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[1803.00085] Chinese Text in the Wild
We introduce Chinese Text in the Wild, a very large dataset of Chinese text in street view images.

...

We give baseline results using several state-of-the-art networks, including AlexNet, OverFeat, Google Inception and ResNet for character recognition, and YOLOv2 for character detection in images. Overall Google Inception has the best performance on recognition with 80.5% top-1 accuracy, while YOLOv2 achieves an mAP of 71.0% on detection. Dataset, source code and trained models will all be publicly available on the website.
nibble  pdf  papers  preprint  machine-learning  deep-learning  deepgoog  state-of-art  china  asia  writing  language  dataset  error  accuracy  computer-vision  pic  ocr 
20 hours ago by nhaliday
Basic Error Rates
This page describes human error rates in a variety of contexts.

Most of the error rates are for mechanical errors. A good general figure for mechanical error rates appears to be about 0.5%.

Of course the denominator differs across studies. However only fairly simple actions are used in the denominator.

The Klemmer and Snyder study shows that much lower error rates are possible--in this case for people whose job consisted almost entirely of data entry.

The error rate for more complex logic errors is about 5%, based primarily on data on other pages, especially the program development page.
org:junk  list  links  objektbuch  data  database  error  accuracy  human-ml  machine-learning  ai  pro-rata  metrics  automation  benchmarks  marginal  nlp  language  density  writing  dataviz  meta:reading 
21 hours ago by nhaliday
Should I go for TensorFlow or PyTorch?
Honestly, most experts that I know love Pytorch and detest TensorFlow. Karpathy and Justin from Stanford for example. You can see Karpthy's thoughts and I've asked Justin personally and the answer was sharp: PYTORCH!!! TF has lots of PR but its API and graph model are horrible and will waste lots of your research time.

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Updated Mar 12
Update after 2019 TF summit:

TL/DR: previously I was in the pytorch camp but with TF 2.0 it’s clear that Google is really going to try to have parity or try to be better than Pytorch in all aspects where people voiced concerns (ease of use/debugging/dynamic graphs). They seem to be allocating more resources on development than Facebook so the longer term currently looks promising for Google. Prior to TF 2.0 I thought that Pytorch team had more momentum. One area where FB/Pytorch is still stronger is Google is a bit more closed and doesn’t seem to release reproducible cutting edge models such as AlphaGo whereas FAIR released OpenGo for instance. Generally you will end up running into models that are only implemented in one framework of the other so chances are you might end up learning both.
q-n-a  qra  comparison  software  recommendations  cost-benefit  tradeoffs  python  libraries  machine-learning  deep-learning  data-science  sci-comp  tools  google  facebook  tech  competition  best-practices  trends  debugging  expert-experience 
yesterday by nhaliday
Intelligence predicts cooperativeness better than conscientiousness does - Marginal REVOLUTION
Intelligence has a large and positive long-run effect on cooperative behavior. The effect is strong when at the equilibrium of the repeated game there is a trade-off between short-run gains and long-run losses. Conscientiousness and Agreeableness have a natural, significant but transitory effect on cooperation rates

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Note that agreeable people do cooperate more at first, but they don’t have the strategic ability and consistency of the higher IQ individuals in these games. Conscientiousness has multiple features, one of which is caution, and that deters cooperation, since the cautious are afraid of being taken advantage of. So, at least in these settings, high IQ really is the better predictor of cooperativeness, especially over longer-term horizons.

I think Garett Jones commented on this on Twitter or in a podcast?
econotariat  marginal-rev  commentary  study  economics  behavioral-gen  psychology  cog-psych  microfoundations  hive-mind  cooperate-defect  iq  psychometrics  personality  discipline  long-short-run  patience  time-preference  equilibrium 
5 days ago by nhaliday
What’s In A Name? Understanding Classical Music Titles | Parker Symphony Orchestra
Composition Type:
Symphony, sonata, piano quintet, concerto – these are all composition types. Classical music composers wrote works in many of these forms and often the same composer wrote multiple pieces in the same type. This is why saying you enjoy listening to “the Serenade” or “the Concerto” or “the Mazurka” is confusing. Even using the composer name often does not narrow down which piece you are referring to. For example, it is not enough to say “Beethoven Symphony”. He wrote 9 of them!

Generic Name:
Compositions often have a generic name that can describe the work’s composition type, key signature, featured instruments, etc. This could be something as simple as Symphony No. 2 (meaning the 2nd symphony written by that composer), Minuet in G major (minuet being a type of dance), or Concerto for Two Cellos (an orchestral work featuring two cellos as soloists). The problem with referring to a piece by the generic name, even along with the composer, is that, again, that may not enough to identify the exact work. While Symphony No. 2 by Mahler is sufficient since it is his only 2nd symphony, Minuet by Bach is not since he wrote many minuets over his lifetime.

Non-Generic Names:
Non-generic names, or classical music nicknames and sub-titles, are often more well-known than generic names. They can even be so famous that the composer name is not necessary to clarify which piece you are referring to. Eine Kleine Nachtmusik, the Trout Quintet, and the Surprise Symphony are all examples of non-generic names.

Who gave classical music works their non-generic names? Sometimes the composer added a subsidiary name to a work. These are called sub-titles and are considered part of the work’s formal title. The sub-title for Tchaikovsky’s Symphony No. 6 in B minor is “Pathetique”.

A nickname, on the other hand, is not part of the official title and was not assigned by the composer. It is a name that has become associated with a work. For example, Bach’s “Six Concerts à plusieurs instruments” are commonly known as the Brandenburg Concertos because they were presented as a gift to the Margrave of Brandenburg. The name was given by Bach’s biographer, Philipp Spitta, and it stuck. Mozart’s Symphony No. 41 earned the nickname Jupiter most likely because of its exuberant energy and grand scale. Schubert’s Symphony No. 8 is known as the Unfinished Symphony because he died and left it with only 2 complete movements.

In many cases, referring to a work by its non-generic name, especially with the composer name, is enough to identify a piece. Most classical music fans know which work you are referring to when you say “Beethoven’s Eroica Symphony”.

Non-Numeric Titles:
Some classical compositions do not have a generic name, but rather a non-numeric title. These are formal titles given by the composer that do not follow a sequential numeric naming convention. Works that fall into this category include the Symphony Fantastique by Berlioz, Handel’s Messiah, and Also Sprach Zarathustra by Richard Strauss.

Opus Number:
Opus numbers, abbreviated op., are used to distinguish compositions with similar titles and indicate the chronological order of production. Some composers assigned numbers to their own works, but many were inconsistent in their methods. As a result, some composers’ works are referred to with a catalogue number assigned by musicologists. The various catalogue-number systems commonly used include Köchel-Verzeichnis for Mozart (K) and Bach-Werke-Verzeichnis (BWV).

https://music.stackexchange.com/questions/6688/why-is-the-key-included-in-classical-music-titles
I was always curious why classical composers use names like this Étude in E-flat minor (Frédéric_Chopin) or Missa in G major (Johann Sebastian Bach). Is this from scales of this songs? Weren't they blocked to ever use this scale again? Why didn't they create unique titles?

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Using a key did not prohibit a composer from using that key again (there are only thirty keys). Using a key did not prohibit them from using the same key on a work with the same form either. Bach wrote over thirty Prelude and Fugues. Four of these were Prelude and Fugue in A minor. They are now differentiated by their own BWV catalog numbers (assigned in 1950). Many pieces did have unique titles, but with the amounts of pieces the composers composed, unique titles were difficult to come up with. Also, most pieces had no lyrics. It is much easier to come up with a title when there are lyrics. So, they turned to this technique. It was used frequently during the Common Practice Period.

https://fredericksymphony.org/how-are-classical-music-compositions-named/
explanation  music  classical  trivia  duplication  q-n-a  stackex  music-theory  init  notation  multi  jargon 
5 days ago by nhaliday
Preventing the Collapse of Civilization [video] | Hacker News
- Jonathan Blow

NB: DevGAMM is a game industry conference

- loss of technological knowledge (Antikythera mechanism, aqueducts, etc.)
- hardware driving most gains, not software
- software's actually less robust, often poorly designed and overengineered these days
- *list of bugs he's encountered recently*:
https://youtu.be/pW-SOdj4Kkk?t=1387
- knowledge of trivia becomes more than general, deep knowledge
- does at least acknowledge value of DRY, reusing code, abstraction saving dev time
hn  commentary  video  presentation  techtariat  carmack  pragmatic  contrarianism  pessimism  sv  tech  unix  rhetoric  critique  programming  engineering  pls  worrydream  software  hardware  performance  robust  trends  multiplicative  roots  impact  comparison  history  iron-age  the-classics  mediterranean  conquest-empire  gibbon  technology  the-world-is-just-atoms  flux-stasis  increase-decrease  games  graphics  hmm  idk  systems  os  abstraction  intricacy  worse-is-better/the-right-thing  compilers  build-packaging  microsoft  osx  apple  reflection  assembly  c(pp)  expert-experience  things  knowledge  detail-architecture  thick-thin  trivia  info-dynamics  caching  frameworks  generalization  systematic-ad-hoc  universalism-particularism  analytical-holistic  structure  tainter  libraries  tradeoffs  prepping  threat-modeling  network-structure  writing  risk  local-global  trade  composition-decomposition  coupling-cohesion  parsimony  civilization  complex-systems  system-design  multi  error  list  debugging 
8 days ago by nhaliday
Information Processing: Moore's Law and AI
Hint to technocratic planners: invest more in physicists, chemists, and materials scientists. The recent explosion in value from technology has been driven by physical science -- software gets way too much credit. From the former we got a factor of a million or more in compute power, data storage, and bandwidth. From the latter, we gained (perhaps) an order of magnitude or two in effectiveness: how much better are current OSes and programming languages than Unix and C, both of which are ~50 years old now?

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Of relevance to this discussion: a big chunk of AlphaGo's performance improvement over other Go programs is due to raw compute power (link via Jess Riedel). The vertical axis is ELO score. You can see that without multi-GPU compute, AlphaGo has relatively pedestrian strength.
hsu  scitariat  comparison  software  hardware  performance  sv  tech  trends  ai  machine-learning  deep-learning  deepgoog  google  roots  impact  hard-tech  multiplicative  the-world-is-just-atoms  technology  trivia  cocktail  big-picture  hi-order-bits 
8 days ago by nhaliday
Reverse salients | West Hunter
Edison thought in terms of reverse salients and critical problems.

“Reverse salients are areas of research and development that are lagging in some obvious way behind the general line of advance. Critical problems are the research questions, cast in terms of the concrete particulars of currently available knowledge and technique and of specific exemplars or models that are solvable and whose solutions would eliminate the reverse salients. ”

What strikes you as as important current example of a reverse salient, and the associated critical problem or problems?
west-hunter  scitariat  discussion  science  technology  innovation  low-hanging  list  top-n  research  open-problems  the-world-is-just-atoms  marginal  definite-planning  frontier  🔬  speedometer  ideas  the-trenches  hi-order-bits 
12 days ago by nhaliday
Braves | West Hunter
If  Amerindians had a lot fewer serious infectious diseases than Old Worlders, something else had to limit population – and it wasn’t the Pill.

Surely there was more death by violence. In principle they could have sat down and quietly starved to death, but I doubt it. Better to burn out than fade away.
west-hunter  scitariat  reflection  ideas  usa  farmers-and-foragers  history  medieval  iron-age  europe  comparison  asia  civilization  peace-violence  martial  selection  ecology  disease  parasites-microbiome  pop-diff  incentives  malthus  equilibrium 
12 days ago by nhaliday
its-not-software - steveyegge2
You don't work in the software industry.

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So what's the software industry, and how do we differ from it?

Well, the software industry is what you learn about in school, and it's what you probably did at your previous company. The software industry produces software that runs on customers' machines — that is, software intended to run on a machine over which you have no control.

So it includes pretty much everything that Microsoft does: Windows and every application you download for it, including your browser.

It also includes everything that runs in the browser, including Flash applications, Java applets, and plug-ins like Adobe's Acrobat Reader. Their deployment model is a little different from the "classic" deployment models, but it's still software that you package up and release to some unknown client box.

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Servware

Our industry is so different from the software industry, and it's so important to draw a clear distinction, that it needs a new name. I'll call it Servware for now, lacking anything better. Hardware, firmware, software, servware. It fits well enough.

Servware is stuff that lives on your own servers. I call it "stuff" advisedly, since it's more than just software; it includes configuration, monitoring systems, data, documentation, and everything else you've got there, all acting in concert to produce some observable user experience on the other side of a network connection.
techtariat  sv  tech  rhetoric  essay  software  saas  devops  engineering  programming  contrarianism  list  top-n  best-practices  applicability-prereqs  desktop  flux-stasis  homo-hetero  trends  games  thinking  checklists  dbs  models  communication  tutorial  wiki  integration-extension  frameworks  api  whole-partial-many  metrics  retrofit  c(pp)  pls  code-dive  planning  working-stiff  composition-decomposition  libraries  conceptual-vocab  amazon 
12 days ago by nhaliday
AFL + QuickCheck = ?
Adventures in fuzzing. Also differences between testing culture in software and hardware.
techtariat  dan-luu  programming  engineering  checking  random  haskell  path-dependence  span-cover  heuristic  libraries  links  tools  devtools  software  hardware  culture  formal-methods  local-global  golang 
24 days ago by nhaliday
The Architect as Totalitarian: Le Corbusier’s baleful influence | City Journal
Le Corbusier was to architecture what Pol Pot was to social reform. In one sense, he had less excuse for his activities than Pol Pot: for unlike the Cambodian, he possessed great talent, even genius. Unfortunately, he turned his gifts to destructive ends, and it is no coincidence that he willingly served both Stalin and Vichy.
news  org:mag  right-wing  albion  gnon  isteveish  architecture  essay  rhetoric  critique  contrarianism  communism  comparison  aphorism  modernity  authoritarianism  universalism-particularism  europe  gallic  history  mostly-modern  urban-rural  revolution  art  culture 
5 weeks ago by nhaliday
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