nhaliday + jobs   80

some interesting job openings in Sydney listed here
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august 2019 by nhaliday
Analysis of Current and Future Computer Science Needs via Advertised Faculty Searches for 2019 - CRN
Differences are also seen when analyzing results based on the type of institution. Positions related to Security have the highest percentages for all but top-100 institutions. The area of Artificial Intelligence/Data Mining/Machine Learning is of most interest for top-100 PhD institutions. Roughly 35% of positions for PhD institutions are in data-oriented areas. The results show a strong interest in data-oriented areas by public PhD and private PhD, MS, and BS institutions while public MS and BS institutions are most interested in Security.
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june 2019 by nhaliday
Stack Overflow Developer Survey 2018
Rust, Python, Go in top most loved
F#/OCaml most high paying globally, Erlang/Scala/OCaml in the US (F# still in top 10)
ML specialists high-paid
editor usage: VSCode > VS > Sublime > Vim > Intellij >> Emacs
ranking  list  top-n  time-series  data  database  programming  engineering  pls  trends  stackex  poll  career  exploratory  network-structure  ubiquity  ocaml-sml  rust  golang  python  dotnet  money  jobs  compensation  erlang  scala  jvm  ai  ai-control  risk  futurism  ethical-algorithms  data-science  machine-learning  editors  devtools  tools  pro-rata  org:com  software  analysis  article  human-capital  let-me-see  expert-experience  complement-substitute 
december 2018 by nhaliday
Information Processing: US Needs a National AI Strategy: A Sputnik Moment?
FT podcasts on US-China competition and AI: http://infoproc.blogspot.com/2018/05/ft-podcasts-on-us-china-competition-and.html

A new recommended career path for effective altruists: China specialist: https://80000hours.org/articles/china-careers/
Our rough guess is that it would be useful for there to be at least ten people in the community with good knowledge in this area within the next few years.

By “good knowledge” we mean they’ve spent at least 3 years studying these topics and/or living in China.

We chose ten because that would be enough for several people to cover each of the major areas listed (e.g. 4 within AI, 2 within biorisk, 2 within foreign relations, 1 in another area).

AI Policy and Governance Internship: https://www.fhi.ox.ac.uk/ai-policy-governance-internship/

Deciphering China’s AI Dream
The context, components, capabilities, and consequences of
China’s strategy to lead the world in AI

Europe’s AI delusion: https://www.politico.eu/article/opinion-europes-ai-delusion/
Brussels is failing to grasp threats and opportunities of artificial intelligence.

When the computer program AlphaGo beat the Chinese professional Go player Ke Jie in a three-part match, it didn’t take long for Beijing to realize the implications.

If algorithms can already surpass the abilities of a master Go player, it can’t be long before they will be similarly supreme in the activity to which the classic board game has always been compared: war.

As I’ve written before, the great conflict of our time is about who can control the next wave of technological development: the widespread application of artificial intelligence in the economic and military spheres.


If China’s ambitions sound plausible, that’s because the country’s achievements in deep learning are so impressive already. After Microsoft announced that its speech recognition software surpassed human-level language recognition in October 2016, Andrew Ng, then head of research at Baidu, tweeted: “We had surpassed human-level Chinese recognition in 2015; happy to see Microsoft also get there for English less than a year later.”


One obvious advantage China enjoys is access to almost unlimited pools of data. The machine-learning technologies boosting the current wave of AI expansion are as good as the amount of data they can use. That could be the number of people driving cars, photos labeled on the internet or voice samples for translation apps. With 700 or 800 million Chinese internet users and fewer data protection rules, China is as rich in data as the Gulf States are in oil.

How can Europe and the United States compete? They will have to be commensurately better in developing algorithms and computer power. Sadly, Europe is falling behind in these areas as well.


Chinese commentators have embraced the idea of a coming singularity: the moment when AI surpasses human ability. At that point a number of interesting things happen. First, future AI development will be conducted by AI itself, creating exponential feedback loops. Second, humans will become useless for waging war. At that point, the human mind will be unable to keep pace with robotized warfare. With advanced image recognition, data analytics, prediction systems, military brain science and unmanned systems, devastating wars might be waged and won in a matter of minutes.


The argument in the new strategy is fully defensive. It first considers how AI raises new threats and then goes on to discuss the opportunities. The EU and Chinese strategies follow opposite logics. Already on its second page, the text frets about the legal and ethical problems raised by AI and discusses the “legitimate concerns” the technology generates.

The EU’s strategy is organized around three concerns: the need to boost Europe’s AI capacity, ethical issues and social challenges. Unfortunately, even the first dimension quickly turns out to be about “European values” and the need to place “the human” at the center of AI — forgetting that the first word in AI is not “human” but “artificial.”

US military: "LOL, China thinks it's going to be a major player in AI, but we've got all the top AI researchers. You guys will help us develop weapons, right?"

US AI researchers: "No."

US military: "But... maybe just a computer vision app."

US AI researchers: "NO."

AI-risk was a mistake.
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february 2018 by nhaliday
Which industries are the most liberal and most conservative?
How Democratic or Republican is your job? This tool tells you: https://www.washingtonpost.com/news/the-fix/wp/2015/06/03/how-democratic-or-republican-is-your-job-this-tool-tells-you/?utm_term=.e19707abd9f1


What you do and how you vote: http://www.pleeps.org/2017/01/07/what-you-do-and-how-you-vote/

trending blue across white-collar professions:

This is partly because the meaning of left and right changed during that period. Left used to about protecting workers. Now it's mainly about increasing the power of the elite class over the working class - thus their increased support.
yes, it is a different kind of left now


The Legal Academy's Ideological Uniformity: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2953087

Homogenous: The Political Affiliations of Elite Liberal Arts College Faculty: https://sci-hub.tw/10.1007/s12129-018-9700-x
includes crosstab by discipline

Neil Gross, Solon Simmons

another crosstab
description of data sampling on page 21, meant to be representative of all undergraduate degree-granting institutions

Computer science 32.3 58.1 9.7

It’s finally out–The big review paper on the lack of political diversity in social psychology: https://heterodoxacademy.org/2015/09/14/bbs-paper-on-lack-of-political-diversity/
What’s interesting about Haidt’s alternative interpretation of the liberal progress narrative is that he mentions two elements central to the narrative—private property and nations. And what has happened to a large extent is that as the failures of communism have become increasingly apparent many on the left—including social scientists—have shifted their activism away from opposing private property and towards other aspects, for example globalism.

But how do we know a similarly disastrous thing is not going to happen with globalism as happened with communism? What if some form of national and ethnic affiliation is a deep-seated part of human nature, and that trying to forcefully suppress it will eventually lead to a disastrous counter-reaction? What if nations don’t create conflict, but alleviate it? What if a decentralised structure is the best way for human society to function?
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september 2017 by nhaliday
Lessons from a year’s worth of hiring data | Aline Lerner's Blog
- typos and grammatical errors matter more than anything else
[I feel like this is probably broadly applicable to other application processes, in the sense that it's more important than you might guess]
- having attended a top computer science school doesn’t matter
- listing side projects on your resume isn’t as advantageous as expected
- GPA doesn’t seem to matter
career  tech  sv  data  analysis  objektbuch  jobs  🖥  tactics  empirical  recruiting  working-stiff  transitions  progression  interview-prep 
december 2016 by nhaliday
Can California Employers Prohibit Moonlighting? | California Employment Law
lol at the pearl-clutching for employers (won't someone think of the corporations!) but seems like a good site for reading up on california labor laws

Noncompete Pacts, Under Siege, Find Haven in Idaho: https://www.nytimes.com/2017/07/14/business/economy/boise-idaho-noncompete-law.html
Do Non-Compete Covenants Influence State Startup Activity? Evidence from the Michigan Experiment: https://ideas.repec.org/p/fip/fedpwp/17-30.html
compensation  career  jobs  law  advice  business  labor  sv  california  working-stiff  the-west  multi  news  org:rec  regulation  insurance  contracts  usa  midwest  study  economics  microfoundations  natural-experiment  policy  endo-exo  history  mostly-modern  competition  markets  startups  innovation  🎩  endogenous-exogenous  freelance  microbiz 
may 2016 by nhaliday
How to pass a programming interview - Triplebyte
Mostly intuitive (eg, I had also planned to interview in reverse order and use Python but mention C++ experience), but still very good advice. Summoning/faking enthusiasm will prob be hardest part for me.
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march 2016 by nhaliday
Who Y Combinator Companies Want — Triplebyte Blog — Medium
1. There’s more demand for product-focused programmers than there is for programmers focused on hard technical problems. The “Product Programmer” and “Technical Programmer” profiles are identical, except one is motivated by product design, and the other by solving hard programming problems. There is almost twice as much demand for the product programmer among our companies. And the “Academic Programmer” (hard-problem focused, but without the experience) has half again the demand. This is consistent with what we’ve seen introducing engineers to companies. Two large YC companies (both with machine learning teams) have told us that they consider interest in ML a negative signal [ed.: :(]. It’s noteworthy that this is almost entirely at odds with the motivations that programmers express to us. We see ten times more engineers interested in Machine Learning and AI than we see interested in user testing or UX [ed.: duh].
2. (Almost) everyone dislikes enterprise programmers. We don’t agree with this. We’ve seen a bunch of great Java programmers. But it’s what our data shows. The Enterprise Java profile is surpassed in dislikes only by the Academic Programmer. This is in spite of the fact we explicitly say the Enterprise Programmer is smart and good at their job. In our candidate interview data, this carries over to language choice. Programmers who used Java or C# (when interviewing with us) go on to pass interviews with companies at half the rate of programmers who use Ruby or JavaScript. (The C# pass rate is actually much lower than the Java pass rate, but the C# numbers are not yet significant by themselves.) Tangential facts: programmers who use Vim with us pass interviews with companies at a higher rate than programmers who use Emacs, and programmers on Windows pass at a lower rate than programmers on OS X or Linux.
3. Experience matters massively. Notice that the Rusty Experienced Programmer beats both of the junior programmer profiles, in spite of stronger positive language in the junior profiles. It makes sense that there’s more demand for experienced programmers, but the scale of the difference surprised me. One prominent YC company just does not hire recent college grads. And those that do set a higher bar. Among our first group of applicants, experienced people passed company interviews at a rate 8 times higher than junior people. We’ve since improved that, I’ll note. But experience continues to trump most other factors. Recent college grads who have completed at least one internship pass interviews with companies at twice the rate of college grads who have not done internships (if you’re in university now, definitely do an internship). Experience at a particular set of respected companies carries the most weight. Engineers who have worked at Google, Apple, Facebook, Amazon or Microsoft pass interviews at a 30% higher rate than candidates who have not.

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december 2015 by nhaliday

bundles : bizness

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