HN user

urish

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papers.ssrn.com 9y ago

Squaring Venture Capital Valuations with Reality

urish
1pts1
www.nytimes.com 9y ago

Hackers Came, but the French Were Prepared

urish
3pts1
www.quantamagazine.org 9y ago

The Shape-Shifting Army Inside Your Cells

urish
1pts0
medium.com 9y ago

Who would transparency in search algorithms benefit?

urish
1pts0
piazza.com 10y ago

Arvind Narayanan's New Textbook: “Bitcoin and Cryptocurrency Technologies”

urish
4pts0
blog.arty.name 10y ago

Use micromorts to fight terrorism

urish
31pts12
rjlipton.wordpress.com 10y ago

A Little More on the Graph Isomorphism Algorithm

urish
60pts3
www.johndcook.com 10y ago

Taking away a damaging tool

urish
2pts0
www.slate.com 10y ago

Trolling ISIS: How an Internet scam could stymie ISIS’s online recruiters

urish
2pts0
www.ams.org 11y ago

The Surprise Examination Paradox and the Second Incompleteness Theorem [pdf]

urish
9pts0
slatestarcodex.com 11y ago

Beware the Man of One Study

urish
4pts0
blog.mrtz.org 11y ago

Competing in a data science contest without reading the data

urish
37pts4
www.machinedlearnings.com 11y ago

Adversarial Scenarios and Economies of Scale

urish
1pts0
www.nytimes.com 11y ago

Amazon Not as Unstoppable as It May Appear

urish
7pts5
mrtz.org 11y ago

Half the papers at NIPS would be rejected if the review process were rerun

urish
141pts49
arxiv.org 11y ago

Teaching Deep Convolutional Neural Networks to Play Go

urish
71pts17
opencv.org 11y ago

OpenCV computer vision challenge

urish
2pts0
hemiposterical.blogspot.co.il 13y ago

Proper engineering blog: Iron Dome (Israel's rocket defense system)

urish
2pts1
bytesizebio.net 13y ago

Can we make accountable research software?

urish
24pts10

tl;dr by Laura Norén (@digitalFlaneuse on twitter):

Stanford professor, Ilya Strebulaev, and Will Gornall of the University of British Columbia recalculated the valuation of 100+ companies known as unicorns (startups valued at $1bn +) and showed many aren't worth nearly as much as they claim. Why? Because math. Startups typically issue different classes of stock in each fundraising round but their valuations are oversimplified by applying the price of the most recent round to all outstanding shares. Every company they looked at was overvalued, 53 lost their $1bn unicorn status, and 13 were overvalued by more than 100 percent. ... "Some unicorns have made such generous promises to their preferred shareholders that their common shares are nearly worthless," the two professors wrote. In my opinion, this is an example of two things 1) lots of people cannot apply their math skillz and 2) the ethos of finance contains much magical thinking. The entire industry is obsessed with unicorns. According to Scottish myth, unicorns were ruthlessly hounded by clamoring hoards, simultaneously scapegoated for being the aberrant creatures they are and loved to death (e.g. abused, fatally) for their magical powers. Lesson: it's clear that many in finance are not good at applying their history and culture skillz, either.

My only disagreement is with: >Human processes just add additional bias

Bias with respect to what? As you say, there is already bias baked into the data collection and the algorithmic choices.

The bias that human editors introduce is different, but not necessarily larger, however you even measure it. There are also myriad human choices behind the choice and deployment details of the algorithm.

An important plus for human editors is greater interpretability and greater transparency regarding the biases the system ends up showing.

Debt: The First 5,000 Years by David Graeber.

I'm cautious to say it changed my life, but it definitely changed my view on many things. I'm more aware of the ubiquity and power of debt, and I can no longer take those for granted.

It's an extremely interesting read and has a broader intellectual appeal, elucidating the roots of money, morality, and the roles of markets, nations, and friends with regard to those.

I'm surprised by how relatively little machine learning research they have. Microsoft, Google, IBM and Yahoo seem much better represented at the core ML conferences like ICML and NIPS.

But why would you equate "lazy" with unethical?

That's already an assumption that could be challenged. We now don't deem someone who's unwilling to work from sun up to sun down in the rice fields as lazy or unethical, but 300 years ago that might have been the case. Why? Because now a few people grow our food so efficiently that most people can afford to be "lazy" and work 40 hours a week in an office job, or at least an air-conditioned job.

I'm a PhD student working in machine learning, on the border of mathematical optimization. I also have a research project about mapping influence and innovation in the history of contemporary music, with ML tools.

I heard about HN from my brother, who's a psychiatrist. I wonder how he got here though.

"The Fourth Part of the World: The Race to the Ends of the Earth, and the Epic Story of the Map That Gave America Its Name".

A really interesting history book. I'm now in a great part, about how knowledge of geography (and map projections) was disseminated in Europe through a network of scholars and humanists during the 15th century. There was this huge collaborative effort to reconstruct ancient texts and to bring them in line with (then) current knowledge.

Startup idea list 14 years ago

Endless personalized (/-able) photo&image stream.

I want my screen full of an endless stream of images, which I can customize both by "liking" or narrow by keywords, such as "now I want to see artsy black&white photos" or "show me men's fashion". I expect the images shown both in general and in specific cases to cater to my taste.