1. Introduction
NLP has become an important branch of the AI area with rapid AI development. Then GPT comes naturally and becomes the pioneer first. Though a depth learning model, GPT shows a powerful ability in text generation, language understanding, and human-machine interaction, especially transformer architectures. And it has achieved many milestones in the area of language modeling. Despite remarkable achievements, the potential and challenges of GPT in commercial applications still need to be explored. Current research focuses on how effectively to transform the technology into practical applications, drive innovation in business models, and explore its broad impact on socio-economic dynamics.
The integration of artificial intelligence (AI) and natural language processing (NLP) into business practices has brought big changes to many industries. It has made business operations more efficient and helped companies make better decisions. Bahja and Just did some research and found that NLP is important for better communication and finding new ways of doing things in areas like health, education, and early stages of innovation [1,2]. Huang did some more research and found that NLP is also important for marketing strategies in big companies. It helps them understand the market better so they can make good plans for their business, both for the whole world and for their own country [3].
In the financial world, Sirignano and Cont did some work to see how well AI can predict stock prices. Gonçalves and his team showed that a special kind of model called CNN is very good at predicting what will happen in the stock market [4,5]. This shows that AI is crucial for making financial plans and managing risks.
Di Vaio and others talked about how AI can help create business models that are good for the environment. They said that knowledge management systems are important for making sure that AI fits well with business activities [6]. In the healthcare business, Kulkov discovered that AI is used by new companies to make better business plans. These plans help make things run more smoothly and help patients get better care [7]. Garbuio and Lin also said that AI can make it easier for patients to work with the healthcare system [8]. In the industrial sector, Burström, Weber, and Åström did some work to see how AI is used in factories to change business models. They found that AI can help improve traditional business methods [9-11].
Finally, Watanabe and Uchihira said that NLP is important for analyzing a lot of text data quickly. This is important for making new business plans [12]. All of this research shows how AI and NLP help businesses fit into different industries. They help businesses work better and can help make new ways of doing business in today’s world.
2. Features and Advantages of GPT
OpenAI has developed a generated pre-training variable (GPT) that opens a new era in natural language processing (NLP) and artificial intelligence (AI). It is based on a transformer architecture that forms the basis of many AI-driven applications.
2.1. Innovative Architecture
The ability to evaluate every sentence dynamically makes it easier to understand the context, of course ranging from simple talk to reading complex legal documents. This system structure not only resolves operational efficiency problems in business applications but also can respond quickly and accurately in real-time interactions among industries.
2.2. Comprehensive Pre-training
GPT adapts to different language logics and commercial areas by focusing solely on extensive pre-training across different datasets, without the need for additional training. This ability is prominent in expanding AI applications all around the world because it was integrated into the global market and improved the accessibility and utility of localized environments.
2.3. Expandability and Flexibility
GPT’s design has outstanding expandability and flexibility, which is convenient for users who need AI for handling all kinds of tasks but don't want to spend a lot of energy and time. The expandability not only makes it easier to integrate with existing systems but also lays the foundation for AI innovation in the strategies of enterprises.
2.4. Advanced Language Comprehension and Generation
GPT's advanced language ability not only promotes the creation of new information but also enhances interactive customer service tools, especially in commercial applications. Furthermore, GPT narrows the gap between AI and actual business, promotes communication, and increases efficiency by understanding and generating assembled human-written content.
3. Features and Advantages of GPT
3.1. Product Development and Pricing Models
The integration of generated pre-training transformers (GPT) into product development across industries creates innovative solutions that improve operational efficiency and user engagement. This section outlines key products developed using GPT technology and the corresponding pricing models that promote their commercial feasibility.
3.1.1. Chatbot Solutions for Customer Service
Chatbots based on GPT are changing customer service in telecommunications, financial services, and retail industries. These chatbots handle a wide range of interactions, from solving common problems to recommending products. For example, telecom companies deploy GPT-based chat bots to effectively manage customer queries, significantly improving customer satisfaction while reducing the workload of staff representatives.
3.1.2. Content Generation Tools for Media
In the media industry, GPT helps publishers and organizations by automating content creation from press releases to social media posts. This automation helps to maintain a stable content stream, which is vital in a fast-paced media environment. The use of GPT significantly reduces production time and cost, allowing creative teams to focus on strategic tasks such as content planning. Many organizations choose these tools for subscription-based pricing, providing monthly or annual plans to provide continuous access to content-generated GPT features.
3.1.3. Legal and Financial Document Automation
In legal and financial settings, GPT technology simplifies the handling of complex documents. Legal technology companies use GPT automated contract review and risk assessment to improve the speed and accuracy of legal transactions. Financial institutions also use the GPT to effectively process loan applications and investment documents. Customized enterprise solutions are common in these areas, pricing based on the complexity and scope of deployment and customized to specific regulatory and operational needs.
3.1.4. Pricing Flexibility
The versatility of GPT applications allows for flexible pricing strategies to meet different business needs. Usage-based pricing is popular in services that require frequent GPT interactions, and it provides a cost-effective solution for businesses with variable usage patterns. The subscription service is suitable for users who need regular access to GPT features, ensuring predictable costs and stable service delivery. At the same time, custom enterprise solutions address the specific challenges of large customers and are priced according to the complexity of the project and the level of customization.
3.2. Investment and Financing
All kinds of financing strategies provide powerful support to the development of GPT, from venture capital to strategic acquisition. This movement plays an important role in improving the growth and integration of cross-industry GPT applications.
3.2.1. Venture Capital in AI Startups: OpenAI and Microsoft Partnership
In 2019, OpenAI established an important partnership with Microsoft, with an investment of $1 billion. The core of the partnership is the exclusive licensing of parts of the OpenAI technology to Microsoft, thereby enhancing Azure’s cloud computing capabilities. This investment is crucial to driving the development of OpenAI as well as providing powerful computing resources for OpenAI. This strategic alliance has accelerated the adoption of the GPT model in enterprise applications, promoting innovation in Microsoft’s AI solutions and services.
3.2.2. Collaborative Development and Research: Hugging Face and BigScience
Hugging Face, along with the Big Science Research Project, has launched a big project involving more than 500 researchers from around the world. Their goal is to create a large-scale, multilingual model based on the GPT-3 with the help of a technology company that provides computing resources. The project aims to encourage the creation of language models and address major issues such as how to use AI in an ethical way and get rid of prejudice. The collaborative nature of the joint venture helps AI develop in a more open and inclusive way, setting new standards for future research projects involving public-private collaboration.
3.2.3. Strategic Acquisitions: Apple's Acquisition of Xnor.ai
In the beginning of 2020, Apple picked up Xnor.ai, a Seattle-based firm that creates AI tools that are super energy-efficient and run on the edges of devices. The cost of this purchase was about 200 million dollars. This purchase shows that Apple is very interested in improving their hardware products’ AI capabilities. Xnor’s tech can run AI programs on smartphones, smart home gadgets, and wearable things without needing much power or sending lots of data to the internet. Apple wants to put Xnor’s AI and machine learning code into their own gadgets to make them more private by keeping data on the device and not sending it to the internet.
4. Future Prospects
With the continuous development of GPT, it was predicted that the future landscape of AI will be formed by several existing vital developments. These developments will expand AI’s power and influence in every industry.
4.1. Product Development and Pricing Models
These systems can integrate and interpret data from multiple sources, such as text, images, and audio, because the next iteration of GPT is expected to include stronger multi-modal capabilities. For instance, Google and Adobe are exploring using AI to create something new in the marketing and creative industries. These behaviors mean AI can not only understand language but also grasp the backgrounds and subtleties of different media. Thus, AI must be able to facilitate a richer global interaction.
4.2. Product Development and Pricing Models
Progress in ethical AI and collaborative tools is beginning to take shape, as seen in projects like IBM’s Watson, and Wilson is already engaged in ethical AI research. The main point of these subjects is to develop AI systems that can make ethical decisions when put into the health care and finance areas. At the same time, enhanced tools for AI collaboration with humans are being developed to support the creative processes in design and engineering, indicating that future versions of GPT will become more direct partners in a workflow centered on people.
4.3. Product Development and Pricing Models
Governments all around the world begin to respond to the rapid development of AI. For instance, the EU proposed AI regulation in order to set global standards for AI development, with a main point on transparency, accountability, and privacy rights. This regulatory method will play an important role in deploying AIs like GPT, ensuring they are aligned with societal values and economic goals. This also includes addressing potential economic disruptions like job migration and industry transformation by preparing the workforce for the new roles that AI will create and support.
5. Conclusion
This paper researches GPT how to make influence in different areas. Because GPT models are created and trained before they are used, they are better at writing, understanding language, and can interact with people fluently. Due to these changes, it is now easier to make strategic choices in many areas, such as media, telecommunications, and law. This makes business processes more efficient. The study found that simply integrating GPT into many workplace environments can boost innovation and productivity by dealing with challenging tasks and creating interesting, enriching customer experiences. The study also demonstrates the need to develop ethical guidelines and government supervision as GPT technology becomes increasingly widespread. This is to ensure that their use is beneficial to society without jeopardizing security or privacy. As the GPT expands, new growth and innovation opportunities may emerge in the future. As AI-driven models become better at dealing with multiple data and social issues, they are likely to play an important role in how future growth will be. To realize its full potential in a rapidly changing digital world, the GPT will require constant change, which will require not only new technologies but also compliance with rules and ethics.
References
[1]. Bahja M. Natural language processing applications in business[J]. E-Business-higher education and intelligence applications, 2020.
[2]. Just J. Natural language processing for innovation search–Reviewing an emerging non-human innovation intermediary[J]. Technovation, 2024, 129: 102883.
[3]. Huang S. Exploring the Influence of Natural Language Processing Technology on Marketing Strategy Innovation Management in Emerging Markets for Multinational Corporations[J].
[4]. Sirignano J, Cont R. Universal features of price formation in financial markets: perspectives from deep learning[M]//Machine learning and AI in finance. Routledge, 2021: 5-15.
[5]. Gonçalves R, Ribeiro V M, Pereira F L, et al. Deep learning in exchange markets[J]. Information Economics and Policy, 2019, 47: 38-51.
[6]. Di Vaio A, Palladino R, Hassan R, et al. Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review[J]. Journal of Business Research, 2020, 121: 283-314.
[7]. Kulkov I. Next-generation business models for artificial intelligence start-ups in the healthcare industry[J]. International Journal of Entrepreneurial Behavior & Research, 2023, 29(4): 860-885.
[8]. Garbuio M, Lin N. Artificial intelligence as a growth engine for health care startups: Emerging business models[J]. California Management Review, 2019, 61(2): 59-83.
[9]. Burström T, Parida V, Lahti T, et al. AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research[J]. Journal of Business Research, 2021, 127: 85-95.
[10]. Weber M, Beutter M, Weking J, et al. AI startup business models: Key characteristics and directions for entrepreneurship research[J]. Business & Information Systems Engineering, 2022, 64(1): 91-109.
[11]. Åström J, Reim W, Parida V. Value creation and value capture for AI business model innovation: a three-phase process framework[J]. Review of Managerial Science, 2022, 16(7): 2111-2133.
[12]. Watanabe M, Uchihira N. Digital Business Model Analysis Using a Large Language Model[J]. arXiv preprint arXiv:2406.05741, 2024.
Cite this article
Chen,B. (2024). The Impact and Future of Generative Pre-trained Transformers: A Study of GPT in Enhancing Business and Technology. Advances in Economics, Management and Political Sciences,114,215-220.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Bahja M. Natural language processing applications in business[J]. E-Business-higher education and intelligence applications, 2020.
[2]. Just J. Natural language processing for innovation search–Reviewing an emerging non-human innovation intermediary[J]. Technovation, 2024, 129: 102883.
[3]. Huang S. Exploring the Influence of Natural Language Processing Technology on Marketing Strategy Innovation Management in Emerging Markets for Multinational Corporations[J].
[4]. Sirignano J, Cont R. Universal features of price formation in financial markets: perspectives from deep learning[M]//Machine learning and AI in finance. Routledge, 2021: 5-15.
[5]. Gonçalves R, Ribeiro V M, Pereira F L, et al. Deep learning in exchange markets[J]. Information Economics and Policy, 2019, 47: 38-51.
[6]. Di Vaio A, Palladino R, Hassan R, et al. Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review[J]. Journal of Business Research, 2020, 121: 283-314.
[7]. Kulkov I. Next-generation business models for artificial intelligence start-ups in the healthcare industry[J]. International Journal of Entrepreneurial Behavior & Research, 2023, 29(4): 860-885.
[8]. Garbuio M, Lin N. Artificial intelligence as a growth engine for health care startups: Emerging business models[J]. California Management Review, 2019, 61(2): 59-83.
[9]. Burström T, Parida V, Lahti T, et al. AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research[J]. Journal of Business Research, 2021, 127: 85-95.
[10]. Weber M, Beutter M, Weking J, et al. AI startup business models: Key characteristics and directions for entrepreneurship research[J]. Business & Information Systems Engineering, 2022, 64(1): 91-109.
[11]. Åström J, Reim W, Parida V. Value creation and value capture for AI business model innovation: a three-phase process framework[J]. Review of Managerial Science, 2022, 16(7): 2111-2133.
[12]. Watanabe M, Uchihira N. Digital Business Model Analysis Using a Large Language Model[J]. arXiv preprint arXiv:2406.05741, 2024.