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In the mind of today’s technological entrepreneur, the ideal user (and employee) is semi-skilled – or unskilled entirely. The ideal user interface for such a person never rewards learning or experience when doing so would come at the cost of immediate accessibility to the neophyte. This design philosophy is a mistake – a catastrophic, civilization-level mistake. There is a place in the world for the violin as well as the kazoo. Modern computer engineering is kazoo-only, and keyboards are only the most banal example of this fact.
"Humanities students should be more like computer-science students.
I decided that as I sat in on a colleague's computer-science course during the beginning of this, my last, semester in the classroom. I am moving into administration full time, and I figured that this was my last chance to learn some of the cool new digital-humanities stuff I've been reading about. What eventually drove me out of the class (which I was enjoying tremendously) was the time commitment: The work of coding, I discovered, was an endless round of failure, failure, failure before eventual success. Computer-science students are used to failing. They do it all the time. It's built into the process, and they take it in stride."
"My main interest is the overlap between software engineering
and computational science."
"So how quickly can a crowd be put into action.?That's the question tackled today by Michael Bernstein at the Massachusetts Institute of Technology in Cambridge and a few pals.
In the past, these guys have found ways to bring a crowd to bear in about two seconds. That's quick. But the reaction time is limited to how quickly a worker responds to an alert.
Now these guys say they've find a way to reduce the reaction time to 500 milliseconds--that's effectively realtime. A system with a half second latency could turn crowdsourcing into a very different kind of resource.
The idea that Bernstein and co have come up with is straightforward. These guys simply "precruit" a crowd and keep them on standby until a task becomes available. Effectively, they're paying workers a retainer so that they are available immediately when needed"
"Most adults realize that, say, Facebook is engineered to increase the value of our "social graphs" to its customers, the corporations and research firms that buy this data. We understand that we're not the customers, but the product. The more critically we engage with all of the iPhones and Google searches in our lives, the better we can tell what they want from us.
But I no longer think that's enough. It took a few centuries after the invention of text for regular people to learn how to read and write. The printing press, which democratized print by reducing the cost of manuscripts, certainly helped. Now that we live in a world with newspapers, road signs, package labels and drug inserts, almost no one still questions the idea that teaching kids to read is a good thing, or that basic literacy makes us more likely to create value for ourselves or our employers."
The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and many others are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing information from Twitter, Google, Verizon, 23andMe, Facebook, Wikipedia, and every space where large groups of people leave digital traces and deposit data. Significant questions emerge. Will large-scale analysis of DNA help cure diseases? Or will it usher in a new wave of medical inequality? Will data analytics help make people’s access to information more efficient and effective? Or will it be used to track protesters in the streets of major cities? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Some or all of the above?
This essay offers six provocations that we hope can spark conversations about the issues of Big Data. Given the rise of Big Data as both a phenomenon and a methodological persuasion, we believe that it is time to start critically interrogating this phenomenon, its assumptions, and its biases.
"Here's a recap of the three broad use cases for Storm:
Stream processing: Storm can be used to process a stream of new data and update databases in realtime. Unlike the standard approach of doing stream processing with a network of queues and workers, Storm is fault-tolerant and scalable.
Continuous computation: Storm can do a continuous query and stream the results to clients in realtime. An example is streaming trending topics on Twitter into browsers. The browsers will have a realtime view on what the trending topics are as they happen.
Distributed RPC: Storm can be used to parallelize an intense query on the fly. The idea is that your Storm topology is a distributed function that waits for invocation messages. When it receives an invocation, it computes the query and sends back the results. Examples of Distributed RPC are parallelizing search queries or doing set operations on large numbers of large sets."
"Computer scientists have recently undermined our faith in the privacy-protecting power of anonymization, the name for techniques for protecting the privacy of individuals in large databases by deleting information like names and social security numbers. These scientists have demonstrated they can often 'reidentify' or 'deanonymize' individuals hidden in anonymized data with astonishing ease. By understanding this research, we will realize we have made a mistake, labored beneath a fundamental misunderstanding, which has assured us much less privacy than we have assumed. This mistake pervades nearly every information privacy law, regulation, and debate, yet regulators and legal scholars have paid it scant attention. We must respond to the surprising failure of anonymization, and this Article provides the tools to do so. "
"Our everyday data processing activities create massive amounts of data. Like physical waste and trash, unwanted and unused data also pollutes the digital environment by degrading the performance and capacity of storage systems and requiring costly disposal. In this paper, we propose using the lessons from real life waste management in handling waste data. We show the impact of waste data on the performance and operational costs of our computing systems. To allow better waste data management, we define a waste hierarchy for digital objects and provide insights into how to identify and categorize waste data. Finally, we introduce novel ways of reusing, reducing, and recycling data and software to minimize the impact of data wastage "
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