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
Okay, but who cares? “Complex systems are difficult to predict” is a mathematical insight that’s like 2 centuries old at this point… and it hasn’t hindered us at all from gaining deep insights into how both individual complex systems work and how complex systems as a general class of phenomena work. I can’t keep track of all the masses and velocities of every individual air molecule in the room I’m sitting in, but I still know how the interactions of those particles give rise to the temperature and air pressure and general behavior of the atmosphere in the room.
People know how this shit works, and anyone telling you otherwise is either willfully ignorant or internationally lying to you to feed a hype cycle with an end goal of making your life worse. People can’t afford to remain uneducated about this stuff anymore.
What’s interesting is how these complex models produce anything useful at all. We could very well have complex models that don’t produce anything other than random noise.
The reason why “we” have these models because they were deliberately trained not to output random noise. That part is well understood.
The only reason why we don’t know what exactly makes the model output an image of Garfield with boobs is the amount of data to sift through. Not because we don’t understand the processes.
Generalization is not a given. It’s possible to make complex models that perfectly memorizes 100% of the training data, but produces garbage results if the input diverges ever so slightly from the training.
This generalization is a process that’s not fully understood. Earlier architectures struggled with this level of generalization, but transformers seem to handle it well.
Not overfitting is hard, yes. But it’s not “we have no idea how/why this works”-hard.