And there were people who were willing to say, "Nah, no, no, don't worry about that. This is easy. You can do this. Anybody can do this. Twice the work in half the time." From my perspective, that's just a lie.
A good book sweeps a corner of your brain. Clears out a stale assumption. Disturbs the ecosystem of old ideas you've been letting accumulate rent-free.
Excessive manufacturing requirements are a pervasive problem, especially in the field of cell and gene therapy and they are killing countless of promising medicines before they ever reach patients.
Until manufacturing and regulatory risks become easier to underwrite, capital will keep drifting away from one of the most promising technologies of the last century.
it's this nonscalable way to scale your organization and it's through like passing your vampire blood. That's what inside Zinga they called it, Pinkis' vampire blood. What he did that I started to do is you pick someone from the organization who's promising. I usually pick the people who didn't fit in the smart misfits and they become your tech assistant which is not your chief of staff, not your executive assistant.
I think the same kind of principle applies with agents in that they can talk to the compiler. It will tell them what to fix. So I guess this could be a case. We we'll see. But Rust could be a pretty promising candidate for to use for agents because they can get more feedback and it's just hard it's harder to to ship certain type of bugs or maybe impossible to have certain type of bugs.
I think that might be what saves us. I think the fact that no, you can't have one engineer and have him do a thousand projects because after three hours of that he's going to literally pass out in a corner.
Some other comparison is like, Opus is like the coworker that is a little silly sometimes, but it's really funny and you keep him around. And Codex is like the, the weirdo in the corner that you don't wanna talk to, but is reliable and gets shit done.
These corner allocations create a distinct source of cross-market misallocation, separate from the aggregate quantity loss (the Harberger triangle) and from within-market misallocation emphasized in prior work.
I'm now fairly pessimistic about ambitious interpretability (i.e. complete reverse-engineering), and I'm excited about model biology (studying qualitative high-level properties of models) and applied interpretability (rigorously doing useful things with interp).
Another big misunderstanding, and a fundamental one, is that people somehow think that the state suppresses the private sector. It’s not at all as simple as that. For the most part, if we look at the local government’s incentive system, they want to help the best, private companies, which are the most promising, because it makes them look good.
As AI gets more capable, the risks associated with mistaken claims, fraudulent claims, or too-vague-to-be-verified claims will increase, and a cloud of suspicion between competitors could drive corner-cutting (indeed, this is happening already and could get worse).
Contrary to some people’s beliefs, companies do not in fact have sufficient incentives to mitigate all major risks, and competition is driving corner-cutting that needs to be reined in somehow.
But to ever promise a homepage redesign or marketing site redesign in order to drive more acquisition is a failed promise that is going to be led by lots of agency money spending, uh often a million dollars plus
Planning, at its core, is a search problem. You search among different paths towards the goal, predict the outcome (reward) of each path, and pick the path with the most promising outcome.
I've so far not seen good book resources to prepare for the systems design / architecture interview. This book from Alex Xu is the most promising one as of yet.
I do think that string theorists were a bit overly ambitious… Not overly ambitious, but a little bit overly arrogant in the beginning, thinking they could solve many problems that they weren’t going to solve.
Society's response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
Mind candy: Military SF by someone who must've spent a big chunk of time in the (US?) military. The novel is good on the inter-personal dynamics of the service and especially of the small ship.
Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.
Part autobiography, part startup + life philosophy, and part product book, I could have categorized this in a few sections. However, the strength of this book is clearly the advice and insights for how Scott's 7+ year journey led to a fantastically successful exit to Adobe for his startup, Behance. The middle of the book is a great mix of anecdotes and tactical things you can apply if your startup is in "the Messy Middle" to turn the corner for your product.
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