The Choice Generative Artificial Intelligence Challenges And

Home » artificial-intelligence-technologies » Generative AI – Opportunities and Challenges Although artificial intelligence originated in academic research in the 1950s, only recently has it captured the imagination of the general public. This has everything to do with the release of ChatGPT, putting a powerful generative AI tool in the hands of individual consumers. But what are the opportunities it brings to businesses? And what are the challenges we face in using it? To answer these questions, Avasant Senior Partner Frank Scavo recently conducted a video interview with Harbor Rock Wealth Management on this subject.

In this free-flowing conversation, the group discussed: Avasant’s research and other publications are based on information from the best available sources and Avasant’s independent assessment and analysis at the time of publication. Avasant takes no responsibility and assumes no liability for any error/omission or the accuracy of information contained in its research publications. Avasant does not endorse any provider, product or service described in its RadarView™ publications or any other research publications that it makes available to its users, and does not advise users to select only... Avasant disclaims all warranties, expressed or implied, including any warranties of merchantability or fitness for a particular purpose. None of the graphics, descriptions, research, excerpts, samples or any other content provided in the report(s) or any of its research publications may be reprinted, reproduced, redistributed or used for any external commercial purpose...

All rights are reserved by Avasant, LLC. © Copyright 2025 – Avasant and affiliated companies Artificial Intelligence (AI) refers to computer systems designed to perform tasks that typically require human intelligence such as recognizing patterns, making predictions, automate tasks or analyzing information [1]. Traditional AI models often use structured, labeled data to support tasks like classification, forecasting, or automated decision-making [1, 2]. We already encounter AI in everyday tools such as recommendation engines, search engines, grammar checkers, navigation apps, and fraud detection systems [2, 3]. AI literacy is now as important as digital literacy.

Generative AI is a branch of AI that creates new content – text, images, code, audio, or data by learning patterns from large datasets [5]. Unlike traditional AI, which mainly analyzes information, generative AI can draft essays, summarize readings, create images, simulate data, or help brainstorm ideas. Generative AI systems are built using methods such as: A key insight is that generative AI can produce convincing content without truly understanding it, commonly known as the “generative AI paradox” [8]. By clicking Continue, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. By clicking Agree & Join or Continue, you agree to the LinkedIn User Agreement, Privacy Policy, and Cookie Policy.

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Get In Touch For Details! Request More Information A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. You have full access to this open access article Generative Artificial Intelligence (Gen-AI) is a new advancement that has revolutionized the concepts of Natural Language Processing (NLP) and Large Language Model (LLM).

This change impacts various aspects of life, stimulating industry, education, and healthcare progression. This survey presents the potential applications of Gen-AI across various sectors, highlighting the risks and opportunities. Some of the most pressing challenges include ethical consideration, the rise of disinformation (including deepfakes), concerns over Intellectual Property (IP) rights, cybersecurity risks, bias and discrimination. The survey also covers the fundamental models of Gen-AI, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers. These frameworks are extremely important in various sectors, including medical imaging, drug discovery, and personalized medicine, and offer valuable insights into the future of technological advancements in the scientific community. The study contributes substantially by exploring positive elements and addressing the challenges of adequately deploying Gen-AI models.

Using these insights, we hope to provide a comprehensive knowledge of the potential challenges and complexities associated with the widespread implementation of artificial intelligence technologies. Avoid common mistakes on your manuscript. Artificial Intelligence (AI) [1, 2] is a rapidly expanding domain of computer science that deals with all aspects of emulating cognitive functions to solve problems in the real world and develop computers that can... Being considered the oldest field of computer research , it is commonly referred to as machine intelligence [4] to differentiate it from human intelligence [5]. According to Tenenbaum et al. [6], the field centered on cognitive and computer science.

AI is currently receiving great attention because of the achievements made in Machine Learning (ML). Throughout the history of AI, there has always been a solid connection to explainability. In 1958, McCarthy’s Advice Taker, described it is a “program with common sense” [7]. Common sense reasoning abilities were possibly being proposed for the first time as the cornerstone of AI. Rather than only focusing on solving pattern recognition problems, artificial intelligence systems should be able to construct causal models of the world that assist explanation and comprehension, according to recent research [8]. The consistent update of AI technology has led to the introduction of new advanced LLMs models such as GPT, PaLM, and Llama [2].

These models fall under the category of Gen-AI, showcasing significant progress in NLP capabilities. These models employ the neural capabilities required for processing labeled, unlabeled, or semi-supervised data via different learning methods. Adopting advanced transformer architectures characterized by encoder-decoder structures empowers LLMs to process different data modalities, including text, visual, and audio information. This versatility highlights how LLMs are key contributors to the ongoing wave of digital transformation [9].

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