Hi everyone,
As the title indicates, we are all familiar with one certain trend in 2025: AI, AI and AI… From auto-generating marketing copy to composing symphonies or crafting legal drafts, it’s everywhere.
It’s not new, though.
Artificial Intelligence has been a part of our lives for a very long time. In fact, the history of AI predates the internet boom, having started in the middle of the 20th century [1]. For instance, ever since I was a little kid, I have been crying while playing chess against a goddamn wicked computer. From students to Presidents, when did we begin discussing AI as though it were a coworker, tutor, or even a friend?
Since late 2022, something shifted. Previously sci-fi-sounding questions: “Can AI compose my emails? Could you help me with the coding? Build my business plan?” suddenly became part of everyday conversation thanks to ChatGPT. GenAI is transforming not only the capabilities of technology but also the way we engage with knowledge, creativity, and even one another. It’s fast, it’s powerful and again it’s everywhere.
However, this change is not occurring in a vacuum. Every generation Gen Z, Gen X, and Millennials (Gen Y) enters the world with distinct expectations, abilities, and mindsets. Some people are nervous. A few are motivated. Many are both.
For me, I was fortunate to start working with AI about two years ago from building a model for my thesis [2] to being assigned to a chatbot project [3] at workplace. To be honest, I wasn’t really aware of AI at that time, it just felt like a series of random thoughts or an unexpected opportunity that crossed my path. Thanks to that, I had the chance to explore many corners of the vast AI landscape, gaining valuable experience and skills along the way that’s why I feel confident talking about AI with friends and writing this blog today.
AI (the most boring one, feel free to skip)
Human intelligence is diverse and multi-faceted – people excel in different areas like languages, science, or arts. Each can be intelligent in their own way. “There is no standard for human intelligence.” [4] While computers can outperform humans at specific tasks with clear rules (like chess or Go), they don’t actually understand what they’re doing or why. AI is simply very good at pattern matching and following predetermined rules. Really?
In 1980, the philosopher John Searle created what he called the Chinese Room Argument to explain that sometimes systems can seem intelligent, but they’re just mindlessly matching patterns. In the argument, you should imagine yourself in a windowless room with one mail slot on the door. You can only use this slot to communicate with the outside world. In the room, you have a phrase book on a desk and a bunch of Post-It notes with Chinese symbols on the floor. The book shows what response you should use with the note that comes through the slot. It says, “If you see this sequence of Chinese symbols, “then respond with that sequence of Chinese symbols.” Now, imagine a speaker writes something in Chinese mandarin and pushes it through the slot. You can look at the note and match it with your phrase book. Then you paste together the Mandarin response from the Post-It notes on the floor. You have no idea what it says in Mandarin. Instead, you simply go through the process of looking through the book and matching the sequence of symbols. A native Chinese speaker behind the door might believe that they’re having a conversation. In fact, they might even assume that the person in the room is a native speaker. But Searle argues that this is far from intelligence since the person in the room can’t speak Mandarin and doesn’t have any idea what they’re talking about. [5]
“You couldn’t just program intelligence into a system, but maybe you could program a system to become a intelligent through observation” [6]
The symbolic system approach failed because it created an unmanageable explosion of possible combinations that’s a reason why Machine Learning was born. Arthur Samuel’s 1959 checkers program, which learned by playing against itself, is at the heart of The Birth of Machine Learning. Instead of being preprogrammed with particular moves and tactics, the system learned from its own gameplay and gradually got better. The machine eventually outperformed its own programmer thanks to this self-teaching technique, proving that systems could learn from experience rather than explicit programming. [7] From that, later we have artificial neural networks as a powerful advancement in machine learning that mimics how the human brain processes information where we train systems to learn from data and improve over time without being explicitly programmed for every task. Here’s a simplified breakdown of how it works:
Feed the system large datasets: including training, testing, and evaluation sets. These can be labeled (for supervised learning) or unlabeled (for unsupervised learning).
Identify patterns in the data using machine learning algorithms. This could involve classifying data (e.g., spam or not spam) or clustering similar data points together (e.g., customer segmentation).
Make predictions or guesses based on what the system has learned; for example, using algorithms like K-Nearest Neighbors (KNN), Decision Trees, or Neural Networks.
Compare predictions to actual results (in the case of supervised learning) to measure accuracy and adjust the model if needed.
GenAI
With the models, AI was mostly about automation and logic: sorting your inbox, detecting fraud, recommending a playlist. It made decisions based on rules, data, and probabilities. Useful? Definitely. Creative? Not even close. That’s because it’s one of the most popular uses of AI – predictive AI.
Another type is called: generative AI where we use data to create something valuable, new… It can write essays, generate images, compose music, simulate voices, and even mimic your writing style. That leap from logic to creativity is why the world suddenly took notice.
At its core, GenAI uses machine learning models, especially large language models (LLMs like GPT, GANs or VAE…), trained on massive datasets; think billions of pages of text, code, images, and videos. These models learn patterns in language, imagery, and structure, allowing them to “generate” original content based on what they’ve seen.
This is where the trend began to spread globally, changing many jobs by automating repetitive tasks and creating new content. Some people started to feel nervous, some excited.
Why?
You’ve probably heard of deepfakes or other types of frauds that use generative AI and yes, that’s a serious problem we’re facing today. For instance, in Vietnam, my mom or friends have received fake calls from overseas, where scammers impersonate police officers and demand money, threatening court action or even jail if they don’t comply.
However, as the saying goes, “what doesn’t kill you makes you stronger.” they’re updated with the-state-of-art technologies, and easily get rid of scammer from now on. I moved to a new place and sent pictures of my neat, clean apartment to my mom after working so hard to get it all set up.. She just replied. “Is it AI?”
What should we do?
What do we need to consider in terms of ethics and responsibility when working with generative AI?
Many friends who jump on the latest tech trends super fast and try to squeeze them into every project, often with no reason other than, “It’s powerful!”. I want to take it a bit personal at this point since I didn’t have a chance to speak to my friends when we discussed, there is a Vietnamese saying: “dùng dao mổ trâu giết gà.” (to break a butterfly on wheel)
When adapting to new technology, why not start by asking: “Who’s actually benefiting from this?” To focus on highlighting the role that human plays in the creation and use of AI. Honestly, I picked that up from a LinkedIn course. But to be real – as a software engineer, my first question when encountering a new technology is usually: “WTF is this?” Anyway, never mind. Human-centric could be a keyword to take away. What do you think? AI tools rely on human input, guidance, and judgment to produce meaningful, ethical, and relevant outputs. This collaboration reshapes skills, placing greater value on creativity, critical thinking, and empathy. Moreover, by democratizing access to creative tools, GenAI empowers more people to express themselves and solve problems, making humans the true drivers and curators of this new era. In my opinion, moral values and executive skill sets should be our top priority from individual to enterprise level, doesn’t matter which generation we belong to.
From Gen X to Gen Z, we’ve grown up shaping the world; but now, GenAI is stepping in, not just to join the conversation, but to rewrite the rules.
In conclusion, we’ve entered a new era where Generative AI is not just a tool, but a creative partner. This moment isn’t just about technology; it’s also about redefining creativity across generations and changing the way we work and learn. It concerns us.
Whether you’re GenX navigating workplace disruption, a Millennial using AI to streamline a side hustle, or Gen Z (me) blending AI into your everyday routines, one truth stands out: AI is evolving because of us, and with us. It’s still humans who provide the vision, values, and voice behind every prompt.
What do you think? Let’s open to discuss more.
Be positive,
Nguyen
References
[1] tableau. What is the history of artificial intelligence (AI)?. https://www.tableau.com/data-insights/ai/history
[2] Nguyen Hoang. (2023). Developing A Machine Learning-Powered Web Application To Enhance Heart Disease Prediction And Diagnosis. https://www.theseus.fi/handle/10024/798055
[3] KPMG. Generative AI – Changing the game with KymChat. https://kpmg.com/au/en/home/topics/artificial-intelligence-ai/kymchat-trustworthy-ai.html
[4] Doug Rose. (2024, Nov). Introduction to Artificial Intelligence. https://www.linkedin.com/learning/introduction-to-artificial-intelligence-24947908
[5] Swarnabha Sinha. (2023, May 4). The Chinese Room Argument. https://www.scaler.com/topics/artificial-intelligence-tutorial/Chinese-room-argument/
[6] Doug Rose. (2024, Nov). Introduction to Artificial Intelligence. https://www.linkedin.com/learning/introduction-to-artificial-intelligence-24947908
[7] Dr Nivash Jeevanandam. (2024, Feb 01). Mastering Minds: A Journey into the Legacy of the Samuel Checkers-Playing Program (1959). https://indiaai.gov.in/article/mastering-minds-a-journey-into-the-legacy-of-the-samuel-checkers-playing-program-1959
[8] Doug Rose. (2024, Nov). Introduction to Artificial Intelligence. https://www.linkedin.com/learning/introduction-to-artificial-intelligence-24947908