INTRODUCTION
The recent surge in artificial intelligence (AI) hype, fueled by bold promises from influential figures like Elon Musk, suggests that AI development is advancing at an unstoppable pace. Musk’s projections include fully autonomous Tesla within a few years, AI surpassing human intelligence shortly, and a future dominated by AI-driven robots. Despite these claims, the reality of AI
technology will hit a plateau because of diminishing returns in its development.
SYSTEM VIEW OF GENERATIVE AI EVOLUTION
GENERAL LIMITATION TO GROWTH OF AI
AI operates through deep learning and artificial neural networks that identify patterns in massive datasets, which they use to make predictions or generate new data. Initially, AI systems improve rapidly as they train on larger datasets, enhanced by advances in programming and algorithms. However, the gains from increasing data size are diminishing.
This diminishing return is twofold. First, the value of each new data point decreases as the dataset grows, making it increasingly difficult to find new, applicable patterns or content for training. Second, the computational demand of training on these larger datasets grows exponentially, requiring more power and generating more cost, which could eventually make further advancements difficult if not impractical.
Even high-profile AI models like OpenAI’s ChatGPT4, which used a training dataset 571 times larger than its predecessor, exhibit only marginal improvements and continue to struggle with issues like factual accuracy (removal of hallucinations). Estimates suggest that the next significant leap in AI performance could require an impractically large amount of data and energy, rendering the cost and environmental impact prohibitively high.
Further compounding the issue, even potential energy breakthroughs like nuclear fusion are unlikely to offer a cheap and viable solution soon. Research from the University of Massachusetts Amherst suggests that achieving over 95% accuracy in image recognition, for instance, would cost $100 billion and emit as much carbon as New York City does in a month.
The repercussions of these energy demands extend beyond global environmental impact; they also have significant local consequences. Data centers, the powerhouses behind AI capabilities, necessitate extensive cooling and ventilation, further increasing energy consumption and water use. These facilities emit considerable heat and noise, potentially disrupting local ecosystems and diminishing the quality of life for nearby communities. Often situated in regions where electricity is inexpensive and plentiful, these data centers frequently rely on non-renewable energy sources. Thus, the environmental cost of generative AI is a pressing concern not only for our planet but also for the health and well-being of human and ecological communities globally. (Francesco Federico, The Financial and Environmental Cost of Generative AI, Chronicles of Change, December 2023)
Given these challenges, the AI industry may not achieve the futuristic visions being promoted. Instead, it faces a likely stagnation unless new technologies, such as more efficient AI hardware or innovative architectures requiring less data, can be developed. These technologies are still in the early stages and may take a decade to mature. The excitement around AI’s capabilities and future
potential needs to be tempered with an understanding of the technological and practical limitations currently facing the field.
LIMITS TO GROWTH AND ACCEPTANCE OF AI IN DISPUTE RESOLUTION
Fast, fair, and full of potential, artificial intelligence (AI) tools can be used by arbitrators and mediators to boost efficiency, offer deeper insights, and provide an enhanced level of precision in their work as demonstrated by platforms like NextLevelTM Mediation.
However, its adoption into the legal sector, encompassing arbitration, mediation, and litigation, has progressed very slowly. There are a number of factors contributing to this slow adoption:
In summary, there are limits to growth in any ecosystem, and AI is no exception. However, while the AI legal and dispute resolution ecosystems are growing, those professionals who adopt these technologies could gain a significant advantage over those who do not. The legal and dispute resolution fields are thus facing a critical period of transformation. To navigate this transition successfully, these communities must integrate technological advances carefully and thoughtfully, ensuring that ethical standards are upheld even as they innovate. This proactive adaptation is not merely beneficial but essential for those aiming to remain relevant and effective in their practices.
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