 Название: Democratization of Artificial Intelligence for the Future of Humanity
Автор: Chandrasekar Vuppalapati
Издательство: CRC Press
Год: 2021
Страниц: 388
Язык: английский
Формат: pdf (true)
Размер: 23.1 MB
Artificial Intelligence (AI) stands out as a transformational technology of the digital age. Its practical applications are growing very rapidly. One of the chief reasons AI applications are attaining prominence, is in its design to learn continuously, from real-world use and experience, and its capability to improve its performance. It is no wonder that the applications of AI span from complex high-technology equipment manufacturing to personalized exclusive recommendations to end-users. Many deployments of AI software, given its continuous learning need, require computation platforms that are resource intense, and have sustained connectivity and perpetual power through central electrical grid.
In order to harvest the benefits of AI revolution to all of humanity, traditional AI software development paradigms must be upgraded to function effectively in environments that have resource constraints, small form factor computational devices with limited power, devices with intermittent or no connectivity and/or powered by non-perpetual source or battery power.
The aim this book is to prepare current and future software engineering teams with the skills and tools to fully utilize AI capabilities in resource-constrained devices. The book introduces essential AI concepts from the perspectives of full-scale software development with emphasis on creating niche Blue Ocean small form factored computational environment products.
It also:
- Outlines AI software architecture & Cloud architecture with emphasis on Edge Computing
- Provides comprehensive comparison and applicability of AI algorithms in constrained environments: Supervised and unsupervised.
- Emphasizes real-time embedded storages for AI applications, specifically operating in constrained environments.
- Develops AI driver software code with real-time Deep Learning on small footprint frameworks such as TensorFlow, Python, C and Android
- Provides exclusive examples of AI field deployments that operate in remote & non-connected environments
- Explains AI solution development from a product management perspective.
Contents:
SECTION I - INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND FRAMEWORKS
1. Introduction
2. Standard Processes and Frameworks
SECTION II - DATA SOURCES AND ENGINEERING TOOLS
3. Data – Call for Democratization
4. Machine Learning Frameworks and Device Engineering
5. Device Software and Hardware Engineering Tools
Machine Learning Tools
Anaconda
Jupyter Notebook
Spyder
Android Studio
Google Colaboratory
Microsoft Azure Machine Learning
Azure Databricks
TensorFlow
SECTION III - MODEL DEVELOPMENT AND DEPLOYMENT
6. Supervised Models
7. Unsupervised Models
SECTION IV - DEMOCRATIZATION AND FUTURE OF AI
8. National Strategies
9.Future
Appendix
Index
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