diamondage

My point is that they will, and that is always going to be useful. Bundles of simplified knowledge (in whatever form) that make research simpler will be useful to share whether humans can understand them or not...

Almost every country in the world has a patent system for a reason...and they are pretty essential for big pharma to function. Granted there should be better calibration of duration - particularly in software, but rewarding first movers is wise in many market conditions. My point regarding "math as a hash list of theorems" is that Theorems will never lose their value, regardless of how powerful AI becomes; they represent compressed knowledge, and as such it will be more efficient for a more advanced AI to query known theorems than reconstruct each one from scratch - in that gap lies a marginal compute saving; an api service or tool call that someone might charge for. Even AI can benefit from specialists...

This misses the raw advantage of a good proof. It makes conceptualization simpler. In some ways math is like a hash list of of theorems. This list makes it simpler to prove other calculations, and will always be useful, to both humans and AI models. I can see two new directions 1 - the creation of specialist theorem models; that can answer questions efficiently about one topic and 2 - we probably need to incentivize and codify ownership of theorems; charging a proportion of the compute saved by using them. Ultimately enabling mathematicians to be paid our true market value!

Perhaps undiscussed in the paper is how the blending of memories may also be better. If evolutionary pressure encourages the structure of brain to optimise for accurate prediction, not memory, then it is quite possible that older individuals may be making better predictions than younger ones, in situations where they don't remember the details...

Its great. The article summaries frequently have odd phrases in them - eg "keep concise", the precise summary" etc. Might want to check that!

In my experience Azure endpoint versus openAI endpoint was way faster and significantly cheaper.

The uptake forced the bigger companies to act. With image diffusion models too - no corporate lawyer would let a big company release a product that allowed the customer to create any image...but when stable diffusion et al started to grow like they did...there was a specific price of not acting...and it was high enough to change boardroom decisions

Why are they wrong? Surely it depends on how you train it?

I'm not sure if you're correct. In fact there has been a revolution in some areas of social science in the last two decades due to the availability of online behavioural data.

The question 'what is science actually for' can be sidestepped. Everyone can agree that it has value, albeit we disagree on the actual value...this is why you need a market. As to how things get priced in such a market, this is a subject for further research...To start, it just needs to tie to something measurable. Heck we've created memecoins with far less backing. Also, we've carved up the conceptual space on a very course grained level with patents, we just need a more immediate, and granular system for doing so...

Actually, the problem is pricing. If we could identify and correctly value new concepts, then we can dispense with citations and just use the correct sum of concept valuations. Perhaps a correctly designed futures market would not only solve getting the right PhD students the right jobs, but bring a lot of speculative capital into fundamental research?

Low latency starlink orders on hold