Is this what the employees do nowadays while their AI is generating code?

The news in a vacuum can actually be quite misleading and I too believe people should realize that it is not the ‘whole’ truth.

https://github.com/Mk-Chan/libchess/blob/master/internal/Met... https://github.com/Mk-Chan/libchess/blob/master/Square.h

Edit: it should go without saying that once you hire enough people to dwarf the starting population of the startup + consider employee churn, the bias should disappear within the error margin in the real world. This just follows the original posted results and the paper.

OP doesn’t even claim this is the one true doctrine or anything of the sort.

Now I just play casually if a friend wants to.

For this purpose, I like to mentally model a country (or territory which decided to hold an election of some kind) as a collection of points in a multidimensional space of values or policies which are up for election. Each point represents where a voting person would stand according to their personal principles.

Now, I’d like to postulate that the act of voting corresponds to finding the center of the cluster of points. In this model, it’s easy to imagine the scenario where a very large portion of people (and possibly even the majority) are deeply dissatisfied with the result because they are too far on many dimensions from the center that was elected. This is actually further exacerbated by many factors in real life like the asymmetry of information between people.

The obvious solution would be to create more cluster centers instead of one fat cluster that leaves everyone not very happy. This corresponds to states or provinces within a country in the real world.

So coming from that line of thinking, it leads me to believe that sometimes to maintain a democracy, you need to cut the outliers away to move/make the center such that people are happy with the result. Essentially you enforce the will of the majority by cutting away the minority until it is no longer a tyranny to do so.

Of course this is gross simplification of real life. It is ignoring factors such as external threats, instability, unpredictability, information availability and so on. However, I think it’s useful to think this way for many purposes.

Addressing your extreme example of a criminal, that’s a point so far off in the space that you definitely want to cut it out of the system.

For positive reviews, a business will figure out customers who they already know had a positive experience (quick delivery, continuous usage, etc) and only send them invites to review. This is perfectly legal and the fundamental business model of many review websites - selling the ability to push invites and “manage” reviews.

For negative reviews - no business wants these, and customers with bad experiences are likely to post them by themselves.

What gets left out is the average experience because reviews are essentially cherry picked from the head and tail ends of the normal curve of experiences. This doesn’t render reviews useless, of course. Having a large number of positive reviews is still a positive signal but it is nowhere close to free from manipulation.