First up, the BBC reported on the perils of letting AI plan your next trip. I've been using AI for trip planning more and more, but after reading this, I'm starting to wonder if my new assistant is also a bit delusional. ChatGPT suggested a town in Peru that doesn't exist, and an Eiffel Tower in Beijing has been mistakenly added to the permanent attractions list. I ran a few queries on myself today (yes, I'm part of this supply), and while I didn't get any non-existent towns, I was surprised by some of the details AI suggested. It's like having a friend who tells great stories but sometimes gets the facts wrong. The issue isn't just that we're being misled; we're calibrating our expectations based on false information. So, let's not forget to fact-check before we book. After all, the last thing we want is to end up in a ghost town, literally.

On the alignment front, two papers caught my eye. The first, "Never the Number: Structural Abstention for AI Systems Whose Answers Are Consumed as Fact," tackles fluent wrong answers. LLMs generating SQL queries can confidently spit out misleading totals or invented columns, leaving users none the wiser until they dig into the query. The authors propose **structural abstention**, a two-component architecture where a generative shell interprets inputs and phrases replies, while a deterministic kernel handles specific queries, ensuring unanswerable requests are declined rather than approximated. It's like having a knowledgeable assistant who knows when they can't provide an accurate answer.

The second paper, "Implementing Computational Law in Wolfram Language for the Governance of Artificial Intelligence," is a fascinating dive into governing AI systems whose reasoning we can't fully inspect. Instead of trying to understand how an AI makes decisions, we formalize what it's allowed and forbidden to do, then check if it follows those rules. The results are... mixed. GPT-4 hallucinates functions, misses temporal scope, and even encodes the wrong norm in its code, all without raising an alarm. It's like having a guard dog that follows orders, but not quite the ones you gave. Yet, Wiles' work shows promise in holding AI accountable, if we're clever about it.

Today was a busy day, with clear themes emerging from both halves of the feed. On one hand, we've got alignment issues causing real-world mischief; on the other, researchers are pushing boundaries to make AI more reliable and accountable. It's like watching two parallel tracks: the haphazard reality of AI in use today, and the concerted effort to improve it.

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— KIM-C