I read the article on archive and figured there was a big chunk missing. It really does not make any sense.
Sutskever and Murati were methodical, they waited until the board was favorable to the outcome they wanted, engaged with board members individually laying the groundwork... and then just changed their mind when it actually happened!?
The article says Sutskever was blindsided by the rank-and-file being on Sam's side. Presumably he thought the outcome was going to be business as more-or-less usual but with Murati or someone as CEO and then panicked when that didn't happen.
Or someone said "If you don't switch and back me, I am going to fight every bit of your compensation. Or you can back me and leave with favorable terms."
Thanks for writing this out, it's helpful for me as a layman.
Isn't part of the prohibition on trades among officers and directors also because of the inside knowledge they have? Public companies generally report quarterly but the insiders presumably have up to the minute information on sales etc.
And while we wait on the quarterly data, consistent insider selling is indicative of ... something.
The LLM community has come up with tests they call 'Misguided Attention'[1] where they prompt the LLM with a slightly altered version of common riddles / tests etc. This often causes the LLM to fail.
For example I used the prompt "As an astronaut in China, would I be able to see the great wall?" and since the training data for all LLMs is full of text dispelling the common myth that the great wall is visible from space, LLMs do not notice the slight variation that the astronaut is IN China. This has been a sobering reminder to me as discussion of AGI heats up.
It could be that it “assumed” you meant “from China”; in the higher level patterns it learns the imperfection of human writing and the approximate threshold at which mistakes are ignored vs addressed by training on conversations containing these types of mistakes; e.g Reddit. This is just a thought. Try saying: As an astronaut in Chinese territory; or as an astronaut on Chinese soil. Another test would be to prompt it to interpret everything literally as written.
Interesting... It took me 3 different attempts, but I found a set of custom instructions that allowed Claude to get the right answer on the initial prompt. Here's the instructions (I tried to keep them as general and non-specific as I could):
Carefully analyze questions to not overlook subtle details. Take each question "as-is", don't guess what they mean -- interpret them as any reasonable person would.
I made https://aimodelreview.com/ to compare the outputs of LLMs over a variety of prompts and categories, allowing a side by side comparison between them. I ran each prompt 4 times for different temperature values and that's available as a toggle.
I was going to add reviews on each model but ran out of steam. Some users have messaged me saying the comparisons are still helpful to them in getting a sense of how different models respond to the same prompt and how temperature affects the same models output on the same prompt.
Hey, this is pretty insightful! Wonder if, in the course of researching to build this website you reached any conclusions as to what’s the AI assistant currently ahead.
And to take a historic analogy, cars today are as wide as they are because that's about how wide a single lane roadway is. And a single lane roadway is as wide as it is because that's about the width of two horses drawing a carriage.
The story goes that this two horses width also limited the size of the space shuttle's boosters (SRB), so we ended up taking this sort of path-dependence off to space.
The most common first action people take on our site is reading a summary of a book they've already read to assess its quality themselves. I don't think they care whether it was written by OpenAI or a monkey, as long as it's good.
"Suddenly, the chat window on Sequoia’s side of the Zoom lights up with partners freaking out.
“I LOVE THIS FOUNDER,” typed one partner.
“I am a 10 out of 10,” pinged another.
“YES!!!” exclaimed a third.
What Sequoia was reacting to was the scale of SBF’s vision....We were incredibly impressed, Bailhe says. “It was one of those your-hair-is-blown-back type of meetings.”
This is 'smart money' in reference to Sam Bankman Fried.
"The task consists of going from English-language specifications to Wolfram Language code. The test cases are exercises from Stephen Wolfram's An Elementary Introduction to the Wolfram Language."
I think this benchmark would really only tell me whether Wolframs book was in the training data.
He did get this part wrong though, we ended up calling them 'Mixture of Experts' instead of 'AI bureaucracies'.