Google aims for a new level of map customization
Google introduced a new version of Google maps at Google I/O this week that learns from each use to customize itself to individual users, adapting based on user clicks and searches. A post on the Google blog outlines the updates, which include recommendations for places you might enjoy (based upon your map activity), ratings and reviews, integrated Google Earth, and tours generated from user photos, to name a few.
Leo Mirani at The Atlantic says the update “fixes the one thing that has always been wrong with maps” — namely, neutrality. Though maps are typically viewed as neutral objects, Mirani argues, they’re “about as impartial as journalism.” He writes:
“Is the hot dog vendor who stands on a street corner as worthy of inclusion as the bank on the same corner? That decision still lay with mapmakers — in this case a giant internet company. Google’s solution to the problem was to remove itself from the equation.”
Mirani notes that “mental maps,” maps created based on how a person sees the world, is nothing new, but Google has managed to provide the mental map as a service to millions of users.
In a post at The Atlantic Cities, Emily Badger argues that this level of individual customization comes at a cost — “An algorithm that knows you too well,” she writes, “does a terrible job of telling you things you don’t already know.” Badger relates the new maps to Eli Pariser’s “filter bubble” concept: as the algorithms get to know you, you increasingly get content that leans toward your established point of view, preventing you from broadening your experiences. Badger also notes the increasing issue with “the inequality of information online…rendering some real-world people and places virtually invisible” and wonders if the new maps won’t exacerbate the problem. You can read her full report at The Atlantic Cities.
App development trends lean toward predictive, intelligent service offerings
MIT Technology’s Tom Simonite took a look at the growing app development trend to provide users with personalized information, service connections, and recommendations before even being prompted or searched. Noting the departure of this trend from typical “dumb” computers and software that waited for human operator interaction, Simonite looks at such apps as Google Now, which aims to predict a user’s actions in order to provide appropriate assistance, and the Osito iPhone app, which similarly predicts actions to offer helpful information but also provides actionable assistance, such as a button to call a cab when a user’s flight reminder pops up.
Bit.ly chief data scientist Hilary Mason told Simonite that Google Now is far from perfect in the usefulness of the information it provides, but she uses it anyway and finds the technology important “because it’s the first time Google has taken all they know about us to make a product that makes our lives better.” You can read Simonite’s full report at MIT Technology Review.
In related news, Google announced new services at its I/O conference this week that will help developers build apps that can track users as well as Google does — and without draining a user’s battery. Jessica Leber reports at MIT Technology Review that the service will allow developers to build apps that tap into the accelerometer of a user’s device instead of the “power-hungry” GPS sensor to determine whether a person is driving, walking or cycling; the apps would run Google algorithms, Leber notes, that would “learn over time whether a person is stuck in traffic or just out for an evening stroll.” The new services also include the ability for developers to create geofences to trigger actions based on a user’s location. You can read Leber’s full report at MIT Technology Review.
The New Yorker gets an anonymous inbox to protect sources
The New Yorker launched Strongbox this week, a new tool for people to share files, information, and messages with The New Yorker staff with an increased level of anonymity. In a post announcing the launch, Amy Davidson explains that with the way Strongbox is set up, they won’t be able to tell where a piece of information came from, and thus won’t be able to tell anyone where it came from, providing better protection for their sources.
Davidson notes that the Strongbox tool was developed by Aaron Swartz and Kevin Poulsen, and is based on their underlying code dubbed DeadDrop, which will be open source. You can read the development story and history in a post Poulsen wrote for the New Yorker.
Tip us off
News tips and suggestions are always welcome, so please send them along.