ahalan

In other words: breadth-first search is better when dealing with hard problems

their guilt/punishment is a separate legal case, Kasparov said he is planning to sue the arresting officers in court

some papers by Nicira's CTO: http://scholar.google.com/scholar?hl=en&q=martin+casado&...

Porting researchy Matlab code to optimized Fortran or C could potentially give you the same results as throwing more hardware at the problem, but cheaper and probably faster.

http://www.igvita.com/

http://tom.preston-werner.com/

http://adam.heroku.com/

http://www.catonmat.net/

http://mechanical-sympathy.blogspot.com/

http://natishalom.typepad.com/

cycling

what's his email? I have a great idea for a new startup

take the job, Google on your resume is a very strong signal regardless of what you do, it will open lots of doors

"Adding Value as a Non-Technical No Talent Ass-Clown."

haha, I love this guy

http://www.quora.com/Computational-Genomics/What-are-some-go...

http://en.wikipedia.org/wiki/David_Waltz#Selected_Works

>These processes include ignoring distractions to stay focused, switching attention willfully from one thing to another and holding information in mind

I'm trilingual and I can assure you it doesn't help with the ADD problem

Related: Data-Driven modeling course by Jake Hofman (Yahoo Research): http://jakehofman.com/ddm/recent-posts/

lots of practical exercises, playing with real data and APIs

nice collection of resources: http://datascienc.es/resources/

that's why I like Hacker News

News article: http://www.bbc.co.uk/news/science-environment-17191398

Actual paper: http://www.nature.com/ncomms/journal/v3/n2/full/ncomms1701.h...

Nuclear propulsion, what else

Is this Hacker News or Institute for Noble Maidens?

That particular comment conveyed a valid point, it was done effectively and added to the discussion. I'm rather concerned that this site becomes more and more of an echo chamber as I see dozens of comments from different people that could be easily compressed into one.

Related: http://www.quora.com/What-are-some-good-resources-for-learni...

oh, and StackOverflow, Github, Metaoptimize, some Twiter groups, a tiny bit of LinkedIn, and Facebook if I need to talk to my aunty

Quora, where else

TheEconomist>> Hadoop, a new type of database, can be used to sift through big data streams in real time.

HN>> Hadoop is not a new type of database, and it cannot be used to sift through big data streams in real time.

Related>> Why big data is better than small data : http://www.quora.com/Why-the-current-obsession-with-big-data

MapReduce is applicable wherever you can partition the data and process each part independently of others.

I used Hadoop/Hbase for EEG time series analysis, looking for certain oscillation patterns (basically classic time-series classification) and it was an embarrassingly parallel problem:

Map:

1. Partition the data into fixed segments (either temporal, say 1hr chunks or location based, say 10x10 blocks of pixels). Alternatively you can use a 'sliding window' and extract features as you go. In some cases you can use symbolic representation/piecewise approximation to reduce dimensionality, as in iSax: http://www.cs.ucr.edu/~eamonn/iSAX/iSAX.html , "sketches" as described here: http://www.amazon.com/High-Performance-Discovery-Time-Techni... or some other time-series segmentation techniques: http://scholar.google.com/scholar?q=time+series+segmentation

2. Extract features for each segment (either linear statistics/moments or non-linear signatures: http://www.nbb.cornell.edu/neurobio/land/PROJECTS/Complexity... ). The most difficult part here has nothing to do with MapReduce but decide which features carry the most information. I found ID3 criterion helpful: http://en.wikipedia.org/wiki/ID3_algorithm, also see http://www.quora.com/Time-Series/What-are-some-time-series-c... and http://scholar.google.com/scholar?hl=en&as_sdt=0,33&...

Reduce:

3. Aggregate the results into a hash-table where the keys are segment' signatures/features/fingerprints, and the values are arrays of pointers to corresponding segments (Based on the size this table can either sit on a single machine, of be distributed on multiple hdfs nodes)

Essentially you do time-series clustering at the Reduce stage with each 'basket' in a hash-table containing a group of similar segments. It can be used as an index for similarity or range searches (for fast in-memory retrieval you can use HBase which sits on top of HDFS). You can also have multiple indices for different feature sets.

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The hard part is problem decomposition, i.e. dividing work into independent units, replacing one big nested loop/sigma on the entire dataset with smaller loops that can run in parallel on parts of the dataset, when you've done that, MapReduce is just a natural way to execute the job and aggregate the results.

http://www.quora.com/Brogramming/How-does-a-programmer-becom...

on the exchange side - market data/trade/ack latencies can be microsec in the 50th percentile, single digit msec in the 90th, and double digit msec in the 99.99th, the variance can be significant and also change based on hr of the day and how well the exchange handles the load.

So even if you use customized, co-located hardware/FPGAs or even ASICs with the fastest interconnects and unlimited bandwidth and your engine responds in nanosec, still the message you get from/send to an exchange can sit there in a queue for something like eternity.

http://www.quora.com/What-are-essential-references-for-R

Get a job in MLOS (Machine Learning, Optimization and Statistics), leave AI to Popular Science magazine.

here are some links

http://www.ml-class.org/

http://www.quora.com/Machine-Learning/What-are-some-good-res...

http://www.quora.com/Machine-Learning/What-are-some-good-lea...

http://www.quora.com/What-are-some-good-resources-for-learni...

http://www.quora.com/What-are-some-good-resources-to-learn-a...

also see "Bernard Gitton's Liquid Science": http://www.marcdatabase.com/~lemur/dm-gitton.html

Related courses: http://www.quora.com/What-are-some-courses-on-large-scale-le...

Workshops: http://www.quora.com/What-are-some-workshops-on-large-scale-...

Also see the tutorial "Scaling Up Machine Learning" at KDD2011: http://hunch.net/~large_scale_survey/

item 3: "..What data interests you? Structure your studies based on that"