Artificial intelligence has become a major point of discussion and a recent focal point, experiencing remarkable growth in recent years. Artificial intelligence is a branch of computer science that refers to computer systems and machines with the capability to mimic human cognitive abilities, such as learning, critical decision-making, problem-solving, and creativity. Its ability to identify objects, understand human language, and act independently makes it possible to reduce the need for direct human intervention, increasing the feasibility of AI use in industries such as automotive manufacturing, financial services, and fraud detection.

A Market Measured in Billions

As of now, the projected value of the global artificial intelligence market is approximately USD 638.23 billion, marking an 18.6 percent increase from the 2023 value of USD 538.13 billion. This advancement is expected to continue with a compound annual growth rate (CAGR) of 19.1 percent from 2024 to 2034. Major North American countries, including the United States and Canada, have incorporated AI at scale and accumulated the largest market share, accounting for 36.9 percent of the global AI market. For example, the US artificial intelligence market is currently valued at around USD 146.09 billion in 2024, representing approximately 22 percent of global market value.

Machines That Do Not Learn

The evolution of AI has resulted in several distinct model types. IBM’s Deep Blue chess system is a notable example of a reactive machine model. It was designed using brute-force computation and complex algorithms, enabling it to generate approximately 200 million potential chess positions per second. Deep Blue employed a deep search tree to evaluate optimal moves and was used to challenge human grandmasters, achieving a historic milestone by defeating the reigning world chess champion Garry Kasparov in one game of a six-game match. The system relied entirely on predefined responses and immediate input data, as it had no memory or learning capability.

While this marked a historic achievement in computational performance, the lack of adaptability limited the broader applicability of reactive machines. Their inability to learn from experience or respond effectively to unexpected scenarios restricts their use to narrow, task-specific applications. Despite these limitations, reactive AI systems remain widely used in industrial automation due to their precision and reliability in controlled environments.

Learning From the Past

To address these constraints, Limited Memory AI models were developed. These systems retain historical data from previous experiences, enabling improved decision-making over time. Limited Memory AI is widely used in self-driving vehicle technology, where it analyses traffic patterns, road conditions, and obstacles to make informed real-time decisions. Similarly, in financial forecasting, these systems use historical market data to predict trends and risks. Limited Memory AI gathers data through multiple inputs, stores it temporarily, and applies analytical models to enhance accuracy.

However, such systems still struggle with high levels of complexity, particularly in areas such as advanced natural language processing, where long-term contextual understanding and reasoning are required.

Can Machines Read Minds?

More advanced AI concepts, including Theory of Mind and self-aware models, remain largely theoretical and experimental. Theory of Mind AI aims to interpret and predict human emotions, beliefs, and intentions, enabling more natural social interaction between humans and machines. These systems analyse behavioural patterns derived from user interactions and historical context using advanced algorithms.

Researchers such as Neil Rabinowitz at Google DeepMind have made progress in this area. Rabinowitz and his team developed a system known as ToMnet, which uses artificial neural networks inspired by the structure of the human brain to model decision-making behaviour. Despite this progress, Theory of Mind AI cannot accurately replicate the complexity of human mental states. The intricate nature of human cognition, emotions, and social reasoning means real-world applications remain limited, and widespread deployment is likely years away.

The Idea of Self-Aware AI

The final hypothetical stage of AI development involves systems with genuine self-awareness. Such models would possess the ability to understand their own internal states and adapt behaviour accordingly. Self-aware AI systems could potentially tailor interactions based on environmental and emotional cues, making engagement more effective and personalised.

In environmental management, these systems could predict ecosystem changes, recommend conservation strategies, and assist in biodiversity protection. In education, self-aware AI could personalise learning experiences by adapting to individual learning styles and progress patterns. However, AI remains far from achieving self-awareness, largely because significant aspects of human cognition, including consciousness, memory integration, and proactive reasoning, remain poorly understood.

Furthermore, self-aware AI would require vast quantities of data, raising ethical concerns related to privacy, surveillance, and data exploitation. As a result, the development of such systems necessitates strict regulatory frameworks governing data collection, storage, and use.

Finance Goes Algorithmic

The financial services sector is currently undergoing a significant transformation driven by artificial intelligence. AI has reshaped analytics, operational efficiency, and strategic decision-making across the industry. Corporate Performance Management has benefited from AI-driven tools that enhance speed and accuracy in financial planning, budgeting, and forecasting.

Major financial institutions such as JPMorgan Chase and Goldman Sachs employ Natural Language Processing technologies to analyse large volumes of financial data. NLP enables machines to interpret and generate human language, supporting applications such as chatbots, market analysis, and risk assessment.

Reading the Fine Print at Scale

When combined with Optical Character Recognition, which converts image-based financial documents into machine-readable text, NLP systems can rapidly process reports, contracts, and news articles. Document parsing technologies further enhance efficiency by extracting relevant information from unstructured data sources, including social media content.

However, the effectiveness of NLP systems depends heavily on the availability of diverse, high-quality datasets. Biased or inaccurate data can undermine model performance and lead to flawed decision-making.

Machines That Create

Generative AI has emerged as a powerful subset of artificial intelligence within the financial sector. Generative AI systems can create original content, including text, images, audio, video, and three-dimensional models. These capabilities have advanced significantly due to developments in Large Language Models, which learn patterns from vast datasets and generate outputs that resemble human-created content.

Generative AI systems use techniques such as text generation and image synthesis to produce new samples based on training data. A key underlying technology is the Generative Adversarial Network, which consists of two neural networks: a generator and a discriminator.

Fighting Fraud With Fakes

The generator produces synthetic data, while the discriminator evaluates its authenticity, enabling continuous improvement in output quality. This architecture has proven particularly valuable in fraud detection, where simulated fraud scenarios can be used to stress-test detection systems. Financial institutions such as PayPal and American Express employ generative models to improve fraud prevention mechanisms.

The Transformer Revolution

Transformers have revolutionised both Natural Language Processing and generative AI. This deep learning architecture enables the modelling of complex sequences in text, images, and audio. Transformer-based models underpin systems such as OpenAI’s GPT, which can generate coherent, human-like text.

Financial organisations use transformer models for investment research, analysing market trends, generating reports, and summarising extensive financial documents to accelerate decision-making processes.

Bias, Attacks, and Blind Spots

Despite these advantages, generative AI presents several challenges. These systems are vulnerable to adversarial attacks, in which manipulated inputs produce misleading outputs. Bias in training data remains a critical concern, particularly in financial applications such as lending and credit assessment, where biased outputs can reinforce systemic inequalities.

Automation, Upgraded

Artificial intelligence has also become integral to intelligent automation, significantly transforming business process management and robotic process automation. Traditional RPA systems were limited to repetitive, rule-based tasks. The integration of AI has enabled automation systems to perform more complex activities that require judgement and adaptive decision-making.

This advancement has expanded the scope of automation across industries while maintaining high levels of accuracy and efficiency.

Predicting Failure Before It Happens

Predictive maintenance is another area where AI has demonstrated significant value. By analysing data from sensors and machinery, AI systems can anticipate equipment failures before they occur. This reduces downtime, extends equipment lifespan, and lowers maintenance costs.

In manufacturing and automotive industries, AI-powered machine vision systems use cameras and optical sensors to detect defects during production. These systems analyse images with greater precision than traditional inspection methods, ensuring consistent product quality.

The Building Blocks of Machine Learning

Machine learning models represent one of the most influential branches of artificial intelligence. These systems enable computers to learn from data and improve performance without explicit programming. Three foundational models commonly used across industries are decision trees, linear regression, and logistic regression.

Decision trees break complex decision-making processes into structured branches based on informative data features and are widely used in healthcare diagnostics and financial credit assessment.

From Trends to Probabilities

Linear regression models establish statistical relationships between dependent and independent variables and are commonly applied in financial forecasting. Logistic regression predicts the probability of binary outcomes and is widely used in healthcare and credit risk analysis due to its efficiency and interpretability.

Teaching Machines to Learn

Machine learning algorithms enable systems to learn patterns from data and improve performance over time. The three primary categories are supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning uses labelled datasets for classification and regression tasks, including spam detection and image recognition. Unsupervised learning operates on unlabelled data and identifies hidden structures through clustering and association analysis.

Imagine being dropped in a brand-new city where everyone speaks a different language. Some people grab a map and start drawing routes. Others listen intently to locals, some race into the streets to explore by doing, and a few quietly observe from a café, making notes. Which traveler are you? In the world of learning, discovering your “learning style” is like finding your unique superpower—the secret key to faster, deeper, more enjoyable learning.

How do you unlock your own learning?

Let’s dive into the Honey-Mumford and VAK (Visual, Auditory, Kinaesthetic) models—two of education’s most influential maps for personalizing the learning journey.

Why Learning Styles Matter (and Why They Change)

It’s important to realize learning preferences aren’t set in stone. Just as a traveler adapts to new cities, learners shift styles based on the challenge. Most of us lean toward one or two favourite modes, but flexibility is key. According to Peter Honey and Alan Mumford’s classic model, being able to “wear all four hats” is crucial for mastering new skills. If you stubbornly avoid certain ways of learning, you may unknowingly tie your own shoelaces together.

Here’s how the four Honey-Mumford learning styles look in action:

Activist: The daredevils of learning! Activists live for new experiences. They’re first to leap into workshops, group activities, and hands-on challenges. They learn best when they’re doing, discussing, and exploring.

Reflector: These learners are the wise owls. Quietly observing first, they watch from every angle, gathering information before jumping in. Their superpower? Drawing connections and insights from deep thinking.

Theorist: Think of the philosophers and scientists. Theorists want the “why” behind everything. They thrive on models, structures, and clear explanations, asking, “Does this make sense?” and “Is there a theory here?”

Pragmatist: These are the builders and fixers. Pragmatists want practical, real-world application. “How can I use this?” is their guiding question. They flourish when they’re solving problems and trying out ideas.

The Visual, Auditory, and Kinaesthetic Adventure

But wait—there’s more! According to neuro-linguistic programming and the globally popular VAK model, everyone navigates the world of knowledge in their own preferred “language”: seeing, hearing, or doing. Here’s how to spot yours:

Visual Learner: You see the world in pictures. Diagrams, charts, videos, and handouts light up your mind. If you sketch ideas or remember faces better than names, visual is your superpower.

Auditory Learner: Sound is your guide. You remember best what you hear—lectures, podcasts, discussions, even recording and replaying information. You may even talk aloud to “think.”

Kinaesthetic Learner: Your hands lead the way. You learn by doing. Whether painting, building, or physically working through problems, motion and touch fuel your brain.

So, How Do You Find Your Style?

No one is 100% one type. Like expert travelers, the best learners pack more than one compass. Educational researcher Niel Fleming expanded on these ideas, showing that all of us use a mix—sometimes favoring one “sense,” sometimes another. Being stuck with just one style can slow you down; flexibility makes the difference.

Educators, coaches, and students can all benefit by asking simple questions—”Do I remember better what I see, hear, or do?”—and using practical inventories from Honey, Mumford, or Fleming to discover strengths.

Want to unlock your learning superpower? Pay attention to how you most naturally enjoy, remember, and apply new information—and don’t be afraid to experiment with new learning adventures. Your secret strength might just surprise you.

Earlier this year, EdPublica reported on an unsettling truth emerging from a collaborative study by MIT and Indian education researchers: Indian children demonstrate impressive mathematical ability when navigating real-life situations—like calculating change in a vegetable market—but often fail when asked to solve similar problems in the classroom. The findings struck a chord, revealing a deep fracture between what children learn and how they learn it.

Now, new data from the government-backed PARAKH Rashtriya Sarvekshan 2024 confirms the broader scale of that crisis. Together, the two reports offer a sobering diagnosis of foundational learning in India—and an urgent call to rethink how education is delivered.

The transfer gap: Street-smart, classroom-stranded

In the February study we reported on, researchers observed that children who work in markets—some out of necessity—could perform complex mental arithmetic swiftly and accurately. But the same children struggled with formal school problems like structured division or textbook subtraction. Meanwhile, their peers in schools did well on written math tests but faltered when asked to apply the same concepts in spontaneous, real-life situations.

This disconnect isn’t just about math—it’s about transferability. What good is education if it doesn’t translate beyond the exam sheet?

PARAKH’s alarming snapshot

The Performance Assessment, Review, and Analysis of Knowledge for Holistic Development (PARAKH) is India’s new national assessment platform launched under the National Education Policy 2020. Managed by the NCERT in collaboration with CBSE and overseen by the Ministry of Education, PARAKH represents a shift away from traditional rote exams to competency-based evaluation.

Its first large-scale survey, conducted in December 2024 across 23 lakh students from Classes 3, 6, and 9, paints a picture that is both revealing and troubling.

In Class 3, only 55% of students could correctly sequence numbers up to 99 or perform simple addition and subtraction. By Class 6, just 53% had mastered multiplication tables up to 10. Math proficiency hovered at 46% overall. The pattern held across language and environmental studies as well.

Perhaps most alarming is the steady decline in foundational ability as students progress. What begins as a fragile grasp in Class 3 becomes a gaping void by Class 9.

Where you study matters

The data also revealed a curious twist: in Class 3, rural students marginally outperformed their urban peers in both math and language. But by Class 6 and 9, the urban students pulled ahead decisively. It suggests that whatever edge rural systems may offer in the early years is quickly lost due to resource constraints, poor infrastructure, or lack of academic support.

Meanwhile, central government-run schools—such as Kendriya Vidyalayas—consistently outperformed state-run and aided schools, particularly in mathematics. The gaps are not just between regions, but embedded within the structure of the system itself.

A system teaching at children, not with them

What both the MIT study and the PARAKH survey show is this: India’s education system, despite enormous progress in enrolment and infrastructure, still hasn’t solved the puzzle of meaningful learning. It teaches children how to arrive at the “right” answer on paper, but not how to reason, estimate, or solve problems in the real world.

This isn’t simply a curriculum issue—it’s pedagogical. Teachers often default to formulas and procedures, driven by syllabus completion and exam pressures. Conceptual understanding, critical thinking, and the space to make mistakes are rare in crowded classrooms with little support for differentiated learning.

Moving from numbers to nuance

To its credit, the Ministry of Education has recognized this crisis. The PARAKH framework is designed not just to assess but to inform change. Its next phase will involve teacher workshops, state- and district-level consultations, and detailed “health reports” of learning outcomes.

A country with one of the youngest populations in the world cannot afford a foundational crisis

But meaningful change will require more than data. It demands political will, sustained investment in teacher training, reduced pupil–teacher ratios, and a shift in classroom culture. Most of all, it requires a rethinking of what education is meant to do—not just pass students from one grade to the next, but prepare them for life.

The stakes couldn’t be higher

A country with one of the youngest populations in the world cannot afford a foundational crisis. Poor learning in early years compounds over time, leading to disengagement, dropout, and economic vulnerability. The students struggling to divide 96 by 8 today are tomorrow’s workforce—and the gaps in their learning will define the future of the nation.

If India wants to reap its much-discussed demographic dividend, it must invest in the one thing that can turn numbers into citizens, and citizens into leaders: deep, transferable learning.