The term Artificial Intelligence (AI) is well known, often evoking futuristic movies like Robocop, Terminator and The Matrix. Yet what many don’t realize is that AI has been part of our lives for decades.
When you finish watching a movie on a streaming platform and instantly see recommendations for similar content, that’s AI. When you’re shopping online and an entire page of suggested products appears, that’s AI. Even the digital ads you see are powered by machine-learning algorithms designed to optimize engagement, clicks, and conversions.
Companies have relied on AI rule-based automation, routines, and machine-learning algorithms for years to power their systems and business processes.
So when you hear conversations about the “urgent need to start using AI,” pause for a moment. AI itself isn’t new, the real transformation we’re witnessing comes from Generative AI, a newer branch of deep learning, which itself is a subset of machine learning.
From AI to Generative AI: What’s actually new
Artificial Intelligence broadly covers machines that can perform tasks requiring human-like intelligence. Machine Learning trains systems to learn from data. Deep Learning uses multi-layered neural networks to recognize complex patterns. Generative AI goes one step further. It doesn’t just analyze data or perform tasks, it creates new content: text, images, code, music, and more.
That’s the real breakthrough.
Navigating the hype: The importance of trusted sources
I was among the early adopters of ChatGPT, just a few months after its public launch in late 2022. It was a defining moment in the history of technology, the first time we could produce analyses, insights, and even complete outputs simply by typing natural-language prompts, without programming or complex tools.
I knew immediately that this would change the world. However, this accessibility has created a double-edged sword. Today, the digital landscape is flooded with “AI experts,” unverified tutorials, and get-rich-quick schemes that often promote the misuse of these tools.
There is a vast amount of misinformation circulating, ranging from overhyped capabilities to fundamental misunderstandings of how Large Language Models (LLMs) process truth and data.
To move beyond the noise, it’s important to seek out education from rigorous, academically backed sources rather than social media trends. The goal when using Generative AI tools shouldn’t be just to generate text, but to understand the capabilities and limitations well enough to apply it securely.
A sesson from the internet era
I was in university when the Internet first became commercialized. My first job, in 1996, was selling Internet access to individuals and companies. I witnessed firsthand the anxiety, resistance, and eventual transformation that followed. Yes, the Internet eliminated certain jobs, but it created far more, and often better ones.
Generative AI is bringing a similar paradigm shift today. The key is not to fear it but to learn it, to develop the literacy and critical understanding to use it responsibly. A sentiment often shared among industry leaders during my Generative AI research has stayed with me ever since: “AI will not steal your job; a human using AI will.”
That sentence should be a constant reminder for everyone: life is an ongoing evolution. Technologies disrupt, jobs disappear, and new ones emerge. So the next time you hear someone say, “We need to get into AI,” ask, “Which AI?”
Traditional AI analyzes what already exists, and chances are, you and your company are already using it. Generative AI creates what’s next. This distinction matters. If you treat Generative AI like a search engine or a calculator, you miss its true potential. If you treat it as an absolute source of truth, you risk misuse.
That is why deep literacy is essential. Understanding the specific capabilities of these tools is an act of digital responsibility. By educating ourselves, grounding our knowledge in fact, and embracing change with intention, we prepare to lead this next technological era, not be replaced by it.
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