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Mustafa Erbay
Technology · 4 min read · görüntülenme Türkçe oku

Things AI Still Can't Do: A Look Through 20 Years of Experience

As artificial intelligence rapidly enters our lives, I discuss the limits of AI and what it has yet to achieve, drawing on my 20 years of experience in system.

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The most expensive mistake of my career wasn’t a line of code; it was a “yes.” Years ago, captivated by the promises of a new technology in a project, I decided to speed up the process by saying, “AI will handle this.” Since that day, even though I see the immense potential of artificial intelligence in new ways every day, I haven’t forgotten the lessons that “yes” taught me. Artificial intelligence has changed many things and will continue to do so, but it hasn’t yet fully replaced human experience, original thought, and deep problem-solving.

Today, artificial intelligence displays incredible capabilities in many areas, from writing text to generating code, and even analyzing complex systems. However, knowing the limits of these capabilities is critical to using the right tool in the right place. In this post, drawing on my 20 years of experience in system architecture and software development, I will share a few key points where AI still struggles, or even fails, illustrated with examples from my own experiences.

Deeply Understanding Context and Real-World Complexity

Artificial intelligence can produce great results by extracting patterns from large datasets. However, this doesn’t always mean it fully grasps the nuances and context of the real world. Once, we were working on an AI model to optimize shipping dates in a manufacturing ERP system. The model was finding mathematically the most efficient routes, but it wasn’t accounting for unexpected disruptions in the supply chain, physical transportation constraints, or even “the factory manager’s mood that day.”

Optimization based solely on data sometimes can’t be as effective as the simplest “human touch.” Artificial intelligence can struggle to understand the motivations underlying the “why” question or the tacit knowledge behind a decision. This situation becomes especially pronounced in areas where human interaction is intense or ethical dimensions come to the fore.

Original Creativity and “Out-of-the-Box” Thinking

Artificial intelligence can produce new combinations and solutions based on existing data. It is great at “making what exists better.” However, it hasn’t yet reached the level of human creativity in creating a completely new paradigm or presenting a concept that hasn’t been thought of before. While developing my own Android spam blocker app, I had to account not only for known spam patterns but also for how humans perceive spam and in which situations a false positive is unacceptable.

This also manifests itself in software development processes. AI can analyze existing code bases and improve them, even suggest new functions. However, the ability to design an architecture from scratch, anticipating future needs and establishing a flexible and scalable structure, is still a significant human privilege. While developing my custom financial calculators on a VDS, I laid a foundation by considering not only technical requirements but also new features that might be added in the future.

Empathy, Emotional Intelligence, and Human Relations

As the integration of technology into our lives increases, we see more clearly that artificial intelligence falls short in areas requiring emotional intelligence and empathy. A chatbot can solve the user’s problem, but it cannot truly feel a human’s frustration, worry, or joy. When designing an operator screen, just showing the data isn’t enough; you have to account for the operator’s stress at that moment, their fatigue, and their psychology at the moment of decision-making.

Even in the bilingual technical blog I write, I try to establish a sincere bond with the reader. This isn’t just about transferring information; it’s also about understanding the reader’s perspective and finding a common language with them. Artificial intelligence is still insufficient at establishing this level of relationship, building trust, and creating long-term loyalty.

Conclusion: A Tool, A Partner, But Not a Replacement

Artificial intelligence is undoubtedly becoming one of humanity’s most powerful tools. It provides us with incredible benefits in many areas, from system optimization to software development. However, what I’ve seen from my 20 years of experience is that artificial intelligence cannot yet replace human intelligence, creativity, empathy, and deep problem-solving skills.

Instead of rejecting this technology, we should see it as a partner and use it by knowing its limits and blending our human capabilities with it. Artificial intelligence is a tool that helps us think better, not a replacement for thinking for us.

So, what do you think? Where do you think AI struggles the most? Share in the comments!

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Frequently Asked Questions

Common questions readers have about this article.

In what situations does AI struggle to deeply understand context?
In my experience, the situations where AI struggles to deeply understand context usually involve real-world complexity. For example, while working on an AI model to optimize shipping dates in a manufacturing ERP system, the model found mathematically the most efficient routes. However, it didn't account for unexpected disruptions in the supply chain or physical transportation constraints. Therefore, knowing AI's limits and using the right tool in the right place is critically important.
What should I pay attention to when analyzing systems using AI?
When analyzing systems using AI, you must remember that you have the ability to produce great results by extracting patterns from large datasets. However, you should also consider that this doesn't always mean AI fully grasps the nuances and context of the real world. In my experience, when analyzing systems using AI, the human factor, experience, and a deep understanding of the event are also important.
What are the things AI can't do, and why does it matter?
The things AI can't do generally relate to the fact that human experience, original thought, and deep problem-solving haven't been fully replaced yet. In my experience, the things AI can't do show that in real-world scenarios, numbers and algorithms aren't always enough. The human factor, experience, and deep understanding of events are topics AI hasn't yet fully mimicked.
Which tools and technologies should I prefer when using AI?
When deciding which tools and technologies to prefer while using AI, you should consider the uniqueness and requirements of your project. In my experience, when using AI, you must have access to large datasets, strong infrastructure, and an expert team. Also, to know AI's limits and use the right tool in the right place, you should consider the human factor and experience as well.
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Mustafa Erbay

Sistem Mimarisi · Network Uzmanı · Altyapı, Güvenlik ve Yazılım

2006'dan bu yana sistem mimarisi, network, sunucu altyapıları, büyük yapıların kurulumu, yazılım ve sistem güvenliği ekseninde çalışıyorum. Bu blogda sahada karşılığı olan teknik deneyimlerimi paylaşıyorum.

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