leads the way in an easy , transparent interface to download all your data.
leads the way in an easy , transparent interface to download all your data.
1) It takes 5 seconds to switch your Search Engine Provider.
2) It consistently does not lock you with added services of its own (like Youtube runs on Iphones and Facebook and on Internet Explorer and so does the search engine and so does Google Maps).
3) It has leveraged the maximum from goodwill from open source developers without investing too much money back into them Goodwill is precious and once you lose dev cred you lose it for some time (as some companies found out in the last decade).
4) It has very well funded rivals from MS, Oracle,and Apple and Facebook. That alone will guarantee competition more than any lawyer.
Some Google Strengths-
5) Its portfolio remains under monetized – that gives it plenty of flexibility. The following products are the best in class, and yet are mostly free for retail customers.
6) Its main product is free to use. You cant beat free. You can try. but cant.
7) It remains the prime source of CS/ Math/Stat related talent on this planet (remember Map Reduce paper). Only the NSA has more bad ass geeks.
8) It tries not to be evil. Mostly it is not evil. Sometimes the ads get irritating. But never evil.
The lovely lovely diagram at https://developer.linkedin.com/documents/oauth-overview is worth a thousand words and errors.
Very useful if you are trying to coax rCurl to do the job for you.
Also a great slideshare in Japanese (no! Google Translate didnt work on pdf’s and slideshares and scribds (why!!) but still very lucid on using OAuth with R for Twitter.
Why use OAuth- you get 350 calls per hour for authenticated sessions than 150 calls .
I tried but failed using registerTwitterOAuth
There is a real need for a single page where you can go and see which social netowork /website is using what kind of oAuth, which url within that website has your API keys, and the accompanying R Code for the same. Google Plus,LinkedIn, Twitter, Facebook all can be scraped better by OAuth. Something like this-
At $39.23 Billion , Facebook is now cheaper than what Steve Ballmer was prepared to pay for Yahoo (when Yahoo CEO Jerry Yang famously turned him down). Can Microsoft buy Facebook? or Can Apple buy Facebook?
Both would be okay from an anti trust perspective- and both have the cash. Note you need only to buy 51% of shares for controlling and Mark Zuckerberg seems a bit down (never mind Sean Parker’s voting arrangement).
Can Google plunk 20% of FB for 8 billion – less than they paid for Motorola, so they can sell Ads there while FB concentrates on thee social aspects.
FB has innovated with good UI, apps, cassandra,the like button, the FB connect network, and of course socially targeted ads. I dont think it’s stock price deserves to be dog with fleas.
Where is a good leveraged buy out (LBO) or hedge fund when you need one?
Here is an interview with one of the younger researchers and rock stars of the R Project, John Myles White, co-author of Machine Learning for Hackers.
Ajay- What inspired you guys to write Machine Learning for Hackers. What has been the public response to the book. Are you planning to write a second edition or a next book?
John-We decided to write Machine Learning for Hackers because there were so many people interested in learning more about Machine Learning who found the standard textbooks a little difficult to understand, either because they lacked the mathematical background expected of readers or because it wasn’t clear how to translate the mathematical definitions in those books into usable programs. Most Machine Learning books are written for audiences who will not only be using Machine Learning techniques in their applied work, but also actively inventing new Machine Learning algorithms. The amount of information needed to do both can be daunting, because, as one friend pointed out, it’s similar to insisting that everyone learn how to build a compiler before they can start to program. For most people, it’s better to let them try out programming and get a taste for it before you teach them about the nuts and bolts of compiler design. If they like programming, they can delve into the details later.
Ajay- What are the key things that a potential reader can learn from this book?
John- We cover most of the nuts and bolts of introductory statistics in our book: summary statistics, regression and classification using linear and logistic regression, PCA and k-Nearest Neighbors. We also cover topics that are less well known, but are as important: density plots vs. histograms, regularization, cross-validation, MDS, social network analysis and SVM’s. I hope a reader walks away from the book having a feel for what different basic algorithms do and why they work for some problems and not others. I also hope we do just a little to shift a future generation of modeling culture towards regularization and cross-validation.
Ajay- Describe your journey as a science student up till your Phd. What are you current research interests and what initiatives have you done with them?
John-As an undergraduate I studied math and neuroscience. I then took some time off and came back to do a Ph.D. in psychology, focusing on mathematical modeling of both the brain and behavior. There’s a rich tradition of machine learning and statistics in psychology, so I got increasingly interested in ML methods during my years as a grad student. I’m about to finish my Ph.D. this year. My research interests all fall under one heading: decision theory. I want to understand both how people make decisions (which is what psychology teaches us) and how they should make decisions (which is what statistics and ML teach us). My thesis is focused on how people make decisions when there are both short-term and long-term consequences to be considered. For non-psychologists, the classic example is probably the explore-exploit dilemma. I’ve been working to import more of the main ideas from stats and ML into psychology for modeling how real people handle that trade-off. For psychologists, the classic example is the Marshmallow experiment. Most of my research work has focused on the latter: what makes us patient and how can we measure patience?
Ajay- How can academia and private sector solve the shortage of trained data scientists (assuming there is one)?
John- There’s definitely a shortage of trained data scientists: most companies are finding it difficult to hire someone with the real chops needed to do useful work with Big Data. The skill set required to be useful at a company like Facebook or Twitter is much more advanced than many people realize, so I think it will be some time until there are undergraduates coming out with the right stuff. But there’s huge demand, so I’m sure the market will clear sooner or later.
(TIL he has played in several rock bands!)
Ajay- What are some of the privacy guidelines that you keep in mind- given the fact that you collect individual information but also have government agencies as potential users.
Prior to his election as ACM president, Chesnais was vice president from July 2008 – June 2010 as well as secretary/treasurer from July 2006 – June 2008. He also served as president of ACM SIGGRAPH from July 2002 – June 2005 and as SIG Governing Board Chair from July 2000 – June 2002.
As a French citizen now residing in Canada, he has more than 20 years of management experience in the software industry. He joined the local SIGGRAPH Chapter in Paris some 20 years ago as a volunteer and has continued his involvement with ACM in a variety of leadership capacities since then.
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