Automated online assistant Automated online assistants have the ability to provide customer service during 24 hours a day and 7 days a week, and may, at least, be a complement to customer service by humans.[2] One report estimated that an automated online assistant produced a 30% decrease in the work-load for a human-provided call centre.[3] Usage[edit] Large companies such as Lloyds Banking Group and Royal Bank of Scotland are now using automated online assistants instead of call centres with humans to provide a first point of contact.[citation needed]Also, IKEA has an automated online assistant in their help center.[4] Automated online assistants can also be implemented via Twitter, or Windows Live Messenger, such as, for example, Robocoke for Coca Cola Hungary. This automated online assistant provides users with information about the brand Coca Cola, but he can also give users party and concert recommendations all over Hungary.[5] Components[edit] Dialog system[edit] Avatar[edit] Other components[edit] See also[edit]
Seth Grimes information technology strategy consulting Seth Grimes is the leading industry analyst covering text analytics, sentiment analysis, and analysis on the confluence of structured and unstructured data sources. He founded Washington DC based Alta Plana Corporation, an information technology strategy consultancy, in 1997 and is longtime TechWeb contributor (InformationWeek, AllAnalytics, Internet Evolution, and before them, Intelligent Enterprise). He created and organizes the twice-yearly Sentiment Analysis Symposium and was founding chair of the Text Analytics Summit (2005-13). Seth consults, writes, and speaks on business intelligence, data management and analysis systems, text mining, visualization, and related topics. Follow Seth on Twitter at @SethGrimes. Grimes earned a master's in mathematics at the Univ. of Washington in Seattle and a bachelor's in mathematics and philosophy at Wesleyan Univ. in Middletown, Connecticut. Interviewed Quoted
Collective intelligence Types of collective intelligence Collective intelligence is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making. The term appears in sociobiology, political science and in context of mass peer review and crowdsourcing applications. It may involve consensus, social capital and formalisms such as voting systems, social media and other means of quantifying mass activity. Collective IQ is a measure of collective intelligence, although it is often used interchangeably with the term collective intelligence. Collective intelligence has also been attributed to bacteria[1] and animals.[2] Collective intelligence strongly contributes to the shift of knowledge and power from the individual to the collective. History[edit] Dimensions[edit] Howard Bloom has discussed mass behavior—collective behavior from the level of quarks to the level of bacterial, plant, animal, and human societies.
Hunch Digi face Research and Development: Client-side recommendations Intelligence amplification Intelligence amplification (IA) (also referred to as cognitive augmentation and machine augmented intelligence) refers to the effective use of information technology in augmenting human intelligence. The idea was first proposed in the 1950s and 1960s by cybernetics and early computer pioneers. IA is sometimes contrasted with AI (Artificial Intelligence), that is, the project of building a human-like intelligence in the form of an autonomous technological system such as a computer or robot. AI has encountered many fundamental obstacles, practical as well as theoretical, which for IA seem moot, as it needs technology merely as an extra support for an autonomous intelligence that has already proven to function. Moreover, IA has a long history of success, since all forms of information technology, from the abacus to writing to the Internet, have been developed basically to extend the information processing capabilities of the human mind (see extended mind and distributed cognition). .." J.
Semantic search Guha et al. distinguish two major forms of search: navigational and research.[3] In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. Semantic search is not applicable to navigational searches. In research search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. There is no particular document which the user knows about and is trying to get to. Rather, the user is trying to locate a number of documents which together will provide the desired information. Semantic search lends itself well with this approach that is closely related with exploratory search. Rather than using ranking algorithms such as Google's PageRank to predict relevancy, semantic search uses semantics, or the science of meaning in language, to produce highly relevant search results. Disambiguation[edit] Commonly used searching methodologies[edit]
Google Now History[edit] In late 2011, reports surfaced that Google was greatly enhancing their product Google Voice Search for the next version of Android. It was originally code named "Majel" after Majel Barrett, the wife of Gene Roddenberry, and well known as the voice of computer systems in his Star Trek franchise; it was also codenamed "assistant".[4] On June 27, 2012, Google Now was unveiled as part of the premier demonstration of Android 4.1 Jelly Bean at the Google I/O.[5] On October 29, 2012, Google Now received an update through the Google Play Store bringing the addition of Gmail cards.[6] Google Now displays cards with information pulled from the user's Gmail account, such as flight information, package tracking information, hotel reservations and restaurant reservations. Functionality[edit] Google Now is implemented as an aspect of the Google Search application. Specialized cards currently comprise:[15] Reception[edit] See also[edit] References[edit] External links[edit] Official website
Just a guy in a garage One of my daughter's friends suggested that sequels would, on average, recieve lower scores than the original movies - as, at least in her experience, they were invariably worse. I thought I'd just confirm her suspicions so that I could let her know that she was thinking about the problem in a good way. However, to my surprise the opposite appears to be true. Here is the mean score - the 0.5879992 number (adjusted for various things) for each episode of Sex in the City. Sex and the City: Season 1 0.5879992 41138Sex and the City: Season 2 0.5824835 43795Sex and the City: Season 3 0.6523933 38983Sex and the City: Season 4 0.7066851 34616Sex and the City: Season 5 0.7359862 33380Sex and the City: Season 6: Part 1 0.8097552 33532Sex and the City: Season 6: Part 2 0.8241694 27914 As you can see the later the sequel the better the result. This might be interpreted as some form of signalling.
Latest Digital Marketing Trends, Insights and News from SmartFocus In previous posts, I’ve looked at what personalization was not. In the last post of this series, I’m looking at the future of personalization – and how you should approach it. Part 3: The future of Ecommerce Personalization “Groupon knows that targeting by regions increases conversion and sales, but imagine how much they could amplify that effect if they were targeting based on a rich and sophisticated understanding of the individual person that receives each offer?” (Techcrunch) Online retailers should be itching to move beyond recommendations into personalization. Here are just a few examples of where personalization can make a difference and change the face of ecommerce. Product Recommendations Initially, driving product recommendation areas with personalization logic will lead to much more relevant product suggestions. I think that crowd-based recommendations have their place in a personalization strategy. Search Emails Social What’s next for Personalization?