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A summary of the “Medicina Personalizada y Computación Cuántica: Simulaciones a Medida de tu Genoma” (Oct 2026)

  Here's a summary of the “Medicina Personalizada y Computación Cuántica: Simulaciones a Medida de tu Genoma” post from Hive (27 Oct 2026): Key Takeaways from the Article What works today (without quantum): Classical pharmacogenomics (CYP2D6, CYP2C19, VKORC1 genes for drug metabolism) Tumor sequencing for targeted cancer therapies Risk panels for immune response, coagulation, drug processing Current limitations: These are statistical/pattern-based approaches that work well for populations but have variable individual responses. Complex biological interactions are hard to model classically. How quantum computing could change things: Molecular simulation by genome: Quantum computers can natively simulate quantum systems, potentially modeling drug-receptor binding, protein folding, and metabolism for specific genomes with far greater precision than classical computers. Three concrete research areas (still experimental): Oncology precision: Simulating binding energies of cancer dr...

My last reading: Artificial Intelligence, M. Wooldridge

It is already December 2, 2025.

Since last month I wanted to share my last reading...
And here we are!

**My Review of *Artificial Intelligence* by Michael Wooldridge**

Michael Wooldridge’s *Artificial Intelligence* stands out as a remarkably accessible yet intellectually rigorous introduction to the field. Rather than diving straight into algorithms or code, Wooldridge adopts a **pedagogical style rooted in conceptual clarity, historical grounding, and vivid metaphor**. He patiently unpacks foundational ideas—search, knowledge representation, reasoning, learning—not as isolated technical modules but as interconnected inquiries into the very nature of intelligence. His explanations are scaffolded with real-world analogies and gentle humor, making complex topics digestible for non-specialists without sacrificing depth. This approach reflects a teacher’s instinct: to meet learners where they are, then guide them thoughtfully toward deeper understanding.



At the heart of the book hovers a **persistent and haunting question**: *What distinguishes human intelligence from artificial intelligence?* Wooldridge doesn’t offer a definitive answer; instead, he lets the question float through every chapter like an undercurrent. He shows how AI systems can mimic aspects of human cognition—playing chess, recognizing images, even generating language—yet consistently underscores the gap between simulation and sentience. The distinction isn’t merely technical but philosophical: humans possess intentionality, embodiment, social context, and consciousness (however poorly understood), while AI operates through pattern matching and optimization within predefined boundaries. Wooldridge is careful not to anthropomorphize machines, yet he also avoids dystopian hype, maintaining a balanced tone that invites readers to reflect critically rather than react emotionally.

In doing so, the book becomes more than a primer on AI—it becomes a meditation on what it means to be human in an age of intelligent machines. Wooldridge’s pedagogy shines precisely because it nurtures this reflective stance, equipping readers not just with knowledge of AI, but with the intellectual tools to navigate its ethical and existential implications.

Thank you for Reading! 

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