Tag: <span>AI</span>

In praise of imperfection

In praise of imperfection

We spend our time hunting down mistakes as if they were the enemy of progress. Yet without them, no knowledge would ever emerge, no learning would ever truly stand. Error is the quiet engine of all intelligence.

The problem begins when we confuse it with deception, and when we project onto our machines the fantasy of a perfection we have never managed to reach ourselves. Without awareness of our own biases, artificial intelligence can only become a magnifying mirror of our blind spots.

This article invites a step aside, not to condemn technology, but to recall one simple and demanding truth: it is not the machine that must become perfect, it is our gaze on our own limits that must become clearer.

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OPINION

AI BIAISES ETHICS

Cognitive laziness in the age of universal assistance

Cognitive laziness in the age of universal assistance

What if, by letting machines think for us, we were slowly forgetting how to think at all?

Once, we had to get lost to learn how to find our way. Today, a synthetic voice guides us step by step, and our mind quietly drifts to sleep. We outsource everything: memory to the cloud, logic to algorithms, decisions to recommendations. It feels smooth, effortless, almost magical. But comfort comes at a cost, the slow erosion of intellectual effort.

We call it progress. Yet behind this promise of efficiency lies a silent drift: cognitive laziness. That subtle surrender where we stop reasoning, doubting, searching, and simply validate what a machine suggests.

This article explores that phenomenon, not to condemn technology, but to question what it’s turning us into: ever-assisted beings, seemingly brilliant, yet increasingly absent from their own thinking.

And perhaps, in this age of constant assistance, thinking is our last true act of freedom.

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OPINION

AI BIAISES ETHICS PARADOX

The jailers of ourselves

The jailers of ourselves

We believe we navigate freely, but we move through a strange bestiary of revisited myths. Like Narcissus, we lean over the digital mirror, fascinated by a reflection that ends up engulfing us. Like Sisyphus, we bear the burden of a memory without forgetting: each piece of data adds to the rock that crushes us without ever rolling back down. Like Prometheus, we offer our traces to a system that feasts on us endlessly. As in the Panopticon, we live under an invisible gaze, but worse still: we have learned to anticipate it, becoming our own jailers.

We are not only losing data; we are losing essential dimensions of the human: the interiority that allows thinking without witness, the forgetting that makes rebirth possible, the autonomy to be oneself, the heteronomy to be several.

Digital servitude needs no chains; it imposes itself through fluidity, seduction, habit. So the real question is no longer: “do I have something to hide?”, but: “how much longer will I remain capable of preserving what makes me a free being?”

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OPINION

AI ETHICS PRIVACY

With AI, the end of doctors?

With AI, the end of doctors?

What if the rise of AI in medicine did not mark the end of doctors, but the beginning of a new era of care?

Since Hippocrates, physicians have drawn their legitimacy from knowledge. Yet, for the first time in modern history, they are no longer necessarily the ones who know the most. AI diagnoses faster, sees what the human eye cannot, and sometimes even drafts responses that patients find more reassuring than those of a professional.

So, should we fear the disappearance of doctors? Or should we rethink their place, their role, their unique value in a world where expertise is shared between human and machine?

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OPINION

AI ETHICS MEDECINE

An LLM is not a knowledge base

An LLM is not a knowledge base

Last week, I talked about a point that’s often misunderstood: for AI, truth doesn’t exist.

Today, I’m taking the reasoning one step further. Because there’s an even deeper misconception: believing that an LLM is a knowledge base. It’s not. A language model generates probable word sequences, not verified facts. In other words, it recites with ease, but it never cites.

That’s exactly what I explore in my new article: why this confusion persists, and how to clearly distinguish between parametric memory and explicit memory, so we can finally combine them the right way.

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OPINION

AI BIAISES