🤖〽️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 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 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 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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🤖〽️Prompt Engineering: Copilot Unabridged (Part 9: The Perils of AI - Risks and Challenges in a Rapidly Evolving Technology)

Prompt Engineering Series

The Perils of AI: Risks and Challenges in a Rapidly Evolving Technology

Artificial Intelligence (AI) is transforming industries, automating processes, and enhancing human capabilities. However, alongside its benefits, AI presents serious risks that must be carefully managed. From ethical concerns to security vulnerabilities, understanding the perils of AI is crucial for responsible development and regulation.

1. Bias and Discrimination in AI

One of the most concerning risks is algorithmic bias, where AI systems unintentionally reinforce societal prejudices. AI models learn from historical data, and if that data reflects racial, gender, or socioeconomic biases, AI decisions may disadvantage certain groups.

For example, AI-powered hiring algorithms have been found to favor male candidates over female candidates due to past hiring patterns. Similarly, facial recognition technology has lower accuracy rates for people with darker skin tones, leading to misidentifications and unfair treatment.

2. Privacy and Surveillance Threats

AI depends on massive amounts of personal data, which raises concerns about privacy and surveillance. Governments and corporations use AI for monitoring behavior, tracking movements, and collecting user data, often without explicit consent.

Examples of AI-driven privacy risks include:

• Social media monitoring: AI analyzes online activity to shape advertisements or influence user behavior.
• Mass surveillance systems: AI-powered cameras and tracking tools can infringe on personal freedoms.
• Data breaches: AI-driven cyberattacks can compromise sensitive information.

Without strict data protection laws, AI could erode personal privacy and digital security.

3. AI in Cybersecurity: An Increasing Threat

While AI enhances security by detecting cyber threats, it also serves as a powerful weapon for hackers. Malicious actors leverage AI to create sophisticated cyberattacks, including:

• Deepfake scams – AI-generated videos and voice impersonations for fraud.
• AI-driven malware – Malicious software that adapts to evade detection.
• Automated phishing attacks – AI personalizes scam messages for higher success rates.

AI-powered hacking tools outpace traditional security systems, making cybersecurity an ongoing challenge.

4. Job Displacement Due to Automation

AI-driven automation reduces workforce demand in sectors like manufacturing, transportation, customer service, and finance. While AI creates new roles in software engineering, AI ethics, and data science, it eliminates jobs requiring routine tasks and repetitive labor.

Industries facing AI-related job losses include:

• Retail and call centers: AI chatbots and self-checkout systems reduce human employment.
• Manufacturing: AI-powered robotics replace manual labor.
• Finance: AI automates investment decisions, reducing demand for financial analysts.

Governments and businesses must invest in workforce reskilling to ensure workers transition to AI-augmented roles.

5. Ethical Concerns and Unregulated AI

The rapid expansion of AI raises ethical dilemmas, including concerns about autonomous weapons, misinformation, and lack of accountability. Without clear regulations, AI could exacerbate global security risks rather than solving them.

Critical ethical debates surrounding AI include:

• AI in warfare: Autonomous drones and robotic weapons could make war more unpredictable and destructive.
• Misinformation and propaganda: AI-generated fake news threatens democracy and trust.
• Unregulated decision-making: AI lacks human ethics, empathy, and context awareness.

Conclusion: Navigating AI’s Risks

AI presents immense possibilities, but also substantial risks. Bias, privacy threats, cybersecurity vulnerabilities, job displacement, and ethical dilemmas must be addressed through responsible development, transparent regulation, and ethical AI frameworks.

The future of AI depends on how humans govern its growth, ensuring it remains a tool for innovation rather than a source of harm.

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 7: The Risks Associated with AI: Challenges in a Rapidly Advancing World)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on the risks associated with AI"

Introduction

Artificial Intelligence (AI) has become an integral part of our society, driving innovation across industries and reshaping how businesses, governments, and individuals operate. While AI offers efficiency, automation, and data-driven decision-making, it also introduces significant risks that must be carefully managed. In this blog post, we explore the major risks associated with AI and why ethical considerations are crucial for its responsible development.

1. Bias and Discrimination in AI

One of the most critical risks in AI development is algorithmic bias, which occurs when AI systems inherit prejudices from their training data. AI models are trained on vast datasets collected from real-world sources, but these sources may contain historical inequalities, societal biases, and skewed representations.

For example, AI-driven hiring systems have been found to favor male candidates over female candidates, simply because historical hiring data reflects gender disparities in certain fields. Similarly, AI-powered facial recognition has lower accuracy when identifying people from racial minorities due to biased training datasets.

Mitigating bias in AI requires diverse training data, continuous audits, and transparent AI decision-making. Without these safeguards, AI can reinforce existing biases rather than eliminate them.

2. Privacy and Data Security Risks

AI relies on massive amounts of data to function effectively, but this dependence raises serious privacy concerns. With AI-driven automation and surveillance technologies, individuals face increased risks of data breaches, unauthorized data collection, and loss of personal privacy.

For example, AI-powered marketing tools analyze consumer behavior through social media and online activity. While this allows businesses to deliver personalized advertisements, it also raises concerns about data misuse and manipulation.

Moreover, AI-based cybersecurity threats, such as deepfake technology, enable malicious actors to impersonate individuals and spread misinformation. If AI is not regulated properly, society could face a loss of trust in digital interactions.

3. AI in Cybersecurity: A Double-Edged Sword

AI is both a tool for cybersecurity and a threat to cybersecurity. While AI enhances security by detecting patterns in cyberattacks and automating threat detection, hackers can also use AI to bypass traditional security measures.

Some AI-driven cyberattacks include:

• Deepfake scams: AI-generated videos and audio impersonate real individuals, enabling fraud or misinformation.
• AI-powered malware: Malicious software adapts in real-time to evade detection.
• Automated phishing attacks: AI personalizes fraudulent emails to increase success rates.

Cybersecurity professionals must stay ahead by leveraging AI to counter threats, but the arms race between cybercriminals and security systems continues to evolve.

4. Job Displacement Due to AI Automation

AI automation is transforming industries by replacing repetitive human tasks with machines, but this shift raises concerns about mass job displacement. While AI creates new roles in data science, robotics, and AI ethics, it also replaces traditional jobs in manufacturing, customer service, and transportation.

For example, AI-powered chatbots have reduced the need for human customer service representatives, while autonomous vehicles threaten to disrupt the transportation industry. AI-driven automation in retail, finance, and healthcare could replace millions of jobs unless reskilling programs and workforce adaptations are prioritized.

Governments and businesses must take proactive steps to ensure AI complements human labor rather than completely replacing it.

5. Ethical and Regulatory Challenges

AI's lack of human intuition, morality, and accountability introduces ethical dilemmas that society must address.

Key ethical concerns include:

• AI in warfare: The development of autonomous weapons raises fears about unregulated warfare and unintended consequences.
• Manipulation of information: AI-driven fake news generation threatens democracy by spreading misinformation.
• Lack of transparency: Many AI systems operate as “black boxes”, meaning users cannot fully understand how decisions are made.

To manage these risks, governments, businesses, and researchers must collaborate on ethical AI development and policies that regulate its usage.

Conclusion: AI Requires Responsible Growth

While AI offers groundbreaking possibilities, its risks must be addressed through ethical considerations, regulation, and transparency. Bias, privacy concerns, cybersecurity threats, job displacement, and ethical dilemmas require proactive solutions to ensure AI benefits society without causing unintended harm.

The future of AI depends on how responsibly we shape its development. By implementing accountable AI governance, ethical oversight, and workforce adaptation strategies, society can leverage AI’s advantages while mitigating its risks.

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 6: Why There Are No Limits to AI Growth)

Prompt Engineering Series

Prompt: "write a blogpost of 600 words on why there are no limits to growth for AI"

Introduction

Artificial Intelligence (AI) has transcended its early stages of development and is now an integral part of business, science, and daily life. Unlike traditional technologies that have clear limits on scalability, AI operates under a unique framework - it evolves, learns, and refines itself over time. This adaptability ensures that AI's growth remains exponential, breaking past barriers that once seemed insurmountable.

AI’s Ability to Learn and Evolve

AI differs from other technologies because it isn’t bound by static programming - it learns and improves through machine learning (ML) and deep learning. These processes enable AI to:

• Optimize itself over time, making operations more efficient.
• Develop new patterns that even human researchers may not anticipate.
• Enhance accuracy and predictive capabilities across industries.

Unlike traditional software, which requires manual updates and modifications, AI continuously refines itself based on real-world interactions. This ability ensures perpetual growth, keeping AI ahead of conventional limitations.

Computational Power Is Increasing Rapidly

One of the biggest factors enabling AI’s endless growth is the exponential increase in computational power. Advances in quantum computing, cloud-based GPUs, and edge computing allow AI models to process massive datasets faster than ever. The more computing resources AI has access to, the more complex problems it can solve.

• Cloud computing democratizes AI, making it accessible to businesses worldwide.
• Quantum computing will eventually enable AI to process unimaginable volumes of data, far surpassing current limitations.
• Neuromorphic computing mimics the human brain, allowing AI models to improve efficiency while consuming less energy.

As technology progresses, AI's capabilities will continue expanding - faster, smarter, and more adaptable than ever before.

AI Has Infinite Applications Across Industries

AI's impact isn’t confined to a single sector; its growth is fueled by its versatility. From healthcare to finance, retail to space exploration, AI finds new ways to integrate into human progress.

• In medicine, AI assists in diagnosing diseases, customizing treatments, and even predicting outbreaks.
• In business, AI automates operations, reduces inefficiencies, and enhances data-driven decision-making.
• In research, AI enables discoveries in fields like genetics, material science, and astrophysics.
• In entertainment, AI creates realistic CGI, automates music composition, and personalizes media experiences.

Since AI isn’t restricted to one field, its expansion is self-perpetuating - each industry unlocks new AI-driven possibilities, further fueling innovation.

AI Growth Is Driven by Human Curiosity

AI’s evolution isn’t happening in isolation; human ingenuity fuels its progress. Researchers, engineers, and businesses continuously explore new ways to push AI beyond current capabilities.

• Developers refine AI models to improve accuracy and efficiency.
• Businesses invest billions in AI research, ensuring advancements continue.
• Governments and educational institutions integrate AI into policies and learning frameworks to accelerate adoption.

As long as humans remain curious about solving problems, enhancing intelligence, and optimizing processes, AI will continue to grow indefinitely.

Ethical Challenges Won’t Halt AI Growth

While ethical concerns - such as AI bias, surveillance, and job displacement - must be addressed, they won’t stop AI’s progress. Instead, they shape its development, ensuring responsible AI integration.

• Regulations will guide ethical AI use rather than stifle growth.
• Bias reduction strategies will make AI more inclusive while improving accuracy.
• AI augmentation will reshape workforces, enabling humans to focus on creative and strategic tasks.

Rather than presenting limitations, ethical considerations ensure that AI’s limitless growth remains aligned with human values and societal needs.

Conclusion: AI’s Infinite Expansion

Unlike previous technological revolutions that reached plateaus, AI’s adaptive nature, increasing computational power, and infinite applications ensure continued growth. With every breakthrough in machine learning, automation, and intelligent computing, AI inches closer to solving problems previously deemed impossible.

The future of AI is limitless - and as long as humans continue pushing boundaries, AI will evolve indefinitely.

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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#️⃣Software Engineering: Mea Culpa (Part X: A Look Beyond AI)

Software Engineering Series

What’s the point of blogging when AI can do a better job than the average blogger? Frankly, one can generate a huge volume of content that’s comparable with the one of the best bloggers. One just needs a set of well-suited prompts and a bit of formatting, though also the formatting can be handled over prompts. So, what’s the point of blogging anyway? Frankly, the more AI takes over, the less space is available for bloggers! Taking a trendy way of formulating titles: is blogging dead? I really hope not!

I’ve been blogging for almost 20 years. Even if I haven’t managed to blog constantly, given the volume of work and learning, I still made it over time! Often it was late in the night, in between other activities, or a bit of work while watching a football game. With AI life changes more than we want. AI can currently write about 20-30% of the code, at least the initial structure on which it can build upon, and it saves a lot of time!

So, the average programmer needs to adapt to the new reality or die?! Becoming unimportant in the programming equation is equivalent with a slow death we witness in our job. Of course, when used wisely AI can provide higher productivity, at least for the moment, though what will happen when the average programmer is not able to keep the pace? What will happen when we can’t review what AI does for us? Probably, we’ll be able to build a smarter AI which can evaluate the output of less smart AI, though what will be the limit?

Just playing the devil’s advocate, though what happens when the devil outsmarts us? Cases of programmers who lost their jobs because of AI become more frequent in the news. This is probably the new reality we need to be accustomed to. AI can write better code, better stories and probably can make faster discoveries than the average programmer, scientist, or any other professions in whose fields AI can be used.

Of course, we can learn to use AI to our advantage, though how many will be capable of doing that? Many programmers will lose their jobs because of AI. Probably, the ones who are still better problems solvers than AI will remain in the business, though until when?! The ones who change jobs early will probably be more adapted to the new paradigm, though at what price? Who will be able to pay the price?

In the first instance the programmers are the ones who’ll suffer, but the chances are high that AI will take over other jobs as well. The shift in teaching and other jobs could change from knowledge transmission to supervision, from creation or simple stand-byers. Does it make sense? Does it help us to live a decent life? Does it help us in the long term?

Probably, all the jobs that need a human touch will be still needed, though it’s challenging to predict how everything will evolve, in which directions, on what terms. The problem is not necessarily AI alone, but the way it is used and misused. Of course, the future doesn’t necessarily need to look that bleak, though the chances of evolving in this direction are high.

AI develops at a faster pace than the human mind can progress. Probably, we’ll be able to benefit from cognitive boosters, developed probably with the help of AI. To any gain is expected also a loss. In which areas should we expect losing something? Is it worth the price we pay for the sake of progress?

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#️⃣Software Engineering: Mea Culpa (Part IX: A Look Inward)

Software Engineering Series

Paraphrasing a saying attributed to Lao Tzu, "a journey starts with a look inward". Of course, there are also people walking just for the sake of walking, without a purpose or afterthought, though usually when we follow a path we ask ourselves: where we are? what's the destination? how long is the road? how much it takes to reach the intermediary and final goals? what do I need for the road? what can I find on the road? what else do I need to know? who can help me? with whom or what can I travel? does the journey make sense? am I missing something? am I fit for the road and obstacles ahead? what challenges I might meet?

The traveler's metaphor can be easily applied to the professional life as well, especially when one reached a junction or a point on which a decision must be made about the road ahead. Usually, the first step is being aware where one is, and this applies from all points of view: geographical, physical, mental, spiritual, social and even ideological or religious. Of course, not everything is equally important, and it depends on each individual's values, state of mind, situation in life, etc. The more dimensions one considers into the equations of life, the more challenging it is to find a solution, even if not optimal. Even if some things can be ignored, the overall texture can become quite complex, more complex than our linear way of thinking allows us to cope with!

It would be great if one could always choose what's the destination! Unfortunately, the destination is available based on a serios of variables we are constrained to: our capabilities and knowledge, our experience, the work ahead, on what others want to achieve, and this can change from day to day according to the priorities set. Ideally, we should be aware of all the strings attached to us, respectively which strings are pulled and who's pulling the strings, however life is usually at least one step ahead of us! The lower we find ourselves in the texture that decides upon our life, the more challenging it becomes. Ideally, we should have no attachment to things, situations, people or other material things. However, we aren't hermits living in a cave!

Once we set a destination, is important to consider what one needs for the road, respectively for reaching the intermediary steps. That's probably one of the most challenging aspects of all experience. How can I prepare one for the road not taken? The past is probably the only one that can offer a sense of the future, though what if the future doesn't resemble the past in many essential points? One can always make jumps of faith, though faith alone is not enough! Blind faith without knowledge or wisdom can one bring in the opposite or other wrong directions. One can but try to map the territory and establish a plan, however the contact with reality can be harsher than one can imagine. One can but try to adapt, though one must be built for this! Some fundament must be already there!

A good recommendation for the road ahead is to expect the unexpected. Planning is good for preparing the mind, though what one finds on the way might be surprising as well challenging to overcome. Harsh long-distance journeys are not for everybody! Starting small and training the qualities needed for such journeys might be the best advice one can get. Though, the beginning of wisdom takes more steps than this, no matter the road, no matter the experience or one’s aims!

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#️⃣Software Engineering: Mea Culpa (Part VIII: A Look Beyond)

Software Engineering Series

With AI on the verge, blogging and bloggers can easily become obsolete. Why bother navigating through the many blogs to get a broader perspective when the same can be obtained with AI? Just type in a prompt of the type "write a blogpost of 600 words on the importance of AI in society" and Copilot or any other similar AI agent will provide you an answer that may look much better than the first draft of most of the bloggers out there! It doesn't matter whether the text follows a well-articulated idea, a personal perspective or something creative! One gets an acceptable answer with a minimum of effort and that's what matters for many.

The results tend to increase in complexity the more models are assembled together, respectively the more uncontrolled are the experiments. Moreover, solutions that tend to work aren't necessarily optimal. Machines can't offer instant enlightenment or anything close to it. Though they have an incomparable processing power of retrieval, association, aggregation, segregation and/or iteration, which coupled with the vast amount of data, information and knowledge can generate anything in just a matter of seconds. Probably, the only area in which humans can compete with machines is creativity and wisdom, though how many will be able to leverage these at scale? Probably, machines have some characteristics that can be associated with these intrinsic human characteristics, though usually more likely the brute computational power will prevail.

At Microsoft Build, Satya Nadella mentioned that foundry encompasses already more than 1900 supported models. In theory, one can still evaluate and test such models adequately. What will happen when the scale increases with a few orders of magnitude? What will happen when for each person there are one or more personalized AI models? AI can help in many areas by generating and evaluating rapidly many plausible alternatives, though as soon the models deal with some kind of processing randomization, the chances for errors increase exponentially (at least in theory).

It's enough for one or more hallucinations or other unexpected behavior to lead to more unexpected behavior. No matter how well a model was tested, as long as there's no stable predictable mathematical model behind it, the chances for something to go wrong increase with the number of inputs, parameters, uses, or changes of context the model deals with. Unfortunately, all these aspects are seldom documented. It's not like using a formula and you know that given a set of inputs and operations, the result is the same. The evolving nature of such models makes them unpredictable in the long term. Therefore, there must always be a way to observe the changes occurring in models.

One of the important questions is how many errors can we afford in such models? How long it takes until errors impact each other to create effects comparable with a tornado. And what if the tornado increases in magnitude to the degree that it wrecks everything that crosses its path? What if multiple tornadoes join forces? How many tornadoes can destroy a field, a country or a continent? How many or big must be the tornadoes to trigger a warning?

Science-Fiction authors love to create apocalyptic scenarios, and all happens in just a few steps, respectively chapters. In nature, usually it takes many orders of magnitude to generate unpredictable behavior. But, as nature often reveals, unpredictable behavior does happen, probably more often than we expect and wish for. The more we are poking the bear, the higher the chances for something unexpected to happen! Do we really want this? What will be the price we must pay for progress?

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#️⃣Software Engineering: Mea Culpa (Part VII: A Look Forward)

Software Engineering Series

I worked for more than 20 years in various areas related to ERP systems - Data Migrations, Business Intelligence/Analytics, Data Warehousing, Data Management, Project Management, (data) integrations, Quality Assurance, and much more, having experience with IFS IV, Oracle e-Business Suite, MS Dynamics AX 2009 and during the past 3-7 years also with MS Dynamics 365 Finance, SCM & HR (in that order). Much earlier, I started to work with SQL Server (2000-2019), Oracle, and more recently with Azure Synapse and MS Fabric, writing over time more than 800 ad-hoc queries and reports for the various stakeholders, covering all the important areas, respectively many more queries for monitoring the various environments. 

In the areas where I couldn’t acquire experience on the job, I tried to address this by learning in my free time. I did it because I take seriously my profession, and I want to know how (some) things work. I put thus a lot of time into trying to keep actual with what’s happening in the MS Fabric world, from Power BI to KQL, Python, dataflows, SQL databases and much more. These technologies are Microsoft’s bet, though at least from German’s market perspective, all bets are off! Probably, many companies are circumspect or need more time to react to the political and economic impulses, or probably some companies are already in bad shape. 

Unfortunately, the political context has a broad impact on the economy, on what’s happening in the job market right now! However, the two aspects are not the only problem. Between candidates and jobs, the distance seems to grow, a dense wall of opinion being built, multiple layers based on presumptions filtering out voices that (still) matter! Does my experience matter or does it become obsolete like the technologies I used to work with? But I continued to learn, to keep actual… Or do I need to delete everything that reminds the old?

To succeed or at least be hired today one must fit a pattern that frankly doesn’t make sense! Yes, soft skills are important though not all of them are capable of compensating for the lack of technical skills! There seems to be a tendency to exaggerate some of the qualities associated with skills, or better said, of hiding behind big words. Sometimes it feels like a Shakespearian inaccurate adaptation of the stage on which we are merely players.

More likely, this lack of pragmatism will lead to suboptimal constructions that will tend to succumb under their own structure. All the inefficiencies need to be corrected, or somebody (or something) must be able to bear their weight. I saw this too often happening in ERP implementations! Big words don’t compensate for the lack of pragmatism, skills, knowledge, effort or management! For many organizations the answer to nowadays problems is more management, which occasionally might be the right approach, though this is not a universal solution for everything that crosses our path(s).

One of society’s answers to nowadays’ problem seems to be the refuge in AI. So, I wonder – where I’m going now? Jobless, without an acceptable perspective, with AI penetrating the markets and making probably many jobs obsolete. One must adapt, but adapt to what? AI is brainless even if it can mimic intelligence! Probably, it can do more in time to the degree that many more jobs will become obsolete (and I’m wondering what will happen to all those people). 

Conversely, to some trends there will be probably other trends against them, however it’s challenging to depict in clear terms the future yet in making. Society seems to be at a crossroad, more important than mine.

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#️⃣Software Engineering: Programming (Part XVII: More Thoughts on AI)

Software Engineering Series

I've been playing with AI-based prompting in Microsoft 365 and Edge Copilot for SQL programming tasks and even for simple requests I got wrong or suboptimal solutions. Some of the solutions weren’t wrong by far, though it was enough for the solution to not work at all or give curious answers. Some solutions were even more complex than needed, which made their troubleshooting more challenging, to the degree that was easier to rewrite the code by myself. Imagine when such wrong solutions and lines of reasoning propagate uncontrolled within broader chains of reasoning! 

Some of the answers we get from AI can be validated step by step, and the logic can be changed accordingly, though this provides no guarantee that the answers won't change as new data, information, knowledge is included in the models, or the model changes, directly or indirectly. In Software Development, there’s a minimum set of tests that can and should be performed to assure that the input generated matches the expectations, however in AI-based solutions there’s no guarantee that what worked before will continue to work.

Moreover, small errors can propagate in a chain-effect creating curious wrong solutions. AI acts and probably will continue to act as a Pandora's box. So, how much can we rely on AI, especially when the complexity of the problems and their ever-changing nature is confronted with a model highly sensitive to the initial or intermediary conditions? 

Some of the answers may make sense, and probably also the answers can be better to some degree than the decisions made by experts, though how far do we want to go? Who is ready to let his own life blindly driven by the answers provided by an AI machine just because it can handle certain facts better than us? Moreover, the human brain is wired to cope with uncertainty, morality and other important aspects that can enhance the quality of the decisions, even if the decisions aren't by far perfect

It’s important to understand the sensitivity of AI models and outputs to the initial and even intermediate conditions on which such models are based, respectively what is used in their reasoning and how slight changes can result in unexpected effects. Networks, independently whether they are or not AI-based, lead to behavior that can be explainable to some degree as long full transparency of the model and outcomes of the many iterations is provided. When AI models behave like black boxes there’s no guarantee of the outcomes, respectively transparence on the jumps made from one state of the network to the other, and surprises can appear more often than we expect or are prepared to accept. 

Some of the successes rooted in AI-based reasoning might happen just because in similar contexts people are not ready to trust their reasoning or take a leap of faith. AI tends to replace all these aspects that are part of human psychology, logic and whatever is part of the overall process. The eventual successes are thus not an immediate effect of the AI capabilities, but just that we took a shortcut. Unfortunately, this can act like a sharp blade with two edges. 

I want to believe that AI is the solution to humanity's problems, and probably there are many areas of applicability, though letting AI control our lives and the over-dependence on AI can on long term cause more problems than AI and out society can solve. The idea of AI acting as a Copilot that can be used to extrapolate beyond our capabilities is probably not wrong, though one should keep the risks and various outcomes in sight!

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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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🧭Business Intelligence: Perspectives (Part XXVII: A Tale of Two Cities II)

Business Intelligence Series

There’s a saying that applies to many contexts ranging from software engineering to data analysis and visualization related solutions: "fools rush in where angels fear to tread" [1]. Much earlier, an adage attributed to Confucius provides a similar perspective: "do not try to rush things; ignore matters of minor advantage". Ignoring these advices, there's the drive in rapid prototyping to jump in with both feet forward without checking first how solid the ground is, often even without having adequate experience in the field. That’s understandable to some degree – people want to see progress and value fast, without building a foundation or getting an understanding of what’s happening, respectively possible, often ignoring the full extent of the problems.

A prototype helps to bring the requirements closer to what’s intended to achieve, though, as the practice often shows, the gap between the initial steps and the final solutions require many iterations, sometimes even too many for making a solution cost-effective. There’s almost always a tradeoff between costs and quality, respectively time and scope. Sooner or later, one must compromise somewhere in between even if the solution is not optimal. The fuzzier the requirements and what’s achievable with a set of data, the harder it gets to find the sweet spot.

Even if people understand the steps, constraints and further aspects of a process relatively easily, making sense of the data generated by it, respectively using the respective data to optimize the process can take a considerable effort. There’s a chain of tradeoffs and constraints that apply to a certain situation in each context, that makes it challenging to always find optimal solutions. Moreover, optimal local solutions don’t necessarily provide the optimum effect when one looks at the broader context of the problems. Further on, even if one brought a process under control, it doesn’t necessarily mean that the process works efficiently.

This is the broader context in which data analysis and visualization topics need to be placed to build useful solutions, to make a sensible difference in one’s job. Especially when the data and processes look numb, one needs to find the perspectives that lead to useful information, respectively knowledge. It’s not realistic to expect to find new insight in any set of data. As experience often proves, insight is rarer than finding gold nuggets. Probably, the most important aspect in gold mining is to know where to look, though it also requires luck, research, the proper use of tools, effort, and probably much more.

One of the problems in working with data is that usually data is analyzed and visualized in aggregates at different levels, often without identifying and depicting the factors that determine why data take certain shapes. Even if a well-suited set of dimensions is defined for data analysis, data are usually still considered in aggregate. Having the possibility to change between aggregates and details is quintessential for data’s understanding, or at least for getting an understanding of what's happening in the various processes. 

There is one aspect of data modeling, respectively analysis and visualization that’s typically ignored in BI initiatives – process-wise there is usually data which is not available and approximating the respective values to some degree is often far from the optimal solution. Of course, there’s often a tradeoff between effort and value, though the actual value can be quantified only when gathering enough data for a thorough first analysis. It may also happen that the only benefit is getting a deeper understanding of certain aspects of the processes, respectively business. Occasionally, this price may look high, though searching for cost-effective solutions is part of the job!

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References:
[1] Alexander Pope (cca. 1711) An Essay on Criticism