Google and the Association of American Publishers, have finally managed to settle a deal, after 7 years of legal disputes about the rights of the later for Google’s digital libraries.
The details of the deal haven’t been available to audience, but both Google and the Association of American Publishers have announced that publishers can decide whether or not to provide their books for digitalization for Google’s “Library Project”.
The president of publishers, Tom Allen, have quoted: “It seems that digital services can provide innovative ways of discovering content, while respecting owners’ copyrights.”
But it seems that this deal isn’t enough to relieve Google from its current litigation with the Authors Guild. The executive director of Authors Guild has stated that Google keeps having huge profits from copyright- protected novels and that they will not hold back with the law suits.
There had been a former deal for the deposit of 125 million dollars as compensation to the rightful creators, but after objections about competitive advantages, the federal court had cancelled it, so they signed a new deal 3 days ago.
Google has already published about 15 millions of digital books to enhance their efforts to provide “easier access to world knowledge” . For that cause, Google collaborates with big libraries from all over the world, like the New York Public Library and Stanford’s University Libraries.
Microsoft is soon getting its own film studio in Hllywood, with the purpose of distributing prototype video material through the XBOX Live service. The “boss” of the studio will be Nancy Tellem, a former CBS executive. Nancy Tellem has taken part in the past, in the creation of successful TV series like “CSI” and “Two and A Half Men”.
As Tellem stated at her interview at Los Angeles Times, the studio is built from scratch. So many predict that the studio won’t be complete until late 2013. She also referred to the movies and series that are going to be produced and said that they will be similar to those playing on TV. Microsoft will also produce interactive material.
Microsoft is hoping that with that move, they can give a clearly advantage to their console, and transform it from a game console to a complete entertainment machine. Already, in USA, XBOX users can watch movies and series from “online video clubs” such as Netflix.
Meanwhile, they hope making more attractive both the computers that are using Windows 8 and the smartphones with Windows 8 operation system, as both have XBOX Live integrated. Microsoft has not yet made clear if the material will be free of charge or not, and if it will be ad-free or not.
It isn’t also clear how the players in TV market are going to react, as Microsoft can be a potential competitor. The more time people will spent watching Microsoft productions, the less spectacularity the competitors will get.
Finally, Tellem stated that, Microsoft will cooperate with Hollywood producing companies and that there will be deals with TV companies, so that their products will be available through XBOX Live.
How do you think will Sony and Nintendo, the main competitors of XBOX, will react?
And what impact you think will the Microsoft productions have on consumers?
In this week’s Technology of the Week we have chosen to evaluate and compare the recommendation systems algorithm used by two of the world’s biggest online retailers: Amazon and Best Buy.
Amazon uses a recommendation platform that is unique for each customer. The recommendations are personalized for each user separately so they provide a unique shopping experience depending on the customer’s interests and preferences. The algorithm Amazon uses is called Item-to-Item collaborative filtering. The effectiveness of this algorithm is based on product grouping. Amazon builds a group of neighboring products for each product on its lists. That means that every time a customer purchases or browses a product X, items from the X neighboring group will be recommended. The question that arises is how does the algorithm recognizes which products are connected. In order to define the similar products and to form the product groups referred above, the algorithm finds items that customers tend to purchase together. Amazon has hundreds of million products listed on its catalogs and over 30 million active customers. That fact makes recommendation a complicated procedure. There are numerous data sources that have to be mined in order to deliver quality recommendations to the users. Useful information can be provided by the shopping cart of each customer, by their wish lists, by dwell time, by the websites that users were before directing to Amazon, by demographical data, by promotional offers through e-mails and so on. Amazon released in 2010, a feature that could prove crucial for information mining and to improve their algorithm. This is a Facebook application that enables integration of the user’s Facebook and Amazon profile. Amazon retrieves information from the users’ Facebook profiles and their friends profiles and it provides useful product recommendations for the user and gift recommendations when a friend of the social network has birthday. It also recommends products that are popular among friends, depending on the products that have purchased or liked.
Best Buy is one of the biggest electronics retailing companies. In 2009, Best Buy has introduced the semantic web in their online store in an effort to make basic store information easier accessible. Initially, semantic web was used to inform customers about the operation hours, location, and contact information for its physical stores. After the success of the store finder, the idea of the semantic web was applied to the online store. The semantic web is an extension of the web 2.0 where information is positioned in context and is linked to each other by means of formal languages, so it is understood by computers and users alike. With the integration of sematic web to online retailer shops, information can be structured beyond mere product categories. Connections between products are becoming more clear and understandable to both the computer systems and the customers, and changes in product specifications from the manufacturers are automatically updated to the online shops. The semantic web encodes all relevant features for products in RDF triples rather than simple HTML lists. The computer can understand these triples and organize them as required by the customer on a level of detail that goes beyond product categories and includes every detail and specification of the products. The costumer can, then, specify the aspects of the product that he considers essential and get the feedback of the products he was looking for, without having to browse through product catalogs. Another feature that is possible with the semantic web is to crosslink products between categories for supplementary or related goods. In this regard it is important to notice that Best Buy can also suggest compatible accessories easily. A simple RDF triple stating compatibility on the company site spares the consumer reading and understanding about compatibility.
Each recommendation system has advantages and disadvantages. Amazon provides quality and fast recommendation to its customers by analyzing their preferences and their interests. But there has been speculation about the trustworthiness of the reviews and ratings that are highly connected to the recommendations. Amazon has been rewarding its top reviewers with free products. That’s the reason that has led people to believe that there are indulgent or even fake comments and reviews.
Best Buy enable customers to find the items they are looking for more intuitively and can compare them easily to other relevant products. But the transition from simple HTML to RDF has a high upfront cost, plus the recommendation system overemphasis on product details at the expense of other important activities (for instance product placement, or how to best utilize the data).
Since both firms have success on the online retailing market, we cannot conclude which recommendation system is better. The conclusion that we can make is that Amazon recommendation algorithms is more suitable for experience goods and Best Buy’s system for search goods, due to its effectiveness comparing and finding technical characteristics.
Group A / Team 8
Because I was working for 3 years at a solar company I have a really great interest on the energy sector.
Last night I found a youtube video from a discussion panel at Stanford University, about the opportunities that arise from the implementation of IT at the energy sector. The speakers are Pat House, Co-founder,Vice chairman and senior vice president of strategy at C3,Paul De Martini, Vice President and Chief Technology Officer at Cisco, Bill Weihl, Green Energy Manager at Google and Balaji Prabhakat, Professor of Electrical Engineering and Computer Science at Stanford university.
For those who are interested about energy, I recommend watching this video.
Anyway, I am giving a small summary of the video. The first subject of discussion was the synergy of IT and energy, and how IT could help solving future energy problems. The total world population is estimated to raise at 10 billion after 30 years and the amount of energy needs that will occur will be increased by 40%. IT can help to avoid any future problems regarding energy efficiency. Large systems and applications will be created to forecast uncertainty, to calculate the exact amount of energy used and,regarding companies, to optimize energy usage along the whole supply chain, A common issue at the energy sector, is the deficiancy of means to predict accurately energy productivity from a wind station. So progress has to be done at that point too. A major problem, regarding bad use and loss of energy, is traffic congestion. The first step towards solving, or decreasing, this problem is to create a reward system for energy saving, by taking longer routes or driving at non-peak hours. To achieve that, Professor Prabhakat stated that efforts should be made to bring together the physical networks and social and communication networks. ( I have some difficulties understanding how this will work, so I would like to discuss it with someone who will watch the video 🙂 )
The biggest obstacle going to an energy sufficient era is the inability of collecting and analysing the huge amount of data from the hundreds of million energy-spending devices. For that reason, the biggest challenge is the creation of reliable,effective and robust systems that will help collecting,storing and analysing these large scale data sets.
Finally, it is a huge opportunity for people who want to combine their IT skills with the energy sector. At the future there will be investments of 1 trillion dollars , just in the USA, for replacing the basic infrastructure of utilities, for designing communication networks that connect the intelligent devices, for building powerful data analyzing and optimisation systems and so on. So it’s a good chance for us to get a share of that big pie 🙂