I'm not understanding this part. If these drugs have solved obesity and the whole host of associated diseases, including the number one killer; heart disease, shouldn't the insurance companies be clambering over each other to cover these drugs and heavily encouraging their use considering the cost reduction on the overall health system.

And if the incentives are misaligned with insurance companies why are governments not handing out GLP-1s to anyone who asks?

My thinking was to just make the responses available to users and let them see how models perform. But from some feedback, turns out users don't want to have to evaluate the answers and would rather see a leaderboard and rankings.

The scalable solution to that would be LLM as judge that some benchmarks already use, but that just feels wrong to me.

LM Arena tries to solve this with the crowd sourced solution, but I think the right method would have to be domain expert human reviewers, so like Wirecutter VS IMDb, but that is expensive to pull off.

I’ll make the bold claim that the following industries / companies would not exist without the USPS:

The Airline Industry: In the early days of American aviation, air transportation was unproven and not financially viable, until the USPS built the necessary infrastructure and gave contracts to airlines to allow them financial feasibility… starting in 1918! [1]

Machine Learning: In 1989 Yann LeCun wrote his seminal paper “Backpropagation Applied to Handwritten ZIP Code Recognition”, which used the USPS’s data set and has today become the hello world of machine learning tasks. More importantly this is the first commercial or industrial application of machine learning. [2]

Netflix: Before Streaming became a thing, Netflix was shipping DVDs via the USPS. The Postal Service adapted its processes and equipment to make this financially feasible, supporting Netflix through its transition to streaming. [3]

Amazon: Early Amazon was only a book vendor, the USPS offered special rates for books that made it possible for Bezos to be profitable from his garage … in 1994, thus birthing the behemoth it is today. [4]

Chickens: okay, not really. But the USPS ships millions of pounds of live chickens and other animals each year! [5]

[1] https://www.history.com/news/us-aviation-airmail-passenger-f...

[2] http://yann.lecun.com/exdb/publis/pdf/lecun-89e.pdf

[3] https://www.zdnet.com/article/u-s-postal-service-to-netflix-...

[4] https://faq.usps.com/s/article/What-is-Media-Mail-Book-Rate

[5] https://pe.usps.com/text/pub52/pub52c5_008.htm

China built their own.

I understand from your post that you are a business person, buying product, performing value added services and selling for profit. Although I know little about business, I would guess that if one of your suppliers raised the prices on one of the inputs to your finished goods, you would likely increase the price of your product to preserve your profits and continue your business as a venture. I would guess that you would not pay the additional cost out of your own pocket.

My question is; why did you not expect the same logic to play out in the tariffs situation? That any country would pay the additional cost of doing business out of their own pocket and not pass it on to the consumer?

He did get this part wrong though, we ended up calling them 'Mixture of Experts' instead of 'AI bureaucracies'.

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!?

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.

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.

[1] https://github.com/cpldcpu/MisguidedAttention

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.

"...next we have this rubber sheet, which is very clever, and very patented!"

“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.

I think this benchmark would really only tell me whether Wolframs book was in the training data.