McGurk effect - Auditory Illusion - BBC Horizon Clip
sixesfullofnines1 HN recommenders · 2021–2025
745K views · demo · evergreen · Uploaded 2016-03-16
A two-minute BBC Horizon clip demonstrating the McGurk effect, where what you see of a speaker's mouth changes the sound you hear.
Recommended as a quick demo of vision overriding hearing, and cited as an example of perception producing confident wrong answers.
57:151. Introduction to Human Behavioral Biology
Stanford
52:29Stanford's Sapolsky On Depression in U.S. (Full Lecture)
Stanford
52:07What Bodies Think About: Bioelectric Computation Outside the Nervous System - NeurIPS 2018
The Artificial Intelligence Channel
16:29The great porn experiment | Gary Wilson | TEDxGlasgow
TEDx Talks
2:16:32Controlling Your Dopamine For Motivation, Focus & Satisfaction
Andrew Huberman
59:57Personal Growth Series: Dr. William Dement on Healthy Sleep and Optimal Perfo...
Google TechTalks
1:55:33Joe Rogan Experience #1109 - Matthew Walker
PowerfulJRE
6:49The man with 1,000 Klein Bottles UNDER his house - Numberphile
Numberphile
4 comments from Hacker News
The visual processing part of the brain can completely overwhelm auditory processing: see the mcgurk effect. [VIDEO] I am not at all surprised that there are also olifactory illusions which are impossible to “unsee”.
Are you aware that the same exact uncompressed recording sounds different depending on context? This is known as the McGurk effect. Very worth your two minutes if you're not yet familiar with the effect: [VIDEO]
The McGurk effect [VIDEO] is another example, which might be described as people learning to lip read without them being aware that they have done so.
This is, I think, a better way to think about LLM mistakes compared to the usual "hallucinations". I think of them as similar to human optical illusions. There are things about the human visual cortex (and also other sensory systems, see the McGurk Effect [0]), that, when presented with certain kinds of inputs, will consistently produce wrong interpretations/outputs. Even when we are 100% ware of the issue, we can't prevent our brains from generating the incorrect interpretation. LLMs seem to have similar issues along dramatically different axes, axes that humans are not used to seeing these kinds of mistakes; where nearly no human would make this kind of mistake and so we interpret it (in my opinion incorrectly) as lack of ability or intelligence. Because these are engineered systems, we may figure out ways to solve these problems (although I personally think the best we will ever do is decrease their prevalence), but more important is probably learning to recognize the places that LLMs are likely to make these errors, and, as your comment suggests, design work flows and systems that can deal with them. [0] [VIDEO]