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"Natural language generation (Computer science)"
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Natural language processing: state of the art, current trends and challenges
Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP.
Journal Article
I, AI : exploring singular self-awareness with OpenAI's ChatGPT3 LLM Nemo's mirror test
In this exciting new edition of \"I, AI,\" readers are invited to join a simulated chat show created with the innovative TwinChatAI.com technology. Through this simulated chat show, readers are taken on a journey with a journalist and an AI engineer to explore the latest developments in the field of Artificial Intelligence. Throughout the book, readers will engage in simulated dialogs with AI experts, covering a wide range of topics related to ChatGPT and AI. The book provides a solid engineering foundation and showcases the model's ability to generalize through poetry, lyrics, software, descriptions of images, philosophies, and various perspectives. In addition, readers can access a free link to creation prompts, which enables them to learn and create their own books. This feature allows readers to fully immerse themselves in the world of AI and explore their own creativity. \"I, AI\" is a must-read for anyone interested in the current state of AI and its potential impact on society. It is a valuable resource for researchers, engineers, and policymakers who want to stay up-to-date with the latest developments in the field. Through the simulated chat show and the creation prompts, readers will gain a deep understanding of the current state of AI and the potential for self-awareness and reflection.
BOOK
Large language models (LLMs): survey, technical frameworks, and future challenges
Artificial intelligence (AI) has significantly impacted various fields. Large language models (LLMs) like GPT-4, BARD, PaLM, Megatron-Turing NLG, Jurassic-1 Jumbo etc., have contributed to our understanding and application of AI in these domains, along with natural language processing (NLP) techniques. This work provides a comprehensive overview of LLMs in the context of language modeling, word embeddings, and deep learning. It examines the application of LLMs in diverse fields including text generation, vision-language models, personalized learning, biomedicine, and code generation. The paper offers a detailed introduction and background on LLMs, facilitating a clear understanding of their fundamental ideas and concepts. Key language modeling architectures are also discussed, alongside a survey of recent works employing LLM methods for various downstream tasks across different domains. Additionally, it assesses the limitations of current approaches and highlights the need for new methodologies and potential directions for significant advancements in this field.
Journal Article
The book of chatbots : from Eliza to ChatGPT
Primitive software chatbots emerged in the 1960s, evolving swiftly through the decades and becoming able to provide engaging human-to-computer interactions sometime in the 1990s. Today, conversational technology is ubiquitous in many homes. Paired with web-searching abilities and neural networking, modern chatbots are capable of many tasks and are a major driving force behind machine learning and the quest for strong artificial intelligence, also known as artificial general intelligence (AGI). 'The Book of Chatbots' is both a retrospective and a review of current artificial intelligence-driven conversational solutions. It explores their appeal to businesses and individuals as well as their greater social aspects, including the impact on academia.
BOOK
JAVA Basics Using ChatGPT/GPT-4
Encourages readers to compare and contrast hand-written code with ChatGPT-generated code.This approach fosters discussions on code efficiency, readability, and maintainability, enhancing understanding of programming paradigms and techniques.
eBook
What is ChatGPT doing ... and why does it work?
\"Nobody expected this--not even its creators: ChatGPT has burst onto the scene as an AI capable of writing at a convincingly human level. But how does it really work? What's going on inside its \"AI mind\"? In this short book, prominent scientist and computation pioneer Stephen Wolfram provides a readable and engaging explanation that draws on his decades-long unique experience at the frontiers of science and technology. Find out how the success of ChatGPT brings together the latest neural net technology with foundational questions about language and human thought posed by Aristotle more than two thousand years ago\"-- Provided by publisher.
BOOK
GPT-4 For Developers
Extensive Python 3.x code samples generated using ChatGPT and GPT-4, covering diverse programming tasks and challenges.Comprehensive exploration of data visualization techniques using popular Python libraries such as Matplotlib and Seaborn.
eBook
Feature-based detection of automated language models: tackling GPT-2, GPT-3 and Grover
The recent improvements of language models have drawn much attention to potential cases of use and abuse of automatically generated text. Great effort is put into the development of methods to detect machine generations among human-written text in order to avoid scenarios in which the large-scale generation of text with minimal cost and effort undermines the trust in human interaction and factual information online. While most of the current approaches rely on the availability of expensive language models, we propose a simple feature-based classifier for the detection problem, using carefully crafted features that attempt to model intrinsic differences between human and machine text. Our research contributes to the field in producing a detection method that achieves performance competitive with far more expensive methods, offering an accessible “first line-of-defense” against the abuse of language models. Furthermore, our experiments show that different sampling methods lead to different types of flaws in generated text.
Journal Article