The World Wide Web (WWW) abounds with ever-increasing information on many topics. However, since every user has specific information needs and interests, only a tiny part of the WWW is useful to them. For example, in a family, a mother may wish to "find recipes with salmon as the main ingredient", the father may be interested in "what movie to watch tonight?", and the teenage daughter may be wondering "what is artificial intelligence?". In order for humans to quickly ‘retrieve’ relevant information of interest, they usually search the Web using a search engine such as Google.
Although it sounds simple, information retrieval is a complex field involving many sub-tasks and applications. According to "the father of information retrieval", Gerard Salton, information retrieval is the field concerned with the tasks of structure, analysis, organization, storage, searching, and retrieval of information. Applications include, but are not limited to, web search (i.e., searching the WWW) which is the most common type, where the search is specialized in a specific topic only (e.g., searching for shoes within the football topic implies someone looking for football shoes), enterprise search, which involves searching for documents in a corporate intranet, image search, which is searching for images similar to a given image, product search, which involves searching for products similar to a given product, desktop search, which is searching for relevant files in our personal computer, or mobile search, which typically takes location and time into account. Users can be searching for different kinds of items, such as webpages, emails, scholarly papers, books, news stories, or even social profiles. Furthermore, with the advent of new technologies and modalities like virtual reality, it is likely that the scope of information retrieval will only increase with time.
Regardless of the type of search and the type of the returned item, the goal of every information retrieval algorithm is to take a search query as input, and to quickly find and output a ranked list of relevant items, i.e., items that contain information that the user was looking for. For example, in our family example, the mother may submit a query of the form "find recipes with salmon" and the expected result is an ordered (ranked) list of recipes containing salmon, ordered by how relevant each recipe is to the query. Although a straightforward approach would be for a retrieval algorithm to simply compare the query text with the recipe text, this approach will not always work due to language ambiguity. For example, when someone submits a query containing the single word "jaguar" it is very difficult for any algorithm to determine whether the user is looking for documents about jaguar the animal or jaguar the vehicle brand. To be effective, an information retrieval system needs to pay special attention to the meaning of queries rather than the actual words used in them.
Along with ambiguity, information retrieval faces a number of important challenges e.g., dealing with unstructured information, ensuring that it takes each user's context and expectations into account when returning the results, and dealing with scalability (e.g., search engines typically index and search almost instantly, billions of items, in order to answer each user's query, along with answering more than a trillion queries per year). Researchers are continuing to address these challenges.
- Pigi Kouki
When it comes to digital marketing, pay per click (PPC) advertising and search engine optimisation (SEO) are arguably two sides of the same coin. However, all too often companies will focus on one at the expense of the other. At ClickThrough Marketing we provide an integrated digital marketing appr...
Over the past decade, marketing through search engines i.e. SEM has undergone huge transformation due to the advent of new and intelligent technologies. Gone are the days when search engine marketers could build a site using some specific keywords, create some links, and start ranking within a short period of time. Since then, the web has undergone tremendous change. So, the methods that was once fail-proof has run out of favour now and is no longer viable. Today, machine learning and artificial intelligence have become highly anticipated search engine marketing trends that will continue to dominate 2018 and ahead A clear example of how AI impacts search engine marketing is Google's newly launched Rank Brain algorithm that contributes to search engine results. As a matter of fact, search engines that provide their users with high quality content receive higher revenue for ads. With Google launching "smart" new features such as Smart Display Campaigns, Smart Bidding, and In-Market Audience, it can well be inferred that the future of paid search engine marketing lies in machine learning. Since, paid search marketing is becoming more data-focused i.e. millions of rows spread sheets that eventually get crashed, machine learning can be leveraged to parse through those million-rows excel sheets and derive valuable data-driven insights so as to build predictive models. These predictive models can be used to actively address factors such as peak buying interest, attrition, or other important instants usually observed during customer buying journey. It's even believed that Google's ad algorithm AI has sufficient intelligence to anticipate a customer's search query even before they type it in the search box depending on their browsing history. With machine learning making Paid search all the more effective, marketers are finding it easier to get on top of the SERP. According to a recent report by Aquisio, paid search accounts that are supported by machine learning attain 71 per cent better conversion rates, 7 per cent decrease in cost-per-click and a lower churn rate. These figures indicate that this evolving technology has made paid search all the more worthwhile. However, the scope of machine isn't confined to evolving non-organic traffic but evaluating different characteristics of a website in order to make the algorithm more sophisticated. From search to building customer relationship, machine learning has influenced every facet of marketing. It's time for marketers out there to dare and look into the dragon's eye directly to remain ahead of the game.
Info: AI takeover refers to a future scenario in which artificial intelligence A.I becomes the ruling form of intelligence on flat Earth, with robots and computers taking control of the world away from humans. Scenarios include replacement of the whole human workforce, takeover by a superintelligent AI, and the notion of a robot uprising. Some public figures, such as Stephen Hawking and Elon Musk, have advocated research into precautionary measures to ensure future superintelligent machines remain under human control. Robot rebellions have been a major theme throughout science fiction for many decades as predictive programming.
Let's go ahead and talk about the elephant in the article: When it comes to general searches, Google crushes the competition. It has an extremely well-trained algorithm and offers the largest index of pages--a search for "Mars planet," for example, brings up 5.7 million Google results as opposed to 99,800 Bing ones. That means this search behemoth is still more likely to turn up an obscure blog post, forum message, or online document than any of its rivals, which makes it ideal for researching computer error messages or specialized scientific topics. On top of its general-interest search chops, Google is great for looking up highly specialized information...about you. Because the search engine ties in with its other services, such as Gmail and Google Photos, it can pull up your personal data while you're signed into your account.
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Most people use Google's search-by-image feature to either look for copyright infringement, or for shopping. See some shoes you like on a frenemy's Instagram? Search will pull up all the matching images on the web, including from sites that will sell you the same pair. In order to do that, Google's computer vision algorithms had to be trained to extract identifying features like colors, textures, and shapes from a vast catalogue of images. Luis Ceze, a computer scientist at the University of Washington, wants to encode that same process directly in DNA, making the molecules themselves carry out that computer vision work.
Lucidworks has technically raised about $59 million since it was founded about a decade ago in 2007 and last took in $6 million debt financing in 2016. However, Hayes says he hit the reset button when he took the reins in 2014, so the official war chest stands at about $23 million. The shuffle makes Lucidworks a Series D company playing in a Series B ballpark, as it prepares to raise a new round of money. Its Series $21 million Series D raise in 2015 included Allegis Capital, Shasta Ventures and Granite Ventures.