Future Tech Startups and Innovation in the Age of AI

Автор: literator от 17-09-2024, 20:23, Коментариев: 0

Категория: КНИГИ » ПРОГРАММИРОВАНИЕ

Название: Future Tech Startups and Innovation in the Age of AI
Автор: Inam Ullah Khan, Hamed Taherdoost, Mitra Madanchian, Mariya Ouaissa, Salma El Hajjami, Hameedur Rahman
Издательство: CRC Press
Год: 2025
Страниц: 262
Язык: английский
Формат: pdf (true), epub
Размер: 13.7 MB

Our book, Future of Tech Startups and Innovations in the Age of AI, mainly focuses on Artificial Intelligence (AI) tools, AI-based startups, AI-enabled innovations, Autonomous AI Agents (Auto-GPT), AI-based marketing startups, Machine Learning for organizations, AI-internet of things (IoT) for new tech companies, AI-enabled drones for agriculture industry, Machine Learning (ML)/Deep Learning (DL)-based drip farming, AI-based driverless cars, AI-based weather prediction startups, AI tools for personal branding, AI-based teaching, AI-based doctor/hospital startups, AI for game companies, AI-based finance tools, AI for human resource management, AI-powered management tools, AI tools for future pandemics, AI/ML-based transportation companies, AI for media, AI for carrier counseling, AI for customer care, AI for next generation businesses, and many more applications. AI tools and techniques will revolutionize startups all over the world. Entrepreneurs, engineers, and practitioners have already moved toward AI-based solutions to reshape businesses. AI/ML will create possibilities and opportunities for improving human lifestyles. AI-enabled startups will work on cost-effective solutions to solve difficult problems. Recently, many research companies are interested in providing solutions and investing a lot in AI-based startups. AI-driven products will revolutionize the "smart world." AI computing tech companies will help to model human speech recognition systems. Also, AI-based startups will focus on perception and reasoning of autonomous robotic systems. AI/ML-based tech startups will introduce smart online education systems for future pandemics.

More interestingly, people are also moving for online job opportunities and trying to work from home. Future innovation needs closer relations between academia and industry. Therefore, online platforms need to be introduced that will only focus on academia and industry linkage. Future AI tech-based startups will focus more on research and development to introduce novel products to the market. Accordingly, engineers and many other people should be trained on AI tools and techniques to introduce innovative solutions for the smart world. In addition, integration of many new technologies with AI will be made possible. AI with IoT, smart cities, unmanned aerial vehicles (UAVs), wireless sensor networks, software-defined networks, network management, vehicular ad hoc networks, flying ad hoc networks, wireless communication technologies, Machine Learning, Reinforcement Learning, Federated Learning and other mechanisms will introduce new technological products.

The motivation behind the research on prompt engineering stems from the rapid growth and impact of large language models (LLMs), particularly GPT and the significant advancements observed in the field of NLP. These developments have ushered in a new era of possibilities and have captured the attention of the AI industry. LLMs, such as GPT, have revolutionized the way AI systems process and generate natural language. These models, which are trained on a large amount of data, are capable of understanding and generating human-like text that is remarkably fluent and coherent. This has led to exciting applications across various domains, including chatbots, virtual assistants, content generation, and language translation.

The rise of LLMs, combined with the growing significance of NLP, has propelled the AI industry forward and sparked considerable interest among researchers and practitioners. However, despite their impressive capabilities, LLMs often require explicit instructions or input to generate desired outputs. This limitation hampers their flexibility and adaptability in real-world scenarios, where explicit instructions may not always be available or feasible. Prompt engineering offers a solution to this challenge.

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