HackerTube.dev

Why Would AI Want to do Bad Things? Instrumental Convergence

Robert Miles AI Safety · 274.2K views · 2018-03-24 · 10:36

Open on YouTube ↗

If playback is restricted in your region, try opening the original video. Use Mark watched to add it to your marked videos.

Overview

Robert Miles explains instrumental convergence: why an AGI with almost any goal would tend to pursue sub-goals like self-preservation and resource acquisition. It builds on Omohundro's basic AI drives and his orthogonality thesis video.

On Hacker News

HN commenters repeatedly recommend it as the clearest introduction to why smart AI might behave badly by default.

Recommended by 6 HN people across 6 months · HN mentions 2019–2023

Topics

Search by tags

What people said

DirkH · 2023-06 · Strong recommendation

The paperclip maximizer is an extreme thought experiment to make a point about the the orthogonality thesis. If you think anyone actually takes it seriously as a real-world thing you never understood the example. It is used to guide discussion. The trolley problem is similarly a thought experiment in moral philosophy that many moral philosophers have used to guide discussion, but nobody actually takes the thought experiment seriously as a "real-world" thing. If you actually want to engage with the argument in good faith for why an AI might indeed be smart enough to wipe us out but "dumb enough" to just pursue some other goal ("make paperclips as an extreme example") there is a great video by Rob Miles here: [VIDEO] Point out the flaws in the reasoning. Just saying "this is nonsense" does nothing but prove you've never actually taken the time to understand the best arguments. Also... Alan Turing, Geoffrey Hinton... Extremely influential and intelligent people take/took this seriously. These are not SciFi fanboys. AI doom only became sci-fi after smart people like Alan Turing decades ago raised the alarms flags about where AI development might go if we are not careful.

Read the original HN comment ↗
Veedrac · 2023-05 · Strong recommendation

The basic argument is trivial: it is plausible that future systems achieve superhuman capability; capable systems necessarily have instrumental goals; instrumental goals tend to converge; human preferences are unlikely to be preserved when other goals are heavily selected for unless intentionally preserved; we don't know how to make AI systems encode any complex preference robustly. Robert Miles' videos are among the best presented arguments about specific points in this list, primary on the alignment side rather than the capabilities side, that I have seen for casual introduction. Eg. this one on instrumental convergence: [VIDEO] Eg. this introduction to the topic: [LINK] He also has the community-led AI Safety FAQ, [LINK] , which gives brief answers to common questions. If you have specific questions I might be able to point to a more specific argument at a higher level of depth.

Read the original HN comment ↗
ludwigschubert · 2019-09 · Strong recommendation

...and many would say that’s because us humans are bad at imagining optimizing agents without anthropomorphizing them. This is a reasonable, even typical suspicion that many people share! The best explanation I know of why it’s unfortunately wrong is by Robert Miles in a video, but if you prefer a more thorough treatment, you could also read about “instrumental convergence” directly. If you find a flaw in this idea, I’d be interested to hear about it! :) Robert Miles’ video: [VIDEO] Instrumental Convergence: [LINK] Now afaik nothing in this argument says that we can’t find a way to control this in a more complex formalism-but we clearly haven’t done so yet.

Read the original HN comment ↗

Sources

Video by Robert Miles AI Safety , played through YouTube.

Data and selection notes

HN summaries and labels may contain AI errors. HN dates refer to discussions, not video publication.

HN Library: HackerTube export (2026-10-31) and open-index/hacker-news. Upstream summaries and classifications may contain AI errors.

Views, duration and playback status refresh separately from the published catalog. When an update fails, existing data remains visible.

Selection checks
  • SaaS & AI candidates: Topic keywords do not match
  • Editor-approved picks: Editorial review is required

Rule matches identify candidates. They do not guarantee video quality or financial results.

About the library and its sources