igorlev
Joined 17 years ago
Joined 17 years ago
Cockney would also be a great comparison as both it and AAVE can be seen as class signifiers while the Australian dialect currently isn’t.
1. Maintaining a navy that reduces piracy enough to have global JIT delivery networks that reduce costs for corps
2. Protects corp interests abroad allowing a larger market and higher profits
3. Enforces regulations that attract a larger than otherwise likely share of people to put their money in the stock market, increasing the value of your equities.
It seems to be a good time in the debugging of these complex pathways as there's another, related avenue of signal manipulation research that may lead to scar-less tissue healing (https://science.sciencemag.org/content/372/6540/eaba2374)
https://www.goodreads.com/book/show/134474.An_Engine_Not_a_C...
Would be fascinating to map the cross referencing and co-author graph here.
Some of the comments on pubpeer are hilarious.
Granted that's usually the case when there's a competitive fundraising round that's coming up but it still not completely crazy.
https://cs.uwaterloo.ca/~rtholmes/papers/msr_2015_labuschagn...
The best part? It was loaded into a running service by parsing a complex Excel spreadsheet which lead to so much fun with debugging.
There are some issues with relevance ranking for price where junk rises to the top when you sort by price (https://medium.com/@dtunkelang/why-is-it-so-hard-to-sort-by-...) and Amazon may just be cutting off lower relevancy items when you sort by price as a heuristic.
Freebase, after being bought by Google became the foundation of the Google Knowledge Graph (aka "things not links"). This kicked off an arms race between all the major search providers to build the largest and most complete knowledge graphs (or at least keep pace with Google [1]). Instead of waiting for folks to tag every single page, it turned out that simple patterns cross referenced across billions of pages were good enough to extract useful knowledge from unstructured text.
Some companies who had easier access to structured but dirty data (like LinkedIn and Facebook) were also able to utilize (and contribute to) all of that research by building their own knowledge graphs with names like the Social Graph and Economic Graph. Those in turn are helping to power a decent amount of their search and ad targeting capabilities as well as spawning some interesting work[2]
All those knowledge graphs became a major part of Siri, Alexa and Google Home's ability to answer a wide range of natural language queries. As well as being pretty fundamental to a lot of tech like semantic search, improved ecommerce search and a bunch of intent detection approaches for chatbots.
So yeah while the technology and associated research did turn out to be incredibly useful, adding fancier meta-tags to pages was not the direction that proved the most useful.
[1] https://ai.google/research/pubs/pub45634 [2] https://research.fb.com/publications/unicorn-a-system-for-se...
There were no other factories and even small business was mostly nonexistent until the late 80s and in some cases punishable under the law as “speculation”.