andreshb

The Night of Terror during the 1977 blackout in NYC led to 1000 fires, 4000 arrests, and 550 injured police.

The blackout in 2003 in NYC had no looting or violence.

The blackout in Maracaibo Venezuela in 2019 had 350 stores looted and 550+ people arrested.

The difference was the economic and social challenges in 1977 in NYC and 2019 in Venezuela. A recession and high unemployment.

Username checks out :)

I use prezi video. But it’s very clunky. So really I use Canva for creating the slides and then Prezi video.

This seems much much better.

If it’s as good or better I’ll drop Prezi and switch completely. Pricing seems good, I don’t share the same opinion of many commenters here. Perhaps because I’m on zoom all day both in 1:1 and 1:many including workshops where I’m the speaker then I might be the right ICP

Could you do a comparison with Prezi Video ?

Do you have samples of time-based cohort analysis? Most other solutions out there struggle to do the steps to generate time-based heatmaps and line graphs of cohort analysis. Averages, medians, and anything that can be done on a spreadsheet by a high schooler, GPT does well with.

One of the flights is from New Jersey to Brooklyn … what a waste

Every time I try to do time-series cohort analysis or Lifetime LTV expandability on many of the analytics tools out there I get frustrated and up resorting to Tableau. It took mixpanel a decade to finally get some of it right but it's still too simple. I don't like tableau but it sure beats doing custom SQL queries all the time. How do you handle cohort analysis?

This and how you actually managed the data being sent / code. Compliance won’t let me touch this otherwise

I can’t use ChatGPT in too many cases because of lack of data transfer agreement.

Aside from an appliance in some local servers, what vendors do you use that solve this problem well ?

tl:dr by GPT3

Paper contributes to debate about abilities of large language models like GPT-3

Evaluates how well GPT performs on the Turing Test

Examines limits of such models, including tendency to generate falsehoods

Considers social consequences of problems with truth-telling in these models

Proposes formalization of "reversible questions" as a probabilistic measure

Argues against claims that GPT-3 lacks semantic ability

Offers theory on limits of large language models based on compression, priming, distributional semantics, and semantic webs

Suggests that GPT and similar models prioritize plausibility over truth in order to maximize their objective function Warns that widespread adoption of language generators as writing tools could result in permanent pollution of informational ecosystem with plausible but untrue texts.