281 karma · joined May 26, 2016
For example you can look at the calibration of the forecasts: Take all the outcomes where you estimated ~10% probability. How many of them actually happened? If you are an well-calibrated forecaster, the actual number should be fairly close to 10%.
Brier Score:
I'm 100% on board with charging data centers more for their power usage. They can afford to pay the extra costs. They would much rather pay in dollars vs. in time.
If someone wants me to spend my time and attention on something they have shared, I would like them to demonstrate that they put a proportionate amount of time and effort into its production.
You mention "debt servicing", but OpenAI has no debt. All the money they have raised is equity not debt.
They are working on making a new measurement approach that will be more accurate.
There may be some other regulations that are avoided by a partial acquisition, but it doesn't bring it wholly outside of the relevant antitrust laws.
As long as the demand curve is downward sloping, there will be some pass through of Apple's cut to customers, though the fraction that is passed through will depend on price elasticity.
1. `from polars import col` which at least shortens each use by a few characters 2. For columns that I use very frequently, define a "constant" for them e.g. ID=col("id").
This lets you do df.groupby(ID) instead of df.groupby(pl.col("ID")). Another advantage of defining these column "constants" is that it makes it much easier to refactor to rename all usages of the column (without needing to check whether each string "id" is being used as a column name vs. something else)
There are a lot of business models that are feasible with tracking for ads, but are not feasible to offer for free without tracking.
There has been some attempts at researching the question of how to design a intelligent system that is "corrigible" = willing to allow humans to change the goal it is set to optimize. This is unfortunately still an open question where no great solutions have been found that seem to be reliable when faced with a highly intelligent and capable AI system.
If you are interested in reading more, a few relevant search terms are "Off-switch game" and "corrigibility".
It is true that a LVT will have a larger percentage impact on the market value of undeveloped land who bought land for speculation without any investments in making the land more valuable. This will especially affect people who bought up lots of land speculating on the value going up. This includes people who bought up lots of land for farm/lumber/mining, but also people who buy up urban plots and keep them as a flat land parking lot while waiting for the value to go up.
Any change in taxes will have some winners and some losers. For example an increase in traditional property taxes hurts anyone who bought expensive houses. An sales tax/consumption taxes hurts anyone who saved up financial assets. An increase in income taxes hurts people who invested in education to increase their labor value.
The question is which is the least unfair way to tax to meet the needs of society. Most of those other taxes will actively discourage valuable investments that make society as a whole more prosperous. Property taxes discourage building. Wealth taxes discourage saving. Income taxes discourage earning and investment towards future earning. In contrast a tax on land value, doesn't cause there to be any less land. It does discourage speculation in future increases in land value, but that speculation is mostly zero sum (it might make the individual speculator richer, but doesn't increase the wealth of society as a whole).
Here is a thing that Google does do: The user searches for "children's clothing". An advertiser has created a search ad for the keyword "NIKOLAI-brand kidswear". If the advertiser has enabled "broad match" for their ad, then their ad will be eligible to be shown on the user's page in one of the advertising slots, even though there are no words in common between the user's query and the advertiser's keyword.
Does anyone have evidence of Google manipulating the user's query to affect the organic search results shown to the user?
The "main" training step using huge amounts of inputs is called pre-training. The idea is that after that pre-training, you might fine tune the model for your specific use case.