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The author argues that the AI hype wave is draining engineering resources without improving security, and that basic practices like inventory and automated patching are far more valuable. He warns that over‑reliance on AI‑generated code erodes understanding and makes debugging harder.
Martin Fowler shares a personal, skeptical take on LLMs, noting their usefulness but criticizing their hallucinations, cultural bias, and the discomfort of interacting with them as if they were human.
This article argues that advanced AI will create an overwhelming number of "control points" requiring human oversight across all industries, leading to a shortage of skilled human jobs. It proposes that committing to the axiom of "human flourishing" justifies the continued need for human experts to maintain control and steer AI development.
A philosophical essay warning against attributing welfare or rights to LLMs, citing recent incidents, alignment research, and ethical debates, but offering no concrete engineering guidance.
DeepMind announced the DeepMind Institute to foster interdisciplinary thinking on the profound implications of AGI. It highlights initial essays covering AI reasoning transparency, economic policy for AGI, principles for a new utopianism, and a framework for frontier AI testing.
The authors release a 12k‑instance multilingual dilemma dataset (English + Hindi, Arabic, Spanish, Chinese) covering three pairwise value conflicts (Honesty‑Justice, Justice‑Autonomy, Autonomy‑Honesty). Benchmarking GPT‑5‑mini shows a consistent Honesty‑over‑Autonomy bias across languages. Llama‑3.2‑1/3B models exhibit a first‑option bias that can be eliminated (>98% accuracy) via plain fine‑tuni…
Apple researchers fine‑tune LLMs on curated subsets of value‑oriented preference data and measure cross‑value effects, safety, and anthropomorphic language. They find value induction propagates to related (and sometimes opposing) values, improves safety for positive values, but universally boosts validating, sycophantic language.
Mustafa Suleyman warns against treating AI models as if they have feelings or rights, arguing that such thinking hinders alignment work. The quote cautions against model‑welfare narratives.
The piece compares generative AI to a powerful laser cutter, highlighting an "ignorance problem" where users lack awareness of risks. It suggests three mitigation paths—labeling, education, and social pressure—each with distinct trade‑offs.
A niece shares a final tea with her aunt, who reveals her plan to get fired from the Museum to expose its unethical "fieldwork" practices. The niece, bound by a deep, engineered loyalty to the Council, has been tasked with assassinating her aunt via a bio-engineered aneurysm.
