Society
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
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.
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.
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.

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
Climate
Could Global Warming Make Greenland, Norway and Sweden Much Colder?
A Nordic Council report warns that global warming could make Norway colder if the Atlantic ocean circulation collapses, triggering severe climate impacts.
Global warming is usually associated with rising temperatures—but a new Nordic report warns it could drive parts of northern Europe into far colder conditions if a major Atlantic ocean current collapses.
Greenland, Norway and Sweden could experience significantly colder climates as the planet warms, according to a new report by the Nordic Council of Ministers that examines the risks linked to a possible collapse of the Atlantic Meridional Overturning Circulation (AMOC).
The report, A Nordic Perspective on AMOC Tipping, brings together the latest scientific evidence on how global warming is slowing the AMOC—one of the world’s largest ocean circulation systems, responsible for transporting heat from the tropics to the North Atlantic. While a full collapse is considered unlikely, the authors warn that it remains possible even at relatively low levels of global warming, with potentially disruptive consequences for northern countries.
The Reversal
If the circulation were to weaken rapidly or cross a tipping point, the report notes, northern Europe could cool sharply even as the rest of the world continues to warm. Such a reversal would have wide-ranging effects on food production, energy systems, infrastructure, and livelihoods across the Nordic region.
“The AMOC is a key part of the climate system for the Nordic region. While the future of the AMOC is uncertain, the potential for a rapid weakening or collapse is a risk we need to take seriously,” said Aleksi Nummelin, Research Professor at the Finnish Meteorological Institute, in a media statement. “This report brings together current scientific knowledge and highlights practical actions for mitigation, monitoring and preparedness.”
A climate paradox
The AMOC plays a central role in maintaining the relatively mild climate of Northern Europe. As global temperatures rise, melting ice from Greenland and increased freshwater input into the North Atlantic are expected to weaken this circulation. According to the report, such changes could reduce heat transport northwards, leading to colder regional conditions—particularly during winter—even under a globally warming climate.
Scientists caution that the impacts would not simply mirror gradual climate change trends. Instead, an AMOC collapse could trigger abrupt and uneven shifts, including expanded sea ice, stronger storms, altered rainfall patterns, and rising sea levels along European coastlines. Some of these impacts would occur regardless of when or how quickly the circulation weakens.
The report also highlights global ripple effects. A slowdown of the AMOC could shift the tropical rain belt southwards, with potentially severe consequences for monsoon-dependent regions such as parts of Africa and South Asia, underscoring that AMOC tipping is not a regional concern alone.
Calls for precaution and preparedness
Given the uncertainty surrounding when—or if—the AMOC might cross a critical threshold, the report urges policymakers to adopt a precautionary approach. It stresses that any additional global warming, and prolonged overshoot of the 1.5°C target, increases the risk of triggering a collapse.
Key recommendations include accelerating emissions reductions, securing long-term funding for ocean observation networks, and developing an early warning system that integrates real-world measurements with climate model simulations. The authors argue that such systems should be embedded directly into policymaking to enable rapid responses.
The report also calls for climate adaptation strategies that account for multiple futures—including scenarios in which parts of Northern Europe cool rather than warm. It emphasises that AMOC collapse should be treated as a real and significant risk, requiring comprehensive risk management frameworks across climate, ocean, and disaster governance.
Science driving policy attention
The findings were developed through the Nordic Tipping Week workshop held in October 2025 in Helsinki and Rovaniemi, bringing together physical oceanographers, climate scientists, and social scientists from across Nordic and international institutions. The initiative was partly motivated by an open letter submitted in 2024 by 44 climate scientists, warning Nordic policymakers that the risks associated with AMOC tipping may have been underestimated.
By consolidating current scientific understanding and translating it into policy-relevant recommendations, the report aims to shift AMOC collapse from a theoretical concern to a concrete risk requiring immediate attention.
Society
Science Is Talking – Why Aren’t We Hearing?
Why the world still struggles to communicate science, and how researchers, journalists, and
institutions can rebuild a broken chain
Have you ever listened to an expert discuss their work and felt like they were speaking a completely different language? You’re not alone. Scientific breakthroughs have the power to shape our health, environment, and future, yet they often remain locked behind a wall of jargon and complexity, failing to reach the public or the policymakers who write our laws.
This communication breakdown creates a “broken chain of knowledge,” with crucial information stuck at its source. The path from a scientific discovery to public understanding and sound policy is fraught with obstacles, from the culture inside the lab to systemic barriers in government.
Let us look at the most significant reasons for this disconnect. By understanding the challenges from the perspectives of scientists, journalists, and policymakers, we can begin to see how we might mend the chain and ensure that knowledge flows to where it’s needed most.

The First Barrier Isn’t a Wall, It’s a Mindset
The communication problem often begins not with external hurdles, but within the culture of science itself. Before a single word is spoken to the public, an internal mindset can prevent scientists from effectively sharing their work. Some researchers operate with what former Indian minister Jairam Ramesh calls a “high pad” mentality, believing their specialized knowledge places them above the need for public engagement.
As Ramesh recently points out at the Science Journalists Conference of India, Ahmedabad University, this attitude is a primary barrier: “Too often I find scientists sitting on a high pad thinking that they have a better knowledge than the rest of the people… they speak in jargon they speak in their own language and they are really appealing to the community and not necessarily to the non-scientific community.”
Dr. Abhijit Majumdar of IIT Bombay acknowledges that scientists are often poor communicators — but he stresses a deeper issue: “Before learning how to communicate, scientists must first appreciate the need to communicate with the general public.” That awareness, he says, is still lacking in many settings. Experts note that this gap persists for two key reasons. The first is mindset: a cultural tendency to work in isolation — an “ivory tower inside their own ego.” The second is Language: after years of specialization, many scientists use technical vocabulary without realizing it’s incomprehensible jargon to outsiders, effectively building a wall where they intend to build a bridge. Overcoming this internal culture is the first step toward unlocking the mutual benefits of communication.

It’s Not ‘Dumbing Down,’ It’s a Two-Way Street
A fundamental misunderstanding of science communication is that it’s simply “dumbing down” complex work; in reality, it is a transformative, two-way exchange that can lead to deeper insights for the researchers themselves.
When scientists are challenged to explain their work to non-experts, they must distill complex ideas to their “’observable conceptual’ level.” This act of translation often forces them to see their own work from a new perspective, uncovering fresh insights. As Dr. Majumdar states, the benefits flow in both directions:
“It’s a two-way street, it is beneficial for the sides if we learn how to communicate.”
Furthermore, this process can generate questions from the public that are “much more superior” to those scientists typically receive from their peers, pushing their research in new and unexpected directions.

A Scientist’s Silence Creates a Vacuum for Misinformation
In our modern digital world, many scientists are hesitant to speak publicly, “scared that one wrong use of the work can be taken out of the context,” potentially leading to professional backlash. While this caution is understandable, it creates a dangerous paradox.
When credible experts stay silent on a complex issue, they create an “information vacuum.” That empty space will not remain empty for long. It is inevitably filled by less informed, less qualified, or even malicious actors eager to become the spokesperson on the topic. The silence of experts, therefore, directly enables the spread of false narratives.
In an era with a “lot of misinformation,” the proactive solution is a strong partnership between cautious, responsible scientists and trusted journalists. This collaboration is the public’s best and most powerful defense against falsehoods.

The System Itself Is Designed to Fail
Even when individual scientists are willing to engage, they are often crushed by systemic and structural barriers. The larger systems governing science and media are frequently not built to support public communication, a problem that is truly global in scope.
Studies reveal a stark reality. Nearly 46% of academics in one study had never communicated their findings beyond peer circles, with 80% citing a lack of time as a major barrier. A global survey of geoscientists found that while 90% believe they have a moral duty to engage, 87% identified a lack of funding as a key obstacle. This isn’t confined to one region; a study in Zimbabwe found nearly half of academics had never shared their research with public audiences.
In India, this is compounded by institutional support that suffers from “irregular funding” and offers little incentive for sustained engagement. Interestingly, a 2020 Pew Research survey found that 75% of Indians believe government investment in science is worthwhile, suggesting a public appetite for knowledge that the system is failing to meet.
Further straining the system is the inherent conflict between the clashing timelines of science and journalism. Science is slow, careful, and methodical, prioritizing peer review and accuracy. The news cycle is instantaneous and reactive, demanding immediate responses for a public hungry for information. This friction between a scientist’s verification process and a journalist’s deadline puts constant stress on the very relationship needed to bridge the knowledge gap.

In Policy, There’s a Structural Wall Between Science and Law
Even when science successfully reaches the public, the final link in the chain—influencing policy—is often completely broken. In India, for instance, Jairam Ramesh describes a profound structural disconnect between the nation’s scientific community and its lawmakers.
He explains that Members of Parliament receive their information almost exclusively from “government bodies” and “ministries,” not from the independent scientific institutions that house the country’s experts. This has led to a glaring absence of science-informed debate on some of the most critical issues facing the nation, including:
- GM crops
- Nuclear policy
- The increasing frequency of landslides and earthquakes
Global warming and its impact on agriculture, health, and energy
To fix this, Ramesh proposes that India’s scientific academies must take a more “active role.” Instead of relying on individuals, these institutions should consolidate a “collective view” from the scientific community and present it directly to legislators, providing an authoritative voice that is much harder for policymakers to ignore.
Building the Bridge, Together
Mending the broken “chain of knowledge” is not a simple task, nor is it the responsibility of a single group. It requires a collaborative effort from scientists who see communication as a duty, journalists who build trust and provide context, and institutions that create systems that reward and support public engagement.
Breaking down these barriers is a critical responsibility for any society that wishes to be guided by evidence and shared understanding. By strengthening every link in the chain—from the lab to our laws—we can build the bridge to a future shaped by insight and reason. If knowledge is power, how can we each help ensure it flows to where it’s needed most?
Climate
A Green Turn with Gaps: India’s Budget Backs Clean Tech but Skips Climate Adaptation
India’s Budget 2026–27 doesn’t shout climate ambition—but it hardwires it into clean manufacturing, carbon capture and energy supply chains, quietly reshaping the country’s green economy from the inside out.
India’s Union Budget 2026–27 may not carry a standalone climate chapter, but its green intent runs deep through the fine print. From carbon capture and battery storage to critical minerals and clean manufacturing, the budget signals a strategic shift: climate action is no longer framed as an environmental add-on, but as industrial policy and economic risk management rolled into one.
Presented by Finance Minister Nirmala Sitharaman on February 1, 2026, the budget places clean energy and climate-aligned manufacturing at the heart of India’s growth narrative. With a GDP growth target of around 7 percent and a sharp focus on fiscal discipline, sustainability is being embedded into supply chains, cities, transport and finance—quietly but deliberately.
Carbon Capture Takes Centre Stage
The most striking climate-linked announcement is the Rs 20,000 crore allocation over five years for Carbon Capture, Utilisation and Storage (CCUS), aimed at hard-to-abate sectors such as power, steel, cement, refineries and chemicals. For the first time, industrial decarbonisation is being backed at scale through public finance, signalling recognition that renewables alone cannot carry India’s net-zero journey.
As Arunabha Ghosh of CEEW notes, the budget’s “prioritisation of carbon capture, utilisation and storage across power, steel, cement, refineries, and chemicals” places these sectors squarely at the centre of India’s long-term climate pathway. This marks a decisive move from aspiration to infrastructure.

Building the Clean Energy Ecosystem
The energy transition is supported by coordinated allocations across key ministries: Rs 32,915 crore for New and Renewable Energy, Rs 29,997 crore for Power, and Rs 24,124 crore for Atomic Energy. Customs duty exemptions have been extended to lithium-ion cells used in battery energy storage systems, inputs for solar glass manufacturing, and nuclear power project imports till 2035.
Aarti Khosla of Climate Trends captures this shift succinctly: “Coupled with the exemption given to battery manufacturing, VGF for BESS and grant to CCUS, the focus of the government is rightly tilting towards building an energy transition ecosystem.” She adds that continued reforms in power distribution could bring “360-degree improvement in India’s green energy supply chain.”
At the household level, the PM Surya Ghar Muft Bijli Yojana receives a major boost, reinforcing decentralised clean energy as a pillar of inclusive growth. Rooftop solar is increasingly being positioned not just as a climate solution, but as a competitiveness tool for small businesses and urban households.
Supply Chains, Not Just Solar Panels
Rather than headline-grabbing renewable capacity targets, Budget 2026–27 leans into industrial resilience. Duty exemptions for critical minerals processing equipment, solar glass inputs, and battery storage components underline a focus on domestic value addition.
Energy analyst Duttatreya Das of Ember observes that while there are “no big-ticket announcements for renewables,” the continued duty exemptions and manufacturing reforms are expected to “quietly strengthen clean energy supply chains.” This reflects a broader policy philosophy: competitiveness before capacity, foundations before scale.
Rare Earth Corridors and incentives for mineral-rich coastal states further indicate a push to secure upstream inputs essential for EVs, batteries, wind turbines and electronics—areas where geopolitical vulnerabilities are growing.
Clean Mobility and Greener Cities
Sustainability also shapes transport and urban planning. The budget proposes 20 new national waterways over five years, aims to double the share of inland and coastal shipping by 2047, and identifies seven high-speed rail corridors as environmentally sustainable growth connectors. Municipal finance incentives—such as Rs 100 crore support for cities issuing large bonds—open space for green urban infrastructure, including pollution control and climate-resilient services.
Labanya Prakash Jena,Director, Climate and Sustainability Initiative, highlights that such incentives can catalyse “green municipal bonds, particularly for pollution control and urban environmental projects,” linking fiscal reform directly with urban sustainability.
The Gaps That Remain
Despite these advances, the budget remains notably silent on climate adaptation. Heat stress, floods, water scarcity and climate-resilient agriculture receive no scaled-up fiscal roadmap. Vibhuti of IEEFA points out that while support for decentralised renewables and bioenergy has increased, spending on transmission and energy storage has stagnated or declined—areas that are “not optional but indispensable” for a high-renewables grid.
The absence of strong EV demand-pull measures and limited risk-sharing instruments for private capital also signal unfinished business in India’s clean transition.
A Budget of Signals, Not Slogans
Budget 2026–27 is not a climate manifesto. Instead, it is a signal budget—one that rewires incentives, de-risks clean manufacturing, and treats decarbonisation as an economic strategy rather than a moral appeal. Its strength lies in industrial tools and fiscal realism; its weakness, in adaptation and social resilience.
Whether this quiet green turn translates into measurable emissions reductions and climate resilience will depend on execution, state capacity, and private investment. But one thing is clear: India’s clean-tech transition has now entered the core of its economic planning.
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