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Understanding AI: The Science, Systems, and Industries Powering a $3.6 Trillion Future

Explore how artificial intelligence is transforming finance, automation, and industry — and what the $3.6 trillion AI boom means for our future

Jay Magiya

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The global artificial intelligence market growing from $538 billion in 2023 to $3.68 trillion by 2034, with steady year-on-year increases and a projected 18.6% CAGR. Image credit: Satheesh Sankaran/Pixabay

Artificial Intelligence (AI) has become a major point of discussion and a defining technological frontier of our time. Experiencing remarkable growth in recent years, AI refers to computer systems capable of mimicking intelligent human cognitive abilities such as learning, problem-solving, critical decision-making, and creativity. Its ability to identify objects, understand human language, and act autonomously makes it increasingly feasible for industries like automotive manufacturing, financial services, and fraud detection.

As of 2024, the global AI market is valued at approximately USD 638.23 billion, marking an 18.6% increase from 2023, and is projected to reach USD 3,680.47 billion by 2034 (Precedence Research). North America leads this global growth with a 36.9% market share, dominated by the United States and Canada. The U.S. AI market alone is valued at around USD 146.09 billion in 2024, representing nearly 22% of global AI investments.

The Early Evolution of AI: From Reactive Machines to Learning Systems

Our understanding of AI has evolved through different models, each representing a step closer to mimicking human intelligence.

Reactive Machines: The First Generation

One of the earliest and most famous AI systems was IBM’s Deep Blue, the chess-playing computer that defeated world champion Garry Kasparov in 1997. Deep Blue was a reactive machine model, relying on brute-force algorithms that evaluated millions of possible moves per second. It could process current data and generate responses, but lacked memory or the ability to learn from past experiences.

Reactive machines are task-specific and cannot adapt to new or unexpected conditions. Despite these limitations, they remain integral to automation, where precision and repeatability are more important than learning—such as in manufacturing or assembly-line robotics.

Artificial intelligence is evolving from a niche technology to a global economic powerhouse. According to Precedence Research, the global AI market is expected to expand nearly sevenfold between 2023 and 2034, driven by applications in finance, healthcare, and manufacturing.

Limited Memory AI: Learning from Experience

To overcome the rigidity of reactive machines, researchers developed Limited Memory AI, a model that can store and recall past data to make more informed decisions. This model powers technologies such as self-driving cars, which constantly analyze road conditions, objects, and obstacles, using stored data to adjust their behaviour.

Limited Memory AI is also valuable in financial forecasting, where it uses historical market data to predict trends. However, its memory capacity is still finite, making it less suited for complex reasoning or tasks like Natural Language Processing (NLP) that require deeper contextual understanding.

Theoretical Models: Towards Human-Like Intelligence

Theory of Mind AI

The next conceptual step is Theory of Mind AI, a model designed to understand human emotions, beliefs, and intentions. This approach aims to enable AI systems to interact socially with humans, interpreting emotional cues and behavioral patterns.

Researchers like Neil Rabinowitz from Google DeepMind have developed early prototypes such as ToMnet, which attempts to simulate aspects of human reasoning. ToMnet uses artificial neural networks modeled after brain function to predict and interpret behavior. However, replicating the complexity of human mental states remains a distant goal, and these systems are still largely experimental.

Self-Aware AI: The Future Frontier

The ultimate ambition of AI research is self-aware AI — systems that possess conscious awareness and a sense of identity. While this remains speculative, the potential applications are vast. Self-aware AI could revolutionize fields like environmental management, creating bots capable of predicting ecosystem changes and implementing conservation strategies autonomously.

In education, self-aware systems could understand a student’s cognitive style and deliver personalized learning experiences, adapting dynamically to each learner.

However, replicating human self-awareness is extraordinarily complex. The human brain’s intricate memory, emotion, and decision-making systems remain only partially understood. Additionally, self-aware AI raises profound ethical and privacy concerns, as such systems would require massive amounts of sensitive data. Strict guidelines for data collection, storage, and usage would be essential before such systems could be deployed responsibly.

Artificial Intelligence in the Financial Services Industry

The financial sector has undergone a massive transformation powered by AI-driven analytics, automation, and predictive intelligence. AI enhances Corporate Performance Management (CPM) by improving speed and precision in financial planning, investment analysis, and risk management.

From financial services (38%) to healthcare (35%), sectors are integrating AI to improve efficiency, prediction, and decision-making. Manufacturing, retail, and education are close behind, showing that automation and data intelligence are reshaping every industry

Natural Language Processing and Automation

Leading financial firms such as JPMorgan Chase and Goldman Sachs employ Natural Language Processing (NLP) — AI systems that understand human language — to streamline customer interaction and analyze market information. NLP tools like chatbots handle millions of customer queries efficiently, while advanced systems process unstructured text data from financial reports and news sources to inform investment decisions.

Paired with Optical Character Recognition (OCR) and document parsing, NLP systems can convert scanned or image-based documents into machine-readable text, accelerating compliance checks, fraud detection, and financial forecasting.

However, the accuracy of NLP models depends on the quality and diversity of training data. Biased or incomplete data can lead to errors in analysis, potentially influencing high-stakes financial decisions.

Generative AI in Finance

Another major shift in finance comes from Generative AI, a branch of AI that creates new content — including text, images, videos, and even financial models — based on learned patterns. Using Large Language Models (LLMs) and Generative Adversarial Networks (GANs), these systems simulate complex financial scenarios, improving fraud detection and stress testing.

For instance, PayPal and American Express use generative AI to simulate fraudulent transaction patterns, strengthening their security systems. Transformers — deep learning architectures behind tools like OpenAI’s GPT — enable these models to understand and generate human-like language, allowing them to summarize extensive reports, produce research briefs, and assist analysts in decision-making.

Yet, Generative AI also presents challenges. It can be manipulated through adversarial attacks, producing misleading or biased outputs if trained on flawed data. Ensuring transparency and fairness in training datasets remains critical to prevent discriminatory outcomes, especially in credit scoring and loan assessment.

AI and Automation: Revolutionizing Industry Operations

Artificial Intelligence has become a cornerstone of intelligent automation (IA), reshaping business process management (BPM) and robotic process automation (RPA). Traditional RPA handled repetitive, rule-based tasks, but with AI integration, these systems can now manage complex workflows that require contextual decision-making.

AI-driven automation enhances productivity, reduces operational costs, and increases accuracy. For example, in manufacturing, AI-enabled systems perform predictive maintenance by analyzing sensor data to detect machinery issues before failures occur, minimizing downtime and extending equipment lifespan.

In the automotive sector, AI-powered machine vision systems inspect car components with higher accuracy than human inspectors, ensuring consistent quality and safety. These innovations make automation not only efficient but also economically advantageous for large-scale industries.

Machine Learning: The Engine of Artificial Intelligence

At the heart of AI lies Machine Learning (ML) — algorithms that allow computers to learn from data and improve over time without explicit programming. Three fundamental ML models underpin most modern AI applications: Decision Trees, Linear Regression, and Logistic Regression.

Decision Trees

Decision trees simplify complex decision-making processes into intuitive, branching structures. Each branch represents a decision rule, and each leaf node gives an outcome or prediction. This makes them powerful tools for disease diagnosis in healthcare and credit risk assessment in finance. They handle both numerical and categorical data, offering transparency and interpretability.

Linear Regression

Linear regression models relationships between one dependent and one or more independent variables, making it useful for predictive analytics such as stock price forecasting. It applies mathematical techniques like Ordinary Least Squares (OLS) or Gradient Descent to optimize prediction accuracy. Its simplicity, efficiency, and scalability make it ideal for large datasets.

Logistic Regression

While linear regression predicts continuous outcomes, logistic regression is used for classification problems — determining whether an instance belongs to a particular category (e.g., yes/no, fraud/genuine). It calculates probabilities between 0 and 1 using a sigmoid function, providing fast and interpretable results. Logistic regression is widely used in healthcare (disease prediction) and finance (loan default assessment).

Types of Machine Learning Algorithms

Machine Learning can be broadly classified into Supervised, Unsupervised, and Reinforcement Learning — each suited for different problem types.

Supervised Learning

In supervised learning, algorithms train on labelled datasets to identify patterns and make predictions. Once trained, they can generalize to new, unseen data. Applications include spam filtering, voice recognition, and image classification.

Supervised models handle both classification (categorical predictions) and regression (continuous predictions). Their strength lies in high accuracy and reliability when trained on quality data.

Unsupervised Learning

Unsupervised learning, in contrast, deals with unlabelled data. It identifies hidden patterns or groupings within datasets, commonly used in customer segmentation, market basket analysis, and anomaly detection.

By autonomously discovering relationships, unsupervised learning reduces human bias and is valuable in exploratory data analysis.

Reinforcement Learning (Optional Expansion)

While not yet as mainstream, reinforcement learning trains algorithms through trial and error, rewarding desired outcomes. It is foundational in robotics, autonomous systems, and game AI — including the systems that now outperform humans in complex strategic games like Go or StarCraft.

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Image credit: Gerd Altmann/Pixabay

Ethical and Societal Considerations of AI

Despite its transformative potential, AI raises significant ethical and privacy challenges. Issues such as algorithmic bias, data exploitation, and job displacement are increasingly at the forefront of public discourse.

Ethical AI demands transparent data practices, accountability in algorithm design, and equitable access to technology. Governments and academic institutions, including Capitol Technology University (captechu.edu), emphasize developing AI systems that align with social good, human rights, and sustainability.

Furthermore, the rise of generative AI has intensified debates about content authenticity, intellectual property, and deepfake misuse, underscoring the urgent need for comprehensive AI regulation.

A Technology Still in Transition

Artificial Intelligence stands at the intersection of opportunity and uncertainty. From Deep Blue’s deterministic algorithms to generative AI’s creative engines, the technology has redefined industries and continues to evolve at an unprecedented pace.

While self-aware AI and full cognitive autonomy remain theoretical, the rapid integration of AI across industries signals an irreversible shift toward machine-augmented intelligence. The challenge ahead is ensuring that this evolution remains ethical, inclusive, and sustainable — using AI to enhance human potential, not replace it.

References

  • Artificial Intelligence (AI) Market Size to Reach USD 3,680.47 Bn by 2034 – Precedence Research
  • What is AI, how does it work and what can it be used for? – BBC
  • 10 Ways Companies Are Using AI in the Financial Services Industry – OneStream
  • The Transformative Power of AI: Impact on 18 Vital Industries – LinkedIn
  • What Is Artificial Intelligence (AI)? – IBM
  • The Ethical Considerations of Artificial Intelligence – Capitol Technology University
  • What is Generative AI? – Examples, Definition & Models – GeeksforGeeks

Jay Magiya is a contributor for EdPublica, based in Abu Dhabi, and an A-Level student of Mathematics, Further Mathematics, Economics, and Physics with a deep interest in Artificial Intelligence and its applications in finance. His research explores how machine learning and generative AI models — including decision trees, regression, and GANs — are transforming financial systems and automation. He is keen to continue researching the intersection between the financial industry with theoretical models and their practical implementations of AI in the real world.

Climate

From Fighting Water to Saving It: The Netherlands Faces a Growing Drought Challenge

A land built to keep water out is now struggling to keep enough of it in — forcing a world leader in water management to rethink its infrastructure

Sebin Pious

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Low water levels on the Nederrijn near Arnhem's Andrej Sacharovbrug, 5 August 2026. Photo: Tomas Guus / Wikimedia Commons (CC0)

The Netherlands built its global reputation by keeping water out. Now, longer dry spells and intensifying heatwaves are forcing the country to confront a very different problem: how to keep enough fresh water in the landscape. From greenhouse agriculture to homes built on wooden foundation piles, the Netherlands drought challenge is exposing the limits of infrastructure designed primarily for flood protection.

When people think of the Netherlands, the images that come to mind are windmills, tulip fields and the great sea walls that have kept the ocean at bay for centuries. The Dutch built their reputation, and much of their nation, on mastering water — pumping it away, holding it back, and reclaiming land from the sea to build a prosperous country on ground that, by rights, shouldn’t exist. Yet beneath that carefully engineered landscape, the Netherlands is now facing an unfamiliar problem: it is running out of fresh water.

As repeated summer heatwaves sweep across Western Europe, Dutch water authorities say they have reached the limit of what engineering can do. In several regions, officials have exhausted every standard measure available to them and are left with what amounts to a last resort — waiting, and hoping, for rain.

Netherlands Drought Challenge: From Floods to Water Scarcity

To understand how a country famous for its rainfall and rivers has arrived at this point, it helps to look at how the land itself was designed. For generations, the Dutch water system had one job: get excess water out to sea as fast as possible, to prevent flooding. That same efficiency has become a liability as weather patterns shift towards longer dry spells and more intense heat. The pressure peaks in late summer, when temperatures regularly cross 35°C and water evaporates faster than rainfall can replace it.

The consequences of shrinking water reserves go well beyond the daily weather report. They are already reaching into the economy, and into the foundations — quite literally — of Dutch homes.

Thousands of historic Dutch houses stand on wooden foundation piles. When groundwater levels drop, those piles are exposed to air and begin to rot. On clay and peat soils, the ground shrinks unevenly, pulling foundations down and cracking brick walls

Economic Strain and Sinking Homes

In Westland, the heart of Dutch greenhouse horticulture, the Delfland water authority has banned growers from drawing irrigation water from local ditches and canals — the first such ban in its history. According to the growers’ umbrella body Glastuinbouw Nederland, the ban affects around 150 commercial growers, with potential damages running as high as €150 million.

At the same time, a quieter crisis is unfolding beneath people’s homes. Thousands of historic Dutch houses stand on wooden foundation piles. When groundwater levels drop, those piles are exposed to air and begin to rot. On clay and peat soils, the ground shrinks unevenly, pulling foundations down and cracking brick walls. The Council for the Living Environment and Infrastructure estimates that close to half a million buildings across the country could show foundation damage by 2035, with repair costs reaching as much as €54 billion.

From Water Battle to Water Sponge

This reality is forcing a fundamental shift in how the Netherlands manages its resources. For centuries, Dutch policy was simple: fight the water, and push it away. Today, water authorities are engaged in a delicate balancing act, trying to save every drop using canal locks and storage basins. But holding onto existing water can only do so much once the rain stops altogether.

Long-term resilience will require redesigning the landscape itself. Rather than treating rainwater as a threat to be flushed out to sea, experts increasingly argue that the Netherlands needs to function more like a giant sponge — capturing heavy winter rain and storing it safely to survive the dry summer months that are becoming the norm.

A Lesson Beyond Borders

What is unfolding in the Netherlands carries a lesson well beyond it. If a nation this experienced in water engineering is struggling to keep pace with a changing climate, it says something about how quickly conditions can outrun even the most sophisticated infrastructure. As riverbeds stay low and the dry spells drag on, the Dutch find themselves in an unfamiliar position for a country built on water: waiting for the skies to open.

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Society

Urban Women Hit by a Stark 8.7% Unemployment Rate

India’s unemployment rate rose to 5.4% in April–June 2026, but the sharpest divide was among urban workers: women faced 8.7% unemployment, while only 22.8% were employed compared with 70.7% of men.

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Women wearing masks march in a protest, holding a sign that reads “WE WANT JOB SECURITY.”
Women participate in a protest demanding job security, highlighting concerns over employment and workplace stability. Representational image. Image credit: Rsapmech/Pexels

India’s urban labour market continues to show a persistent gender imbalance, even as overall employment trends remain relatively stable. While unemployment rates are often used as the primary indicator of job stress, they do not fully capture who is able to access work in the first place.

India’s latest employment data reveal a divide larger than the headline unemployment rate. In April–June 2026, 8.7% of urban women aged 15 years and above who were in the labour force were unemployed, compared with 6.1% of urban men. But the sharper gap lies in employment itself: only 22.8% of urban women were working, compared with 70.7% of urban men.

The figures come as India’s overall unemployment rate rose to 5.4%, from 5.0% in January–March, according to the latest Periodic Labour Force Survey (PLFS) Quarterly Bulletin released by the Ministry of Statistics and Programme Implementation. Rural unemployment rose from 4.3% to 4.8%, while urban unemployment remained almost unchanged at 6.7%.

For urban women, unemployment actually fell from 9.1% to 8.7% over the quarter. Yet it remains considerably higher than the rate for men.

That makes the story less about a sudden rise in female unemployment and more about a persistent question: why are so few urban women participating in paid work?

The Bigger Divide is Participation

The unemployment rate counts people who are working or actively seeking and available for work. Those outside the labour force are not counted as unemployed.

That distinction is crucial. The urban Worker Population Ratio (WPR) stood at 46.8% overall in April–June. But the gender gap was stark: 70.7% for men and just 22.8% for women.

The nearly 48-percentage-point difference means that looking only at the 8.7% female unemployment rate captures only part of the employment problem. India can therefore have a relatively stable urban unemployment rate while still having a large pool of women who are not participating in the labour market.

Unemployment and gender gap in employment.
Women work on a production floor, illustrating the growing importance of women’s participation in India’s urban workforce amid persistent gender gaps in employment. Image credit: EqualStock IN/Pexels

Urban Jobs are Changing, But the Gender Gap Remains

The urban labour market itself is not showing signs of a broad collapse. Urban unemployment edged up only marginally from 6.6% to 6.7% during the quarter. At the same time, the share of urban workers in regular wage or salaried employment increased from 48.9% to 49.3%.

Urban employment is also dominated by services. The tertiary sector accounted for 62% of urban employment in April–June, compared with 61.7% a year earlier.

Yet these shifts have not translated into comparable employment outcomes for women.

Rural Unemployment Rose Faster

The national increase in unemployment was partly driven by rural India. Rural unemployment rose by 0.5 percentage points, compared with a 0.1-point increase in urban areas. At the same time, rural employment continued to shift away from agriculture: agriculture’s share fell from 55.8% to 52.9%, while the secondary sector rose from 22.6% to 24.4%.

chart visualization

The figures point to an economy undergoing changes in where and how people work, even as access to employment remains uneven.

Women’s Participation Also Fell

The gender gap extends beyond cities. Overall female labour-force participation declined from 34.7% in January–March to 33.2% in April–June. The overall LFPR for people aged 15 and above also fell, from 55.5% to 54.6%.

The latest figures should not be interpreted as proof that women simply lost jobs. LFPR measures participation in the labour force, while WPR measures actual employment.

But together, the indicators highlight a persistent challenge: India’s employment story cannot be understood through unemployment alone.

The Question of Gender Gap

The PLFS does not establish why urban women participate in the labour market at much lower rates than men. Factors such as childcare, household responsibilities, transport, workplace conditions, safety and access to suitable jobs require separate evidence and reporting.

What the data do establish is the scale of the divide. Urban female unemployment is 8.7%, compared with 6.1% for men. But the much larger gap is in actual employment: 22.8% of urban women were working, against 70.7% of urban men.

As India’s urban economy becomes increasingly service-led and regular salaried employment expands, the central employment question is no longer only how many jobs are being created. It is also who is able to enter the workforce and stay in it.

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From Bell Labs to the Classroom: Finding Purpose After Retirement

In the previous part of my story, I described how retirement led me to pursue certification as a high school mathematics teacher after my career at Bell Labs.

Sudhir M. Ambekar

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Second Act is Education Publica’s column on professionals who reinvent themselves after retirement or at major turning points in life. In the previous issue, former Bell Labs researcher Sudhir M. Ambedkar recounted his journey from IIT Bombay and the University of California, Berkeley to nearly three decades of research and innovation at Bell Labs. In this concluding part, he reflects on how retirement opened the door to an equally rewarding second career—as a mathematics teacher, mentor and lifelong learner.

In the previous part of my story, I described how retirement led me to pursue certification as a high school mathematics teacher after my career at Bell Labs. Standing in a classroom for the first time as a teacher marked the beginning of an entirely new chapter in my life.

After completing my certification, I taught mathematics full-time for about seven years. Teaching mathematics in the United States was a new experience for me, and it took some time to adjust to a different way of teaching. While teaching, I learned a few things about some fundamental mathematics concepts that I had never thought about before.

I used a hybrid approach, combining the Indian method and the American method of teaching mathematics. During my tenure as a teacher, I made several observations about the education system where I was teaching, although many of them were generally applicable to the education system in New Jersey as well. I documented those observations in a white paper and sent them to a few education professors at major universities. Some agreed with my observations but said I could not publish the paper in an academic journal because I was not an education researcher.

One observation was the amount of repetition in the curriculum across Algebra I, Algebra II and Pre-Calculus. I felt it would be better to build a stronger foundation in the earlier stages rather than repeat many of the same topics in subsequent courses.

I also noticed that calculators were introduced in very early grades. As a result, many students became overly dependent on them and often lacked the ability to perform mental mathematics. I believe it is important to build a strong understanding of basic mathematical operations and concepts—including addition, subtraction, multiplication, division, fractions and solving equations—before relying heavily on technology.

The mathematics textbooks used in American schools are excellent. They contain colourful illustrations, graphics and worked examples that make concepts easier to understand. At the same time, students often expected teachers to explain every step in detail, rather than working through some of the reasoning independently.

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Another aspect that interested me was the grading system. Homework and classwork accounted for a significant portion of students’ grades, while tests and quizzes carried comparatively less weight. In my opinion, this did not always accurately reflect a student’s mathematical skills and knowledge.

The students hardly ever received failing grades, and almost everyone progressed to the next level. On one occasion, I was under pressure to pass a student so that the school could maintain a 100 per cent graduation rate—a highly valued performance indicator.

These observations are not intended as criticism of the education system, but simply to illustrate how it differed from the one I had experienced in India. Bright students always rise to the top in this system as well.

I thoroughly enjoyed teaching, and it was gratifying to see students succeed and gain admission to excellent universities. Unlike research, where results often take years to become visible, I could see the impact of my work immediately.

After about seven years of teaching, I underwent knee surgery. As a result, I gave up classroom teaching and began focusing more seriously on preparing students for the SAT and ACT, the standardised tests widely used for college admissions in the United States.

I worked with two learning centres that referred students to me for SAT and ACT coaching. Over the years, both examinations changed considerably in their format and content, so I had to keep up with those changes and adjust my teaching accordingly.

Initially, I taught students in person. During the COVID-19 pandemic, all instruction shifted to Zoom. Even after the pandemic, I continued teaching online because, since most of my classes were one-to-one, I found Zoom to be both efficient and physically less demanding.

As a result of teaching these examinations, I developed several practical strategies that enabled students to complete the tests accurately within the allotted time. Some students later told me they found these strategies useful not only in the SAT and ACT but also in their regular mathematics and English classes.

I eventually published these strategies as a series of guidebooks on Amazon. The feedback from students and parents has been consistently encouraging.

In terms of years, my teaching career has now lasted about 75 per cent as long as my engineering career. In both professions, I could see the results of my work, but teaching has been more gratifying because I had direct contact with the people who benefited from it.

I expect to continue this second career for as long as possible.

Sudhir M. Ambedkar is a mechanical engineer trained at IIT Bombay and the University of California, Berkeley. He spent nearly three decades at Bell Labs working in telecommunications research and development. After retirement, he became a certified mathematics teacher and now tutors students preparing for the SAT and ACT while authoring test-preparation guides.

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