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Florence Ion


What if you could privately use an application and manage its permissions to keep ill-intending apps from accessing your data? That’s exactly what Steve Kondik at CyanogenMod—the aftermarket, community-based firmware for Android devices—hopes to bring to the operating system. It’s called Incognito Mode, and it’s designed to help keep your personal data under control.

Kondik, a lead developer with the CyanogenMod team, published a post on his Google Plus profile last week about Incognito Mode. He offered more details on the feature:

I've added a per-application flag which is exposed via a simple API. This flag can be used by content providers to decide if they should return a full or limited dataset. In the implementation I'm working on, I am using the flag to provide these privacy features in the base system:

  • Return empty lists for contacts, calendar, browser history, and messages.
  • GPS will appear to always be disabled to the running application.
  • When an app is running incognito, a quick panel item is displayed in order to turn it off easily.
  • No fine-grained permissions controls as you saw in CM7. It's a single option available under application details.

The API provides a simple isIncognito() call which will tell you if incognito is enabled for the process (or the calling process). Third party applications can honor the feature using this API, or they can choose to display pictures of cats instead of running normally.

Every time you install a new application on Android, the operating system asks you to review the permissions the app requests before it can install. This approach to user data is certainly precarious because users can't deny individual permissions to pick and choose what an application has access to, even if they still want to use that app. Incognito Mode could potentially fix this conundrum, enabling users to restrict their data to certain applications.

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Today's video is an interview with the Corporate Alliance Director and the Chief Technology Officer of the International Association of Privacy Professionals (IAPP), a non-profit organization that claims it is "...the largest and most comprehensive global information privacy community and resource, helping practitioners develop and advance their careers and organizations manage and protect their data." In other words, it's not the same as the much-beloved Electronic Privacy Information Center (EPIC), but is -- as its name implies -- a group of people engaged in privacy protection as part of their work or whose work is about privacy full-time, which seems to be the case for more and more IT and Web people lately, what with HIPAA and other privacy-oriented regulations. This is a growing field, well worth learning more about.

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hal380The advent of Salesforce Marketing Cloud and Adobe Marketing Cloud demonstrates the need for enterprises to develop new ways of harnessing the vast potential of big data. Yet these marketing clouds beg the question of who will help marketers, the frontline of businesses, maximize marketing spending and ROI and help their brands win in the end. Simply moving software from onsite to hosted servers does not change the capabilities marketers require — real competitive advantage stems from intelligent use of big data.

Marc Benioff, who is famous for declaring that “Software Is Dead,” may face a similar fate with his recent bets on Buddy Media and Radian6. These applications provide data to people who must then analyze, prioritize and act — often at a pace much slower than the digital world. Data, content and platform insights are too massive for mere mortals to handle without costing a fortune. Solutions that leverage big data are poised to win — freeing up people to do the strategy and content creation that is best done by humans, not machines.

Big data is too big for humans to work with, at least in the all-important analytical construct of responding to opportunities in real time — formulating efficient and timely responses to opportunities generated from your marketing cloud, or pursuing the never-ending quest for perfecting search engine optimization (SEO) and search engine marketing (SEM). The volume, velocity and veracity of raw, unstructured data is overwhelming. Big data pioneers such as Facebook and eBay have moved to massive Hadoop clusters to process their petabytes of information.

In recent years, we’ve gone from analyzing megabytes of data to working with gigabytes, and then terabytes, and then petabytes and exabytes, and beyond. Two years ago, James Rogers, writing in The Street, wrote: “It’s estimated that 1 Petabyte is equal to 20 million four-door filing cabinets full of text.” We’ve become jaded to seeing such figures. But 20 million filing cabinets? If those filing cabinets were a standard 15 inches wide, you could line them up, side by side, all the way from Seattle to New York — and back again. One would need a lot of coffee to peruse so much information, one cabinet at a time. And, a lot of marketing staff.

Of course, we have computers that do the perusing for us, but as big data gets bigger, and as analysts, marketers and others seek to do more with the massive intelligence that can be pulled from big data, we risk running into a human bottleneck. Just how much can one person — or a cubicle farm of persons — accomplish in a timely manner from the dashboard of their marketing cloud? While marketing clouds do a fine job of gathering data, it still comes down to expecting analysts and marketers to interpret and act on it — often with data that has gone out of date by the time they work with it.

Hence, big data solutions leveraging machine learning, language models and prediction, in the form of self-learning solutions that go from using algorithms for harvesting information from big data, to using algorithms to initiate actions based on the data.

Yes, this may sound a bit frightful: Removing the human from the loop. Marketers indeed need to automate some decision-making. But the human touch will still be there, doing what only people can do — creating great content that evokes emotions from consumers — and then monitoring and fine-tuning the overall performance of a system designed to take actions on the basis of big data.

This isn’t a radical idea. Programmed trading algorithms already drive significant activity across stock markets. And, of course, Amazon, eBay and Facebook have become generators of — and consummate users of — big data. Others are jumping on the bandwagon as well. RocketFuel uses big data about consumers, sites, ads and prior ad performance to optimize display advertising. Turn.com uses big data from consumer Web behavior, on-site behaviors and publisher content to create, optimize and buy advertising across the Web for display advertisers.

The big data revolution is just beginning as it moves beyond analytics. If we were building CRM again, we wouldn’t just track sales-force productivity; we’d recommend how you’re doing versus your competitors based on data across the industry. If we were building marketing automation software, we wouldn’t just capture and nurture leads generated by our clients, we’d find and attract more leads for them from across the Web. If we were building a financial application, it wouldn’t just track the financials of your company, it would compare them to public filings in your category so you could benchmark yourself and act on best practices.

Benioff is correct that there’s an undeniable trend that most marketing budgets today are betting more on social and mobile. The ability to manage social, mobile and Web analysis for better marketing has quickly become a real focus — and a big data marketing cloud is needed to do it. However, the real value and ROI comes from the ability to turn big data analysis into action, automatically. There’s clearly big value in big data, but it’s not cost-effective for any company to interpret and act on it before the trend changes or is over. Some reports find that 70 percent of marketers are concerned with making sense of the data and more than 91 percent are concerned with extracting marketing ROI from it. Incorporating big data technologies that create action means that your organization’s marketing can get smarter even while you sleep.

Raj De Datta founded BloomReach with 10 years of enterprise and entrepreneurial experience behind him. Most recently, he was an Entrepreneur-In-Residence at Mohr-Davidow Ventures. Previously, he was a Director of Product Marketing at Cisco. Raj also worked in technology investment banking at Lazard Freres. He holds a BSE in Electrical Engineering from Princeton and an MBA from Harvard Business School.

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The Economics of Interaction: How We Can Use Microeconomics to Describe System Interaction

The Economics of Interaction: How We Can Use Microeconomics to Describe the Interaction Between User and System A Google Tech Talk June 7, 2012 Presented by Leif Azzopardi ABSTRACT Searching is inherently an interactive process usually requiring numerous iterations of querying and assessing in order to find the desired amount of relevant information. Essentially, the search process can be viewed as a combination of inputs (queries and assessments) which are used to "produce'' output (relevance). Under this view, it is possible to adapt microeconomic theory to analyze and understand the dynamics of Interactive Information Retrieval. In this talk, I will describe how the search process can be treated as an economics problem and then go on to describe a series of simulations on TREC test collections where I analyzed various combinations of inputs in the "production'' of relevance. The analysis reveals that the total Cumulative Gain obtained during the course of a search session is functionally related to querying and assessing. Furthermore, this relationship can be characterized mathematically by the Cobbs-Douglas production function. Then in a subsequent analysis using cost models, I show which search strategies minimize the cost of interaction for a given level of output. And these developments establishes the theoretical foundations of Interactive Information Retrieval, providing numerous directions for empirical experimentation that are motivated directly from theory <b>...</b>
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The technology reporters and editors of The New York Times scour the Web for important and peculiar items. Tuesday's selection includes 30 of India's technology leaders, a photo-sharing iPhone app jumping to Android and a comic strip look at a possible future technological discovery,

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holy_calamity writes "This article talks about software from Microsoft Research that looks like a smarter, more private version of Facebook's timeline. Lifebrowser uses machine learning techniques to process photos, emails, web history, documents and other data on your computer and automatically create an interactive timeline with an awareness of what's important and what's not. Lifebrowser is intended to be a prosthetic for memory. When a user searches their archive for specific information, Lifebrowser presents notable photos and other information to aid recollection."


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I woke up this morning...ish... to discover that Hacker News had finally had enough of me being at Google, so they forced me into early retirement.

On Monday I was honored to be able to deliver a keynote talk at OSCON Data. In the talk, I announce at the end that I am quitting a project that I had very publicly signed up for, one that I am not passionate about and don't personally think is very important to the human race. Though others clearly do, and that's a legitimate viewpoint too.

But the power of suggestion can make you see and hear something entirely different. If, for instance, someone tells you that I gave the talk wearing a gorilla suit, then when you watch it, I will magically appear to be wearing a gorilla suit. It's actually a gray jacket over a black shirt, but you will perceive the jacket as the back-hair of a male silverback gorilla! And to be honest the talk could have benefited from the judicious application of a gorilla suit, so no harm there.

Similarly, if someone on Hacker News posts that "Steve Yegge quits Google in the middle of his speech" and links to the video, then you will watch the video, and when I say the word "project" at the end of my speech, a magical Power of Suggestion Voice-Over will interrupt -- in a firm manly voice totally unlike my own quacking sounds -- with "Gooooooogle". And then you will promptly sink into a 15-minute trance so that the voice-over can occur in the middle of my speech where Hacker News said it happened, instead of 96.7% of the way through the talk where it actually happened.

I am going to harness this amazing Power of Suggestion, right here, right now. Here goes.

You are going to come work at Google! You are going to study up, apply, interview, and yes, you are going to work there! And it will be the most awesome job you've ever had or ever will have!

I hope for your sake that this little experiment works, because Google is frigging awesome, and you'll love it here. And they'll be happy to have you here. It's a match made in heaven, I'm tellin' ya. It might take you a couple tries to get in the door, because Google's interview process -- what's the word I'm looking for here -- ah yes, their process sucks at letting in all the qualified people. They're trying to get better at it, but it's not really Google's fault so much as the fault of interviewers who insist that you're not qualified to work there unless you are exactly like them.

Of course, there are interviewers like that wherever you go. The real problem is the classic interview process, which everyone uses and which Google hasn't innovated on, not really. It's like deciding whether to marry someone after four one-hour dates that all happen on the same day in a little room that looks kind of like a doctor's office except that the examining table is on the wall.

The reason I haven't been blogging lately is that working at Google is so awesome that I just don't feel like doing anything else. My project is awesome, the people are awesome, the work environment is over-the-top-crazy-awesome, the benefits are awesome, even the corporate mission is awesome. "Organize the world's hardline goods in little brown boxes delivered straight to your doorstep" -- that's an awesome mission, yeah?

Wait, sorry, that was a flashback to the Navy or something. "Organize the world's information" -- that's the one. It's a mission that is changing the course of human events. It is slowly forcing governments to be more open, forcing corporations to play more fairly, and helping all of us make better decisions and better use of our time.

In that vein, the part of my brain that makes Good Decisions was apparently broken a few weeks ago, when I allowed myself to be cajoled into working on something that I wasn't passionate about. I am an eternal optimist, and I figured I could teach myself to be passionate about it. And I tried! I spent a few weeks pretending that I was passionate about it -- that's how I got through my Physics classes in college with A grades, so I know it's a mental trick that can sometimes work.

But then I wrote my OSCON Data speech, in which I basically advise everyone to start working on important problems instead of just chasing the money. Or at the very least, go ahead and chase the money in the short term, but while you are doing that, prepare yourself to help solve real problems.

And after writing the speech I realized I'd completely failed to follow my own advice. I'm getting old and I only have so many "big projects" left that I can actually participate in. So in my mind it's a complete cop-out for me to take the easy path and work on a project that my company is excited about but I am not.

Now, as it happens, I am in fact working on a very cool project at Google. It's not important in the same sense that curing cancer or getting clean water to impoverished cities are important. But it's a project that has the potential to revolutionize software development, and NOT through some new goddamn dependency-injection framework or web framework or other godawful embarrassing hacky workaround for a deficient programming language. No. It is a project that aims to turn source code -- ALL source code -- from plain text into Wikipedia. I've been on it for three and a half years, and I came up with the idea, and the team running with the idea is fantastic. The work may not be directly important, but it is an enabler for important work, much like scaling infrastructure is an enabler.

So I am happy to continue working on that project for now. Yes, at Google. I may even blog it up at some point. But I'm very serious about brushing up on my math and statistics, some of which I haven't applied directly in 20 years, and start focusing on machine learning problems. Particulary, if I may be so fortunate, the problem of curing cancer. I may not be able to participate directly for a few years, as I need to keep working and paying the bills just like you. But I'm studying hard -- I started up again a few days ago -- and I've demonstrated to myself quite a few times that if I do anything daily for a few years I can get pretty good at it.

Anyway, I'm late for work. Isn't that nice? I like the sound of it. It has a nice ring to it: "I'm late... for my job."

So come work with me! Unless you are curing cancer, of course.

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