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

Prompt Engineering Series

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

Introduction

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

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

The Foundation of AI Mimicry: Learning from Data

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

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

Reasoning vs. Pattern Recognition

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

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

Creativity: Genuine or Simulated?

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

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

Limitations of AI Intelligence

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

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

Conclusion

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

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

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

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

Prompt Engineering Series

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

Introduction

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

The Shift from Traditional BI to AI-Driven Analytics

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

AI-driven analytics can:

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

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

Why AI Might Replace Traditional BI Tools

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

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

Challenges and Limitations of AI in Business Intelligence

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

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

The Future: AI-Augmented Business Intelligence

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

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

Conclusion

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

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

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

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

Prompt Engineering Series

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

Introduction

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

1. AI for Personal Assistance and Organization

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

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

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

2. AI for Learning and Education

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

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

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

3. AI for Creativity and Content Generation

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

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

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

4. AI for Financial Management

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

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

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

5. AI for Health and Wellness

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

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

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

6. AI for Shopping and Recommendations

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

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

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

Conclusion: AI Is for Everyone

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

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

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

Prompt Engineering Series

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

Introduction

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

1. AI Already Outperforms Humans in Specific Domains

AI has already surpassed human capabilities in several areas:

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

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

2. Predictions on AI Surpassing Human Intelligence

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

3. AI’s Rapid Learning and Self-Improvement

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

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

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

4. AI’s Performance Benchmarks Are Constantly Evolving

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

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

5. The Challenge of Artificial General Intelligence (AGI)

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

Challenges in achieving AGI include:

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

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

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

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

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

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

Prompt Engineering Series

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

Introduction

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

1. AI Revolutionizing Healthcare and Medicine

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

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

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

2. AI Enhancing Education and Learning

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

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

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

3. AI Tackling Climate Change and Environmental Issues

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

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

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

4. AI Advancing Social Good and Humanitarian Efforts

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

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

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

5. AI Driving Economic Growth and Innovation

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

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

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

Conclusion: AI’s Role in Shaping a Better Future

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

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

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

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

Prompt Engineering Series

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

Introduction

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

1. Humanity Can’t Fully Reverse AI Integration

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

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

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

2. Humanity Can’t Stop AI Learning

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

3. Humanity Can’t Prevent AI from Being Exploited

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

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

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

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

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

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

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

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

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

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

6. Humanity Can’t Unleash AI Without Consequences

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

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

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

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

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

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

ERP Implementations Series

 

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

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

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

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

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

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

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

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

Software Engineering Series

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

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

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

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

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

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

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

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

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

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

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

"A bad excuse is better than none"

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

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

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

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

"A little learning is a dangerous thing"

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

"A picture is worth a thousand words"

"A stitch in time (saves nine)"

"Actions speak louder than words"

"All good things must come to an end"

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

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

"All roads lead to Rome"

"All is well that ends well"

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

"Another day, another dollar"

"As you sow so shall you reap"

"Beauty is in the eye of the beholder"

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

"Better safe than sorry": Deleting obsolete companies

"Big fish eat little fish"

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

"Calm seas never made a good sailor"

"Count your blessings"

"Dead men tell no tales"

"Do not bite the hand that feeds you"

"Do not change horses in midstream"

"Do not count your chickens before they are hatched"

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

"Do not judge a book by its cover"

"Do not meet troubles half-way"

"Do not put all your eggs in one basket"

"Do not put the cart before the horse"

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

"Do not try to walk before you can crawl"

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

"Easier said than done"

"Every cloud has a silver lining"

"Every little bit helps"

"Every picture tells a story"

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

"Faith will move mountains"

"Fake it till you make it"

"Fight fire with fire"

"First impressions are the most lasting"

"First things first": Ways of looking at data

"Fish always rots from the head downwards"

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

"Half a loaf is better than no bread"

"Haste makes waste"

"History repeats itself"

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

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

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

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

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

"Ignorance is bliss"

"Imitation is the sincerest form of flattery"

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

"It is a small world"

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

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

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

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

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

"It takes a whole village to raise a child"

"It will come back and haunt you"

"Judge not, that ye be not judged"

"Kill two birds with one stone"

"Knowledge is power, guard it well"

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

"Less is more"

"Life is what you make it"

"Many hands make light work"

"Moderation in all things"

"Money talks"

"More haste, less speed"

"Necessity is the mother of invention"

"Never judge a book by its cover"

"Never say never"

"Never too old to learn"

"No man can serve two masters"

"No pain, no gain"

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

"Oil and water do not mix"

"One-man show": series

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

"One swallow does not make a summer"

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

"Patience is a virtue"

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

"Practice makes perfect"

"Practice what you preach"

"Prevention is better than cure"

"Rules were made to be broken"

"Seek and ye shall find"

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

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

"Strike while the iron is hot"

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

"The best defense is a good offense"

"The bets are off":  A look forward

"The bigger they are, the harder they fall"

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

"The die is cast"

"The exception which proves the rule"

"The longest journey starts with a single step"

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

"There are two sides to every question"

"There is no smoke without fire"

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

"There is no I in team"

"There is safety in numbers"

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

"Time is money"

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

"Too little, too late"

"Too much of a good thing"

"Truth is stranger than fiction"

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

"Two heads are better than one": Pair programming

"Two wrongs (do not) make a right"

"United we stand, divided we fall"

"Use it or lose it"

"Unity is strength"

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

"Virtue is its own reward"

"Well begun is half done"

"What does not kill me makes me stronger"

"Well done is better than well said"

"What cannot be cured must be endured"

"What goes around, comes around"

"When life gives you lemons, make lemonade"

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

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

"Where there is a will there is a way"

"With great power comes great responsibility"

"Work expands so as to fill the time available"

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

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

"You cannot make an omelet without breaking eggs"

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

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

"Zeal without knowledge is fire without light"

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

🧭Business Intelligence: Mea Culpa (Part IV: Generalist or Specialist in an AI Era?)

Business Intelligence Series

Except the early professional years when I did mainly programming for web or desktop applications in the context of n-tier architectures, over the past 20 years my professional life was a mix between BI, Data Analytics, Data Warehousing, Data Migrations and other topics (ERP implementations and support, Project Management, IT Service Management, IT, Data and Applications Management), though the BI topics covered probably on average at least 60% of my time, either as internal or external consultant. 

I can consider myself thus a generalist who had the chance to cover most of the important aspects of a business from an IT perspective, and it was thus a great experience, at least until now! It’s a great opportunity to have the chance to look at problems, solutions, processes and the various challenges and opportunities from different perspectives. Technical people should have this opportunity directly in their jobs through the communication occurring in projects or IT services, though that’s more of a wish! Unfortunately, the dialogue between IT and business occurs almost only over the tickets and documents, which might be transparent but isn’t necessarily effective or efficient! 

Does working only part time in an area make one person less experienced or knowledgeable than other people? In theory, a full-time employee should get more exposure in depth and/or breadth, but that’s relative! It depends on the challenges one faces, the variation of the tasks, the implemented solutions, their depth and other technical and nontechnical factors like training, one’s experience in working with the various tools, the variety of the tasks and problem faced, professionalism, etc. A richer exposure can but not necessarily involve more technical and nontechnical knowledge, and this shouldn’t be taken as given! There’s no right or wrong answer even if people tend to take sides and argue over details.

Independently of job's effective time, one is forced to use his/her time to keep current with technologies or extend one’s horizon. In IT, a professional seldom can rely on what is learned on the job. Fortunately, nowadays one has more and more ways of learning, while the challenge shifts toward what to ignore, respectively better management of one’s time while learning. The topics increase in complexity and with this blogging becomes even more difficult, especially when one competes with AI content!

Talking about IT, it will be interesting to see how much AI can help or replace some of the professions or professionals. Anyway, some jobs will become obsolete or shift the focus to prompt engineering and technical reviews. AI still needs explicit descriptions of how to address tasks, at least until it learns to create and use better recipes for problem definition and solving. The bottom line, AI and its use can’t be ignored, and it can and should be used also in learning new things. It’s amazing what one can do nowadays with prompt engineering! 

Another aspect on which AI can help is to tailor the content to one’s needs. A high percentage in the learning process is spent on fishing in a sea of information for content that is worth knowing, respectively for a solution to one’s needs. AI must be able to address also some of the context without prompters being forced to give information explicitly!

AI opens many doors but can close many others. How much of one’s experience will remain relevant over the next years? Will AI have more success in addressing some of the challenges existing in people’s understanding or people will just trust AI blindly? Anyway, somebody must be smarter than AI, and here people’s collective intelligence probably can prove to be a real match. 

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🧭🏭Business Intelligence: Microsoft Fabric (Part V: One Person Can’t Learn or Do Everything)

Business Intelligence Series

Today’s Explicit Measures webcast [1] considered an article written by Kurt Buhler (The Data Goblins): [Microsoft] "Fabric is a Team Sport: One Person Can’t Learn or Do Everything" [2]. It’s a well-written article that deserves some thought as there are several important points made. I can’t say I agree with the full extent of some statements, even if some disagreements are probably just a matter of semantics.

My main disagreement starts with the title “One Person Can’t Learn or Do Everything”. As clarified in webcast's chat, the author defines “everything" as an umbrella for “all the capabilities and experiences that comprise Fabric including both technical (like Power BI) or non-technical (like adoption data literacy) and everything in between” [1].

For me “everything” is relative and considers a domain's core set of knowledge, while "expertise" (≠ "mastery") refers to the degree to which a person can use the respective knowledge to build back-to-back solutions for a given area. I’d say that it becomes more and more challenging for beginners or average data professionals to cover the core features. Moreover, I’d separate the non-technical skills because then one will also need to consider topics like Data, Project, Information or Knowledge Management.

There are different levels of expertise, and they can vary in depth (specialization) or breadth (covering multiple areas), respectively depend on previous experience (whether one worked with similar technologies). Usually, there’s a minimum of requirements that need to be covered for being considered as expert (e.g. certification, building a solution from beginning to the end, troubleshooting, performance optimization, etc.). It’s also challenging to roughly define when one’s expertise starts (or ends), as there are different perspectives on the topics. 

Conversely, the term expert is in general misused extensively, sometimes even with a mischievous intent. As “expert” is usually considered an external consultant or a person who got certified in an area, even if the person may not be able to build solutions that address a customer’s needs. 

Even data professionals with many years of experience can be overwhelmed by the volume of knowledge, especially when one considers the different experiences available in MF, respectively the volume of new features released monthly. Conversely, expertise can be considered in respect to only one or more MF experiences or for one area within a certain layer. Lot of the knowledge can be transported from other areas – writing SQL and complex database objects, modelling (enterprise) semantic layers, programming in Python, R or Power Query, building data pipelines, managing SQL databases, etc. 

Besides the standard documentation, training sessions, and some reference architectures, Microsoft made available also some labs and other material, which helps discovering the features available, though it doesn’t teach people how to build complete solutions. I find more important than declaring explicitly the role-based audience, the creation of learning paths for the various roles.

During the past 6-7 months I've spent on average 2 days per week learning MF topics. My problem is not the documentation but the lack of maturity of some features, the gaps in functionality, identifying the respective gaps, knowing what and when new features will be made available. The fact that features are made available or changed while learning makes the process more challenging. 

My goal is to be able to provide back-to-back solutions and I believe that’s possible, even if I might not consider all the experiences available. During the past 22 years, at least until MF, I could build complete BI solutions starting from requirements elicitation, data extraction, modeling and processing for data consumption, respectively data consumption for the various purposes. At least this was the journey of a Software Engineer into the world of data. 

References:
[1] Explicit Measures (2024) Power BI tips Ep.328: Microsoft Fabric is a Team Sport (link)
[2] Data Goblins (2024) Fabric is a Team Sport: One Person Can’t Learn or Do Everything (link)

🧭🏭Business Intelligence: A One-Man Show III (The Microsoft Fabric)

Business Intelligence Series

Announced at the end of the last year, Microsoft Fabric (MF) become a reality for the data professional, even if there are still many gaps in the overall architecture and some things don't work as they should. The Delta Lake and the various data consumption experiences seem to bring more flexibility but also raise questions on how one can use them adequately in building solutions for Data Analytics and/or Data Science. 

Currently, as it happens with new technologies, data professionals seem to try to explore the functionality, see what's possible, what's missing, and that's a considerable effort as everybody is more or less on his own. The material released by Microsoft and other professionals should facilitate in theory this effort, though the considerable number of features and the effort needed to review them do the opposite. Some professionals do this as part of their jobs, and exploring the feature seems to be a full job in each area, while others, like myself, do it in their own time. 

There are organizations that demand from their employees to regularly actualize their knowledge in their field of activity, respectively explore how new technologies can be integrated in organization's architecture. Having a few hours or even a day a weak for this can go a long way! Occasionally, I could take 1-2 hours a week during the program and take maybe a few many more hours from my own time. Unfortunately, most of the significant progress I made in a certain area (SQL Server, Dynamics 365, Software Engineering, Power BI, and now MF) it was done in my own time, which became in time more and more challenging to do given the pace with which new features and technologies develop.

By comparison, it was relatively easy to locally install SQL Server in its various CTP or community versions, deploy one of the readily-available databases, and start learning. I'm still doing it, playing with a SQL Server 2022 instance whenever I find the time. Similarly, I can use Power BI and a few other tools, depending again on the time available to make progress. However, with MF things start slowly to get blurry. The 60 days of trial won't cut it anymore as there are so many things to learn - Spark SQL, PySpark, Delta Lake, KQL, Dataflows, etc. Probably, there will be ways for learning any of this standalone, though not together in an integrated manner. 

The complexity of the tools demands more time, a proper infrastructure and a good project to accommodate them. This doesn't mean that the complexity of the solutions need to increase as well! Azure Synapse allowed me to reuse many of the techniques I used in the past to build a modern Data Analytics solution, while in other areas I had to accommodate the new. The solution wasn't perfect (only time will tell), though it provided the minimum of what was needed. I expect the same to happen in Microsoft Fabric, even if the number of choices is bigger. 

There's a considerable difference between building a minimal viable solution and exploring, respectively harnessing MF's capabilities. The challenge for many organizations is to determine what that minimum is about, how to build that knowledge into the team, especially when starting from zero. 

Conversely, this doesn't mean that the skillset and effort can't be covered by one person. It might be more challenging though achievable if the foundation is there, respectively if certain conditions are met. This depends also on organization's expectations, infrastructure and other characteristics. A whole team is more likely to succeed than one person, but not certainty! 

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Data Science: Back to the Future I (About Beginnings)

Data Science Series

I've attended again, after several years, a webcast on performance improvement in SQL Server with Claudio Silva, “Writing T-SQL code for the engine, not for you”. The session was great and I really enjoyed it! I recommend it to any data(base) professional, even if some of the scenarios presented should be known already.

It's strange to see the same topics from 20-25 years ago reappearing over and over again despite the advancements made in the area of database engines. Each version of SQL Server brought something new in what concerns the performance, though without some good experience and understanding of the basic optimization and troubleshooting techniques there's little overall improvement for the average data professional in terms of writing and tuning queries!

Especially with the boom of Data Science topics, the volume of material on SQL increased considerably and many discover how easy is to write queries, even if the start might be challenging for some. Writing a query is easy indeed, though writing a performant query requires besides the language itself also some knowledge about the database engine and the various techniques used for troubleshooting and optimization. It's not about knowing in advance what the engine will do - the engine will often surprise you - but about knowing what techniques work, in what cases, which are their advantages and disadvantages, respectively on how they might impact the processing.

Making a parable with writing literature, it's not enough to speak a language; one needs more for becoming a writer, and there are so many levels of mastery! However, in database world even if creativity is welcomed, its role is considerable diminished by the constraints existing in the database engine, the problems to be solved, the time and the resources available. More important, one needs to understand some of the rules and know how to use the building blocks to solve problems and build reliable solutions.

The learning process for newbies focuses mainly on the language itself, while the exposure to complexity is kept to a minimum. For some learners the problems start when writing queries based on multiple tables -  what joins to use, in what order, how to structure the queries, what database objects to use for encapsulating the code, etc. Even if there are some guidelines and best practices, the learner must walk the path and experiment alone or in an organized setup.

In university courses the focus is on operators algebras, algorithms, on general database technologies and architectures without much hand on experience. All is too theoretical and abstract, which is acceptable for research purposes,  but not for the contact with the real world out there! Probably some labs offer exposure to real life scenarios, though what to cover first in the few hours scheduled for them?

This was the state of art when I started to learn SQL a quarter century ago, and besides the current tendency of cutting corners, the increased confidence from doing some tests, and the eagerness of shouting one’s shaking knowledge and more or less orthodox ideas on the various social networks, nothing seems to have changed! Something did change – the increased complexity of the problems to solve, and, considering the recent technological advances, one can afford now an AI learn buddy to write some code for us based on the information provided in the prompt.

This opens opportunities for learning and growth. AI can be used in the learning process by providing additional curricula for learners to dive deeper in some topics. Moreover, it can help us in time to address the challenges of the ever-increase complexity of the problems.

🌡Performance Management: Mastery (Part II: First Time Right - The Aim toward Operational Excellence)

 

Performance Management Series

Rooted in Six Sigma methodology as a step toward operational excellence, First Time Right (FTR) implies that any procedure is performed in the right manner the first time and every time. It equates to minimizing the waste in its various forms (inventory, motion, overprocessing, overproduction, waiting, transportation, defects). Like many quality concepts from the manufacturing industry, the concept was transported in the software development process as principle, process, goal and/or metric. Thus, it became part of Software Engineering, Project Management, Data Science, and any other similar endeavors whose outcome results in software products. 

Besides the quality aspect, FTR is rooted also in the economic imperative – the need to achieve something in the minimum amount of time with the minimum of effort. It’s about being efficient in delivering a product or achieving a given target. It can be associated with continuous improvement, learning and mastery, the aim being to encompass FTR as part of organization’s culture. 

Even if not explicitly declared, FTR lurks in each task planned. It seems that it became common practice to plan with the FTR in mind, however between this theoretical aim and practice there’s as usual an important gap. Unfortunately, planners, managers and even tasks' performers often forget that mistakes are made, that several iterations are needed to get the job done. It starts with the communication between people in clarifying the requirements and ends with the formal sign off. All the deviations from the FTR add up in the deviations between expected and actual effort, though probably more important are the deviations from the plan and all the consequences deriving from it. Especially in complex projects this adds up into a spiral of issues that can easily reinforce themselves. 

Many of the jobs that imply creativity, innovation, research or exploration require at least several iterations to get the job done and this is independent of participants’ professionalism and experience. Moreover, the more quality one needs, the higher the effort, the 80/20 being sometimes a good approximation of the effort needed. In extremis, aiming for perfection instead of excellence can make certain tasks a never-ending story. 

Achieving FTR requires practice - the more novelty, the higher the complexity, the communication or the synchronization needs, the more practice is needed. It starts with the individual to master the individual tasks and ends with the team, where communication, synchronization and other aspects need to be considered. The practice is usually achieved on hands-on work as part of the daily duties, project work, and so on. Unfortunately, it’s based primarily on individual experience, and seldom groomed in advance, as preparation for future tasks. That’s why sometimes when efficiency is needed in performing critical complex tasks, one also needs to consider the learning curve in achieving the required quality. 

Of course, many organizations demand from job applicants experience and, when possible, they hire people with experience, however the diversity, complexity and changing nature of tasks require further practice. This aspect is somehow recognized in the implementation in organizations of the various forms of DevOps, though how many organizations adopt it and enforce it on a regular basis? Moreover, a major requirement of nowadays businesses is to be agile, and besides the mere application of methodologies, being agile means to have also a FTR mindset. 

FTR starts with the wish for mastery at individual and team level and, with the right management attention, by allocating time for learning, self-development in the important areas, providing relevant feedback and building an infrastructure for knowledge sharing and harnessing, FTR can become part of organization’s culture. It’s up to each of us to do it!

🧭🪄Business Intelligence: Power BI (Part I: Learning Curve I)

A learning curve attempts depicting the (average) time it takes a person to learn how to use a method, tool, or technique, tracing the path from newbie to mastery. A common definition of the learning curve is based on the correlation between a learner’s performance on a task or activity and the number of attempts or amount of time required to complete it.

There are several diagrams in circulation which depict the correlation between the difficulty of Power BI concepts and probably their implementation as functionality. Even if they reflect to some degree the rate of learning, their simplicity and fuzziness can easily make one question their accuracy in reflecting the reality.

Researchers tend to categorize the curves associated with the learning process in simple idealized patterns like S-curve (aka sigmoid), exponential growth, exponential rise and fall to limit, or power law, however the learning process in IT-based endeavors is seldom characterized by a linear or exponential curve, given that the tasks seldom allow a steady path. The jumps of knowledge between tasks can be wide enough to appear insurmountable, and they can prove to be quite of a challenge without some help.

Like a baby’s first steps, we, as learners, must learn first to crawl, before making some unsteady steps, and it can take long time until visible progress is made. It’s a slow progress until we suddenly hit a (tipping) point from which everything seems easy, fact that increases our confidence in us. On the other side, when we find that we make no visible progress for a long period, it’s easy to arrive to the opposite, a critical zone, which in extremis could make one lose interest.

As beginners, after the first tipping point on the learning journey, it’s easy to arrive at a plateau in which there seem no need to learn new things, the current knowledge allowing to handle a range of tasks of small to average complexity. This can last for a long time, and then, a big thing comes our way – a hard problem to solve or a concept hard to understand. It’s the point where we stagnate, and the deeper we go, and the more such challenges are thrown in our way, the more difficult the learning seems to be. However, with new understanding, small steps are made, one step after the other, the pace makes us to evolve faster until we reach again a critical point from which the process increases smoothly until we seem to stagnate again. We meet again a hard limit to growth, which seems to be more solid than the previous one.

Both limits to growth can appear to be hard, however, considering that the knowledge in the field expands, more opportunity for growth appear, thus, the limits are apparent. Even if knowledge tends to increase ‘indefinitely’, the limits are there in terms of complexity, time available, knowledge quality (incl. availability) or any other dimension of the learning process. Moreover, these successions of tipping points, growth limits, plateaus, critical, steady and fast progress zones can occur in several iterations in the learning path. Thus, the path seems to resemble a snakelike curve with many ups and downs.

For the learner is important to be aware of this last aspect, there are always ups and downs, taking effort, patience and maybe an expert’s help to bridge the gaps in between. The chances are high that the gap between what we think we know and what we know is considerable, therefore a reality check is useful from time to time. A new problem to tackle will provide that occasion!

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