- Top 10 Chinese Internet Memes of 2012 — most are political, unlike Overly Attached Girlfriend.
- Evaporative Cooling — thoughtful piece about the tendency of event quality to trend down unless checked by invisible walls. (via Hacker News)
- What Was It Like to Browse the Web in the 90s? (Quora) — it was awesome, because the alternative was television. Couple of whiny “you won’t believe how hard we had it” posts, from people who obviously believe that everyone in history has been miserable because they don’t have it as good as we do now. And, thus, by extension, we are miserable because we don’t have it as good as future generations of silver-robot-bearing flying-car-driving humans.
- Why Are Dead People Liking Stuff on Facebook? (ReadWrite Web) — a good question.
Motivated Learning, Better Hadoopery, Poignant Past Product, and Drone Imagery
- Teaching Programming to a Highly Motivated Beginner (CACM) — I don’t think there is any better way to internalize knowledge than first spending hours upon hours growing emotionally distraught over such struggles and only then being helped by a mentor. Me, too. Not struggle for struggle’s sake, but because you have built a strong mental map of the problem into which the solution can lock.
- Corona (GitHub) — Facebook opensources their improvements to Hadoop’s job tracking, in the name of scalability, latency, cluster utilization, and fairness. (via Chris Aniszczyk)
- One Man’s Trash (Bunnie Huang) — Bunnie finds a Chumby relic in a Shenzhen market stall.
- Dronestagram — posting pictures of drone strike locations to Instagram. (via The New Aesthetic)
Obstacles for big data, big data intelligence, and a privacy plugin puts Google and Facebook settings in the spotlight.
Here are a few stories from the data space that caught my attention this week.
Big obstacles for big data
For the latest issue of Foreign Policy, Uri Friedman put together a summarized history of big data to show “[h]ow we arrived at a term to describe the potential and peril of today’s data deluge.” A couple months ago, MIT’s Alex “Sandy” Pentland took a look at some of that big data potential for Harvard Business Review; this week, he looked at some of the perilous aspects. Pentland writes that to be realistic about big data, it’s important to look not only at its promise, but also its obstacles. He identifies the problem of finding meaningful correlations as one of big data’s biggest obstacles:
“When your volume of data is massive, virtually any problem you tackle will generate a wealth of ‘statistically significant’ answers. Correlations abound with Big Data, but inevitably most of these are not useful connections. For instance, your Big Data set may tell you that on Mondays, people who drive to work rather than take public transportation are more likely to get the flu. Sounds interesting, and traditional research methods show that it’s factually true. Jackpot!
“But why is it true? Is it causal? Is it just an accident? You don’t know. This means, strangely, that the scientific method as we normally use it no longer works, because there are so many possible relationships to consider that many are bound to be ‘statistically significant’. As a consequence, the standard laboratory-based question-and-answering process — the method that we have used to build systems for centuries — begins to fall apart.”
Pentland says that big data is going to push us out of our comfort zone, requiring us to conduct experiments in the real world — outside our familiar laboratories — and change the way we test the causality of connections. He also addresses issues of understanding those correlations enough to put them to use, knowing who owns the data and learning to forge new types of collaborations to use it, and how putting individuals in charge of their own data helps address big data privacy concerns. This piece, together with Pentland’s earlier big data potential post, are this week’s recommended reads.
Square cab fares, Wal-Mart looks to beat Amazon to the same-day punch, and a major player update in the mobile payments war.
Here are a few stories that caught my attention in the commerce space this week.
Square may be courting cabs
Square not only is gearing up to launch in Starbucks stores in November — it may also be looking to enter the New York City taxi cab market. Ryan Mac reports at Forbes this week that negotiations may be underway:
“Late Monday, private company expert PrivCo said that the San Francisco-based startup and the city of New York will be announcing an official partnership with the city of New York to implement Square’s payment systems across the city’s cabs. If negotiations are completed as expected, said New York City-based PrivCo, the partnership may be announced as early as this month.”
Mac reports that neither Square nor New York City’s Taxi & Limousine Commission (TLC) would confirm that a deal was in place, but he notes Square has been testing iPad credit card swipers with TLC since March.
As to its forthcoming foray into Starbucks, Lisa Baertlein at Reuters reports that further innovations are in the works even ahead of the launch. At launch, customers will be able to pay for a coffee by having a barcode scanned off a smartphone, but plans are already in the works to use Square’s GPS to identify a customer in a Starbucks location, who can then pay by giving his or her name to the cashier. Also, Cliff Burrows, president of Starbucks’ Americas region, told Baertlein that by summer 2013, customers will have the option and ability to tip using the technology.
One-click Facebook campaigns, PayPal redesigns, and a Best Buy exec identifies in-store mobile issues.
Payvment launches a one-click Facebook ad service, PayPal revamps its website with consumers and mobile in mind, and a Best Buy exec says in-store mobile use has a scale issue. (Commerce Weekly is produced as part of a partnership between O'Reilly and PayPal.)