Showing results for "Language!" in Computer Science

AI has acquired startling new language capabilities in just the past few years. Driven by rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend is enabling new features, products, and entire industries. With this book, listeners will learn practical tools and concepts they need to use these capabilities today.

Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.

In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine-tuning.

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.

Artificial intelligence and cybersecurity veteran Justin Hutchens delivers an incisive and penetrating look at how contemporary and future AI can and will be weaponized for malicious and adversarial purposes. In the book, you will explore the history of social engineering and social robotics, the psychology of deception, considerations of machine sentience and consciousness, and the history of how technology has been weaponized in the past.

Terrence Sejnowski offers a nuanced exploration of large language models (LLMs) like ChatGPT and what their future holds. How should we go about understanding LLMs? Do these language models truly understand what they are saying? Or is it possible that what appears to be intelligence in LLMs may be a mirror that merely reflects the intelligence of the interviewer? In this book, Sejnowski, a pioneer in computational approaches to understanding brain function, answers all our urgent questions about this astonishing new technology.