Artificial Intelligence (AI) is becoming a part of everyday life and consequently becoming one of the fastest growing industries globally. The AI has enormous potential for the growth and economic impact. per a recent statistics compiled in the Forbes Advisor report, the global AI market size, which was valued at $136.55 billion in 2022, is projected to reach $1,811.8 billion by 2030 with an estimated compound annual growth rate (CAGR) of 37.3%. Based on the PWC-Sizing the Price data, the estimated contribution of AI to the global economy is likely to touch $15.7 trillion by 2030, which would be more than the combined current output of China and India. There are numerous factors that have triggered exponential growth of AI. However, at the same time various ethical and legal concerns are also being raised about both the short-term and long-term impacts of AI on society and economy, particularly in view of the evolving generative AI applications.
The development and adaptation of generative AI in industries is motivated by a number of factors including increasing efficiency, reducing operational costs and enhanced customer experiences. It is believed and tested that application of AI in automation and optimization of routine business processes can lead to significant benefits in terms saving time and money and thus, increasing productivity and profits. It can also help in improving speed of operations and avoiding human errors. However, despite these benefits, looking at the fast pace of generative AI growth leads to some fundamental questions that need urgent attention and collective efforts of the stakeholders.
The generative AI refers to the algorithms which has power to create new contents in form of texts, audio, video, images simulations or any combination of these by continuously building on the size and use of the existing user data. That implies that like human brain, the generative AI applications can interpret and analyze the existing data sets to produce new content as per the requirements. However, the limitation of the generative AI is rooted in two fundamental issues: first, it works on the basis of the database provided initially and learn continuously from the data generated by the use pattern; and second, it can not completely replace the processes where larger ethical considerations are involved which require human brain to understand and analyze the issues with emotion and feelings. On the one side, the AI enthusiasts are making claims to completely replace the most of the human driven functions by AI over a period of time while on the other hands most of the recent research indicating that businesses can attain the better and optimized performances when both machine and humans are working together.
Some of the most popular generative AI applications today include: ChatGPT (generative pretrained transformer) for text generation; DALL.E which can connect words to images and generate images based on the prompts; and Bard, which is a chatbot developed by Google. These applications can be used for a variety of functions at faster speed including image generation and manipulation, audio/video creation and manipulation, text generation and summarization to name a few.
In industrial domains, where data continually pours in from machinery, sensors, and day-to-day operations,‘AI-powered data management’ emerges as a game-changer, offering several compelling advantages:Streamlined Data Integration by acting as a mediator, bridging these systems, ensuring synchronisation, and simplifying data integration to eliminate operational bottlenecks; Enhanced Decision-Making Insights by functioning as an intuitive detective, connecting the dots. Leveraging a deep understanding of the data, it empowers industries to make more informed, thoughtful decisions. It is not just about having information; it is about gaining actionable insights; Precision in Predictive Maintenancebyensuring the smooth functioning of machinery and equipment is paramount. AI-powered data management analyses data patterns and predicts maintenance needs, reducing unplanned downtime and resource wastage; Optimising Operational Excellenceby acting as a GPS for processes, guiding them along the most efficient routes, cutting costs, and maximising productivity; Contextual Search Understanding by taking search to the next level, interpreting context and relationships; Environmental Impact Reductionby analysing resource consumption and emissions data, helping industries identify opportunities to reduce their environmental footprint; Product Quality Enhancementby ensuring consistency and adherence to quality standards; Resource Allocation Optimisationby analysing resource utilisation patterns and helping industries allocate resources efficiently, directing manpower, materials, and equipment where they are needed most, reducing waste and costs; Market Trend Analysis through real-time processing and analysis of market data; and Customised Product Developmenton the basis of customer data and feedback.
On the other side the increasing AI applications are also leading to various ethical and legal challenges. For example; the use of AI generated fake or manipulated contents on social media has been often identified as one of the major causes for creating distrust and affecting harmony. The use of generative AI in educational settings is challenging the conventional examinational and assessment systems in the institutions and universities across the world. The various scams and campaigns based on the misinformation has been further fuelled by use of some of the generative AI applications. The increasing number of court cases related to violation of copyright and patents are alarming the stakeholders in the legal system across the world. The increasing number of lawsuits against the leading companies are indicating the bigger challenges caused by the fast adaptation of the generative AI. For example; a law suit filed against Google in 2023 alleges the misuse of personal information and copyright violations with estimated impact of at least $5 billion.
The emerging challenges from use of generative AI indicates that multi stakeholder driven collective action is required at different levels ranging from national level policies and regulations for use and protection of data to company or institution level policies and guidelines. No doubt that with the increasing technological advancements, the generative AI will continue to grow in terms of reach and impact but the bottom-line is are we prepared to protect and safeguard the legitimate rights and interests of individuals?
Dr Farshad Badie Vice-Dean of the Faculty of Computer Science and InformaticsBerlin School of Business and Innovation
Dr. Shiv K. Tripathi Vice Chancellor Atmiya University, India
