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            1. Showing posts with label crowdsourcing. Show all posts
              Showing posts with label crowdsourcing. Show all posts

              Monday, April 30, 2012

              Fixing Software Patents, One Hack At Time



              Software patents are broken and patent trolls are seriously hurting innovation. Companies are spending more money on buying patents to launch offensive strikes against other companies instead of competing by building great products. There are numerous patent horror stories I could outline where they are being used for all purposes except to innovate. In fact the software patent system as it stands today has nothing to do with innovation at all. This is the sad side of the Silicon Valley. While most people are whining about how software patent trolls are killing innovation some are trying to find creative ways to fix the problems. This is why it was refreshing to see Twitter announcing their policy on patents, Innovator's Patent Agreement, informally called IPA. As per IPA, patents can only be used in an offensive litigation if the employees who were granted the patents consent to it. I have no legal expertise to comment on how well IPA itself might hold up in a patent litigation but I am thrilled to see companies like Twitter stepping up to challenge the status quo by doing something different about it. If you're an employee you want three things: innovate, get credit for your innovation, and avoid your patents being used as an offensive tool. IPA is also likely to serve as a hiring magnet for great talent. Many other companies are likely to follow the suit. I also know of a couple of VCs that are aggressively pushing their portfolio companies to adopt IPA.

              The other major challenge with software patents is the bogus patents granted based on obvious ideas. I really like the approach taken by Article One Partners to deal with such patent trolls. Article One Partners crowdsources the task of digging the prior art to identify bogus patents and subsequently forces the US patent office to invalidate them. Turns out that you don't have to be a lawyer to find prior art. Many amateurs who love to research this kind of stuff have jumped into this initiative and have managed to find prior art for many bogus patents. It's very hard to change the system but it's not too hard to find creative ways to fix parts of the system.

              I would suggest going beyond the idea of crowdsourcing the task to find the prior art. We should build open tools to gather and catalog searchable prior art. If you have an idea just enter into that database and it becomes prior art. This would make it incredibly difficult for any company to patent an obvious idea since it would already be a prior art. We should create prior art instead of reactively research for it. Open source has taught us many things and it's such a vibrant community. I can't imagine the state of our industry without open source. Why can't we do the same for patents? I want to see Creative Commons of patents.

              The industry should also create tools to reverse translate patents by taking all the legal language out of it to bring transparency to show for what purposes that patents are being granted for.

              I would also want to see an open source like movement where a ridiculously large set of patents belong to one group - a GitHub of patents. And that group will go after anyone who attempt to impede innovation by launching an offensive strike. If you can't beat a troll then become one.

              Silicon valley is a hacker community and hackers should do what they are good at, hack the system — to fix it — using creative ways.

              Photo: Opensource.com

              Monday, December 1, 2008

              Does Cloud Computing Help Create Network Effect To Support Crowdsourcing And Collaborative Filtering?

              Nick has a long post about Tim O'Reilly not getting the cloud. He questions Tim's assumptions on Web 2.0, network effects, power laws, and cloud computing. Both of them have good points.

              O'Reilly comments on the cloud in the context of network effects:

              "Cloud computing, at least in the sense that Hugh seems to be using the term, as a synonym for the infrastructure level of the cloud as best exemplified by Amazon S3 and EC2, doesn't have this kind of dynamic."

              Nick argues:

              "The network effect is indeed an important force shaping business online, and O'Reilly is right to remind us of that fact. But he's wrong to suggest that the network effect is the only or the most powerful means of achieving superior market share or profitability online or that it will be the defining formative factor for cloud computing."

              Both of them also argue about applying power laws to the cloud computing. I am with Nick on the power laws but strongly disagree with him on his view of cloud computing and network effects. The cloud at the infrastructure level will still follow the power laws due to the inherent capital intensive requirements of a data center and the tools on the cloud would help create network effects. Let's make sure we all understand what the powers laws are:

              "In systems where many people are free to choose between many options, a small subset of the whole will get a disproportionate amount of traffic (or attention, or income), even if no members of the system actively work towards such an outcome. This has nothing to do with moral weakness, selling out, or any other psychological explanation. The very act of choosing, spread widely enough and freely enough, creates a power law distribution."

              Any network effect starts with a small set of something and it eventually grows bigger and bigger - users, content etc. The cloud makes it a great platform for such systems that demand this kind of growth. The adoption barrier is close to zero for the companies whose business model actually depends upon creating these effects. They can provision their users, applications, and content on the cloud and be up and running in minutes and can grow as the user base and the content grows. This actually shifts the power to the smaller players and help them compete with the big cloud players and yet allow them to create network effects.

              The big cloud players, that are currently on the supply side of this utility mode, have few options on the table. They either can keep themselves to the infrastructure business and I would wear my skeptic hat and agree with a lot of people on the poor viability of this capital intensive business model that has very high operational cost. This option alone does not make sense and the big companies have to have a strategic intent behind such large investment.

              The strategic intent could be to SaaS up their tools and applications on the cloud. The investment and control over the infrastructure would provide a head start. They can also bring in partner ecosystem and crowdsource large user community to create a network effect of social innovation that is based on collective intelligence which in turn would make the tools better. One of the challenges with the recommendation systems that uses collaborative filtering is to be able to mine massive information that includes users' data and behavior and compute the correlation by linking it with massive information from other sources. The cloud makes a good platform for such requirements due to its inherent ability to store vast amount of information and perform massive parallel processing across heterogeneous sources. There are obvious privacy and security issues with this kind of approach but they are not impossible to resolve.

              Google, Amazon, and Microsoft are the supply side cloud infrastructure players that are already moving in the demand side of the tools business though I would not call them the equal players exploring all the opportunities.

              And last but not the least, there is a sustainability angle around the cloud providers. They can help consolidate thousands of data centers into few hundreds based on the geographical coverage, availability of water, energy, and dark fiber etc. This is similar to consolidating hundreds of dirty coal plants into few non-coal green power plants that can produce clean energy with efficient transmission and distribution system.

              Monday, August 4, 2008

              Social computing in enterprise software - leveraging Twitter like microblogging capabilities

              Twitter was buzzing with posts on the recent L.A earthquake nine minutes before AP officially broke the news. This Twitter phenomenon once again proved that unintended consequences are always larger than intended consequences. As we would have never imagined people find amusing ways of using Twitter ranging from keeping buddies updated and getting caught drinking when they called in sick and the boss followed their tweets to ensue wave of media coverage to get out of jail. A recent proposal to use Twitter as an emergency system met with stark criticism citing Twitter's availability issues. I don't see this as an "either or" proposition. The answer is "and" and not "yes, but". Let's use Twitter for what it is worth. It's a great microblogging and crowdsourcing tool to tap into the wisdom of crowd with a very little overhead and almost no barrier to entry.

              Enterprise software should seriously consider this social computing phenomenon and leverage its capabilities by integrating such a tool in their offerings. For instance a social CRM application can use such a tool to help sales people effectively follow, collaborate, and close opportunities. The customer support system can provide transparency into the defect resolution process by service representatives tweeting the progress instead of logging it in semi-static IT ticket systems.

              Following individual tweets has its obvious advantages but correlating multiple tweets could be extremely powerful and could yield to interesting nontraditional usage models such as using it to run predictive markets, sentiment analysis, or to track a recall on salmonella tainted tomatoes in real-time.

              Saturday, July 12, 2008

              Make to think and think to make - Design thinking helps a start-up radio show compete with NPR's Morning Edition

              The upstart radio show Takeaway's producers worked with the d.school at Stanford to apply design thinking approach to their show that competes with NPR's Morning Edition. It is quite an interesting story about how a legacy media industry can discard a traditional approach and embrace design thinking to rapidly iterate on the design of a radio show.

              "A three-day crash course taught the producers the basic steps of d.school innovation: observe, brainstorm, prototype, and implement; repeat as necessary."


              "The program's central idea is a daily question that audiences are asked to riff upon, either by calling in or by emailing. Their responses are then woven into the rest of the show's programming."

              Not spelled out in so many words in the story but this is a good example of user-centered and participatory design with a crowdsourcing twist to it.

              "But recognizing shortcomings and criticism and iterating quickly is one of the design process's core principles. The students in a d.school course called Design + Media, who are using the show as a class project, are helping producers generate ideas and track online response. For example, they're following Twitter streams to find out which questions and other parts of the broadcast are producing the strongest reactions."


              Once again this story reinforces that design is an ongoing process and design thinking is not about talking but making and generating more ideas while making to change what you just made.

              Wednesday, March 19, 2008

              User-generated content, incentives, reputation, and factual accuracy

              Not all user-generated content is factually accurate and it does not have to be that way. I don't expect Wikipedia to be completely accurate and some how many people have a problem with that. Traditionally the knowledge-bases that upfront requires high factual accuracy have been subjected to slow growth due to high barrier to entry. Wikipedia's prior stint, Nupedia, had a long upfront peer review process that hindered the growth and eventually led to the current Wikipedia model as we all know. Google Knol is trying to solve this problem by introducing the incentives to promote the quality of the thoughtocracy. I haven't seen Knol beyond this mockup and I am a little skeptical of a system that can bring in accuracy and wild growth at the same time. I would happy to be proven wrong here.

              For an incentive-based system it is important not to confuse factual accuracy with popularity of the content. If content is popular it does not necessarily have to be accurate. If we do believe that incentives can bring in the accuracy, we need to be careful in associating incentives to the accuracy and not to the popularity and that is much harder to accomplish since the incentive scheme needs to rate the content and the author based on the sources, up-front fact-checking, and not just the traffic which could indicate popularity. Mahalo is trying to solve the popularity problem and not the accuracy problem. There have been some attempts to try out the reputation model for Wikipedia but the success has been somewhat underwhelming. I see many opportunities and potential in this area, especially if you can cleverly combine the reputation with the accuracy.

              In reality what we need is a combination of restriction free content creation, fact-checking, incentives, and reputation. These models are not mutually exclusive and not necessarily required at all the times and should not be enforced to all the content across all the users. Guided or informative content tend to be more popular irrespective of the factual accuracy since it is positioned as a guide and not as a fact. The people who are in the business of working off the facts such as media reporters, students working on a thesis etc. should watch for the content that is useful, looks reputable, current, and may be factual but is pure wrong and should go through a systematic due diligence fact-checking process.
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