The deeper lesson is that education has to be research-based and proven to work measurably, not just based on positive emotions or customer satisfaction.
The most positive use case if you said, you know, what's the number one thing we could use AI for? It's not giving kids chatbots that's going to work. It's giving them an individualized lesson plan based on their level to catch them up to grade level. And that would be the single best thing we could do to fix education in America.
Basically, the story would end up being that to get five years of AI progress, you're probably going to need around I would say like maybe eight years of algorithmic progress very roughly. Um, which is a lot a lot of algorithmic progress.
Abdul's victory is a blow to the Democratic establishment in a crucial swing state-and a lesson in the kinds of fearless, populist, "fighter" candidates that today's voters increasingly want to support.
So, the lesson here is to bet on a system that's maximally learned and minimally constrained and leverage structure intentionally to boost performance and scaling laws both in training and in evaluation.
the the big lesson is that for me is technology is changing all the time, and so long as you're able to confront the reality, so long as you are able to learn, the technology itself actually doesn't matter.
We kind of expect that we can go to work and we can work really hard for eight hours straight during the day, but like the brain doesn't really work like that.
but I mean in reality when you look at the data on sleep loss and dementia risk The cut off is really around 6 hours. So if you're consistently sleeping fewer than 6 hours, that's where risk starts to increase.
That’s a lesson we could all learn about our approach to supplements and even drugs: not to fall for hazy promises that a single molecule will deliver vaguely-defined benefits for all aging persons, but to demand rigorous evidence for well-defined benefits for well-defined problems in well-defined populations.
You look under the underneath though at the code and it was just a horrible unmaintainable mess. But the fact that people could program the programs that they wanted was a a significant step forward as opposed to I'm going to write a thousandpage requirements document and then wait 8 years and not get what I want which was the alternative that we were offering at the at the time.
So a a big challenge in object-oriented programming is dividing the responsibilities because you're moving the computation to where the data is. Saying, "Well, this object does this and that object does that is a really critical decision because you want to you want the computation near to the data so that there's less coupling between them." which is a lesson that I I think uh kind of got lost in the noise. That's the fundamental design move in ob in designing object-oriented programs and I I think I stand behind that.
One of my favorite lecture series he has given is The Evidence for Modern Physics where he breaks down the experiments that validate some of the weird laws of physics we have, and what it would take to validate even the weirder ones.
Canada recognised these developments earlier than most, and our response reflects the core lesson we have taken from these tectonic shifts: We have to take care of ourselves and be true to ourselves.
The more ants you put in the puzzle, the faster the solution. But the more humans you add, the worse. Unless the humans are very carefully aligned, this is the key lesson for organizational design.
And humans max out at about like 3 to 4 hours a day. Like really really hard deep work, right? Um which always makes me laugh when executives are like, "We need 8 hours of intense work."
The bitter lesson is don't try to be smarter than the AI, okay? You think that you've got special knowledge. The humans bring special domain knowledge to this problem and we're going to teach it something to AI and it will be smarter. What we found was bigger is smarter. Always.
let's say an engineer can be 100 times as productive, just just for sake of argument, all right? Who captures that value? If the if the engineer goes to work and works for 8 hours a day and produces 100 times as much, the company captured all of that value.
Consider using a noise machine to offset environmental sounds or earplugs if necessary. Temperature controlled mattresses such as Eight Sleep are great.
This book has more of a DGAF tone than Hold Still, I think, and it works best for me when Mann is telling stories instead of trying to extract a lesson for the reader.
Brandy Melville underwear and sweaters. 100% cotton and I can't believe how good they are for how cheap they are. I've owned a couple of their sweaters for like eight years and they really hold up.
The lesson is you just got to get something that you can that you can have that people can see the vision of where you're going. Um, but don't don't do what we did. Get to market faster. I wish we had.
The bitter lesson. Oh, who cares about that? That's that's an empirical observation about a particular period in history. 70 years in history no longer doesn't necessarily have to apply the next 70 years.
I think there are so many lessons from World War II that could have been brought into the history of the last 30 years, which weren’t, such as if you decapitate an incredibly strong leader, you get a power vacuum. And if you don’t have a solution for that power vacuum, lots of bad elements are going to sweep into that in very quick order, which of course, is exactly what happens in Iraq.
I think the lesson from all of that is that humans talking to humans and being together in the real world or a virtual world, is a naturally empathetic medium, which naturally leads to bonding, and though conflict sometimes occurs, it's just generally so much more promoting of our social norms and good interactions between people and positivity promoting.
And number two, the superhuman opportunity deserves everyone who works at the company to spend as much time as possible in their zone of genius. And so that includes me as well as everybody else. So what I did is I hired a really great president. I went from eight direct reports to two.
The longevity of that consensus is itself remarkable: in 79 years of data, Republican presidents spent an average of $34 billion a year on aid, while Democrats spent $40 billion.
The scale word gets a lot of attention in this. The interpretation that I use is effectively to avoid adding the human priors to your learning process. And if you read the original essay, this is what it talks about is how researchers will try to come up with clever solutions to their specific problem that might get them small gains in the short term while simply enabling these deep learning systems to work efficiently, and for these bigger problems in the long term might be more likely to scale and continue to drive success.
I’d say for humanity, it’s both a tribute to the ability of discovery and the ability of really believing in things so that you have the confidence to go look for them, but it’s also a cautionary tale that you don’t want to assume things before they’ve been actually found.
The problem that companies like Apple need to solve is not preventing exploits forever, but a much simpler one: they need to screw up the economics of NSO-style mass exploitation.
This economics thought experiment is a classic book originally written in 1946. Like most books that follow Nassim Taleb's "all books worth reading are at least 20 years old" rule, it has timeless lessons throughout. It tackles all kinds of ongoing political/economic concepts like rent control, minimum wage, tariffs, "saving industries", and more.
The lesson is that connecting with others starts with recognizing that people are so different, especially in how they communicate. But there’s a much broader lesson embedded in this book if you read between the lines.
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