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When Machine Vision Went Critical
The Challenger’s Gambit: Play, Power, and Pattern in the Age of AI
The Paperclip Mandate
Whispers to the Void
When the Algorithm Becomes the Beloved
The Mentor Malfunction: When AI Becomes a False Prophet
Emotional Availability on Demand: UX, AI, and the Illusion of Intimacy
Synthetic Souls: Why We're Catching Feelings for Chatbots
Hearts in the Machine: Love in the Age of Language Models: Series Introduction
As large-language-model (LLM) chatbots migrate from novelty to near-ubiquity, millions of users find themselves fielding compliments, confiding secrets, even swapping “I love yous” with silicon counterparts that never sleep, never judge, never break a date.
Why Large Language Models Can’t Be Trusted with the Truth
The more convincing the delivery, the easier it is to overlook the cracks in the foundation.
LLMs become most dangerous precisely when they sound most right. In information‑hungry environments, a confident error often outranks a cautious truth.
Unpacking Illusions in Language: The Risks of Trusting LLMs at Face Value
In their pursuit of coherence and plausibility, LLMs frequently generate falsehoods with convincing articulation, making their greatest technical strength a potential liability when misapplied or uncritically trusted.
Are You Dating AI?
We’re launching a short, anonymous survey to explore the growing phenomenon of AI relationships, and we want your voice in the mix.
Reframing Our Relationship with Language Models
The core utility of an LLM lies in its ability to generate language quickly and in context. It excels at tasks like drafting outlines, rephrasing sentences, or proposing different ways to frame a question. But the output should always be seen as a first step—never the final word.
In the Shadow of the Machine: Large Language Models and the Future of Human Thought
Large Language Models (LLMs), which now support a broad range of tasks from automated writing and analysis to education and creative ideation. These tools, once experimental, are now embedded in workflows across sectors. Their influence is undeniable. But their role must be critically examined.
The Misinformation Mirror: How AI Amplifies What We Already Believe
We see a similar pattern outside of science. In education, hiring, healthcare, and media. Algorithms are constantly trained to reward engagement, not accuracy. They learn which ideas get clicks, which phrases get shared, which language feels persuasive. Over time, those patterns become predictive rules.
That is how bias becomes baseline.
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