textminer

Tens of thousands?

For those interested in his work, I really recommend viewing the recent documentary Jodorowsky's Dune, a failed film project Giger and several other proto-luminaries worked on (inspiring much of the iconic imagery in Alien, Star Wars, and Indiana Jones). Giger appears throughout the documentary. I believe it's still playing in the Bay Area.

Had a set theory professor who taught us that for the non-negative integers, m^n was just the number of unique mappings from a set of cardinality n to one of cardinality m. Ergo, for all sets A such that |A| = k, k^0 is just all mappings from Ø, which is necessarily the one with empty image and pre-image. So 0^0 = 1.

Fascinating story, but I don't see how the town's larger economic forces drove the actions of either Rosa or Cantrelli. Mind elaborating?

Is this a disco-pants selling company? That's pretty sexy.

Why sell the data when you can sell targeted ads to the desired demographic? Why give away the cash cow of actual people-to-properties key-value maps?

Me too. Forty minute drive from Oakland to Menlo Park.

I've adopted a similar attitude as you here when it comes to past machine learning jobs, and discussion of detail. What ends up being your bright shiny line that you don't cross? I tend to just not talk about the specific feature engineering, being relatively upfront about such basic things as "I used a random forest".

Is "unlimited PTO" ever not window dressing for a more predatory policy? Anywhere I've worked with this policy had implemented it so that ever taking vacation was a negotiation with your boss (not something you could just comfortably declare), and it led to not being paid for any surplus vacation days (as none exist but an "infinite" amount) when you do finally leave said job because of burnout.

I bounced from one to a liberal arts college to Columbia. Also an anomaly, but it was a fantastic learning experience (and quite the redemption from terrible high school performance).

The four-weeks-vacation-but-feel-bad-about-that-and-occasionally-working-from-home thing does seem archaic and counterproductive for knowledge workers who are best when they're creative. I can understand how putting a fire out or having chance encounters with coworkers makes the full-time thing beneficial on the long tail, but the accumulated self-inflicted guilt and burn out go a long way toward that eventual decision of switching jobs (if just for the reprieve while ramping down and switching).

If Facebook/Google were in SF, I reckon many of its workers would still live in San Jose, Fremont and Oakland, due to affordability concerns and a lack of density in the city proper.

Pragmatic question: I'm a mathematician by training, and currently a machine learning engineer at a large Silicon Valley company. How does one in their late twenties jump into really working robotics? Machine learning was something I could watch lectures on, read papers about, work the math out on paper, then spend years in Python/C++ getting a feel for across various problem domains. Physical robots don't seem as accessible.

Touché. :)

There's a functional and chemical different between the amphetamine salts in popular ADD drugs and methamphetamine (namely, a methyl group.)

Not Nebraska.

Employ rolling averages above historical inflation?

Posted details to another respondent.

A friend was let go, as was an acquaintance who was hired in senior management of the group (Todd Beaupré). Investigating his LinkedIn page, he had circular recommendations with five others on his team, all who had finished working there at the same time (not including my friend).

Surprised I've heard nothing about the bloodbath at Dr. Dre's Beats Music/MOG online service. They hired up a huge data scientist contingent, then fired them all. Strange considering how aggressive their recruiting was. Also strange to ponder hiring data scientists for a yet-to-be-launched service (with little data).

That's sort of the basis of Plato's Republic, no?

How much has Facebook's open-source contributions to PHP (say, the HHVM) helped the development and adoption of the language?

Yeah. It's long seemed to me that the empirical argument against socialism/communism was the flawed implementation of central planning for price-setting in the last century. This seems like a strange rhetorical bent to hear from professionals who work in algorithms and frequently see the short-sighted local minima produced by capitalism.

My goto recommendation for a fresh installation: http://fonnesbeck.github.io/ScipySuperpack/

It was personally strange to start programming in earnest with Python, and think namedtuples (or classes with __slots__) were so cool and efficient, and then learn C and just think "oh yeah. Structs."

Ah, ain't that the truth. I loved my HP Pavillion, until its motherboard glue melted and internal parts detached. Twice. Shame for them, too-- circa 2007, you saw as many of those on college campuses as you did MacBooks or netbooks. No longer.

I've basically freshly come to C++ in a work environment, and C++11 is all I've learned so far. Range-for loops and smart pointers and move semantics are wonderful. I still haven't adopted to a perfect idiom for calling functions, though: It's straightforward if I call a function and it returns a single object with move semantics. But what if I want to mutate multiple things with a function call (say, go through a vector and throw out indexes below a certain threshold, updating an unordered_map so as to specify the names and locations of certain columns). Do I return one, while changing the other by reference? Do I return a pair or a struct that only exists for me to quickly unpack? Do I make the function void and change both by reference?

What's the best way for an average person to invest? Can and should ones buy index fund, or just lower-risk hedge funds?

I've actually seen "data scientist" used to indicate "probably not that proficient as an actual engineer, but knows what he or she is talking about in terms of theory and can usually get a working model done in R, Matlab, or Python". There seems to be an additional jump in knowledge of systems, data structures, and algorithms in building up performant machine learning stacks (something which can add in practical knowledge and praxis, but steals a little from raw-ideas work).

Citation: working in data science and machine learning engineer roles for nearly three years straight out of grad school in math.