Most code written are not novel. Actually most code written should not be novel. Eg: the number of lines of code written isn't spent on writing git, it's spent on writing that landing page no one ever sees.
The key difference is that the model is able to write the program as it’s executing it.
Before it needs to write the code and have an external program execute it. Here it can change its mind mid execution. Kinda like what was observed in the CoT’s ah ha moment
Was hoping to upgrade from my 2017 iMac and this new studio display is quite a bummer. 9 years later and it's basically the same display spec. (500 nits spec bump to 600 nits)
It’s probably because their paid advertising spots are being viewed by bots instead of humans. Amazon still charges the advertisers, since it detects the traffic as coming from a non-bot user based on the user agent.
The puzzle assumes that the room temperature is greater than the cold milk's temperature. When I added that the room temperature is, say, -10 °C, Mercury fails to see the difference.
The difference in the dates example seems right to me
20 October 2024 and 2024-20-10 are not the same.
Months in different locales can be written as yyyy-MM-dd. It can also be a catalog/reference number. So, it seems right that their embedding similarity is not perfectly aligned.
So, it's not a tokenizer problem. The text meant different things according to the LLM.
Github Copilot definitely improved my coding productivity. However, most of my time is spent thinking about the problem to be solved than the actual coding.
Stupid question but I thought transformers have an O(n^2) memory usage. With 2M tokens, won't I need dozens and dozens of GPUs just to run the base LLaMA2 models?
Helping teams know what to say "no" to is the real power of OKR.
You do that by having the entire organization's O and KR roll up and cascade down. My Objectives directly roll up to my parent organization's Key Result. My parent org's Objectives rolls up to their parent's KR, and so on to the top. Then, you have the top-level check downwards if the sets of OKRs still make sense in their entirety.
Unfortunately, I have never seen any organization I was with ever do this...