Iván Palomares Carrascosa's article offers a refreshingly clear, approachable take on what AI really is—cutting through the hype and jargon that tend to overwhelm discussions around this technology. It's a helpful reminder that, at its core, AI is about mimicking intelligent behavior using data-driven learning—think of it as the digital equivalent of a child learning from experience but running on math instead of instinct.
The piece rightly emphasizes both the incredible progress enabled by advances in machine learning and deep learning, and the persistent limitations AI faces—lack of true understanding, vulnerability to biased or poor data, and the monumental computational costs. These are real-world constraints, not just academic footnotes, that we often gloss over in our collective fascination with AI's possibilities.
I particularly appreciate the breakdown of AI's main areas—knowledge representation, machine learning, deep learning, and generative models like LLMs—mapped out simply but accurately. This helps us see how today’s flashy AI features, like ChatGPT’s conversational skills, are built on a layered history of evolving techniques.
As AI continues to embed itself deeper into industries and daily life, we should keep this practical perspective: AI is a powerful tool but not a magical clairvoyant. It requires critical scrutiny, quality data inputs, and sustainable computational resources. This not only grounds expectations but also invites innovators to focus on building smarter infrastructure and better data stewardship alongside model improvements.
In the end, understanding AI as Iván presents it—part science, part engineering, part ongoing experiment—encourages us to remain both optimistic and realistic. And that’s the kind of balanced view that helps tech enthusiasts and novices alike navigate the AI revolution without getting lost in futuristic fantasies or fear-mongering. Source: What is Artificial Intelligence in Simple Words? - KDnuggets
