7 A.I. Workers for WLB

AI policy is rapidly evolving worldwide. In 2024–25 a wave of national AI strategies and international frameworks has emerged to steer AI development and address its societal impacts. Governments are seeking both to capture AI’s economic benefits and to build public trust. Below we compare key country strategies and multilateral ethics efforts, then examine recent regulatory trends and standardization work on AI governance. 6.4.1 International AI Strategies and Collaborations EU (European Union): The EU has pursued an “excellence and trust” strategy. It aims to be an AI leader by heavily investing in R&D (e.g. €1 billion per year from EU funds) and building data/computing infrastructure. At the same time Europe is finalizing the AI Act (the world’s first comprehensive AI law) to ensure AI systems respect fundamental rights. In 2024–25 the Commission launched an “AI Continent” action plan and innovation package to boost startups, develop so-called AI “Gigafactories”, a...

6.3.1 Key Roles in the AI Ecosystem AI Researcher: AI researchers (often PhD-level) conduct fundamental R&D in machine learning and neural algorithms. They design and test novel AI models, often in academia or corporate labs. This role is growing rapidly: computer and information research scientists (which include AI researchers) are projected to grow about 26% over the next decade. Because of the specialized skills required, AI researchers command high salaries (e.g. Glassdoor reports averages around $200K). These researchers are most often employed at tech companies, research institutes, or universities, especially in AI-intensive industries (software, autonomous vehicles, etc.). Data Scientist: Data scientists collect, clean, and analyze large datasets to extract insights and build predictive models. They bridge business and technical teams by using statistics, visualization, and machine learning to inform decisions. Demand for data scientists remains very stron...

6.2.1 AI in Climate Change and Sustainability AI is playing an increasing role in climate science by processing vast, complex data sets to improve predictions and planning. For example, MIT researchers have built an “Earth Intelligence Engine” that combines machine learning with physical flood models to generate realistic satellite images of potential future flooding. In general, AI models are being integrated with traditional climate simulations to reduce uncertainty and improve forecast accuracy for extreme weather and long-term trends. These AI-driven projections can inform better adaptation and mitigation strategies (e.g. improved flood maps or forest-fire risk forecasts). Climate modeling improvements: Deep learning can ingest satellite, oceanographic and sensor data to identify subtle patterns that physics-based models may miss. Initiatives like Columbia’s LEAP project embed physical laws into neural networks to sharpen long-range climate forecasts and extreme-event pr...

The field of artificial intelligence is rapidly moving beyond task-specific (“narrow”) systems toward more general and adaptive models. Artificial General Intelligence (AGI) refers to AI that can understand, learn, and apply knowledge across a wide range of domains, akin to human cognition. In contrast to today’s narrow AI (which excels only at predefined tasks), AGI aims to reuse knowledge , operate in novel situations, and autonomously learn new domains. For example, while current AI might master one game or one type of medical image, an AGI could potentially analyze genetics one day and drive a car the next without retraining. This shift promises transformative impacts: AGI is expected to revolutionize fields from biomedical research to nanotechnology, and could even spark an “intelligence explosion” where advanced AI designs even more advanced AI. Yet building AGI involves profound scientific and philosophical challenges. Researchers debate whether truly intelligent machines...

This chapter surveys anticipated technological, societal, and professional developments in AI. It highlights emerging research directions (Section 6.1), new applications and impacts (6.2), evolving career and education paths (6.3), and global/regulatory trends (6.4). The goal is to equip learners with insight into the next wave of AI evolution. 6.1 Predictive Analysis of AI Research and Development 6.1.1 AI Beyond Narrow Intelligence Traditional AI excels at narrow tasks but cannot generalize across domains. Artificial General Intelligence (AGI) aims to replicate broad human cognition – reasoning, learning, and creativity – in machines. AGI would shift AI from specialized tools to generalists that understand and adapt to novel problems. For example, AGI could autonomously design new scientific experiments, draft original literature, or manage complex systems. Researchers stress that AGI could “revolutionize” fields like medicine, materials science, and biotech by discovering unfor...