in-troduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc. Our method fo-cuses on editing profiles as …
Character is Destiny: Can Role-Playing Language Agents Make …
In this paper, we systematically study the capa-bility of RPLAs to simulate persona-driven deci-sions, based on characters from high-quality nov-els. In high-quality novels, characters’ life …
From Characters to Words: Hierarchical Pre-trained Language …
trained language model with a hierarchical two-level architecture. Our method does not rely on pre-dened word or sub-word vocabu-lary. We propose a novel adaptive and learnable …
Characteristic AI Agents via Large Language Models - arXiv.org
Several commercial products, such as Character.AI (Char-acter.AI, 2022a) and AI-Utopia (LingxinAI, 2022a), have leveraged large language models (LLMs) to provide users with …
CLOWER: A Pre-trained Language Model with Contrastive …
To fully leverage the semantic information of multi-granularity and preserve the flexibility of single-grained models in the fine-tuning stage, we propose a novel PLM named CLOWER to efi …
Evaluating Language Model Character Traits - arXiv.org
We formalise a behaviourist view of LM character traits: qualities such as truth-fulness, sycophancy, or coherent beliefs and intentions, which may manifest as consistent patterns of …
STYLEFUSION DAPTIVE MULTI TYLE GENERATION IN …
Adapter, a novel approach to enhance style awareness and consistency in character-level language models, addressing a critical gap in the current landscape of natural language …
Character-Aware Neural Language Models - Harvard University
CNNs in NLP typically involve temporal (rather than spatial) convolutions over words. 1. Apply a convolution between C and H to obtain a vector. where hA; Bi = Tr(ABT ) is the Frobenius …
CharBERT: Character-aware Pre-trained Language Model
In this paper, we propose a character-aware pre-trained lan-guage model named CharBERT improving on the previous methods (such as BERT, RoBERTa) to tackle these problems.
Character-Aware Neural Language Models
• Derive a powerful, robust language model effective across a variety of languages. • Encode subword relatedness: eventful, eventfully, uneventful… • Address rare-word problem of prior …
CharacterBench: Benchmarking Character Customization of …
To address these issues, we propose CHARACTERBENCH, the largest bilingual generative benchmark, with 22,859 human-annotated samples covering 3,956 characters from 25 …
Abstract - arXiv.org
l needs. On top of CharacterGLM, we can customize various AI characters or social agents by configuring their attributes (identities, interests, viewpoints, experiences, achievements, social …
UCM - The first steps Universal Character Model The first …
Sep 22, 2022 · In the Universal Character Model (UCM) white paper, we outlined a direction to advance immersive narratives and evolve interactive characters through the combination of …
Better Character Language Modeling through Morphology
We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition im-proves bits-per-character (BPC) performance …
A Character-Based Model for Pragmatic Caption Generation
We extend previous work by making use of a character LSTM in our neural model, which allows for improved prag-matic captioning. We use Visual Genome for data, which allows us to obtain …
Character-Level Language Modeling with Deeper Self …
In this paper, we show that a deep (64-layer) transformer model (Vaswani et al. 2017) with fixed context outperforms RNN variants by a large margin, achieving state of the art on two popular …
Character-Level Language Modeling with Deeper Self …
In this paper, we show that a non-recurrent model can achieve strong results on character-level language modeling. Specifically, we use a network of transformer self-attention layers …
Evaluating Language Model Character Traits - ACL Anthology
We formalise a behaviourist view of LM character traits: qualities such as truth- fulness, sycophancy, or coherent beliefs and intentions, which may manifest as consistent patterns of …
Character-LLM: A Trainable Agent for Role-Playing - arXiv.org
In this work, we in-troduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc. Our method fo-cuses on editing profiles as …
Character is Destiny: Can Role-Playing Language Agents …
In this paper, we systematically study the capa-bility of RPLAs to simulate persona-driven deci-sions, based on characters from high-quality nov-els. In high-quality novels, characters’ life …
arXiv:2407.11484v9 [cs.AI] 10 Jan 2025
role-playing with language models, focusing on their development from early persona-based models to advanced character-driven simula-tions facilitated by Large Language Models …
From Characters to Words: Hierarchical Pre-trained Language …
trained language model with a hierarchical two-level architecture. Our method does not rely on pre-dened word or sub-word vocabu-lary. We propose a novel adaptive and learnable …
Characteristic AI Agents via Large Language Models - arXiv.org
Several commercial products, such as Character.AI (Char-acter.AI, 2022a) and AI-Utopia (LingxinAI, 2022a), have leveraged large language models (LLMs) to provide users with …
CLOWER: A Pre-trained Language Model with Contrastive …
To fully leverage the semantic information of multi-granularity and preserve the flexibility of single-grained models in the fine-tuning stage, we propose a novel PLM named CLOWER to efi …
Abstract - arXiv.org
that large language models have the ability to act as specific characters, and communities for sharing prompts have even emerged (e.g. AIPRM (AIPRM, 2023) ). Many companies have …
