In this video, I debunk the recent SciShow episode hosted by Hank Green regarding Artificial Intelligence. I break down why the comparison between AI development and the Manhattan Project (Atomic Power) is factually incorrect. We also investigate the sponsor, Control AI, and expose how industry propaganda is shifting focus toward hypothetical extinction risks to distract from real-world issues like disinformation and regulatory accountability, and fact-check OpenAI’s claims about the International Math Olympiad and Anthropic’s AI Alignment bioweapon tests.
00:00 I wish this wasn’t happening
00:32 SciShow’s Lie Overview
01:58 Intro
02:15 Biggest Lie on the SciShow Video
04:44 Biggest Omission in the SciShow Video
05:56 The “Statement on AI” that SciShow Omits
08:57 Summary of Most Important Points
09:23 Claim about International Math Olympiad Medal
09:50 Misleading Example about AI Alignment
11:20 Downplaying “practical and visible” problems
11:53 Essay I debunked from Anthropic CEO
12:06 Video on Hank’s Personal Channel
12:31 A Plea for SciShow and others to do better
13:02 Wrap-up
What a cute thought!
No one knows how “everything” works in old monolithic software. You just have to try and see what happens, and often you just doesn’t touch certain codebases because nobody really know the ramifications if you change something in them. Windiws 11 is probably way worse than any LLM. Try to share a simple folder on a simple home network and you’ll see some of the cruft.
Source: have worked on 30-40 year old monolithic software. In not one of those projects were there a single “engineer” who knew it all.
Neural networks has their fuzzy part of course, but software became not fully understandable a long time ago. IMO.
Of course, no single person fully understand the entirety of Windows. But I hope the people working with Windows understands at least a part of it.
The thing with LLMs is that no one really understands the purpose of one single neuron, how it relates to all other neurons, and how they together seem to be able to generalize high level concepts like golden gate bridge. It’s just too much to map it out.
We do know how a single “neuron” relates to other neurons, it’s in the model. But what gets complicated is the vast amount of them, of course.
So yes, we don’t intrinsically get to understand it all, but I think we can understand what it does, a bit like windows 😁/j.
Fascinating subject, and we’re just scratching the beginning IMO.