Archive for category: Sarah Williams

In his article “How Big Data Can Boost Weather Forecasting” for Wired, author Steve Hamm discusses how Big Data analysis techniques are being used to more accurately predict weather patterns. As his first example, Hamm discusses how the Korean Metrological Administration (KMA) is working to upgrade its predictive systems in order to better prepare the Korean peninsula for storms that “[carry] dense clouds of yellow dust from China’s Gobi Desert that are sometimes loaded with heavy metals and carcinogens”. As part of their upgrades, the KMA is dramatically increasing the agency’s storage capabilities in hopes of more accurately forecast weather patterns through an increased ability to quickly analyze large amounts of information.

Such efforts to increase predictive capabilities are also being made in other parts of the world. Following the destruction caused by Hurricane Sandy, “leaders of the city of Hoboken…are considering building a wall around the city to keep the tidal Hudson River at bay”, but such efforts will be in vain if scientists are unable to predict how the changing climate will affect the river’s future depth and behavior. Due to the scale of the issue, IBM is assisting in researching more accurate predictive methods through a project called Deep Thunder, a “long-term weather analysis project”.

Currently, Deep Thunder has been used to accurately predict “the snowfall totals in New York City during the mammoth snowstorm” in February, and was also able to predict “when the snowfall would start and stop”. IBM is currently working to implement Deep Thunder in Rio de Janeiro for the 2016 Olympics, and provide attendees access to the predictive information through “iPad and cloud applications”. The accuracy and speed of Deep Thunder have great implications for the future of climate prediction; if the planet’s weather can be consistently be predicted, the damages and injuries caused by catastrophic weather could be greatly mitigated during future events.

In Amy Scott’s article “Creating the Next Silicon Valley in Chicago” for Marketplace, she discusses the possibility of an upcoming technological boom in Chicago. Specifically, she references the presence of the University of Illinois at Urbana Champaign as a potential source of talent for such a boom; the school itself is widely respected, but “its students are often drawn to other shores”.  Talent retention has been a concern for Chicago in the past, as “[founders] of Youtube, Paypal, and Yelp also studied there before heading west”. To help solve this issue, Chicago Mayor Rahm Emanuel “[wants] to draw the talent [to Chicago]”, and believes that “the companies will follow”.

To achieve this goal, Chicago is emphasizing the creation and maintenance spaces in which companies and new technologies can grow. The Merchandise Mart, “a massive building of showrooms and shops”, is becoming such a hub. Motorola Mobility now occupies space within the building, as do 100 startups sharing office space.

An issue that Chicago is facing, however, is the overall culture within the city; according to Margaret O’Mara of the University of Washington, Silicon Valley is unique in its “extraordinary tolerance or risk and failure”, and that such a culture doesn’t yet exist in Chicago. If that cultural impediment can be overcome, however, Chicago holds great potential for new companies. Lightbank, “a venture capital firm run by the co-founders of Groupon” has already begun changing the economic culture of tech startups in the city, having invested in 53 companies after only 18 months. If such trends continue, the potential technological success of Chicago would likely have far-reaching economic implications for the Midwest as a whole.

In Computer Weekly’s article “Apple iPad App Pushes the Location-Based Cloud”, author Adrian Bridgwater discusses how location-based service technology is currently being used in apps for Apple devices. In particular, he discusses how these technologies relate to the increasing prevalence of cloud computing-based resources and devices. Additionally, he discusses to main types of location-based services: “push” LBS, “pull” (also known as “query”) LBS, and a multi-user LBS service, which is the article’s primary focus.

Specifically, Bridgwater explores the benefits and potential pitfalls of an app called Find My Friends. Find My Friends allows users to view each other’s current locations using their iCloud Apple accounts. Additionally, the app can be used in more specific situations; rather than broadcasting one’s location to one’s friends at all times, Find My Friends allows users to “share [their] location with a group of friends for a limited time”. It can be used to monitor when others leave or arrive at a certain location, and can also distribute that information to one’s own contacts.

While Find My Friends certainly has great potential benefit for users, it also presents a new opportunity for developers to create “a new breed of apps that allows us to interact with each other based on where we are [and] what we’re doing”. However, such possibilities do come with potential privacy concerns. As location-based sharing requires the permission of each user to be fully utilized, individuals’ particular privacy settings could affect the efficacy of LBS-reliant applications. Nevertheless, the increased prevalence of cloud and LBS applications identifies an area of great potential within the apps market.

In hopes of discovering and recording new and creative words, the staff at the UK’s Collins English Dictionary has begun crowdsourcing the entries in their dictionary. Collins, which added “crowdsourcing” to its own dictionary in 2009, is asking internet users to contribute their own words to this project. So far, there’s been a good response; “[in] the first two weeks of the initiative, there were 2,637 suggestions from more than 2,000 different users.”

While the Boston Globe’s article “Crowdsourcing the Dictionary” initially suggests that Collins’ project may initially seem more similar in nature to online dictionaries such as Urban Dictionary, it soon becomes clear that the concept of dictionaries incorporating user-submitted content is not in fact a new one; the Oxford English Dictionary, for example, put out its first call for user submissions in 1879. Rather, it is the ease of submission to and user influence on the dictionary that makes the Collins English Dictionary unique. While Merriam-Webster has a similar word submission project called Open Dictionary that has received “nearly 20,000 suggestions from users since…2005”, the Merriam-Webster editors use it simply as research inspiration. In contrast, the Collins English Dictionary aims integrate user submissions with their current online dictionary.

This project has interesting implications for future projects both within and outside of the lexical world. While soliciting user input is not a new business model for dictionary groups, the amount of moderated, user-generated content within Collins’ final product is unique. Though Collins’ model of user-based submissions makes improvements on past models, there is still much progress that could be made in terms of user interactivity and influence. However, such concessions are necessary if a specific level of quality is desired for the final product.

In this article from PandoDaily, Farhad Manjoo predicts that Nokia’s end as a company is near. Specifically, he claims that Nokia’s reliance on other companies’ software is the primary reason why Nokia is currently struggling; whereas successful mobile companies such as Google and Apple provide the software and, occasionally, the hardware for the massively popular Android and iPhone devices, “Nokia…always thought of itself as being in the device business. It made hardware, and it only cared about software to the extent that it needed code to run on those devices.” A memo sent to Nokia’s employees by recently appointed CEO Stephen Elop supports Manjoo’s claim. In it, Elop points out that while Nokia had continued to create solid hardware, they had failed to create entire device ecosystems.

In spite of Elop’s timely analysis of the issue, Nokia is nevertheless in decline. Referencing Apple’s 1990s similar decline in the face of Microsoft’s market ubiquity, Manjoo suggests that Apple was able to regain strength by “changing the rules of the game”, offering a solution that went beyond simply providing PCs. Nokia, however, isn’t showing the same type of innovation. In an attempt to quickly reverse their deterioration, Nokia has partnered with Windows in distributing their mobile OS. However, their success now relies on that of the Windows mobile OS, and Nokia/Windows Lumia phones aren’t selling as well as either company would hope. While Elop has hopes for the upcoming Windows 8 phones, Manjoo is not as optimistic regarding Nokia’s overall fate.

On a broader scale, however, this article is very telling as to how the device market has changed in the past years. Nokia’s decline suggests that it’s no longer economically feasible for IT production companies to produce only one element of a total product and subsequently expect to be both independent from other companies and financially successful. Rather, to succeed in the current business climate, companies need to consider providing combined IT solutions to consumers’ problems.