Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.
Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We
<?xml version='1.0' encoding='utf-8'?> | |
<?grc format='1' created='3.7.13'?> | |
<flow_graph> | |
<timestamp>Tue Apr 2 21:50:17 2019</timestamp> | |
<block> | |
<key>options</key> | |
<param> | |
<key>author</key> | |
<value></value> | |
</param> |
########loading the Titanic Train Data Set | |
TitanicTrain<-train1 | |
######Checking Missing Values in the Train Data Set | |
sapply(TitanicTrain, function(x)sum(is.na(x))) | |
#######Loading the Titanic Test Data Set | |
TitanicTest<-test11 | |
#######Checking Missing Values in the Test Data Set |
# see also: | |
# https://s3.amazonaws.com/assets.datacamp.com/blog_assets/xts_Cheat_Sheet_R.pdf | |
# Open csv file using read.zoo | |
my_tsdata <- read.zoo("my_tsdata.csv", sep = ",", FUN = as.Date, header = TRUE, index.column = 1) | |
my_tsdata <- as.xts(my_tsdata) | |
I wrote this answer on stackexchange, here: https://stackoverflow.com/posts/12597919/
It was wrongly deleted for containing "proprietary information" years later. I think that's bullshit so I am posting it here. Come at me.
Amazon is a SOA system with 100s of services (or so says Amazon Chief Technology Officer Werner Vogels). How do they handle build and release?
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##### This is a simple RODBC example | |
##### The ODBCDriverName will be the driver name in ODBC Administrator | |
require(RODBC) | |
#open the ODBC connection | |
ch <- odbcConnect("ODBCDriverName") | |
##### Alternative ODBC connection for Microsoft SQL Server | |
ch <- odbcDriverConnect( |