The Wisdom Wall

“by the time you get that out and it's actually scalable and it can really train it and all the software is ready for it. Uh, and I can now go on AWS and spawn up your new hardware, like, Nvidia will also have been a hundred X faster with their latest and greatest GPU.”

“Um, uh, but I, I do think, uh, hallucinations can be, uh, also very helpful, uh, for AI when you want it to explore novel kinds of proteins.”

“If you think about it from first principles, there is nothing you can do to be 10 X better.”

“And yeah, I don't think you should attempt it unless you have, you know, hundreds of millions of, of responses, um, that to really like train your own model, um, versus just fine-tune it a little bit. Um, like certainly not from scratch.”

“And my, my theory here, uh, after thinking about this for quite some time is largely dependent on the elasticity of the demand of the product, uh, when its prices go down.”

“if you want to test how students write essays, you probably have to take away the internet and just have them do some handwritten essays sometimes if that's still what you want to test for.”

“whenever it's very slow to create an artifact in your space, an image, a text document, and so on, very slow to create it, but very quick to verify its correctness, then Gen AI is going to, like, massively disrupt that space.”

“even if your agent is 95% accurate, but now it's doing, like, 14, 15 steps, each of which is 95% accurate, and you multiply, you know, .951549, now, half the time, the overall sequence is wrong, right?”

“It's generally a bad idea. I would discourage everyone from doing it. You need a lot more extra funding. You need a lot of very smart people who execute at extremely high levels. It makes everything harder.”

“truth is people don't like paying for things, um, and they like it even less than advertisements.”

“It is almost impossible nowadays to be the sort of general genius that can dabble in all of these different fields because each field takes years and years and years to get really deep into.”

“There's also some amount of moral panic Um, about chatbot friends, similar fashion to how novels used to be a really bad thing.”

“the very foundational building blocks, which I currently think are prediction, action, and goals, uh, and a combination of those three. Um, those are sort of the three principal components.”

“You can actually, if you want to predict like, oh, what's the next thing of open AI? Just read all the research papers over the last couple of years. And a lot of those will, will come and get into products at scale with all the right engineering and so on. Uh, and then you'll, you can predict the future better.”

“So the best founding teams are often a great AI researcher together with, uh, someone who has great, uh, expertise in, in the domain, and they get along well, and you see the dynamics, uh, when you meet them, and they just, they get along.”

“A lot of companies die because the founders don't get along, but the best companies are usually, um, you know, founded by two founders.”

“Deep learning is a set of algorithms that is really not that different to machine learning in general. It can do anything that general machine learning can do, and in many cases better, but it really shines when you have unstructured data.”

“you don't need an expert in your domain for understanding and representing that data in order to give it to a final classifier. It will actually learn all of that automatically.”