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Software piracy exists because-
1) Lack of appropriate technological controls (like those on DVDs) or on Bit Torrents (an innovation on the centralized server like Napster) or on Streaming etc etc.
Technology to share content has evolved at a much higher pace than technology to restrict content from being shared or limited to purchasers.
2) Huge difference in purchasing power across the globe.
An Itunes song at 99 cents might be okay buy in USA, but in Asia it is very expensive. Maybe if content creators use Purchasing Power Parity to price their goods, it might make an indent.
3) State sponsored intellectual theft as another form of economic warfare- this has been going on since the West stole gunpowder and silk from the Chinese, and Intel decided to win back the IP rights to the microprocessor (from the Japanese client)
4) Lack of consensus in policy makers across the globe on who gets hurt from IP theft, but complete consensus across young people in the globe that they are doing the right thing by downloading stuff for free.
5) There is no such thing as a free lunch. Sometimes software (and movie and songs) piracy help create demand across ignored markets – I always think the NFL can be huge in India if they market it.Sometimes it forces artists to commit suicide because they give up on the life of starving musician.
Mostly piracy has helped break profits of intermediaries between the actual creator and actual consumer.
So how to solve software piracy , assuming it is something that can be solved-
I dont know, but I do care.
I give most of my writings as CC-by-SA and that includes my poems. People (friends and family) sometimes pay me not to sing.
Pirates have existed and will exist as long as civilized men romanticize the notion of piracy and bicker between themselves for narrow gains.
- Ephesians 4:28 Let the thief no longer steal, but rather let him labor, doing honest work with his own hands, so that he may have something to share with anyone in need.
- A clean confession, combined with a promise never to commit the sin again, when offered before one who has the right to receive it, is the purest type of repentance.-Gandhi
- If you steal, I will wash your mouth with soap- Anonymous Mother.
- You shall not steal- Moses
- Steal may refer to: Theft, the illegal taking of another person’s property without that person’s freely-given consent; The gaining of a stolen base in baseball;
some questions in my Mind as I struggle to bet my money and pension savings on Facebook IPO
1) Revenue Mix- What percentage of revenues for Facebook come from Banner ads versus gaming partners like Zynga. How dependent is Facebook on Gaming partners. (Zynga has Google as an investor). What mix of revenue is dependent on privacy regulation countries like Europe vs countries like USA.
2) Do 800 million users of Facebook mean 100 billion valuation ? Thats a valuation of $125 in customer life time in terms of NPV . Since ad revenue is itself a percentage of actual good and services sold- how much worth of goods and services do consumers have to buy per capita , to give $125 worth of ads to FB. Eg . companies spend 5% of product cost on Facebook ads, so does that mean each FB account will hope to buy 2500$ worth of Goods from the Internet and from Facebook (assuming they also buy from Amazon etc)
3) Corporate Governance- Unlike Google, Facebook has faced troubling questions of ethics from the day it has started. This includes charges of intellectual property theft, but also non transparent FB stock option pricing in secondary markets before IPO, private placement by Wall Street Bankers like GoldMan Saachs, major investments by Russian Internet media corporations. (read- http://money.cnn.com/2011/01/03/technology/facebook_goldman/index.htm)
4) Retention of key employees post IPO- Key Employees at Google are actually ex- Microsofties. Key FB staff are ex-Google people. Where will the key -FB people go when bored and rich after IPO.
5) Does the macro Economic Condition justify the premium and Private Equity multiple of Facebook?
Will FB be the next Google (in terms of investor retruns) or will it be like Groupon. I suspect the answer is- it depends on market discounting these assumptions while factoring in sentiment (as well as unloading of stock from large number of FB stock holders on week1).
Baby You Are a Rich Man. but not 100 billion rich. yet. Maybe 80 billion isnt that bad.
Some wry observations from me on the world on economics-
1) 150 years after humiliating their country in the Opium Wars, Chinese mandarins have somehow convinced their leaders and military to park 2 trillion assets in Anglo Saxon debt. If Greece geting a 50% discount on its loan is the new precedent, when will the USA force its lendors to the negotiation table.
2) Income inequality and protests are something the Arabs and Israelis have in common. Besides being the sons of Abraham of course. Note the Persians are not considered the same as Arabs.
3) Advance knowledge of geo-political events can and ensures Western financial dealers have an edge on the sovereign funds in the other hemisphere. What used to be the playgrounds of Eton has now shifted to the pubs of Boston and So Cal.
4) After spending 1 trillion USD on arms in the past one decade (funded by guys in item 1), the United States military forces is in a much better more advanced position to wage simultaneous war.
5) Can a war in Korean peninsula affect war in the Persian sphere of influence. Just follow the money , baby.
6) Saudi Wahabis continue to fund terror despite losing a lot of money in the economic meltdown in past few years. For every 1 $ increase in Saudi oil revenue, western oil companies ,traders, financiers make more, much more.
7) Demographics is an important key to economics. An aging Japan, and stagnant West is one cause to shift from manpower intensive warfare to cyber warfare. Plus Cyber warfare is good business . Underpopulated Russia and Arabs continue to lack true economic potential.
8) There are new economic incentives to develop tools to disseminate as well as distort information flow in real time in a hyper connected digital world.
Here is an interview with Dan Steinberg, Founder and President of Salford Systems (http://www.salford-systems.com/ )
Ajay- Describe your journey from academia to technology entrepreneurship. What are the key milestones or turning points that you remember.
Dan- When I was in graduate school studying econometrics at Harvard, a number of distinguished professors at Harvard (and MIT) were actively involved in substantial real world activities. Professors that I interacted with, or studied with, or whose software I used became involved in the creation of such companies as Sun Microsystems, Data Resources, Inc. or were heavily involved in business consulting through their own companies or other influential consultants. Some not involved in private sector consulting took on substantial roles in government such as membership on the President’s Council of Economic Advisors. The atmosphere was one that encouraged free movement between academia and the private sector so the idea of forming a consulting and software company was quite natural and did not seem in any way inconsistent with being devoted to the advancement of science.
Ajay- What are the latest products by Salford Systems? Any future product plans or modification to work on Big Data analytics, mobile computing and cloud computing.
Dan- Our central set of data mining technologies are CART, MARS, TreeNet, RandomForests, and PRIM, and we have always maintained feature rich logistic regression and linear regression modules. In our latest release scheduled for January 2012 we will be including a new data mining approach to linear and logistic regression allowing for the rapid processing of massive numbers of predictors (e.g., one million columns), with powerful predictor selection and coefficient shrinkage. The new methods allow not only classic techniques such as ridge and lasso regression, but also sub-lasso model sizes. Clear tradeoff diagrams between model complexity (number of predictors) and predictive accuracy allow the modeler to select an ideal balance suitable for their requirements.
The new version of our data mining suite, Salford Predictive Modeler (SPM), also includes two important extensions to the boosted tree technology at the heart of TreeNet. The first, Importance Sampled learning Ensembles (ISLE), is used for the compression of TreeNet tree ensembles. Starting with, say, a 1,000 tree ensemble, the ISLE compression might well reduce this down to 200 reweighted trees. Such compression will be valuable when models need to be executed in real time. The compression rate is always under the modeler’s control, meaning that if a deployed model may only contain, say, 30 trees, then the compression will deliver an optimal 30-tree weighted ensemble. Needless to say, compression of tree ensembles should be expected to be lossy and how much accuracy is lost when extreme compression is desired will vary from case to case. Prior to ISLE, practitioners have simply truncated the ensemble to the maximum allowable size. The new methodology will substantially outperform truncation.
The second major advance is RULEFIT, a rule extraction engine that starts with a TreeNet model and decomposes it into the most interesting and predictive rules. RULEFIT is also a tree ensemble post-processor and offers the possibility of improving on the original TreeNet predictive performance. One can think of the rule extraction as an alternative way to explain and interpret an otherwise complex multi-tree model. The rules extracted are similar conceptually to the terminal nodes of a CART tree but the various rules will not refer to mutually exclusive regions of the data.
Ajay- You have led teams that have won multiple data mining competitions. What are some of your favorite techniques or approaches to a data mining problem.
Dan- We only enter competitions involving problems for which our technology is suitable, generally, classification and regression. In these areas, we are partial to TreeNet because it is such a capable and robust learning machine. However, we always find great value in analyzing many aspects of a data set with CART, especially when we require a compact and easy to understand story about the data. CART is exceptionally well suited to the discovery of errors in data, often revealing errors created by the competition organizers themselves. More than once, our reports of data problems have been responsible for the competition organizer’s decision to issue a corrected version of the data and we have been the only group to discover the problem.
In general, tackling a data mining competition is no different than tackling any analytical challenge. You must start with a solid conceptual grasp of the problem and the actual objectives, and the nature and limitations of the data. Following that comes feature extraction, the selection of a modeling strategy (or strategies), and then extensive experimentation to learn what works best.
Ajay- I know you have created your own software. But are there other software that you use or liked to use?
Dan- For analytics we frequently test open source software to make sure that our tools will in fact deliver the superior performance we advertise. In general, if a problem clearly requires technology other than that offered by Salford, we advise clients to seek other consultants expert in that other technology.
Ajay- Your software is installed at 3500 sites including 400 universities as per http://www.salford-systems.com/company/aboutus/index.html What is the key to managing and keeping so many customers happy?
Dan- First, we have taken great pains to make our software reliable and we make every effort to avoid problems related to bugs. Our testing procedures are extensive and we have experts dedicated to stress-testing software . Second, our interface is designed to be natural, intuitive, and easy to use, so the challenges to the new user are minimized. Also, clear documentation, help files, and training videos round out how we allow the user to look after themselves. Should a client need to contact us we try to achieve 24-hour turn around on tech support issues and monitor all tech support activity to ensure timeliness, accuracy, and helpfulness of our responses. WebEx/GotoMeeting and other internet based contact permit real time interaction.
Ajay- What do you do to relax and unwind?
Dan- I am in the gym almost every day combining weight and cardio training. No matter how tired I am before the workout I always come out energized so locating a good gym during my extensive travels is a must. I am also actively learning Portuguese so I look to watch a Brazilian TV show or Portuguese dubbed movie when I have time; I almost never watch any form of video unless it is available in Portuguese.
Dan Steinberg, President and Founder of Salford Systems, is a well-respected member of the statistics and econometrics communities. In 1992, he developed the first PC-based implementation of the original CART procedure, working in concert with Leo Breiman, Richard Olshen, Charles Stone and Jerome Friedman. In addition, he has provided consulting services on a number of biomedical and market research projects, which have sparked further innovations in the CART program and methodology.
Dr. Steinberg received his Ph.D. in Economics from Harvard University, and has given full day presentations on data mining for the American Marketing Association, the Direct Marketing Association and the American Statistical Association. After earning a PhD in Econometrics at Harvard Steinberg began his professional career as a Member of the Technical Staff at Bell Labs, Murray Hill, and then as Assistant Professor of Economics at the University of California, San Diego. A book he co-authored on Classification and Regression Trees was awarded the 1999 Nikkei Quality Control Literature Prize in Japan for excellence in statistical literature promoting the improvement of industrial quality control and management.
His consulting experience at Salford Systems has included complex modeling projects for major banks worldwide, including Citibank, Chase, American Express, Credit Suisse, and has included projects in Europe, Australia, New Zealand, Malaysia, Korea, Japan and Brazil. Steinberg led the teams that won first place awards in the KDDCup 2000, and the 2002 Duke/TeraData Churn modeling competition, and the teams that won awards in the PAKDD competitions of 2006 and 2007. He has published papers in economics, econometrics, computer science journals, and contributes actively to the ongoing research and development at Salford.