🤖〽️Prompt Engineering: Copilot Unabridged (Part 47: The Future of AI - How Artificial Intelligence Could Evolve in the Next Decade)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI can evolve in the next 10 years"

Introduction

Artificial intelligence (AI) has transformed industries and daily life in ways that once seemed like science fiction. Over the next decade, AI is expected to become even more sophisticated, reshaping business, healthcare, entertainment, and how humans interact with technology. While predicting the exact trajectory of AI is challenging, emerging trends indicate several exciting advancements and challenges ahead.

1. AI’s Integration into Everyday Life

AI-powered virtual assistants will become more intuitive, personalized, and proactive. Instead of simply responding to commands, they will anticipate user needs, offering recommendations before being asked. For example, AI could book travel arrangements, schedule appointments, and manage daily tasks with minimal input from users. AI companions will evolve to provide emotional support, becoming increasingly adept at understanding human emotions and social dynamics.

2. AI’s Role in Healthcare

Healthcare is already benefiting from AI-driven innovations, but the next decade will bring even more breakthroughs. AI will enhance early disease detection through advanced predictive analytics, improving diagnostic accuracy for conditions like cancer and neurological disorders. AI-powered robots and virtual doctors will assist in surgeries, monitor patient health, and offer personalized treatment plans tailored to an individual’s genetic makeup and lifestyle. Drug discovery will also accelerate, allowing pharmaceutical companies to create life-saving treatments at a faster rate.

3. AI and Creativity

Rather than replacing human creativity, AI will act as a collaborative partner in art, music, writing, and design. AI-generated music and visual art will continue evolving, assisting creators in refining and expanding their work. AI will also play a major role in storytelling, helping authors create immersive worlds, develop characters, and generate plots with unprecedented depth. AI-powered movie scripts and video game narratives will push the boundaries of interactive entertainment, blurring the lines between human and machine creativity.

4. AI in Business and Automation

AI-driven automation will significantly transform the workforce. AI-powered machines will take over repetitive and hazardous tasks in manufacturing, reducing workplace accidents and increasing efficiency. Personalized AI customer service bots will enhance business interactions, offering instant, intelligent responses to customer inquiries. AI-driven financial analysis will provide businesses with better forecasting models, enhancing decision-making processes and reducing financial risks.

5. Ethical and Regulatory Challenges

As AI becomes more integrated into society, concerns about privacy, bias, and security will intensify. Governments and organizations will need to implement strong AI governance frameworks to regulate AI ethics and prevent misuse. AI models will undergo rigorous bias audits to ensure fairness, minimizing algorithmic discrimination in hiring, loan approvals, and criminal justice decisions. Striking the balance between AI innovation and ethical oversight will remain a key challenge in the coming years.

6. AI-Powered Autonomous Systems

Self-driving cars will become safer, more reliable, and widespread. AI will enhance traffic management systems, reducing congestion and optimizing travel routes in real time. AI-powered drones and robots will assist in disaster response, delivering medical supplies, conducting search-and-rescue missions, and monitoring environmental hazards. AI will also play a role in space exploration, powering autonomous rovers on Mars and assisting in deep-space research.

7. AI’s Potential in Scientific Discovery

AI will accelerate scientific breakthroughs in fields such as physics, biology, and climate research. AI-powered simulations will help scientists predict climate change trends more accurately, develop sustainable energy solutions, and discover new materials with groundbreaking applications. AI will also aid in decoding complex data in quantum computing, potentially revolutionizing computation beyond classical computing limitations.

Conclusion

AI will become an integral part of society in ways that are both exciting and challenging. The next decade will see AI evolving into an indispensable tool, assisting humanity in ways that were unimaginable just a few years ago. As AI continues to advance, ethical considerations, regulatory frameworks, and responsible development will shape how AI integrates into everyday life. With careful oversight and continued research, AI has the potential to uplift societies, solve pressing global challenges, and redefine the boundaries of human capability.

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 46: Understanding AI Governance - Balancing Innovation and Responsibility)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI governance works"

Introduction

Artificial intelligence (AI) is rapidly transforming industries, societies, and economies worldwide. Its immense potential has led to groundbreaking advancements in healthcare, finance, transportation, and more. However, with these capabilities come risks - bias in AI models, privacy concerns, and misuse of AI-powered systems. To address these challenges, AI governance has emerged as a critical framework for ensuring responsible AI development and deployment.

What is AI Governance?

AI governance refers to the policies, laws, regulations, and ethical frameworks that guide AI development and usage. It encompasses a broad spectrum of considerations, including data privacy, security, accountability, transparency, and fairness. The goal is to balance the rapid advancement of AI technology with societal norms and ethical principles.

Governance mechanisms differ across regions and industries, but they typically involve collaboration between governments, tech companies, academic researchers, and civil society groups. The underlying challenge in AI governance is ensuring AI systems benefit humanity while mitigating risks such as bias, discrimination, and security vulnerabilities.

Key Principles of AI Governance

Several fundamental principles shape AI governance frameworks across the globe:
Transparency: AI systems should be understandable and explainable. Black-box models, where the decision-making process remains obscure, can lead to concerns regarding bias and accountability.

Explainability helps foster trust among users and regulators.

• Accountability: Organizations developing and deploying AI must take responsibility for their systems’ behavior. This includes ensuring ethical use, addressing unintended consequences, and establishing mechanisms for legal recourse when AI causes harm.
• Privacy and Data Protection: AI systems rely on vast amounts of data, raising concerns about privacy breaches and misuse. Strong governance frameworks require compliance with data protection laws such as GDPR in Europe, ensuring users have control over their personal information.
• Bias and Fairness: AI can inherit biases from training data, leading to discriminatory outcomes. Ethical AI governance emphasizes fairness, reducing disparities in AI-driven decisions affecting hiring, law enforcement, healthcare, and financial services.
• Security and Safety: As AI applications expand, cybersecurity threats, deepfake technology, and AI-driven autonomous weapons become pressing concerns. Governance frameworks must enforce security protocols to prevent malicious use of AI systems.

Global AI Governance Initiatives

Different nations and organizations are approaching AI governance in diverse ways:

• European Union (EU): The EU’s Artificial Intelligence Act seeks to regulate AI based on risk categories. High-risk applications, such as biometric identification and critical infrastructure management, face stricter requirements, while lower-risk systems have minimal oversight.
• United States: The U.S. government has taken a more hands-off approach, emphasizing AI innovation while promoting ethical guidelines through the National Institute of Standards and Technology (NIST) AI Risk Management Framework. States such as California have begun implementing stricter AI policies, particularly regarding data privacy.
• China: China has introduced comprehensive AI laws emphasizing security, data control, and algorithmic regulation. The country focuses on AI governance that aligns with state interests while fostering technological leadership in AI innovation.
• United Nations (UN) & Industry Collaborations: The UNESCO AI Ethics Framework and initiatives like the Partnership on AI bring together global stakeholders to promote responsible AI development. Large tech firms, including Microsoft and Google, have also created internal AI governance structures to align their AI systems with ethical standards.

Challenges in AI Governance

While governance frameworks are evolving, challenges remain:

• Regulatory Complexity: AI development is global, but governance laws vary widely, making international collaboration essential yet difficult.
• Balancing Innovation and Regulation: Striking the right balance between enabling innovation and imposing regulations is crucial to avoid stifling progress.
• Enforcement: Ensuring companies adhere to AI regulations requires oversight and accountability mechanisms, which can be difficult to implement.

The Future of AI Governance

AI governance will continue to evolve as AI capabilities expand. Ethical AI development, global cooperation, and transparent policies will play a crucial role in shaping a future where AI benefits society responsibly. Initiatives promoting AI auditing, fairness assessments, and bias reduction will become integral to AI governance frameworks.

Governance is not about restricting AI; rather, it’s about steering its trajectory toward ethical, secure, and beneficial use. By integrating ethics, accountability, and oversight into AI development, we can maximize AI’s potential while mitigating risks, ensuring its contributions to humanity remain positive.

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 40: How Disruptive Is AI as a Technology?)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how disruptive is AI as technology"

Introduction

Artificial Intelligence (AI) is one of the most transformative and disruptive technologies of the modern era. It is reshaping industries, automating processes, and redefining human interactions with machines. While AI brings efficiency, innovation, and economic growth, it also raises concerns about job displacement, ethical dilemmas, and security risks. Understanding AI’s disruptive nature is crucial for navigating its impact on society.

1. AI’s Disruption Across Industries

AI is revolutionizing multiple sectors, leading to both opportunities and challenges:

• Healthcare: AI-powered diagnostics improve accuracy, but automation may reduce the need for human radiologists and pathologists.
• Finance: AI-driven trading algorithms optimize investments, yet they also introduce risks of market instability.
• Retail: AI personalizes shopping experiences, but automation threatens traditional retail jobs.
• Manufacturing: AI-powered robotics enhance efficiency, yet they replace human labor in factories.

AI’s ability to streamline operations and reduce costs makes it highly disruptive, forcing industries to adapt or risk obsolescence.

2. AI’s Impact on Employment and Workforce Dynamics

One of AI’s most significant disruptions is its effect on employment. While AI creates new job opportunities in data science, AI development, and cybersecurity, it also eliminates traditional roles in sectors like customer service, transportation, and manufacturing.

• Automation replaces repetitive tasks, reducing demand for human workers.
• AI-driven hiring processes change recruitment dynamics, making job searches more competitive.
• Reskilling becomes essential, as workers must adapt to AI-integrated industries.

Governments and businesses must invest in workforce retraining to mitigate AI-induced unemployment.

3. AI’s Ethical and Security Challenges

AI’s disruptive nature extends beyond economics - it raises ethical concerns and security risks:

• Bias in AI algorithms: AI models trained on biased data can reinforce discrimination in hiring, healthcare, and law enforcement.
• Privacy concerns: AI-driven surveillance and data collection raise questions about personal security.
• Cybersecurity threats: AI-powered hacking tools exploit vulnerabilities, making digital security more complex.

Without strong ethical guidelines and security measures, AI’s disruption could lead to unintended consequences.

4. AI’s Role in Decision-Making and Governance

AI is increasingly used in government policies, legal judgments, and corporate strategies, raising concerns about autonomy and accountability:

• AI-driven governance: Governments use AI for predictive policing and policy analysis, but transparency is crucial.
• AI in legal systems: AI assists in legal research, yet its role in judicial decisions remains controversial.
• AI-powered corporate decision-making: Businesses rely on AI for strategic planning, but human oversight is necessary.

Balancing AI automation with human judgment is essential for ethical governance.

Conclusion: AI’s Disruption Requires Responsible Management

AI is undeniably disruptive, reshaping industries, employment, ethics, and governance. While its advancements bring efficiency and innovation, they also introduce challenges that require proactive solutions. Governments, businesses, and researchers must develop ethical AI policies, invest in workforce adaptation, and ensure AI remains a tool for progress rather than an unchecked 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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🤖〽️Prompt Engineering: Copilot Unabridged (Part 38: The Growing Backlog of AI Policies - How Much Needs to Be Done?)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how big is the backlog of policies that need to be made" because of AI

Introduction

Artificial Intelligence (AI) is advancing at an unprecedented pace, reshaping industries, governance, and daily life. However, the rapid evolution of AI has outpaced regulatory frameworks, leaving governments, businesses, and institutions struggling to catch up with necessary policies. The backlog of AI-related policies is substantial, covering areas such as ethics, security, employment, privacy, and accountability.

1. The Urgent Need for AI Governance

AI’s widespread adoption has created legal and ethical gaps that require immediate policy intervention. Some of the most pressing areas include:

• AI bias and fairness: Policies must ensure AI systems do not reinforce discrimination.
• Data privacy and security: AI-driven data collection raises concerns about surveillance and personal privacy.
• AI accountability: Governments must define who is responsible when AI makes errors or unethical decisions.
• AI in employment: Regulations must address job displacement and workforce adaptation.

Without clear policies, AI’s impact on society could become unpredictable and potentially harmful.

2. The Backlog of AI Policies Across Industries

Different sectors require specific AI regulations, but many industries are lagging behind in policy development. Some examples include:

• Healthcare: AI-powered diagnostics and treatment recommendations need strict oversight to prevent errors.
• Finance: AI-driven trading algorithms require regulations to prevent market manipulation.
• Education: AI-powered learning tools must be monitored to ensure ethical use and prevent misinformation.
• Law enforcement: AI surveillance and facial recognition technologies raise concerns about civil liberties.

Governments and institutions must accelerate policy development to keep up with AI’s rapid integration into these fields.

3. Global AI Policy Challenges

AI regulation is not uniform across countries, leading to conflicting policies and enforcement difficulties. Some challenges include:

• Different AI ethics standards: Countries have varying perspectives on AI’s role in governance and security.
• Cross-border AI regulations: AI-driven cyber threats and misinformation require international cooperation.
• Corporate AI policies: Businesses must align AI governance with global standards to ensure compliance.

The backlog of AI policies is not just national - it’s global, requiring collaboration between governments, corporations, and researchers.

4. The Role of AI Policy Frameworks

Organizations and governments are working to develop AI policy frameworks, but progress is slow. Some key initiatives include:

• The EU AI Act, which aims to regulate AI applications based on risk levels.
• ISO 42001, an AI management system standard for responsible AI governance.
• Corporate AI policies, guiding ethical AI use within businesses.

Despite these efforts, the backlog of AI policies remains significant, requiring faster implementation and enforcement.

Conclusion: AI Policy Development Must Accelerate

The backlog of AI policies is large and growing, covering ethics, security, employment, privacy, and accountability. Governments, businesses, and institutions must prioritize AI governance, ensuring regulations keep pace with technological advancements. Without proactive policy development, AI’s impact on society could become unmanageable, making responsible AI regulation more urgent than ever.

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 37: How AI Can Increase Unemployment)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI can increase unemployment"

Introduction

Artificial Intelligence (AI) is transforming industries, automating processes, and reshaping the global workforce. While AI offers efficiency and innovation, it also presents significant risks to employment, potentially leading to widespread job displacement. As AI-driven automation replaces human labor, concerns about unemployment, economic inequality, and workforce adaptation continue to grow.

1. AI-Driven Job Displacement

One of the most immediate ways AI increases unemployment is through automation, where machines and algorithms replace human workers. Industries most affected include:

• Manufacturing: AI-powered robotics reduce the need for factory workers.
• Retail and customer service: AI chatbots and self-checkout systems replace human employees.
• Finance: AI automates investment decisions, reducing demand for financial analysts.

A report by McKinsey Global Institute estimates that by 2030, up to 375 million workers worldwide may need to switch occupations due to automation.

2. AI’s Impact on Low-Skilled Jobs

AI disproportionately affects low-skilled and repetitive jobs, making it harder for workers in these roles to transition to new employment. Examples include:

• Warehouse workers replaced by AI-driven logistics systems.
• Data entry clerks replaced by automated processing software.
• Truck drivers replaced by autonomous vehicles.

Without proper workforce retraining, AI-induced unemployment could widen economic inequality.

3. AI’s Role in Economic Inequality

AI benefits corporations that can afford advanced automation, but it disadvantages smaller businesses and low-income workers. AI-driven automation:

• Increases profits for large corporations, widening the gap between wealthy and struggling businesses.
• Reduces wages for workers, as AI replaces human labor.
• Limits economic mobility, making it harder for displaced workers to find new opportunities.

Without proper regulation, AI could exacerbate income inequality, leading to social unrest and economic polarization.

4. AI’s Influence on Hiring Practices

AI-powered recruitment platforms streamline hiring but also eliminate human judgment, leading to:

• Automated resume screening, which may overlook unconventional career paths.
• AI-driven hiring biases, reinforcing discrimination in employment decisions.
• Reduced personal engagement, making job seekers feel disconnected from employers.

While AI improves efficiency, it removes the human element from hiring, making employment less accessible.

5. AI’s Role in Financial Market Instability

AI-driven algorithms dominate stock trading, investment strategies, and financial predictions. While AI enhances efficiency, it also introduces risks:

• AI-powered trading systems can trigger market crashes due to rapid, automated decision-making.
• AI-driven financial models may misinterpret economic trends, leading to poor investment choices.
• Over-reliance on AI in banking could create vulnerabilities in financial security.

AI’s influence on financial markets requires strict oversight to prevent economic instability.

Conclusion: AI’s Economic Risks Require Careful Management

While AI offers efficiency and innovation, its negative economic impact - job displacement, inequality, financial instability, and hiring biases - must be addressed. Governments, businesses, and researchers must develop policies that balance AI’s benefits with economic sustainability, ensuring AI serves society rather than disrupting it.

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 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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🏭🗒️Microsoft Fabric: Result Set Caching in SQL Analytics Endpoints [Notes] 🆕

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 21-Jun-2025

[Microsoft Fabric] Result Set Caching in SQL Analytics Endpoints

• {def} built-in performance optimization for Warehouse and Lakehouse that improves read latency [1]
• fully transparent to the user [3]
• persists the final result sets for applicable SELECT T-SQL queries
• caches all the data accessed by a query [3]
• subsequent runs that "hit" cache will process just the final result set
• can bypass complex compilation and data processing of the original query[1]
• ⇐ returns subsequent queries faster [1]
• the cache creation and reuse is applied opportunistically for queries
• works on
• warehouse tables
• shortcuts to OneLake sources
• shortcuts to non-Azure sources
• the management of cache is handled automatically [1]
• regularly evicts cache as needed
• as data changes, result consistency is ensured by invalidating cache created earlier [1]
• {operation} enable setting
• via ALTER DATABASE <database_name> SET RESULT_SET_CACHING ON
• {operation} validate setting
• via SELECT name, is_result_set_caching_on FROM sys.databases
• {operation} configure setting
• configurable at item level
• once enabled, it can then be disabled 
• at the item level
• for individual queries
• e.g. debugging or A/B testing a query
• via OPTION ( USE HINT ('DISABLE_RESULT_SET_CACHE') 
• {default} during the preview, result set caching is off for all items [1]
• [monitoring] 
• via Message Output
• applicable to Fabric Query editor, SSMS
• the statement "Result set cache was used" is displayed after query execution if the query was able to use an existing result set cache
• via queryinsights.exec_requests_history system view
• result_cache_hit displays indicates result set cache usage for each query execution [1]
• {value} 2: the query used result set cache (cache hit)
• {value} 1: the query created result set cache
• {value} 0: the query wasn't applicable for result set cache creation or usage [1]
• {reason} the cache no longer exists
• {reason} the cache was invalidated by a data change, disqualifying it for reuse [1]
• {reason} query isn't deterministic
• isn't eligible for cache creation [1]
• {reason} query isn't a SELECT statement
• [warehousing] 
• {scenario} analytical queries that process large amounts of data to produce a relatively small result [1]
• {scenario} workloads that trigger the same analytical queries repeatedly [1]
• the same heavy computation can be triggered multiple times, even though the final result remains the same [1]

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References:

[1] Microsoft Learn (2025) Result set caching (preview) [link]

[2] Microsoft Fabric Update Blog (2025) Result Set Caching for Microsoft Fabric Data Warehouse (Preview) [link|aka]

[3] Microsoft Learn (2025) In-memory and disk caching [link]

[4] Microsoft Learn (2025) Performance guidelines in Fabric Data Warehouse [link] 

Resources:
[R1] Microsoft Fabric (2025) Fabric Update - June 2025 [link]

Acronyms:

MF - Microsoft Fabric

SSMS - SQL Server Management Studio

🤖〽️Prompt Engineering: Copilot Unabridged (Part 27: The Negative Impact of AI on the Economy)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how AI can impact the economy negatively"

Introduction

Artificial Intelligence (AI) is transforming industries, automating processes, and reshaping the global economy. While AI offers efficiency, innovation, and productivity gains, it also presents significant economic risks that could disrupt labor markets, widen inequality, and create financial instability. Understanding these challenges is crucial for ensuring AI’s development aligns with sustainable economic growth.

1. AI-Driven Job Displacement and Unemployment

One of the most pressing concerns about AI’s impact on the economy is job displacement. AI-powered automation is replacing human labor in various industries, including:

• Manufacturing: AI-driven robotics reduce the need for factory workers.
• Retail and customer service: AI chatbots and self-checkout systems replace human employees.
• Finance: AI automates investment decisions, reducing demand for financial analysts.

While AI creates new jobs in data science, software engineering, and AI ethics, it eliminates traditional roles, leading to mass unemployment and economic instability.

2. Widening Economic Inequality

AI’s integration into businesses benefits corporations that can afford advanced AI systems, but it disadvantages smaller companies and low-income workers. AI-driven automation:

• Increases profits for large corporations, widening the gap between wealthy and struggling businesses.
• Reduces wages for workers, as AI replaces human labor.
• Limits economic mobility, making it harder for displaced workers to find new opportunities.

Without proper regulation, AI could exacerbate income inequality, leading to social unrest and economic polarization.

3. AI’s Role in Financial Market Instability

AI-driven algorithms dominate stock trading, investment strategies, and financial predictions. While AI enhances efficiency, it also introduces risks:

• AI-powered trading systems can trigger market crashes due to rapid, automated decision-making.
• AI-driven financial models may misinterpret economic trends, leading to poor investment choices.
• Over-reliance on AI in banking could create vulnerabilities in financial security.

AI’s influence on financial markets requires strict oversight to prevent economic instability.

4. AI’s Impact on Tax Revenue and Public Services

As AI replaces human workers, governments face declining tax revenue, affecting public services such as:

• Healthcare: Reduced funding for hospitals and medical programs.
• Education: Limited resources for schools and universities.
• Infrastructure: Less investment in transportation and urban development.

Without a restructured tax system, AI-driven automation could weaken government funding, impacting essential services.

5. Ethical and Regulatory Challenges in AI Economics

AI’s rapid expansion outpaces regulatory frameworks, leading to:

• Unregulated AI-driven monopolies, where a few corporations control AI technologies.
• Privacy concerns, as AI collects vast amounts of consumer data.
• Bias in AI decision-making, affecting hiring, lending, and economic policies.

Governments must implement AI regulations to ensure fair competition and ethical economic practices.

Conclusion: AI’s Economic Risks Require Careful Management

While AI offers efficiency and innovation, its negative economic impact - job displacement, inequality, financial instability, tax revenue decline, and regulatory challenges - must be addressed. Governments, businesses, and researchers must develop policies that balance AI’s benefits with economic sustainability, ensuring AI serves society rather than disrupting it.

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 26: How Humans Can Outsmart AI: The Power of Human Ingenuity)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on how human can outsmart AI"

Introduction

Artificial Intelligence (AI) has made incredible advancements, surpassing human capabilities in data processing, automation, and strategic decision-making. However, despite its speed and efficiency, AI still lacks intuition, creativity, and emotional intelligence - qualities that give humans a unique advantage. While AI can outperform humans in structured environments, there are several ways in which human ingenuity can outsmart AI.

1. Leveraging Creativity and Abstract Thinking

AI excels at pattern recognition and logical reasoning, but it struggles with creativity and abstract thought. Humans can:

• Think outside the box, generating innovative solutions AI cannot predict.
• Create art, music, and literature that reflect emotions and cultural depth.
• Solve problems intuitively, without relying solely on data-driven patterns.

While AI can generate content, it lacks the ability to truly understand human creativity, making human ingenuity a powerful advantage.

2. Using Emotional Intelligence and Social Skills

AI lacks empathy, intuition, and emotional intelligence, which are essential for human relationships, leadership, and negotiation. Humans can:

• Read emotions and body language, adapting communication accordingly.
• Build trust and rapport, essential for teamwork and collaboration.
• Make ethical decisions, considering moral implications beyond logic.

AI may analyze sentiment in text, but it cannot genuinely understand human emotions, giving humans an edge in social interactions and leadership.

3. Adapting to Unpredictable Situations

AI relies on structured data and predefined algorithms, but humans excel in adapting to uncertainty. Humans can:

• Make quick decisions in unpredictable environments, such as crisis management.
• Learn from minimal examples, while AI requires vast datasets.
• Navigate complex social dynamics, where AI struggles with nuance.

AI performs well in controlled settings, but humans thrive in real-world unpredictability, making adaptability a key advantage.

4. Outsmarting AI in Ethical and Moral Reasoning

AI lacks moral judgment and ethical reasoning, making humans essential for guiding AI development responsibly. Humans can:

• Recognize ethical dilemmas that AI may overlook.
• Ensure fairness and inclusivity in AI-driven decisions.
• Prevent AI from reinforcing biases, ensuring responsible AI use.

AI may optimize efficiency, but humans prioritize ethical considerations, ensuring technology aligns with societal values.

5. Controlling AI’s Development and Purpose

Ultimately, humans design, regulate, and oversee AI, ensuring it serves human interests rather than operating autonomously. Humans can:

• Set boundaries for AI, preventing unintended consequences.
• Regulate AI applications, ensuring responsible use.
• Guide AI’s evolution, ensuring it complements human intelligence rather than replacing it.

While AI is powerful, humans remain in control, shaping its development to enhance society rather than disrupt it.

Conclusion: Human Ingenuity Will Always Matter

AI may outperform humans in speed, efficiency, and automation, but it cannot replicate creativity, emotional intelligence, adaptability, ethical reasoning, or human oversight. By leveraging these strengths, humans can outsmart AI, ensuring technology remains a tool for progress rather than an unchecked 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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🤖〽️Prompt Engineering: Copilot Unabridged (Part 24: The Next Steps in the Development of AI)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on the next steps in the development of AI"

Introduction

Artificial Intelligence (AI) is evolving at an unprecedented pace, shaping industries, economies, and daily life. As AI continues to advance, researchers, businesses, and governments are focusing on the next steps in its development - from improving efficiency to addressing ethical concerns. This blog post explores the key areas driving AI’s future growth and the innovations that will define the next phase of AI evolution.

1. Advancing AI Infrastructure and Computing Power

AI’s capabilities depend on high-performance computing, and the next phase of development will focus on enhancing AI infrastructure. Companies and governments are investing in AI supercomputers to support large-scale AI applications.

For example, Ericsson, AstraZeneca, SAAB, SEB, and Wallenberg Investments have formed a consortium to build Sweden’s largest AI supercomputer, powered by Nvidia’s DGX SuperPODs. This infrastructure will enable AI-driven advancements in network intelligence, drug discovery, defense systems, and financial services.

2. AI for Secure and Ethical Software Development

As AI becomes more integrated into software development, security concerns are growing. Companies like Snyk are launching AI-native platforms to govern and secure AI-driven software. These platforms aim to:

• Reduce AI-generated security vulnerabilities in software development.
• Enhance governance and policy enforcement for AI applications.
• Improve AI security posture management to prevent cyber threats.

Ensuring AI is secure and ethically developed will be a major focus in the coming years.

3. AI in Data Analytics and Business Intelligence

AI is revolutionizing data analytics, enabling businesses to process information faster and more efficiently. dbt Labs has introduced the dbt Fusion engine, designed to optimize AI-driven analytics and reduce cloud costs. This innovation will:

• Accelerate AI-powered data processing for enterprises.
• Improve AI-driven decision-making in business intelligence.
• Enhance AI’s ability to manage large-scale data pipelines.

AI’s role in business intelligence and analytics will continue to expand, making data-driven strategies more effective.

4. AI for National and Regional Development

Governments and corporations are investing in AI to drive national and regional innovation. Safaricom, for example, has pledged $500 million to develop AI infrastructure in East Africa. This investment will support:

• AI-powered data centers for regional businesses.
• Edge computing capabilities to improve AI efficiency.
• Digital skills programs to train local developers in AI technologies.

AI’s development is becoming a global priority, with nations investing in AI to boost economic growth and technological leadership.

5. AI’s Role in Scientific Discovery and Automation

AI is transforming scientific research by accelerating discoveries in medicine, engineering, and environmental science. Future AI advancements will focus on:

• AI-driven drug discovery to develop new treatments faster.
• AI-powered automation in manufacturing to improve efficiency.
• AI-enhanced climate modeling to predict environmental changes.

AI’s ability to process vast amounts of data will continue to drive breakthroughs in multiple fields.

Conclusion: AI’s Future Is Shaped by Innovation and Responsibility

The next steps in AI development will focus on enhancing infrastructure, securing AI-driven software, optimizing data analytics, expanding AI’s role in national development, and advancing scientific discovery. As AI continues to evolve, responsible innovation and ethical governance will be crucial to ensuring AI benefits society.

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 5: Why There Are Limits to AI Growth)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on why there are limits to growth for AI"

Introduction

Artificial Intelligence (AI) is evolving rapidly, transforming industries and redefining how humans interact with technology. However, despite its advancements, AI is not without limitations. While many claim that AI’s potential is limitless, several fundamental constraints affect its development, usability, and ethical application. In this blog post, we explore why AI growth is not boundless and the factors that restrict its expansion.

Computational and Hardware Constraints

AI depends heavily on computational power to process large amounts of data efficiently. While improvements in processing speeds, cloud computing, and quantum computing are promising, hardware limitations remain a bottleneck.

• Energy consumption: Training sophisticated AI models requires immense energy, leading to high costs and environmental concerns.
• Hardware limitations: Even cutting-edge processors (GPUs, TPUs) struggle with increasingly complex AI models, limiting performance scalability.
• Data storage needs: AI requires vast amounts of storage and memory, which can be difficult to scale beyond current technologies.

Despite ongoing advancements, these factors impose real-world limitations on AI expansion, preventing it from growing indefinitely.

Dependence on Data and Human Oversight

AI is fundamentally data-driven, meaning its accuracy and capabilities are only as good as the data it receives. However, data availability and quality present major challenges:

• Data scarcity: Not all industries generate sufficient high-quality datasets, limiting AI effectiveness.
• Bias in data: AI models can inherit human biases, leading to ethical concerns and flawed decision-making.
• Human intervention: AI requires constant supervision for algorithm adjustments, data corrections, and error management.

Since AI models learn from existing human-created data, they are restricted by data inconsistencies, biases, and inaccuracies - creating natural growth barriers.

Ethical and Legal Restrictions

AI operates within a world governed by laws, regulations, and ethical constraints, which significantly limit its unrestricted development:

• Privacy concerns: AI depends on personal data for optimization, but privacy regulations (GDPR, CCPA) restrict unchecked data usage.
• Security risks: AI-driven automation increases the risk of hacking, misinformation, and deepfake fraud, requiring strict oversight.
• Job displacement worries: Governments and institutions limit AI adoption to protect workers and avoid societal disruption.

As AI continues to advance, legal frameworks must adapt - but regulatory boundaries will always exist to prevent ethical breaches.

AI’s Lack of True Consciousness

Unlike humans, AI does not possess consciousness, intuition, or creativity in the way people do. While AI mimics intelligence, it lacks genuine understanding:

• No self-awareness: AI does not truly “think”; it only processes patterns based on data inputs.
• Limited creativity: AI can generate art and music, but it does not comprehend emotion, cultural context, or abstract thought.
• Inability to make ethical judgments: AI lacks moral reasoning and cannot replace human values or ethical decision-making.

This inherent lack of cognitive ability prevents AI from achieving true autonomy, reinforcing its growth limitations.

Final Thoughts: AI’s Evolution Has Limits

While AI continues to expand, it is not limitless due to constraints in hardware, data, ethics, and intelligence itself.

AI can optimize processes, enhance decision-making, and revolutionize industries, but it will always require human oversight and face technical and ethical barriers.

Rather than seeing AI as boundless, we should focus on responsible development, ensuring it remains a powerful tool rather than an unchecked 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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🏭🗒️Microsoft Fabric: Materialized Lake Views (MLV) [Notes] 🆕🗓️

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 9-Jun-2025

-- create schema
CREATE SCHERA IF NOT EXISTS <lakehouse_name>.<schema_name>

-- create a materialized view
CREATE MATERIALIZED VIEW IF NOT EXISTS <lakehouse_name>.<schema_name>.<view_name> 
[(
    CONSTRAINT <constraint_name> CHECK (<constraint>) ON MISMATCH DROP 
)] 

[PARTITIONED BY (col1, col2, ... )] 
[COMMENT “description or comment”] 
[TBLPROPERTIES (“key1”=”val1”, “key2”=”val2”, 

AS 
SELECT ...
FROM ...
-- WHERE ...
--GROUP BY ...

[Microsoft Fabric] Materialized Lake Views (MLVs)

• {def} persisted, continuously updated view of data [1]
• {benefit} allows to build declarative data pipelines using SQL, complete with built-in data quality rules and automatic monitoring of data transformations
• simplifies the implementation of multi-stage Lakehouse processing [1]
• ⇐ aids in the creation, management, and monitoring of views [3]
• ⇐ improves transformations through a declarative approach [3]
• streamline data workflows
• enable developers to focus on business logic [1]
• ⇐ not on infrastructural or data quality-related issues [1]
• the views can be created in a notebook [2]
• {benefit} allows developers visualize lineage across all entities in lakehouse, view the dependencies, and track its execution progress [3]
• can have data quality constraints enforced and visualized for every run, showing completion status and conformance to data quality constraints defined in a single view [1]
• empowers developers to set up complex data pipelines with just a few SQL statements and then handle the rest automatically [1]
• faster development cycles 
• trustworthy data
• quicker insights
• {goal} process only the new or changed data instead of reprocessing everything each time [1]
• ⇐  leverages Delta Lake’s CDF under the hood
• ⇒ it can update just the portions of data that changed rather than recompute the whole view from scratch [1]
• {operation} creation
• allows defining transformations at each layer [1]
• e.g. aggregation, projection, filters
• allows specifying certain checks that the data must meet [1]
• incorporate data quality constraints directly into the pipeline definition
• via CREATE MATERIALIZED LAKE VIEW
• the SQL syntax is declarative and Fabric figures out how to produce and maintain it [1]
• {operation} refresh
• refreshes only when its source has new data [1]
• if there’s no change, it can skip running entirely (saving time and resources) [1]
• via REFRESH MATERIALIZED LAKE VIEW [workspace.lakehouse.schema].MLV_Identifier [FULL];
• {operation} list views from schema [3]
• via SHOW MATERIALIZED LAKE VIEWS <IN/FROM> Schema_Name;
• {opetation} retrieve definition
• via SHOW CREATE MATERIALIZED LAKE VIEW MLV_Identifier;
• {operstion} update definition
• via ALTER MATERIALIZED LAKE VIEW MLV_Identifier RENAME TO MLV_Identifier_New;
• {operstion} drop view
• via DROP MATERIALIZED LAKE VIEW MLV_Identifier;
• {warning} dropping or renaming a materialized lake view affects the lineage view and scheduled refresh [3]
• {recommendation} update the reference in all dependent materialized lake views [3]
• {operation} schedule view run
• lets users set how often the MLV should be refreshed based on business needs and lineage execution timing [5]
• depends on
• data update frequency: the frequency with which the data is updated [5]
• query performance requirements: Business requirement to refresh the data in defined frequent intervals [5]
• system load: optimizing the time to run the lineage without overloading the system [5]
• {operation} view run history
• users can access the last 25 runs including lineage and run metadata
• available from the dropdown for monitoring and troubleshooting
• {concept} lineage
• the sequence of MLV that needs to be executed to refresh the MLV once new data is available [5]
• {feature} automatically generate a visual report that shows trends on data quality constraints 
• {benefit} allows to easily identify the checks that introduce maximum errors and the associated MLVs for easy troubleshooting [1]
• {feature} can be combined with Shortcut Transformation feature for CSV ingestion 
• {benefit} facilitate the building of end-to-end Medallion architectures
• {feature} dependency graph
• allows to see the dependencies existing between the various objects [2]
• ⇐ automatically generated [2]
• {feature} data quality report
• built-in Power BI dashboard that shows several aggregated metrics [2]
• doesn't support
• {feature|planned} PySpark [3]
• {feature|planned} incremental refresh [3]
• {feature|planned} integration with Data Activator [3]
• {feature|planned} API [3]
• {feature|planned} cross-lakehouse lineage and execution [3]
• {limitation} Spark properties set at the session level aren't applied during scheduled lineage refresh [4]
• {limitation} creation with delta time-travel [4]
• {limitation} DML statements [4]
• {limitation} UDFs in CTAS [4] 
• {limitation} temporary views can't be used to define MLVs [4]
• 
  

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References:

[1] Microsoft Fabric Update Blog (2025) Simplifying Medallion Implementation with Materialized Lake Views in Fabric [link|aka]

[2] Power BI Tips (2025) Microsoft Fabric Notebooks with Materialized Views - Quick Tips [link]

[3] Microsoft Learn (2025) What are materialized lake views in Microsoft Fabric? [link]

[4] Microsoft Learn (2025) Materialized lake views Spark SQL reference [link]

[5] Microsoft Learn (2025) Manage Fabric materialized lake views lineage [link] 

[6] Microsoft Learn (2025) Data quality in materialized lake views [link] 

Resources:
[R1] Databricks (2025) Use materialized views in Databricks SQL [link]

[R2] Microsoft Learn (2025) Implement medallion architecture with materialized lake views [link] 

Acronyms:

API - 

CDF - Change Data Feed

CTA - 

DML - 

ETL - Extract, Transfer, Load

MF - Microsoft Fabric
MLV - Materialized Lake views

UDF - User-defined functions