Brief Timeline of AI
The roots of artificial intelligence trace back to science fiction short story ‘Runaround’ written by Isaac Asimov in 1942 that detailed the three laws of robotics, planting the seeds of curiosity in machines and artificial intelligence (Haelein & Kaplan, 2019). In the same period, Alan Turing developed the first modern computer in his creation of ‘the bombe’ (Haelein & Kaplan, 2019), a machine that broke the enigma code in WWII and proposed the Turing test, sparking the conversation debating if machines had intelligence. In the 1960s, Natural Language Processing tools and Rule-based systems were the focus of development with the production of computer programs, birthing ‘ELIZA’ written by Joseph Weizenbaum to stimulate conversation (Berry, 2023). The 1960’s brought forth progress in knowledge based systems, also known as expert systems, consisting of a knowledge base of facts and rules and an inference engine which determined the usability of the information (Voiron-Canicio & Gilles Voiron, 2020). The current developments of artificial intelligence encompasses data driven AI, where technology learns from data to produce outcomes. This includes deep learning and generative AI that we see today.
Key Technologies of AI
Deep learning
A subset of machine learning, deep learning draws inspiration from the human brain to simulate the activity of neural networks. Similar to how humans learn through repetition, deep learning performs the task over and over, with adjustments each time in order to improve the outcome (Sarker, 2021). Deep learning uses artificial neural networks to ‘learn’ from large amounts of data, allowing computers to solve complexities that are harder for humans to understand (Sarker, 2021). Current technologies that use deep learning include chatbots and virtual assistants, translations and advanced technologies in the field of medicine and pharmaceuticals.
Natural Learning Processing (NLP)
Using the foundations of machine learning, NLPs are able to comprehend and communicate with human language (Nadkarni et al., 2011). NLPs work through computational linguistics and statistical modelling to produce a specific subset of rule based systems. This is what allows your device to understand text and speech commands in search engines, GPS systems, digital assistants and chatbots. NLPs provide the building blocks for generative AI to give human-like responses. In the world of business and enterprises, NLP can help simplify and automate business processes as well as raise employee productivity, introducing service bots to reduce employee workloads (Holdsworth, 2024).
Computer Vision
Computer vision refers to digital systems that feed from visual information. All the visual data from digital images and videos is processed in a way that the human eye cannot (Szeliski, 2020). Computer vision operates through cameras, databases and algorithms to classify, identify and track objects. Similar to human behaviors, computer visions rely on pattern recognition to gain information (Szeliski, 2020). Current applications of this technology are used in facial recognition biometrics on your phone and was also implemented in the Paris 2024 Olympics to track athletes on the field.
Large Language Model (LLMs)
As a generative AI model using deep learning algorithms, LLMs are the current star in the field with applications in chatbots and content creation. Seen in creations such as ChatGPT and Character.AI, LLMs are capable of generating large chunks of text using large quantities of parameters and training data (Hadi et al., 2023). LLMs work with predictability, providing probable results for given prompts (Hadi et al., 2023). The range of uses are wide, capable of generating stories, scripts, transcripts, songs and more, making it a useful and interesting tool in today’s world.
Economics of AI: Parallels with the Internet Revolution
Artificial Intelligence (AI) is reshaping the way economies and businesses function in a manner similar to the internet’s transformative impact from a few decades ago. Just as the internet revolutionised communication, commerce, and innovation, AI is now driving economic shifts by lowering costs, improving efficiency, and creating entirely new markets (Wamba-Taguimdje et al., 2020). Both technologies serve as catalysts for profound changes across industries, from finance to healthcare to manufacturing, influencing how organisations operate and thrive in a global marketplace.
The number of AI users has increased rapidly in recent years. By 2024, AI tools were being used by more than 314 million people globally, nearly tripling from just a few years earlier in 2020 (Statista, 2024). Projections suggest that by 2030, more than 700 million people will be using AI tools regularly (Statista, 2024).
This rapid increase in AI users closely mirrors the internet’s early growth. The internet went from just over 1 billion users in 2005 to more than 5.4 billion users worldwide by 2023 (Statista, 2024). Both AI and the internet have demonstrated their potential to quickly capture global audiences quickly, with their far-reaching applications across multiple industries.
Market Size
AI’s growth is not just about the increasing number of users – its economic impact is also expanding at an impressive pace. In 2023, the AI market was valued at around $538.1 billion, and it is expected to skyrocket to $2.575 trillion by 2032, with a compound annual growth rate (CAGR) of 19% (Statista, 2024). In comparison, the Internet of Things (IoT), which is a critical part of the digital economy, was valued at $848 billion in 2023 (Statista, 2024). It is forecasted to grow to $1.56 trillion by 2029, with a CAGR of 13% (Statista, 2024).
As AI technologies become increasingly integrated into the core functions of organisations, its economic impact will only grow, potentially outpacing other digital technologies (Gonzales, 2023).
Contribution to GDP
AI’s ability to boost economic growth is often compared to the internet’s impact in its early days. By 2030, AI is expected to drive global GDP up by as much as 16%, due to its broad applications in sectors like healthcare, finance, and manufacturing (Statista, 2024). In particular, China is projected to experience a 26.1% increase in GDP due to AI, adding an impressive US$7 trillion to its economy (Statista, 2024).
For a comparison, the digital economy, which is heavily shaped by the internet, played a significant role in boosting the U.S. economy. In 2021, the digital economy contributed $3.7 trillion to the U.S. gross GDP, accounting for 10.3% of the nation’s total GDP (Highfill & Surfield, 2022). The growth of the digital economy was also significant, with its real GDP increasing by 9.8% from 2020 to 2021, far outpacing the overall economy’s growth of 5.9% during the same period (Highfill & Surfield, 2022).
The Future Implications of AI
The AI space is rapidly growing, with advancements in underlying technologies contributing to the excellence and popularity of AI today. AI technology holds incredible implications across various sectors, including but not limited to medicine, manufacturing, transport, and agriculture (von Braun et al., 2021). On a more personal scale, AI has had a profound impact on our own lives, becoming an extremely useful and accessible tool. As the technology continues to improve, its reach will extend even further, becoming more integrated within our daily lives. Despite the amazing advancements that AI has achieved, others have raised concerns regarding the misuse and consumption of AI (Qian et al., 2024). To manage these concerns and drive technological development in the right direction, recommendations to collaborate on public policies pertaining to AI have become a recurring theme (McKinsey & Company, 2023). The challenges that policymakers will face, however, is the rapid development of the technology itself, which provides complexity and uncertainty in the navigation and development of public policy and the further societal impact AI will have.
The Societal Impacts
A clear societal impact that AI is already having is on the job market. McKinsey & Company (2023) estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across various use cases. This significant economic potential also brings concerns regarding job displacement. While AI is likely to create new job opportunities, it may also automate certain tasks, potentially leading to unemployment in some sectors. The impact is expected to be most pronounced in knowledge work, particularly in activities involving decision-making and collaboration (McKinsey & Company, 2023).
Another significant societal impact is the potential for AI to exacerbate existing inequalities. As AI systems become more prevalent in decision-making processes, there is a risk that biases present in training data could lead to discriminatory outcomes in areas such as hiring, lending, and criminal justice (Qian et al., 2024). This underscores the need for careful consideration of fairness and equity in AI development and implementation.
The Ethical Considerations
The rapid advancement of AI technology has brought forth a myriad of ethical considerations. One primary concern is the potential for AI to be used in ways that infringe upon individual privacy. As AI systems become more sophisticated in processing and analysing personal data, there is a growing need to establish robust safeguards to protect individuals’ privacy rights (Bryson, 2023).
Another ethical concern is the question of accountability. As AI systems become more autonomous in their decision-making, it becomes increasingly challenging to determine who is responsible when these systems make errors or cause harm. This issue is particularly pertinent in high-stakes domains such as healthcare and autonomous vehicles (United States Senate Select Committee on Intelligence, 2024).
The potential for AI to be used in the creation and dissemination of misinformation is another significant ethical concern. Advanced language models can generate highly convincing fake content, which could be used to manipulate public opinion or interfere with democratic processes (Qian et al., 2024).
Advocacy for AI Governance
Given these societal impacts and ethical considerations, there is a growing call for robust AI governance frameworks. Bryson (2023) argues for the importance of human-centered AI regulation, emphasizing the need to prioritize human rights and societal values in AI development and deployment. This includes ensuring transparency in AI systems, protecting privacy, and maintaining human oversight in critical decision-making processes.
The United States Senate Select Committee on Intelligence (2024) has highlighted the national security implications of AI, advocating for policies that promote responsible AI development while maintaining the country’s competitive edge in this technology. They emphasize the need for collaboration between government, industry, and academia to address the challenges posed by AI.
At a global level, there is recognition of the need for international cooperation in AI governance. Given the borderless nature of AI technology, coordinated efforts are necessary to establish global standards and norms for AI development and use (Qian et al., 2024).
As AI continues to evolve and integrate into various aspects of our lives, it is clear that its implications are far-reaching and complex. While the technology offers immense potential for economic growth and societal advancement, it also presents significant challenges that need to be addressed. Moving forward, it will be crucial to foster ongoing dialogue between technologists, policymakers, ethicists, and the public to ensure that AI development aligns with societal values and contributes positively to human welfare. The future of AI governance will require adaptability, foresight, and a commitment to ethical principles to harness the benefits of this transformative technology while mitigating its risks.
Berry, D. M. (2023). The Limits of Computation: Joseph Weizenbaum and the ELIZA Chatbot. Weizenbaum Journal of the Digital Society, 3(3). https://doi.org/10.34669/WI.WJDS/3.3.2
Bryson, J. J. (2023). Human Experience and AI Regulation. Weizenbaum Journal of the Digital Society, 3(3). https://doi.org/10.34669/WI.WJDS/3.3.8
Gonzales, J. T. (2023). Implications of AI innovation on economic growth: A panel data study. Journal of Economic Structures, 12(13). https://doi.org/10.1186/s40008-023-00307-w
Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
Hadi, M. U., Tashi, Q. A., Qureshi, R., Shah, A., Muneer, A., Irfan, M., Zafar, A., Shaikh, M. B., Akhtar, N., Wu, J., & Mirjalili, S. (2023, July 10). A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage. Www.techrxiv.org. https://doi.org/10.36227/techrxiv.23589741.v1
Highfill, T., & Surfield, C. (2022). New and revised statistics of the U.S. digital economy, 2005–2021. U.S. Bureau of Economic Analysis. https://www.bea.gov/system/files/2022-11/new-and-revised-statistics-of-the-us-digital-economy-2005-2021.pdf
Holdsworth, J. (2024, June 6). What Is Natural Language Processing? IBM. https://www.ibm.com/topics/natural-language-processing
Marr, B. (2018, October 1). What Is Deep Learning AI? A Simple Guide With 8 Practical Examples. Forbes. https://www.forbes.com/sites/bernardmarr/2018/10/01/what-is-deep-learning-ai-a-simple-guide-with-8-practical-examples/
McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011). Natural language processing: an introduction. Journal of the American Medical Informatics Association, 18(5), 544–551. https://doi.org/10.1136/amiajnl-2011-000464
Omega: New Technology for Paris 2024 – Swatch Group. (2024). Swatchgroup.com. https://www.swatchgroup.com/en/swatch-group/innovation-powerhouse/internet-things/omega-new-technology-paris-2024
Qian, Y., Siau, K. L., & Nah, F. F. (2024). Societal impacts of artificial intelligence: Ethical, legal, and governance issues. Societal Impacts, 3, 100040. https://doi.org/10.1016/j.socimp.2024.100040
Raghu Bongula. (2023, July 17). Embracing Data-Driven AI And Machine Learning For Efficiency. Forbes. https://www.forbes.com/councils/forbestechcouncil/2023/07/17/embracing-data-driven-ai-and-machine-learning-for-efficiency/
Renze, M. (2020, April 1). The History of AI. Matthewrenze. https://matthewrenze.com/articles/the-history-of-ai/
Sarker, I. H. (2021). Deep Learning: a Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science, 2(6). Springer. https://doi.org/10.1007/s42979-021-00815-1
Statista. (2024, February 13). AI tool user numbers worldwide from 2020 to 2030 [Graph]. Statista. https://www-statista-com.eu1.proxy.openathens.net/forecasts/1449844/ai-tool-users-worldwide
Statista. (2024, April). Artificial Intelligence: In-depth market analysis [Report]. Statista. https://www-statista-com.eu1.proxy.openathens.net/study/138971/insights-compass-2023-unleashing-artificial-intelligences-true-potential/
Statista. (2024, June). Internet of Things: market data & analysis [Report]. Statista. https://www-statista-com.eu1.proxy.openathens.net/study/109197/internet-of-things-market-outlook-report/
Statista. (2024, May 22). Number of internet users worldwide from 2005 to 2023 [Graph]. Statista. https://www-statista-com.eu1.proxy.openathens.net/statistics/273018/number-of-internet-users-worldwide/
Szeliski, R. (2020). COMPUTER VISION : algorithms and applications. Springer Nature.
United States Senate Select Committee on Intelligence. (2024). Open hearing: Advancing intelligence in the era of artificial intelligence: Addressing the national security implications of AI: Hearing before the Select Committee on Intelligence of the United States Senate, One Hundred Eighteenth Congress, first session, September 19, 2023.
Voiron-Canicio, C., & Gilles Voiron. (2020). Assessing the territorial adoption potential of electric mobility: geoprospective and scenarios. Elsevier EBooks, 163–187. https://doi.org/10.1016/b978-0-12-818215-4.00006-7
von Braun, J., Archer, M. S., Reichberg, G. M., & Sánchez Sorondo, M. (2021). Robotics, AI, and humanity: Science, ethics, and policy. Springer.
Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411
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