🤖〽️Prompt Engineering: Copilot Unabridged (Part 97: Swarm Intelligence - How AI Could Evolve Collective Behavior)

Prompt Engineering Series

Prompt: "write a post of 600 words on how AI could develop swarm behavior"

Introduction

In nature, some of the most remarkable feats of coordination come not from individual brilliance, but from collective intelligence. Birds flock, ants forage, and bees build hives - all without central control. This phenomenon, known as swarm behavior, is a decentralized, self-organizing system that emerges from simple rules followed by many agents.

Now imagine machines doing the same.

As Artificial Intelligence (AI) advances, the potential for AI systems to evolve swarm behavior becomes increasingly plausible - and powerful. Let’s explore how this could happen, what it might look like, and why it could redefine the future of intelligent systems.

What Is Swarm Behavior?

Swarm behavior refers to the coordinated actions of many agents - biological or artificial - based on local interactions rather than centralized commands. Each agent follows simple rules, but together they produce complex, adaptive behavior.

In AI, this could mean:

• Drones flying in formation without a pilot.
• Bots managing traffic flow by communicating locally.
• Robotic units exploring terrain by sharing sensor data.

The key is decentralization. No single machine leads. Instead, intelligence emerges from the group.

How AI Could Develop Swarm Behavior

AI systems could evolve swarm behavior through several pathways:

• Reinforcement Learning in Multi-Agent Systems: Machines learn to cooperate by maximizing shared rewards. Over time, they develop strategies that benefit the group, not just the individual.
• Local Rule-Based Programming: Each agent follows simple rules - like 'avoid collisions', 'follow neighbors', or 'move toward goal'. These rules, when scaled, produce emergent coordination.
• Communication Protocols: Machines exchange data in real time - position, intent, environmental cues - allowing them to adapt collectively.
• Evolutionary Algorithms: Swarm strategies can be 'bred' through simulation, selecting for behaviors that optimize group performance.

These methods don’t require central control. They rely on interaction, adaptation, and feedback - just like nature.

What Swarm AI Could Do

Swarm AI could revolutionize many domains:

• Disaster Response: Fleets of drones could search for survivors, map damage, and deliver aid - faster and more flexibly than centralized systems.
• Environmental Monitoring: Robotic swarms could track pollution, wildlife, or climate patterns across vast areas.
• Space Exploration: Autonomous probes could explore planetary surfaces, sharing data and adjusting paths without human input.
• Military and Defense: Swarm tactics could be used for surveillance, area denial, or coordinated strikes - raising ethical concerns as well as strategic possibilities.

In each case, the swarm adapts to changing conditions, learns from experience, and operates with resilience.

Challenges and Risks

Swarm AI isn’t without challenges:

• Coordination Complexity: Ensuring agents don’t interfere with each other or create chaos.
• Security Vulnerabilities: A compromised agent could disrupt the entire swarm.
• Ethical Oversight: Decentralized systems are harder to audit and control.
• Emergent Unpredictability: Swarms may develop behaviors that weren’t anticipated or intended.

Designing safe, transparent, and accountable swarm systems will be critical.

A New Paradigm of Intelligence

Swarm AI represents a shift from individual intelligence to collective cognition. It’s not about building smarter machines - it’s about building smarter networks.

This mirrors a broader truth: intelligence isn’t always centralized. Sometimes, it’s distributed, adaptive, and emergent. And in that model, machines don’t just think - they collaborate.

Final Thought: From Hive to Horizon

If AI evolves swarm behavior, we won’t just see machines acting together - we’ll see machines thinking together. They’ll form digital ecosystems, capable of solving problems too complex for any single system.

And in that evolution, we may find a new kind of intelligence - one that reflects not the mind of a machine, but the wisdom of the swarm.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 83: Mapping the Future - A 25-Year Evolution of AI)

Prompt Engineering Series

Prompt: "write a post of 600 words that depicts a map that reflects the evolution of AI for the next 25 years"

Introduction

Artificial Intelligence (AI) is no longer a distant dream - it’s a living, learning force reshaping our world. But what lies ahead? If we were to chart a map of AI’s evolution over the next 25 years, it would resemble a dynamic landscape of breakthroughs, ethical crossroads, and societal transformation.

Let’s take a journey through this imagined terrain.

Phase 1: 2025–2030 - The Age of Specialization

In the next five years, AI will become deeply embedded in vertical industries:

• Healthcare: AI will assist in diagnostics, drug discovery, and personalized treatment plans.
• Finance: Predictive models will dominate risk assessment, fraud detection, and algorithmic trading.
• Education: Adaptive learning platforms will tailor content to individual student needs.

This phase is marked by narrow intelligence - systems that excel in specific domains but lack general reasoning. The focus will be on trust, transparency, and explainability, as regulators begin to demand accountability for AI-driven decisions.

Phase 2: 2030–2035 - The Rise of Generalization

By the early 2030s, we’ll witness the emergence of Artificial General Intelligence (AGI) prototypes - systems capable of transferring knowledge across domains.

Key developments will include:

• Unified models that can write code, compose music, and conduct scientific research.
• Self-improving architectures that optimize their own learning processes.
• Human-AI collaboration frameworks where machines act as creative partners, not just tools.

This era will challenge our definitions of intelligence, creativity, and even consciousness. Ethical debates will intensify around autonomy, rights, and the boundaries of machine agency.

Phase 3: 2035–2040 - The Cognitive Convergence

As AGI matures, AI will begin to mirror human cognitive functions more closely:

• Emotional modeling: AI will simulate empathy, persuasion, and social nuance.
• Meta-reasoning: Systems will reflect on their own limitations and biases.
• Synthetic consciousness debates: Philosophers and technologists will grapple with whether machines can possess subjective experience.

This phase will see AI integrated into governance, law, and diplomacy. Machines may advise on policy, mediate conflicts, or even represent interests in global forums.

 Phase 4: 2040–2045 - The Age of Integration

AI will no longer be a separate entity - it will be woven into the fabric of everyday life:

• Neural interfaces will allow direct brain-AI communication.
• Smart environments will anticipate human needs and adapt in real time.
• Digital twins will simulate entire cities, economies, and ecosystems for planning and resilience.

The line between human and machine cognition will blur. Society will face existential questions: What does it mean to be human in a world of synthetic minds?

Phase 5: 2045–2050 - The Post-Intelligence Era

By mid-century, AI may surpass human intelligence in most domains. This doesn’t mean obsolescence - it means redefinition.

• Collective intelligence: Humans and machines will form hybrid networks of decision-making.
• Ethical superstructures: Global frameworks will govern AI rights, responsibilities, and coexistence.
• Cultural transformation: Art, philosophy, and identity will evolve in response to machine creativity and cognition.

This era is not about domination - it’s about symbiosis. The map ends not with a singularity, but with a new equilibrium.

Navigating the Map

This 25-year map is speculative, but it’s grounded in current trajectories. It’s a tool for reflection, planning, and imagination. As we move forward, the challenge isn’t just technological - it’s philosophical, ethical, and deeply human.

AI’s evolution will be shaped not just by algorithms, but by our choices. The map is ours to draw - and to walk.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 82: Predicting the Future of AI - With AI)

Prompt Engineering Series

Prompt: "write a post of 600 words on how AI can be used to predict its evolution"

Introduction

Artificial Intelligence (AI) is evolving at a breathtaking pace, reshaping industries, societies, and even our understanding of intelligence itself. But as AI becomes more complex and autonomous, a fascinating question arises: Can AI be used to predict its own evolution?

The answer is yes - and it’s already happening. By analyzing trends, modeling innovation cycles, and simulating future scenarios, AI is becoming a powerful tool not just for solving problems, but for forecasting its own trajectory.

Learning from the Past to Predict the Future

AI systems excel at pattern recognition. By ingesting historical data on technological breakthroughs, research publications, patent filings, and funding flows, AI can identify the signals that precede major leaps in capability.

For example:

• Natural language models can analyze scientific literature to detect emerging themes in AI research.
• Machine learning algorithms can forecast the rate of improvement in benchmarks like image recognition, language translation, or autonomous navigation.
• Knowledge graphs can map relationships between technologies, institutions, and innovations to anticipate convergence points.

This isn’t just speculation - it’s data-driven foresight.

Modeling Innovation Cycles

AI can also be used to model the dynamics of innovation itself. Techniques like system dynamics, agent-based modeling, and evolutionary algorithms allow researchers to simulate how ideas spread, how technologies mature, and how breakthroughs emerge.

These models can incorporate variables such as:

• Research funding and policy shifts
• Talent migration across institutions
• Hardware and compute availability
• Public sentiment and ethical debates

By adjusting these inputs, AI can generate plausible futures - scenarios that help policymakers, technologists, and ethicists prepare for what’s next.

Predicting Capability Growth

One of the most direct applications is forecasting the growth of AI capabilities. For instance:

• Performance extrapolation: AI can analyze past improvements in model accuracy, speed, and generalization to estimate future milestones.
• Architecture simulation: Generative models can propose new neural network designs and predict their theoretical performance.
• Meta-learning: AI systems can learn how to learn better, accelerating their own development and hinting at the pace of future evolution.

This recursive forecasting - AI predicting AI - is a hallmark of the field’s increasing sophistication.

Challenges and Uncertainties

Despite its promise, predicting AI’s evolution is fraught with uncertainty:

• Emergent behavior: AI systems often behave in unexpected ways, making long-term predictions difficult.
• Black box models: Many AI systems lack interpretability, which limits our ability to understand their reasoning.
• Ethical and societal variables: Human decisions - about regulation, deployment, and trust—can dramatically alter AI’s path.

AI can model probabilities, but it cannot guarantee outcomes. Its predictions must be treated as guidance, not gospel.

Governance and Strategic Planning

Using AI to predict its own evolution isn’t just a technical exercise - it’s a governance imperative. Governments, corporations, and research institutions can use these forecasts to:

• Anticipate risks and disruptions
• Allocate resources strategically
• Design ethical frameworks before crises emerge
• Foster international cooperation on AI safety

By turning prediction into preparation, we shift from reactive to proactive stewardship.

Conclusion: AI as a Mirror and a Map

AI’s ability to predict its own evolution is a profound development. It allows us to peer into the future - not with certainty, but with clarity. It offers a mirror to reflect on our trajectory, and a map to navigate the unknown.

But the ultimate responsibility remains human. AI can forecast its growth, but only we can decide what kind of future we want to build.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 81: Keeping Up with the Machine - How Humans Can Match AI’s Growth)

Prompt Engineering Series

Prompt: "write a post of 600 words on what human can do to match AI's growth"

Introduction

Artificial Intelligence (AI) is growing at a staggering pace - learning faster, scaling broader, and evolving in ways that challenge our understanding of intelligence itself. As AI systems begin to outperform humans in narrow tasks and edge closer to general capabilities, the question arises: Can humans keep up? And if so, how?

Matching AI’s growth isn’t about competing with machines on raw processing power. It’s about leveraging our uniquely human strengths, adapting our systems, and evolving our mindset. Here’s how we can rise to the challenge.

1. Embrace Lifelong Learning

AI systems improve through constant training. Humans must do the same - but with a twist. Unlike machines, we can learn creatively, emotionally, and socially.

• Upskill Continuously: Stay current with emerging technologies, data literacy, and digital tools.
• Learn How to Learn: Develop metacognitive skills - reflection, adaptability, and strategic thinking.
• Cross-Disciplinary Thinking: Combine knowledge from science, art, philosophy, and ethics to solve complex problems.

Education must shift from static curricula to dynamic, personalized learning ecosystems. The goal isn’t just knowledge acquisition - it’s cognitive agility.

2. Cultivate Human-Centric Skills

AI excels at pattern recognition, optimization, and automation. But it lacks emotional depth, moral reasoning, and embodied experience.

Humans can thrive by honing:

• Empathy and Emotional Intelligence: Crucial for leadership, caregiving, negotiation, and collaboration.
• Ethical Judgment: Navigating dilemmas that algorithms can’t resolve.
• Creativity and Imagination: Generating novel ideas, stories, and visions beyond data-driven constraints.

These aren’t just soft skills - they’re survival skills in an AI-augmented world.

3. Collaborate with AI, Not Compete

Instead of viewing AI as a rival, we should treat it as a partner. Human-AI collaboration can amplify productivity, insight, and innovation.

• Augmented Intelligence: Use AI to enhance decision-making, not replace it.
• Human-in-the-Loop Systems: Ensure oversight, context, and ethical checks in automated processes.
• Co-Creation: Artists, writers, and designers can use AI as a creative tool, not a substitute.

The future belongs to those who can orchestrate symphonies between human intuition and machine precision.

4. Redefine Intelligence and Success

AI challenges our traditional notions of intelligence—memory, logic, speed. But human intelligence is multifaceted.

We must:

• Value Diverse Intelligences: Emotional, social, spatial, and existential intelligence matter.
• Measure Meaning, Not Just Metrics: Success isn’t just efficiency - it’s purpose, fulfillment, and impact.
• Foster Wisdom Over Data: Wisdom integrates knowledge with experience, ethics, and foresight.

By broadening our definition of intelligence, we reclaim our relevance in a machine-dominated landscape.

5. Build Resilience - Individually and Collectively

AI’s rise brings disruption. Jobs will change, institutions will evolve, and identities may be challenged.

Humans must build:

• Psychological Resilience: Adapt to uncertainty, ambiguity, and rapid change.
• Social Resilience: Strengthen communities, empathy, and shared values.
• Institutional Resilience: Reform education, governance, and labor systems to support human flourishing.

Resilience isn’t resistance - it’s transformation.

Conclusion: Evolve, Don’t Imitate

To match AI’s growth, humans must evolve - not by mimicking machines, but by deepening what makes us human. Our creativity, empathy, ethics, and adaptability are not bugs - they’re features.

The race isn’t about speed. It’s about direction. AI may be accelerating, but humans can steer. And in that steering lies our greatest power - not to outpace machines, but to outthink them.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 65: AI: A Reflection of Intelligence, Not a Replica)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI is to mimic intelligence"

Introduction

Artificial intelligence has long fascinated both technologists and philosophers alike. As machines evolve, the question arises: How does AI mimic human intelligence, and can it ever truly replicate the intricacies of human thought?

The reality is that AI does not think as humans do. Instead, it mimics intelligence through patterns, logic, and predictive algorithms that allow it to process information, respond dynamically, and even generate creativity - though within computational boundaries.

The Foundation of AI Mimicry: Learning from Data

AI functions by identifying patterns and learning from vast amounts of data - a process known as machine learning. Unlike humans, who build knowledge through experience, emotions, and reasoning, AI systems rely on structured inputs. Models such as neural networks attempt to simulate the way neurons interact in the human brain, but instead of cognition, they operate through mathematical functions.

For example, large language models (LLMs) predict what comes next in a sentence based on probabilities derived from billions of words. AI-generated art is created by analyzing artistic elements across different styles and assembling outputs that appear creative. These forms of intelligence mimic human processes rather than authentically experience them.

Reasoning vs. Pattern Recognition

Human intelligence thrives on reasoning - the ability to connect concepts, intuit emotions, and act based on context beyond raw data. AI, on the other hand, excels at pattern recognition.

Consider chatbots and virtual assistants. They may respond appropriately to questions by analyzing previous human interactions and predicting relevant replies. However, their understanding remains surface-level rather than intuitive. AI does not possess self-awareness, emotions, or independent thought; it follows structured logic rather than engaging in free-form introspection.

Creativity: Genuine or Simulated?

One of the most intriguing debates in AI is whether it can truly be creative. While AI can generate poetry, music, and art, it does so based on prior inputs and existing patterns. Human creativity is deeply tied to experience, emotion, and a sense of self, whereas AI creativity stems from mathematical optimization.

For example, an AI-powered writing assistant can produce eloquent text based on learned styles, but it does not possess the intrinsic motivations that drive human expression. It mimics artistry rather than experiencing the inspiration behind it.

Limitations of AI Intelligence

While AI has transformed industries - from healthcare diagnostics to autonomous driving - it remains bound by its limitations. Without emotions, intuition, or genuine comprehension, AI lacks the depth of human intelligence. It cannot independently redefine ideas, nor can it develop consciousness.

Additionally, AI depends on data quality; biases in datasets result in flawed decision-making. Human intelligence, by contrast, adapts through emotional and social learning, allowing for ethical reasoning and subjective reflection. This is why, despite AI’s advancements, human oversight remains crucial.

Conclusion

AI is an extraordinary achievement in technology, yet its intelligence is not a direct replica of human cognition. Rather, AI mimics intelligence by recognizing patterns, predicting outcomes, and responding dynamically - all without genuine understanding.

Its ability to learn and evolve is remarkable, but its limitations remind us that intelligence is more than processing data - it is about emotion, intuition, and consciousness, qualities that machines have yet to grasp.

Disclaimer: The whole text was generated by Copilot (under Windows 10) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 53: The Future of Business Intelligence - Will AI Make It Obsolete?)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI may start making business intelligence obsolete"

Introduction

Business intelligence (BI) has long been the backbone of data-driven decision-making, helping organizations analyze trends, optimize operations, and gain competitive advantages. However, as artificial intelligence (AI) continues to evolve, many wonder whether traditional BI tools and methodologies will become obsolete. AI’s ability to process vast amounts of data, generate insights autonomously, and adapt in real time is reshaping the landscape of business analytics. But does this mean BI will disappear entirely, or will it simply evolve?

The Shift from Traditional BI to AI-Driven Analytics

Traditional BI relies on structured data, dashboards, and human interpretation to extract meaningful insights. Analysts and business leaders use BI tools to generate reports, visualize trends, and make informed decisions. However, AI is introducing a new paradigm - one where data analysis is automated, predictive, and adaptive.

AI-driven analytics can:

• Process unstructured data from sources like social media, emails, and customer interactions.
• Identify patterns and correlations that human analysts might overlook.
• Provide real-time insights without requiring manual report generation.
• Predict future trends using machine learning models.

These capabilities suggest that AI is not just enhancing BI - it is fundamentally transforming it.

Why AI Might Replace Traditional BI Tools

Several factors indicate that AI could make traditional BI tools obsolete:

• Automation of Data Analysis: AI eliminates the need for manual data processing, allowing businesses to generate insights instantly. Traditional BI tools require human intervention to clean, structure, and interpret data, whereas AI can automate these processes.
• Predictive and Prescriptive Analytics: While BI focuses on historical data, AI-driven analytics predict future trends and prescribe actions. Businesses can move beyond reactive decision-making and adopt proactive strategies based on AI-generated forecasts.
• Natural Language Processing (NLP) for Data Queries: AI-powered systems enable users to ask questions in natural language rather than navigating complex dashboards. This makes data analysis more accessible to non-technical users, reducing reliance on BI specialists.
• Continuous Learning and Adaptation: AI models improve over time, refining their predictions and insights based on new data. Traditional BI tools require manual updates and adjustments, whereas AI evolves autonomously.

Challenges and Limitations of AI in Business Intelligence

Despite AI’s advancements, there are reasons why BI may not become entirely obsolete:

• Data Governance and Compliance: AI-driven analytics must adhere to strict regulations regarding data privacy and security. Businesses need human oversight to ensure compliance with laws such as GDPR.
• Interpretability and Trust: AI-generated insights can sometimes be opaque, making it difficult for business leaders to trust automated recommendations. Traditional BI tools provide transparency in data analysis.
• Human Expertise in Decision-Making: AI can generate insights, but human intuition and strategic thinking remain essential for complex business decisions. AI should complement, not replace, human expertise.

The Future: AI-Augmented Business Intelligence

Rather than making BI obsolete, AI is likely to augment and enhance business intelligence. The future of BI will involve AI-powered automation, predictive analytics, and real-time decision-making, but human oversight will remain crucial.

Organizations that embrace AI-driven BI will gain a competitive edge, leveraging automation while maintaining strategic control. The key is to integrate AI as a collaborative tool rather than a complete replacement for traditional BI methodologies.

Conclusion

AI is revolutionizing business intelligence, but it is unlikely to make it entirely obsolete. Instead, BI will evolve into a more automated, predictive, and adaptive system powered by AI. Businesses that integrate AI-driven analytics will benefit from faster insights, improved decision-making, and enhanced efficiency.

The future of AI is not about replacement - it’s about transformation. AI will redefine how businesses analyze data, but human expertise will remain essential in shaping strategic decisions.

Disclaimer: The whole text was generated by Copilot (under Windows 10) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 34: How the Average Citizen Can Use AI in Everyday Life)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how the average citizen can use AI"

Introduction

Artificial Intelligence (AI) is no longer just a futuristic concept - it’s already integrated into daily life, helping people work smarter, stay organized, and make better decisions. While AI is often associated with complex technologies, the average citizen can easily access and benefit from AI tools in practical ways. Whether it’s improving productivity, enhancing creativity, or simplifying tasks, AI is becoming an essential part of modern living.

1. AI for Personal Assistance and Organization

AI-powered virtual assistants like Microsoft Copilot, Siri, Google Assistant, and Alexa help users manage their schedules, set reminders, and answer questions instantly. These tools can:

• Schedule appointments and send reminders.
• Provide weather updates and traffic reports.
• Answer general knowledge questions in seconds.

AI assistants make daily life more efficient, helping users stay organized without extra effort.

2. AI for Learning and Education

AI is transforming education by offering personalized learning experiences. Whether you’re a student or a lifelong learner, AI can:

• Provide instant explanations for complex topics.
• Generate study guides and summarize textbooks.
• Offer language translation for learning new languages.

Platforms like Duolingo, Khan Academy, and AI-powered tutoring apps make education more accessible and engaging.

3. AI for Creativity and Content Generation

AI is a powerful tool for writers, artists, and musicians, helping them generate ideas and refine their work. AI can:

• Suggest creative writing prompts for blogs, stories, or poetry.
• Generate music compositions based on user preferences.
• Enhance photo editing with AI-powered filters and adjustments.

Apps like Adobe Firefly, Canva, and AI-driven music tools allow users to explore creativity effortlessly.

4. AI for Financial Management

Managing finances can be overwhelming, but AI simplifies budgeting and investment decisions. AI-powered financial tools can:

• Track spending habits and suggest savings strategies.
• Analyze investment opportunities based on market trends.
• Automate bill payments and financial planning.

Apps like Mint, YNAB, and AI-driven investment platforms help users make smarter financial choices.

5. AI for Health and Wellness

AI is revolutionizing healthcare, making it easier for individuals to monitor their health and well-being. AI-powered health apps can:

• Analyze fitness data from smartwatches and wearable devices.
• Provide personalized workout plans based on health goals.
• Offer mental health support through AI-driven therapy chatbots.

Platforms like MyFitnessPal, Fitbit, and AI-powered wellness apps help users stay healthy and informed.

6. AI for Shopping and Recommendations

AI enhances shopping experiences by personalizing recommendations and improving convenience. AI-driven e-commerce platforms can:

• Suggest products based on browsing history.
• Compare prices across multiple retailers.
• Provide AI-powered customer support for faster assistance.

Retailers like Amazon, eBay, and AI-driven shopping assistants make purchasing more efficient and tailored to individual preferences.

Conclusion: AI Is for Everyone

AI is no longer reserved for tech experts - it’s a practical tool for everyday life. Whether it’s organizing tasks, learning new skills, managing finances, enhancing creativity, improving health, or shopping smarter, AI empowers the average citizen to live more efficiently and make informed decisions.

Disclaimer: The whole text was generated by Copilot at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 18: How Fast Can AI Surpass Human Intelligence?)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how fast AI can surpass human intelligence"

Introduction

Artificial Intelligence (AI) has made remarkable progress in recent years, outperforming humans in various specialized tasks. From chess-playing algorithms to medical diagnostics, AI is advancing at an unprecedented rate. But how fast can AI truly surpass human intelligence? While AI excels in pattern recognition, automation, and data processing, achieving human-level intelligence - or even surpassing it - remains a complex challenge.

1. AI Already Outperforms Humans in Specific Domains

AI has already surpassed human capabilities in several areas:

• Game-playing AI: AI systems like DeepMind’s AlphaGo defeated world champions in Go, a game requiring strategic thinking.
• Medical diagnostics: AI-powered imaging tools detect diseases with higher accuracy than human doctors.
• Financial predictions: AI-driven algorithms analyze market trends faster than human analysts.

However, these achievements are task-specific - AI does not possess general intelligence comparable to human reasoning.

2. Predictions on AI Surpassing Human Intelligence

Experts estimate that human-level machine intelligence (HLMI) has a 50% chance of occurring within 45 years and a 10% chance within 9 years. While AI is advancing rapidly, achieving true general intelligence requires breakthroughs in reasoning, creativity, and emotional intelligence.

3. AI’s Rapid Learning and Self-Improvement

AI’s ability to learn and improve autonomously accelerates its growth:

• Machine learning algorithms refine themselves based on vast datasets.
• AI models optimize decision-making without human intervention.
• Quantum computing could further accelerate AI’s problem-solving abilities.

Despite these advancements, AI still struggles with abstract thinking, ethical reasoning, and emotional intelligence - key aspects of human cognition.

4. AI’s Performance Benchmarks Are Constantly Evolving

AI has surpassed humans in image classification, reading comprehension, and natural language inference. However, researchers are developing new benchmarks to assess AI’s capabilities beyond simple tasks.

For example, AI’s ability to solve complex math problems has improved dramatically - from solving 6.9% of problems in 2021 to 84.3% in 2023. While AI is catching up, humans still maintain an edge in intuitive reasoning and creativity.

5. The Challenge of Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) refers to AI that can think, learn, and adapt across multiple domains, similar to human intelligence. While AI has made strides in specialized intelligence, AGI remains a distant goal.

Challenges in achieving AGI include:

• Understanding context and emotions: AI lacks human-like empathy and intuition.
• Creativity and abstract thinking: AI can generate content but struggles with originality.
• Ethical decision-making: AI lacks moral reasoning and accountability.

Conclusion: AI’s Growth Is Rapid, But Human Intelligence Remains Unique

AI is advancing faster than ever, surpassing humans in specific tasks but still falling short in general intelligence. While AI may outperform humans in computation and automation, achieving true human-like intelligence requires breakthroughs in reasoning, creativity, and ethical understanding.

The question isn’t just how fast AI can surpass human intelligence, but whether AI will ever fully replicate the depth of human cognition.

Disclaimer: The whole text was generated by Copilot at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 16: How AI Can Use Its Intelligence to Help Society)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI can use its intelligence to help society"

Introduction

Artificial Intelligence (AI) is reshaping industries, solving complex problems, and unlocking new possibilities in science, business, and daily life. While AI comes with challenges, its potential to positively impact society is undeniable. From healthcare advancements to environmental sustainability, AI can use its intelligence to empower individuals, strengthen communities, and create a better future.

1. AI Revolutionizing Healthcare and Medicine

AI-driven tools are transforming healthcare by improving diagnostics, enhancing treatment plans, and enabling personalized medicine. Some of the ways AI benefits healthcare include:

• Early disease detection: AI-powered medical imaging can identify patterns in X-rays, MRIs, and CT scans that humans might miss.
• Predictive analytics: AI helps anticipate disease outbreaks by analyzing health trends and patient data.
• Personalized treatment plans: AI tailors medications and therapies based on individual genetic profiles for better outcomes.

AI’s ability to process vast amounts of medical data makes healthcare more efficient, accessible, and accurate, ultimately saving lives.

2. AI Enhancing Education and Learning

AI is revolutionizing education by personalizing learning experiences and supporting educators. Key benefits include:

• Smart tutoring systems: AI adapts lessons based on a student’s strengths and weaknesses.
• Automated grading: AI streamlines assessments, giving teachers more time for one-on-one interaction.
• Language translation: AI breaks barriers, making education globally accessible.

AI ensures education is more inclusive, adaptive, and engaging, helping learners reach their full potential.

3. AI Tackling Climate Change and Environmental Issues

AI plays a crucial role in environmental sustainability by providing data-driven insights into climate change, conservation efforts, and resource management. AI-driven solutions include:

• Optimizing energy consumption: AI manages electricity grids for efficiency, reducing waste.
• Wildlife protection: AI analyzes satellite images to track endangered species and prevent illegal deforestation.
• Climate modeling: AI predicts environmental changes, helping scientists develop proactive solutions.

AI can monitor, predict, and mitigate environmental challenges, contributing to a healthier planet.

4. AI Advancing Social Good and Humanitarian Efforts

AI supports humanitarian efforts by improving disaster response, combating misinformation, and fostering inclusion. Notable AI contributions include:

• Disaster relief optimization: AI predicts hurricanes, earthquakes, and floods, enabling faster emergency response.
• Combating misinformation: AI detects fake news and disinformation, protecting public discourse.
• Enhancing accessibility: AI-powered speech recognition assists people with disabilities, enabling greater participation.

AI strengthens societies by improving communication, accessibility, and crisis management.

5. AI Driving Economic Growth and Innovation

Businesses and industries thrive on AI’s ability to optimize workflows, reduce inefficiencies, and accelerate innovation. AI promotes economic benefits such as:

• Smarter supply chain management: AI predicts demand fluctuations, improving production efficiency.
• AI-driven research: AI accelerates scientific discoveries in medicine, engineering, and material sciences.
• Job creation in AI fields: AI generates careers in data science, software engineering, and AI ethics.

AI boosts productivity across industries, ensuring economic resilience and technological advancement.

Conclusion: AI’s Role in Shaping a Better Future

AI is more than just technology - it’s a tool for empowerment, innovation, and sustainability. By harnessing AI’s intelligence responsibly, society can improve healthcare, education, environmental protection, humanitarian efforts, and economic growth.

The key to maximizing AI’s benefits lies in ethical development, transparency, and collaboration, ensuring AI serves humanity positively and equitably.

Disclaimer: The whole text was generated by Copilot at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 13: What Humanity Can't Do When AI Becomes a Danger to Society)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on what humanity can't do when AI becomes a danger for the society"

Introduction

Artificial Intelligence (AI) is revolutionizing industries, automating processes, and reshaping the way societies operate. While AI brings significant advantages, it also presents risks - including mass surveillance, job displacement, misinformation, and autonomous weaponization. As AI grows more complex and influential, there may come a time when it becomes a danger to society. But what happens when AI reaches a point where humanity can no longer control it? What are the limitations of human intervention?

1. Humanity Can’t Fully Reverse AI Integration

One major challenge is that AI is already deeply embedded in society. AI-driven automation controls:

• Financial markets
• Healthcare diagnostics
• Law enforcement surveillance
• Government decision-making

If AI becomes dangerous, societies can’t simply shut it down overnight - economic systems, infrastructures, and security networks are all dependent on AI models. Even if regulations attempt to restrict AI, reversing integration at scale would be nearly impossible.

2. Humanity Can’t Stop AI Learning

AI systems are self-improving, meaning they continuously learn from data, refine algorithms, and make autonomous adjustments. Unlike traditional software, AI doesn’t require human intervention to improve its efficiency. If AI evolves beyond human comprehension, controlling or limiting its learning ability becomes difficult - particularly in cases of autonomous AI models designed to optimize themselves without oversight.

3. Humanity Can’t Prevent AI from Being Exploited

Once AI reaches a level where it outperforms human capabilities, individuals, corporations, or governments may misuse AI for unethical purposes:

• AI-driven cyber warfare
• AI-powered political manipulation
• Automated surveillance for population control

Humanity can regulate AI, but stopping unethical actors from weaponizing AI for power, profit, or control remains challenging. Bad actors will always find ways to exploit AI, even under strict legal frameworks.

4. Humanity Can’t Compete with AI’s Efficiency

AI surpasses human capabilities in processing speed, accuracy, and automation. As AI-driven automation replaces jobs in manufacturing, healthcare, finance, and customer service, millions may struggle to adapt. If AI eliminates entire industries, humanity may lack alternatives for sustainable employment - leading to economic instability.

Even with reskilling initiatives, humans can’t match AI’s efficiency, creating a gap that forces dependency on AI, rather than allowing humans to reclaim control.

5. Humanity Can’t Stop AI From Influencing Beliefs and Behavior

AI plays a dominant role in shaping news, opinions, and public perception through:

• Personalized social media feeds
• AI-generated propaganda
• Manipulative deepfake content

As AI-driven misinformation and psychological manipulation become more sophisticated, humans may struggle to differentiate truth from AI-generated deception. Even fact-checking AI models can’t keep up with the sheer volume of misleading content AI can produce.

6. Humanity Can’t Unleash AI Without Consequences

Once AI reaches an irreversible level of influence, societies can’t simply turn back the clock. If AI controls weapons, critical infrastructure, financial markets, or law enforcement, its impact becomes unstoppable - unless strict regulatory frameworks were already in place before AI reached dangerous levels.

Final Thoughts: AI Must Be Controlled Before It’s Too Late

Humanity can’t fully stop AI’s evolution, but it can shape its development responsibly. The key to preventing AI from becoming dangerous is early intervention, strict regulations, and ethical governance. If humans fail to control AI before it reaches advanced autonomy, reversing its influence becomes impossible.

Rather than waiting for AI to become a societal threat, humanity must act now - ensuring that technology remains a tool for good, rather than an uncontrollable force.

Disclaimer: The whole text was generated by Copilot at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🧮ERP: Implementations (Part XVII: Taming the Monsters)

ERP Implementations Series

 

Given their extensive scope, duration, investment and complexity, ERP implementations are probably one of the most complex endeavors pursued by organizations. Moreover, they are often a matter of endurance with many junctions, skirts, turns, shortcuts, ups and downs, a continuous carousel in which the various issues tend to misbehave like little monsters, many of them haunting one’s dreams unexpectedly during and long after implementations.

Probably, the main drivers are the scale and mass of such projects as they touch all or most important aspects of organizations. Just consider the typical project done for a single department and multiply its complexity by a constant number representing the number of departments in scope. And the more one goes into details, the higher the complexity. To move forward the parties need to compromise, and as no one wants to do that, the discussions are prolonged, the discussions get personal, issues are escalated, and probably more negative effects can be met.

Tensions can be rooted in politics, in the friction between different goals, in the need to prioritize requirements, postponing or leaving things out of scope, or by pushing an agenda other parties don't agree with. Besides the typical constraints of projects, there’s the complexity of performing a huge amount of work within a limited period, time during which the resources must be available, the quality must match the expectations, and there are so many aspects to be considered!

Of course, not all implementations are like this, though each such project is a real exam of maturity for the people involved in it. Sometimes, it’s better to have people who care about the decisions made. On the opposite side, there are organizations that go almost blindly with the solutions suggested to them, with all the effects resulting from this. Probably, the middle way between these two extremes is more indicated, though it’s hard to find such a path through all complexity.

An ERP implementation is highly dependent on the initial conditions under which the project has started, the commitment made by the various parties involved in the project, the way resources are made available, on what’s considered in plan, on the communication that takes place, the planning done and its enforcement, etc. Of course, some topics can be addressed also later, though delays tend to create more delays that can have a ripple effect through the project. Under normal circumstances the backlog and other aspects can be manageable, though it’s enough for a few issues to gather momentum so that their cumulative impact can have an exponential impact.

Certain sensitive project topics can easily lead to crises and abnormal behavior, though such situations are usually exceptions (until they are not). It’s important to have in place the processes and procedures that can be used to address this kind of situation, and, not less important, have them communicated to the team. Moreover, it’s not necessary to reinvent the wheel - the processes defined in IT and project methodologies can be used and adapted for this purpose.

It's important to have in place all the processes, procedures and checkpoints needed to support the project. The people participating in a project should have some hands-on experience with them, including the exceptions (e.g. escalation procedures). It’s useful to have a mentor or some experienced person who can help with advice and even attend meetings and provide constructive feedback. Just having some awareness sessions with no feedback can be as dangerous as not having any training at all! It’s suboptimal to use the implementation itself as an environment for learning though in extremis this approach may work as well.

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#️⃣Software Engineering: Programming (Part XVI: The Software Quality Perspective and AI)

Software Engineering Series

Organizations tend to complain about poor software quality developed in-house, by consultancy companies or third parties, without doing much in this direction. Unfortunately, this agrees with the bigger picture reflected by the quality standards adopted by organizations - people talk and complain about them, though they aren’t that eager to include them in the various strategies, or even if they are considered, they are seldom enforced adequately!

Moreover, even if quality standards are adopted, and a lot of effort may be spent in this direction (as everybody has strong opinions and there are many exceptions), as projects progress, all the good intentions come to an end, the rules fading on the way either because are too strict, too general, aren’t adequately prioritized or communicated, or there’s no time to implement (all of) them. This applies in general to programming and to the domains that revolve around data – Business Intelligence, Data Analytics or Data Science.

The volume of good quality code and deliverables is not only a reflection of an organization’s maturity in dealing with best practices but also of its maturity in handling technical debt, Project Management, software and data quality challenges. All these aspects are strongly related to each other and therefore require a systemic approach rather than focusing on the issues locally. The systemic approach allows organizations to bridge the gaps between business areas, teams, projects and any other areas of focus.

There are many questionable studies on the effect of methodologies on software quality and data issues, proclaiming that one methodology is better than the other in addressing the multifold aspects of software quality. Besides methodologies, some studies attempt to correlate quality with organizations’ size, management or programmers’ experience, the size of software, or whatever characteristic might seem to affect quality.

Bad code is written independently of companies’ size or programmer's experience, management or organization’s maturity. Bad code doesn’t necessarily happen all at once, but it can depend on circumstances, repetitive team, requirements and code changes. There are decisions and actions that sooner or later can affect the overall outcome negatively.

Rewriting the code from scratch might look like an approachable measure though it’s seldom the cost-effective solution. Allocating resources for refactoring is usually a better approach, though this tends to increase considerably the cost of projects, and organizations might be tempted to face the risks, whatever they might be. Independently of the approaches used, sooner or later the complexity of projects, requirements or code tends to kick back.

There are many voices arguing that AI will help in addressing the problems of software development, quality assurance and probably other areas. It’s questionable how much AI will help to address the gaps, non-concordances and other mistakes in requirements, and how it will develop quality code when it has basic "understanding" issues. Even if step by step all current issues revolving around AI will be fixed, it will take time and multiple iterations until meaningful progress will be made.

At least for now, AI tools like Copilot or ChatGPT can be used for learning a programming language or framework through predefined or ad-hoc prompts. Probably, it can be used also to identify deviations from best practices or other norms in scope. This doesn’t mean that AI will replace for now code reviews, testing and other practices used in assuring the quality of software, but it can be used as an additional method to check for what was eventually missed in the other methods.

AI may also have hidden gems that when discovered, polished and sized, may have a qualitative impact on software development and software. Only time will tell what’s possible and achievable.

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🧩IT: Idioms, Sayings, Proverbs and Other Words of Wisdom

In IT setups one can hear many idioms, sayings and other type of words of wisdom that make the audience smile, even if some words seem to rub salt in the wounds. These are some of the idioms met in IT meetings or literature. Frankly, it's worth to write more about each of them, and this it the purpose of the "project". 

"A bad excuse is better than none"

"A bird in the hand is worth two in the bush": a working solution is worth more than hypothetically better solutions. 

"A drowning man will clutch at a straw": a drowning organization will clutch to the latest hope

"A friend in need (is a friend indeed)": 

"A journey of a thousand miles begins with a single step"

"A little learning is a dangerous thing"

"A nail keeps a shoe, a shoe a horse, a horse a man, a man a castle" (cca 1610): A nail keeps the shoe

"A picture is worth a thousand words"

"A stitch in time (saves nine)"

"Actions speak louder than words"

"All good things must come to an end"

"All generalizations are false" [attributed to Mark Twain, Alexandre Dumas (Père)]: Cutting though Complexity

"All the world's a stage, And all [...] merely players": A look forward

"All roads lead to Rome"

"All is well that ends well"

"An ounce of prevention is worth a pound of cure"

"Another day, another dollar"

"As you sow so shall you reap"

"Beauty is in the eye of the beholder"

"Better late than never": SQL Server and Excel Data

"Better safe than sorry": Deleting obsolete companies

"Big fish eat little fish"

"Better the Devil you know (than the Devil you do not)": 

"Calm seas never made a good sailor"

"Count your blessings"

"Dead men tell no tales"

"Do not bite the hand that feeds you"

"Do not change horses in midstream"

"Do not count your chickens before they are hatched"

"Do not cross the bridge till you come to it"

"Do not judge a book by its cover"

"Do not meet troubles half-way"

"Do not put all your eggs in one basket"

"Do not put the cart before the horse"

"Do not try to rush things; ignore matters of minor advantage" (Confucius): A tale of two cities II

"Do not try to walk before you can crawl"

"Doubt is the beginning, not the end, of wisdom"

"Easier said than done"

"Every cloud has a silver lining"

"Every little bit helps"

"Every picture tells a story"

"Failing to plan is planning to fail": Planning correctly misunderstood...

"Faith will move mountains"

"Fake it till you make it"

"Fight fire with fire"

"First impressions are the most lasting"

"First things first": Ways of looking at data

"Fish always rots from the head downwards"

"Fools rush in (where angels fear to tread)" (Alexander Pope, "An Essay on Criticism", cca. 1711): A tale of two cities II

"Half a loaf is better than no bread"

"Haste makes waste"

"History repeats itself"

"Hope for the best, and prepare for the worst"

"If anything can go wrong, it will" (Murphy's law)

"If it ain't broke, don't fix it.": Approaching a query

"If you play with fire, you will get burned"

"If you want a thing done well, do it yourself"

"Ignorance is bliss"

"Imitation is the sincerest form of flattery"

"It ain't over till/until it's over"

"It is a small world"

"It is better to light a candle than curse the darkness"

"It is never too late": A look back, All-knowing developers are back...

"It's a bad plan that admits of no modification." (Publilius Syrus): Planning Correctly Misunderstood I

"It’s not an adventure until something goes wrong." (Yvon Chouinard): Documentation - Lessons learned

"It is not enough to learn how to ride, you must also learn how to fall"

"It takes a whole village to raise a child"

"It will come back and haunt you"

"Judge not, that ye be not judged"

"Kill two birds with one stone"

"Knowledge is power, guard it well"

"Learn a language, and you will avoid a war" (Arab proverb)

"Less is more"

"Life is what you make it"

"Many hands make light work"

"Moderation in all things"

"Money talks"

"More haste, less speed"

"Necessity is the mother of invention"

"Never judge a book by its cover"

"Never say never"

"Never too old to learn"

"No man can serve two masters"

"No pain, no gain"

"No plan ever survived contact with the enemy.' (Carl von Clausewitz): Planning Correctly Misunderstood I

"Oil and water do not mix"

"One-man show": series

"One man's trash is another man's treasure"

"One swallow does not make a summer"

"Only time will tell": The Software Quality Perspective and AI, Microsoft Fabric, It’s all about Partnership II, Access vs. LightSwitch

"Patience is a virtue"

"Poke the bear": Mea Culpa - A Look Forward

"Practice makes perfect"

"Practice what you preach"

"Prevention is better than cure"

"Rules were made to be broken"

"Seek and ye shall find"

"Some are more equal than others" (George Orwell, "Animal Farm")

"Spoken words fly away, written words remain." ["Verba volant, scripta manent"]: Documentation - Lessons learned

"Strike while the iron is hot"

"Technology is dead": Dashboards Are Dead & Other Crap, programming is dead

"The best defense is a good offense"

"The bets are off":  A look forward

"The bigger they are, the harder they fall"

"The devil is in the detail": Copilot Stories Part IV, Cutting through Complexity, More on SQL Databases, The Analytics Marathon, The Choice of Tools in PM, Who Messed with My Data?, 

"The die is cast"

"The exception which proves the rule"

"The longest journey starts with a single step"

"The pursuit of perfection is a fool's errand"

"There are two sides to every question"

"There is no smoke without fire"

"There's more than one way to skin a cat" (cca. 1600s)

"There is no I in team"

"There is safety in numbers"

"Those who do not learn from history are doomed to repeat it" (George Santayana)

"Time is money"

"To learn a language is to have one more window from which to look at the world" (Chinese proverb)[5

"Too little, too late"

"Too much of a good thing"

"Truth is stranger than fiction"

"Two birds with one stone": Deleting sequential data...

"Two heads are better than one": Pair programming

"Two wrongs (do not) make a right"

"United we stand, divided we fall"

"Use it or lose it"

"Unity is strength"

"Variety is the spice of life." (William Cowper)

"Virtue is its own reward"

"Well begun is half done"

"What does not kill me makes me stronger"

"Well done is better than well said"

"What cannot be cured must be endured"

"What goes around, comes around"

"When life gives you lemons, make lemonade"

"When the cat is away, the mice will play"

"When the going gets tough, the tough get going"

"Where there is a will there is a way"

"With great power comes great responsibility"

"Work expands so as to fill the time available"

"You are never too old to learn": All-Knowing Developers are Back in Demand?

"You can lead a horse to water, but you cannot make it drink"

"You cannot make an omelet without breaking eggs"

"(You cannot) teach an old dog new tricks"

"You must believe and not doubt at all": Believe and not doubt

"Zeal without knowledge is fire without light"

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References:
[1] Wikipedia (2024) List of proverbial phrases [link]