Challenges and Responses in the Practice of Large Language Models
- URL: http://arxiv.org/abs/2408.09416v2
- Date: Wed, 21 Aug 2024 11:24:42 GMT
- Title: Challenges and Responses in the Practice of Large Language Models
- Authors: Hongyin Zhu,
- Abstract summary: This paper carefully summarizes extensive and profound questions from all walks of life, focusing on the current high-profile AI field.
It covers multiple dimensions such as industry trends, academic research, technological innovation and business applications.
It specifically classifies and organizes these questions systematically and meticulously from the five core dimensions of computing power infrastructure, software architecture, data resources, application scenarios, and brain science.
- Score: 0.9463895540925061
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper carefully summarizes extensive and profound questions from all walks of life, focusing on the current high-profile AI field, covering multiple dimensions such as industry trends, academic research, technological innovation and business applications. This paper meticulously curates questions that are both thought-provoking and practically relevant, providing nuanced and insightful answers to each. To facilitate readers' understanding and reference, this paper specifically classifies and organizes these questions systematically and meticulously from the five core dimensions of computing power infrastructure, software architecture, data resources, application scenarios, and brain science. This work aims to provide readers with a comprehensive, in-depth and cutting-edge AI knowledge framework to help people from all walks of life grasp the pulse of AI development, stimulate innovative thinking, and promote industrial progress.
Related papers
- A Comprehensive Survey on Integrating Large Language Models with Knowledge-Based Methods [4.686190098233778]
Large Language Models (LLMs) can be integrated with structured knowledge-based systems.
This article surveys the relationship between LLMs and knowledge bases, looks at how they can be applied in practice, and discusses related technical, operational, and ethical challenges.
It demonstrates the merits of incorporating generative AI into structured knowledge-base systems concerning data contextualization, model accuracy, and utilization of knowledge resources.
arXiv Detail & Related papers (2025-01-19T23:25:21Z) - The Generative AI Ethics Playbook [25.61087208802131]
Generative AI Ethics Playbook provides guidance for identifying and mitigating risks of machine learning systems.
Drawing on current best practices in both research and ethical considerations, this playbook aims to serve as a comprehensive resource for AI/ML practitioners.
arXiv Detail & Related papers (2024-12-17T22:47:04Z) - Applications and Advances of Artificial Intelligence in Music Generation:A Review [0.04551615447454769]
This paper provides a systematic review of the latest research advancements in AI music generation.
It covers key technologies, models, datasets, evaluation methods, and their practical applications across various fields.
arXiv Detail & Related papers (2024-09-03T13:50:55Z) - Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives [10.16399860867284]
The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP)
This paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications.
arXiv Detail & Related papers (2024-07-20T18:48:35Z) - Networking Systems for Video Anomaly Detection: A Tutorial and Survey [55.28514053969056]
Video Anomaly Detection (VAD) is a fundamental research task within the Artificial Intelligence (AI) community.
With the advancements in deep learning and edge computing, VAD has made significant progress.
This article offers an exhaustive tutorial for novices in NSVAD.
arXiv Detail & Related papers (2024-05-16T02:00:44Z) - Open-world Machine Learning: A Review and New Outlooks [83.6401132743407]
This paper aims to provide a comprehensive introduction to the emerging open-world machine learning paradigm.
It aims to help researchers build more powerful AI systems in their respective fields, and to promote the development of artificial general intelligence.
arXiv Detail & Related papers (2024-03-04T06:25:26Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - Dealing with Data for RE: Mitigating Challenges while using NLP and
Generative AI [2.9189409618561966]
Book chapter explores the evolving landscape of Software Engineering in general, and Requirements Engineering (RE) in particular.
We discuss challenges that arise while integrating Natural Language Processing (NLP) and generative AI into enterprise-critical software systems.
Book provides practical insights, solutions, and examples to equip readers with the knowledge and tools necessary.
arXiv Detail & Related papers (2024-02-26T19:19:47Z) - The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains.
This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
arXiv Detail & Related papers (2023-12-01T16:00:25Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Machine Unlearning: A Survey [56.79152190680552]
A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning.
This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality.
No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios.
The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
arXiv Detail & Related papers (2023-06-06T10:18:36Z) - A survey of Generative AI Applications [0.0]
We present a comprehensive survey of more than 350 generative AI applications.
The survey is organized into sections, covering a wide range of unimodal generative AI applications.
arXiv Detail & Related papers (2023-06-05T11:14:18Z) - Foundations and Recent Trends in Multimodal Machine Learning:
Principles, Challenges, and Open Questions [68.6358773622615]
This paper provides an overview of the computational and theoretical foundations of multimodal machine learning.
We propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification.
Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches.
arXiv Detail & Related papers (2022-09-07T19:21:19Z) - Machine Knowledge: Creation and Curation of Comprehensive Knowledge
Bases [28.856786775318486]
Large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources.
This article surveys fundamental concepts and practical methods for creating and large knowledge bases.
arXiv Detail & Related papers (2020-09-24T09:28:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.