akshayubhat

Chrome is open source, so in case something like this happen you can either investigate the source code, or create a more privacy compliant version. No such luck with I.E. or Safari.

     help people and businesses realize their full potential

and

     Macs, the best personal computers in the world, along with OS X, iLife, iWork, and professional software. Apple leads the digital music revolution with its iPods and iTunes online store. Apple is reinventing the mobile phone with its revolutionary iPhone and App Store, and has recently introduced its magical iPad which is defining the future of mobile media and computing devices

Maybe It's personal but somehow "helping people realize their full potential" sounds much more worthy cause, while Apple sounds more like a fashionable gadget manufacturer.

Consider Google they dont claim to be creators of Best Search Engine and Great email service, rather its "to organize the world's information and make it universally accessible and useful".

Wow this guy is a real idiot if he is making statement like this, probably he can use same logic to justify Hamas.

Can US government do the same? If it can I see no problem in Indian government expecting a similar capability.

Can US government decrypt the messages or get them via Warrantless Wiretapping, or force RIM? If the answer is yes, then I do not see any reason why India or some other country should not be allowed to do the same.

I agree, I have been using it to call to india, and its better than most of the competitors, also I made few calls within US and they were much better than AT & T.

     And isn't it a bit of a sign of discrimination to merely assume off-hand that another culture's definition of "science and engineering" isn't as "rigorous" as ours, even if we seem to be gearing up for a clash with them? Instead of pooh-poohing their efforts, it seems more appropriate to be astonished by their capabilities in as hostile a cultural paradigm as they live in.

First it was "Technical", which is a confusing term. Second 56% in "natural sciences" is on par with USA. Nat. Sci. include Bio,Chem. Men in developing countries prefer Engineering.

Finally as someone who grew up in India, I am familiar with eastern cultures/ educational systems, and can assure you 70% in "Technical [Science and Engineering]" is near impossible, unless significant men are enrolled in some technical military program and are not counted.

     70% of the technical degree holders living under the Iranian patriarchal theology

Highly unlikely, their must be some difference in definition of "Technical", are they including Biology, Nursing even Teaching under "Technical"?

Even in India, Women in engineering have similar proportion as USA.

    1. A certain amount of group think -- CS at Cornell seems to focus a lot more on theory/research. I know this is the case at any reputable CS school but the top CS students either aim to go get their PhDs and pursue the most abstract research or go work for an IBM/Accenture (in my experience, a lot more people opt for MS over Google and I know of a handful at FB)

Thats is purely beacuse MS hires more people every year. You can obtain good stats from this page: http://www.cs.cornell.edu/ugrad/cscareers/PlacementReport/in... [for undergrad]

For Masters students as well as undergrads: http://www.engineering.cornell.edu/student-services/engineer...

People go in all sort of companies from Zynga to Google to Microsoft to NetApp and to exotic trading companies such as Black Rock and D. E. Shaw .

I am at Cornell, actually I just started, There is a good grad level entrepreneurship course at the Management school, also you can enroll for software engineering course, where you work on delivering a product for a startup/other clients. Also you own the product or some share in resulting enterprise if it is a startup.

This guy http://www.stephenpurpura.com/site/Welcome.html is T.A for the software Engg. course is also an ex-Microsoft guy who was Product Manager for Windows XP.

http://www.seamlessreceipts.com/about was founded as result of the Software Engg. course and it has known to have received 225K in funding.

Also Prof. Claire Cardie has her own startup in area of sentiment detection in texts.

If you are a senior then you can still enroll in the Software Engg. course, its a 5K level course though.

Here is the link to the course: http://www.cs.cornell.edu/courses/cs5150/2010fa/

      given that unlabeled samples are far more abundant than labeled samples

It is also related to Transfer learning. This paper provides a good background http://www.stanford.edu/~hllee/icml07-selftaughtlearning.pdf

It contains a nice graphic describing difference between Supervised, Semi-Supervised, Transfer and Self-Taught learning.

Since twitter data consists of short texts. Unlabeled data from sources other than twitter could also be used, e.g. Google Buzz.

AdMob/Google likely have better targeting algorithms due to amazing amount of data that they posses. Also Google employs army of Machine Learning/Recommendation System engineers, while Apple is just getting in to this field.

The biggest problem with such lists is that unlike novels, academic Books are rarely read cover to cover.

If you try to read the book cover to cover you will most likely start forgetting it after few weeks. However when you learn things slowly over few years and interact with material in multiple ways, such as lectures, reading, test, internships, labs, group/individual project you are more likely to have some hint about the material, may be you will remember a lecture or some project or some lab experience, however in case of just reading a book its harder to recall.

A good process of learning involves not just you alone but also people around you who motivate you to stay on the course and offer assessment of your capabilities. Thus formal classroom education tends to be more useful than an online one. Though it depends a lot on your school and quality of your peers/ friend circle.

I assume I got few upvotes as well, its sad to see HN overtaken by Apple fanboi's.

For those who disliked the comment didn't the POTUS [Obama] himself comment that iPad was a distraction.

is breathing light really a requirement? I would prefer faster and cheaper computers. IMHO I find Apple computers to be equivalent of SUV's. You can get a descent laptop at nearly 20-30% cheaper from Dell/HP/ASUS.

Maybe only Americans can afford such frivolity. The difference in price between a good Dell laptop and Apple one is same as wage for 1-2 months for a middle class person in most parts of the worlds.

I agree your last comment, but I thought this service was for sharing with friends who are intimate enough to care for such pictures.

Also how is sending a link or notification about new pictures different from sending actual images. In case a group link is created.

It is unlikely that without notifications, people will actually go and browse the link for new pictures. In that case isn't it same as posting it on your Facebook profile as an album with appropriate privacy settings?

facebook provides a better way of organizing them via tags. and regarding trusting facebook, people are motivated to learn facebook privacy settings, since they are using it everyday.

Finally a person savvy enough to use such website/ email based service, must already be proficient with Facebook settings.

non parametric statistics isnt Machine Learning.

Only few methods such as K nearest neighbors are non parametric, rest all methods such as SVM's, decision trees are all parametric.

So using it as a jargon is a bad idea.

wouldn't it be easier to email the pics to the friends directly?

Posterous works because people actually intend to publish their thoughts, and they are uncomfortable with using an web interface. But in case of picture I dont think people would be comfortable with idea of sending it to a third party website, why not just add multiple emails addresses to your 'to' field. also threaded conversations make it easier to have all comments / replies in a single place.

Also facebook album with proper privacy settings is much better option.

well you could argue that Theory of CS is same as Mathematics of Computation. or Biology is just Chemistry and Chemistry is just Physics.

Traditionally statistics is more geared towards hypothesis testing and analytics. While Machine Learning is about making prediction.

wow this article is so wrong that I wont even comment about issues with it.

Anyone who wants to read good research in nano tech can browse through this journal: http://pubs.acs.org/journal/nalefd

This is same as saying to someone working on EINACS and early computers that they are a laughing stock and nothing but huge calculators. Reminds me of an article by Scott Aaronson : "Whats taking so long Mr. Babbage" : http://scottaaronson.com/blog/?p=446

     Good luck with that. Barriers to professions - which includes access to research - is what keeps the professionals well paid and thus perhaps allow them to fund more research.

>> No, Public interest in research is what keeps funding higher.

     Isn't that infringement of copyrights by the way? Do you think it is fine too if I start distributing freely an application for which you have worked for years and decided to charge for?

>> Researchers publishing papers dont earn a penny, the only thing that they get is free (no charge to researcher) dissemination. In fact a lot of researchers post a free version on there own web site.

I have published two papers in journal published by American Chemical Society and authors are allowed to put up pre-publication / non edited copy of their papers on their own website. ACS also provides a link from which upto 50 visitors can download the authors paper for free in first year and there is no limit after first year.

E.g. http://pubs.acs.org/cgi-bin/download.pl?ie0704647/P8Lu is free link to my first paper, the article is behind the firewall but if you use above link, you can get it for free.

you are getting confused in semantics of word "prediction".

Weather in theory is an Chaotic system thus there is no simple laws such as F = Ma to predict it.

Time frames of week , seconds and years are meaningless. You can always argue that you can predict weather for next milli or micro or femto seconds, why stop at weeks? but that does not proves that chaos theory is wrong or that there is some generative law which can be used to predict weather without relying on data.

Also from a utilitarian perspective, a long term accurate weather forecast would be so much useful rather than just a week long range [Assuming that is accurate right now,]. But you dont find accurate prediction for monsoon in January, or even of say a hurricane a fortnight before.

read before you type, from my comment above:

     Weather is usually predictable only about a week ahead.

and about

     December in the Northern hemisphere might be colder than this last.

Thats not exactly a prediction, It is a seasonal variation due to inclination of earth w.r.t. sun. Thats same as some product will have higher sale before Christmas, or there will be more traffic on roads before thanksgiving.

http://en.wikipedia.org/wiki/Chaos_theory

     To his surprise the weather that the machine began to predict was completely different from the weather calculated before. Lorenz tracked this down to the computer printout. The computer worked with 6-digit precision, but the printout rounded variables off to a 3-digit number, so a value like 0.506127 was printed as 0.506. This difference is tiny and the consensus at the time would have been that it should have had practically no effect. However Lorenz had discovered that small changes in initial conditions produced large changes in the long-term outcome.[43] Lorenz's discovery, which gave its name to Lorenz attractors, showed that even detailed atmospheric modelling cannot in general make long-term weather predictions. Weather is usually predictable only about a week ahead.[25]

Note this is different from predicting Global Warming, Global warming is a long term trend prediction, not what will be temperature at certain day at a certain place kind of prediction.

     already out of date and is of very limited use

Depends on the type of the information, news: yes,information about biology, or Geo-Information or encyclopedic: not so fast. on other hand we now have access to real time data.

     Weather scientists have been collection data since the beginning of the century and they are still no better at predicting the weather than they were at the beginning of the century. 

Predicting Weather is near impossible in theory forget the practice. its a chaotic system, generally in data science we are trying to predict things which we know can be predicted, such as determining whether an article is relevant to a topic for example. Humans can do that very well but for machines to do it you need more data.

     They have tons of data on markets but they still can't figure out what makes markets tick. 

Again markets are chaotic and are affected by things like low probability events. You can do predict some patterns using information asymmetry, thats what all those traders at Goldman Sachs and other firms do.

    We need generative models that explain how the data is being generated and why it is being generated in a certain way.

The systems that we are interested in have complex models, and generating them from first principles isnt always as easy, additionally even if you do generate you need to test them against real world data. Rather than using the normal hypothesis - experiment cycle, it makes more sense to look for predictable patterns in data.

    I don't think the results have been that impressive.

Thats because you are out of touch with the field. Look at Google's Statistical Translation results, for example, they beat every generative model around the town. Or netflix's prize for recommendation system.

As someone interested in Data Science and been quite involved with it for few years as a student and an intern let me try to explain why data science is now becoming an important area:

1. We now have a lot of data in a machine readable format:

Unlike few years ago we now have huge datasets publicly available they, you have topic specific datasets such as Geo Names, Linked Geo data to much more wide encyclopedic datasets such as Wikipedia Data Dump, Freebase, Open Cyc, DBPedia.

We also have huge amount of user generated data, E.g. I have on my computer right now a huge chunk of twitters follower network consisting of 35 million users(I am writing an open source whom to follow system). Additionally I also have 100 million tweets.

2. Not just data we now have access to real time data:

You can access the public twitter time-line, using their streaming api, there are quite a few pubsubhubub systems out there which combine information from disparate sources and provide you unified source.

3. Moreover we have tools to handle the deluge [well sort off]:

Thanks to Google, Apache, Yahoo, Facebook we now have Hadoop , Map Reduce, Pig and other tools which make job of parallelizing processing of the data easier.

5. We have a Scalable on demand infrastructure in place:

Using AWS we can buy processing power as needed. It would have been impossible earlier, I recently bought a high memory instance with 17GB Ram for 50 cents an hour for 10 hours to run some jobs. It would have been impossible few years ago. We can now also deploy web apps very easily using Google App engine and dont need to even pay a penny, this enables us to create nice interfaces for visualization and querying for the data at a low initial cost.

Finally we are slowly building infrastructure to sell datasets or custom apps. E.g. Amazon Dev Pay or Infochimps.

The name data science can though be misleading, if you are a student that would mean taking course in Machine Learning, Data Mining, Information Retrieval, Statistics, Distributed Computing, Databases.

Depends on your background / intended purpose:

1. if you are making web apps then Google App Engine / Python is a good choice

2. If you are developing Ipod/ipad/iphone touch apps then you should learn obj C

3. If you are more scientifically inclined and working in bio / Chem field then again I would recommend python.

4. If you are you young and have ample spare time at your hand then learn C.

5. if you are just plain beginner with no clue then I would suggest Ptyhon again.

GraphLab is intended for efficiently parallelizing Processing intensive Machine learning algorithms, this is different from "Bring Computation near data" philosophy of Hadoop/HDFS/Map-Reduce. Generally most of enterprise mapreduce application are primarily IO intensive, in that case it is important to have multiple machines accessing data from multiple disks.

GraphLab would be suitable for machines with multiple processors or the new cloud on chip machines which have ~40+ Cores.

Most of the Machine Learning algorithms tend to be processing intensive, and simple abstractions such as Map-Reduce dont help a lot, thus GraphLab has its own niche to serve.

Could you point out specific holes in his arguments?

I find your comment to be too much relying on metaphors and not backed up with example or data and without any specifics?

E.g.

One of the recurrent themes in his books is how forecasters fail, and how when they fail they fail by huge margin.

In My experience I have found this ideas to be quite insightful.

Why do you think that his main Thesis about events with large impact and low expectation and their effect on our ability to make predictions is incorrect?

I agree that he himself gets involved in narrative fallacy and that his arguments could be more succinct and sans the Tony and other character that he uses in his book. But Maybe those are there for the hoi polloi and not for people who understand Machine Learning, Search and stuff.

interesting http://www.forbes.com/2007/08/09/goldman-sachs-alpha-markets... says that the fund failed in ~ 2007, while this article talks latest debacles. Thanks for the reference.