<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Writings · Alejandro Tlaie Boria</title><description>Essays by Alejandro Tlaie Boria.</description><link>https://alejandrotlaie.net</link><language>en</language><item><title>A Trialogue on AI Existential Risks</title><link>https://alejandrotlaie.net/a-trialogue-on-ai-existential-risks</link><guid isPermaLink="true">https://alejandrotlaie.net/a-trialogue-on-ai-existential-risks</guid><description>A fictional story on why parts of the AI Safety community need more nuance in their discourse.</description><pubDate>Sun, 05 Oct 2025 00:00:00 GMT</pubDate></item><item><title>My objection(s) to the &quot;LLMs are just next-token predictors&quot; take</title><link>https://alejandrotlaie.net/my-objections-to-the-llms-are-just-next-token-predictors-take</link><guid isPermaLink="true">https://alejandrotlaie.net/my-objections-to-the-llms-are-just-next-token-predictors-take</guid><description>An outline of why I believe this is a wrong stance to have in light of the current evidence</description><pubDate>Sat, 18 Jan 2025 00:00:00 GMT</pubDate></item><item><title>On the implicit assumptions of the Alignment Problem: is it OK to instil values?</title><link>https://alejandrotlaie.net/is-it-ok-to-instil-values</link><guid isPermaLink="true">https://alejandrotlaie.net/is-it-ok-to-instil-values</guid><description>I argue that there are a series of load-bearing assumptions that people working on the Alignment Problem make and that need further discussion. Surprisingly (or not!), the philosophical stance that one takes does matter for real-world AI development!</description><pubDate>Thu, 15 Aug 2024 00:00:00 GMT</pubDate></item><item><title>On predictability, chaos and AIs that don&apos;t game our goals</title><link>https://alejandrotlaie.net/on-predictability-chaos-and-gaming-ais</link><guid isPermaLink="true">https://alejandrotlaie.net/on-predictability-chaos-and-gaming-ais</guid><description>Exploring why designing AI that aligns with human goals is fundamentally challenging, focusing on the unpredictability of complex systems, limitations of initial conditions, and the risks of reward hacking in reinforcement learning.</description><pubDate>Mon, 15 Jul 2024 00:00:00 GMT</pubDate></item></channel></rss>