It's a well known fact that humans have the ability to effeciently recognize patterns. Some people who work for Google, have highlighted the fact that backliinks, keywords, title tags and meta descritpoions are greate factors which can be utulied to sort and rank websites. However, the concept of recognizing such patterns on a massive scope is something that humans cannot easily do. Machines on the other hand, are extremly effeeint at gathering data. However, unlike humans they cannot recognize patterns as easily in terms of how certain patterns fit into the overal big picture as well as to understand what that pictures mean. MEaning that while mavhines can gather the writing that are made on the wall, they can't read the writign on the …show more content…
wall as effeceintly as humans can. As such, machnes and humans need to work cohesively so that they can conduct the complicated process of gather information and analyzising it, before it appears in the search results. This is the whole concept behing RankBrain. It's Google's machine learning articualr intelligenace that has the abiility to process a signfinanfact fraction of search results. What makes this AI so unqiue is that, not only does it gathers data, it is able to see patterns to a certain extent as well. RankBrain isn't a new algortithm al togther, but rather an important part of Google's complete algorithm which is now responsible for taking search queries, interpreting what users are specifically serachin for, and figures out how to cater to what users search for in new ways as will be disscussed. To put things in retrospsect let's take a at the search term "Matts Cuts." Just a few years sago, if an indiaul were to make insert the term "Matts Cutts" into the seearcch engine, the seac query would result in various pages which match that specifc term. However, Google's algortihm is sisgnifnantly better. As such, if you were to search that specific phrase now, not only would it features pages with that exact phrase, it will pull up nformation that matches SEO or Google spam team. Said in simpler terms, Google is now at a place where they are able to recongiize the relationship between words and the specificss of what those means. It is also able to determine what those words refeerence as well as why they were strung together to create a search term. Rankbrain essnetially deepens thisi level of understanding. Reports indicate that RankBrain utlzies articial intelligence to combine vast amounts of information into mathemrical entities that are called vectors, which the computer can readily understand.
For example if RankBrain comes across a word it's not familzired with,it essnetially makes an educated guess as to what those words/phrases might mean and filter the subseuqnte results accordingly. As such, it is able to effectively hande never before seen search queires more effeceintly. A prime example of how RankBrain operates would be to compare it to a clerk at Bed Bath and Beyond. Bed Bath and Beyond would be Google in this scenario, the clerk would be RankBrain and the customer who has a question would be the typical searcher. Let's say that the customer goes to Bed Bath and Beyond and ask for "a plastic container that can squeeze lemon juice and filter seeds." The clerk would synthesiz those string of words and respond by saying "Oh, you mean a citrus squeezer? Those are in aisle 12. Ranbrains operates simialry to this concept, it synthesiez the words and phrasses that users make and recognize the relationship between those words as to what they mean simailry to how the clerk recognize the relationship between the key words "plastic," "lemon juice" "filter seeds" to determine that the customer wanted a citrus
squeezer. Addionally RankBrain is able to learn. But not in the tradional sense of how humans learn. Instead, it is contisosuly fed large quantities of historical searches and their subsequent results. It then extrapolates this vast amount of information to make more accurate predictions about future searches. Once these predictions have been deemed as being accurate, the humans who are responsible for engineering RankBrain, release its latest version. In essence, RankBrains abiliity to learn is comprable to that of a student who takes multiple practe SATs so that they can score high on the actual thing. As it stands, RankBrain is regagrdd as being the thir-most important signal that contribures to the manifestation of a search query. As such, it would be logical to say that it very well can and does affect search engine optimcation. However, evenm though it was rolled out a few months ago, it hasn't as much as a profudn effect than that of Penguin or Pando. Meaning that, as opposed to derankling sites, it more than likely helped SEO as a whole by gathering more relevant search factors. Meaning that where a local restaurnt may not have ranked high within the local search results page before, as a result of RankBrain, it may now be in the top ranks.
Each person has their unique way of calling themselves an owner. Some are owners of a phone, but were on books today. The importance of being an owner is that we have the fall ownerships of it. However, the important question is why, do we own something we don’t use. From the essay of Mortimer Adler, “How to Mark a Book”, he explained the three way’s which someone own a book. Such as the one that just owns a book but remains unread and untouched. One who reads books left and right but refuse to leave a mark of his own. Lastly, the one who make their books apart of himself from the highlights and underlined quotes for thought, notes on the margins and all the caviar on display(1). Therefore I do agree with Mortimer Adler on How to Mark a Book.
I argue that Queequeg, who speaks but does not write (even his own name), literally embodies writing through his tattoos before it was stuck in the linearity of print and novels. His tattoos violate the linearity and legibility of the text as he himself is a “queer round figure”(Caramello 1983). Illegibility is a central concern as we see characters trying to decipher illegible images in the past chapters as well. Stubb, earlier on, watches Queequeg examine the doubloon and says, “Here comes Queequeg—all tattooed—looks like the signs of the Zodiac himself.” Then Queequeg compares the markings on his body to those on the coin after which Stubb interprets the zodiac decorating the doubloon as a message from the heavens. Ishmael later transfers this analysis on to Queequeg’s tattoos as well as to the markings on the whale. Mirror-like, the reflections flash from Queequeg to Ishmael to the coffin to the ship to the doubloon to the whale and back to the coffin. The legibility of all these characters and items are solidified when Queequeg copies his own tattoos onto the coffin and makes it an immortal replica of his own body as well as a copy of the text that he represents. Later on, Ahab says, “Can it be that in some spiritual sense the coffin is, after all, but an immortality-preserver!” (575). This immortal text can be read as a message from the heavens, the legacy of Queequeg, or the tale of the ship’s
Back in the modern days, the Internet is a whole collection of a media composed of reproductions. It is a virtual space, which has no original and lacks even a master copy. We, human as the user, offer to put the information inside the space. However web pages do not exist until they are uploaded onto the Internet by the author, and “reproduced” on our computer. Nowadays we can even create our own webpage on the cloud. To look for an original on the Internet is such a hard job since there are somehow no real material base to
We live in a world that can’t live without binary code anymore. Computers have pervaded so deep in our lives that they are now being called ubiquitous. With phenomenal increase in users, has come a phenomenal increase in data. We generate a vast amount of data through activities on our computing devices making it necessary to employ intelligent algorithms which enable the system to learn from and analyze this vast dataset. Fortunately, the advent of Distributed Computing has created avenues to access virtually limitless computing power even through mobile devices thus, allowing us to use highly complex and large scale algorithms. However, with all this power under the hood, it is important to make the computers as usable and receptive to users as possible. I believe this interdisciplinary paradigm will have far reaching impact on industries, governments as well as our daily lives which is why I am so interested in research concerning Information Management and Analytics, Artificial Intelligence, Human Computer Interaction, and Mobile and Internet Computing.
In today’s fast paced technology, search engines have become vastly popular use for people’s daily routines. A search engine is an information retrieval system that allows someone to search the...
The fact that the Internet is bristling full of information, too much information for a single human being to comprehend, is not the problem, but the real issue is in the quality of the information therein. The old lesson on Internet searching is when you enter for example, "computers," and the search engine returns 10 of an abominable 8,102,365 matches. You would exclaim, "Wow! There is a lot of information in there." Then you would ask, "How do you know what is good?" Where is the quality? Portals (who run search engines) these days are adding value to their searched information thereby returning higher quality results, often grouped by appropriate categories, thus pinpointing useful information for the learning public.
Pattern recognition is when you look for similarities among and within small, decomposed problems that help solve complex problems more efficiently. An example of this would be drawing a dog, if we wanted to draw a dog we wouldn’t have to think too long because we know all dogs have 4 legs, eyes and a tail so knowing that it would make it easier and quicker to complete many different drawings. Finding patterns in problems makes problem solving a lot easier and it gives you a place to start when fixing a new problem. Pattern recognition is a process based on 5 key
is very much in use today and is prominent in the field of Search Engine Optimization (SEO), with Google and other search-engines using it to help people look up things quicker by offering a wider range of results as well as suggesting and automatically filling up the search-bar with what it thinks you will search for. Google (like a woman) starts suggesting things before you can even finish your sentence, speeding up the searching process.
Imagine asking your computer to do something in the same way you would ask a friend to do it. Without having to memorize special commands that only it could understand. For computer scientists this has been an ambitious goal; that can further simplify computers. Artificial Intelligence, a system that can mimic human intelligence by performing task that usually only a human can do, usually has to use a form of natural language processing. Natural language processing, a sub-field of computer science and artificial intelligence, concerns the successfully interaction between a computer and a human. Currently one of the best examples of A.I.(Artificial Intelligence) is IBM 's Watson. A machine that gained popularity after appearing on the show
Search engines are not very complex in the way that they work. Each search engine sends out spiders to bots into web space going from link to link identifying all pages that it can. After the spiders get to a web page they generally index all the words on that page that are publicly available pages at the site. They then store this information into their databases and when you run a search it matches they key words you searched with the words on the page that the spider indexed. However when you are searching the web using a search engine, you are not searching the entire web as it is presently. You are looking at what the spiders indexed in the past.
Most of the day the human mind is taking in information, analyzing it, storing it accordingly, and recalling past knowledge to solve problems logically. This is similar to the life of any computer. Humans gain information through the senses. Computers gain similar information through a video camera, a microphone, a touch pad or screen, and it is even possible for computers to analyze scent and chemicals. Humans also gain information through books, other people, and even computers, all of which computers can access through software, interfacing, and modems. For the past year speech recognition software products have become mainstream(Lyons,176). All of the ways that humans gain information are mimicked by computers. Humans then proceed to analyze and store the information accordingly. This is a computer's main function in today's society. Humans then take all of this information and solve problems logically. This is where things get complex. There are expert systems that can solve complex problems that humans train their whole lives for. In 1997, IBM's Deep Blue defeated the world champion in a game of chess(Karlgaard, p43). Expert systems design buildings, configure airplanes, and diagnose breathing problems. NASA's Deep Space One probe left with software that lets the probe diagnose problems and fix itself(Lyons).
IR systems receiving such queries need to fill in the gaps of the user’s underspecified query. For example, user typing “nuclear waste dumpling” into the search engine such as Google is probably searching for multiple number of documents that describing the topic. Some of the documents might not archive what user need as the search engine search documents that relate to the three worlds only. The content being searched is typically unstruc...
When a user searches for something such as ‘design blogs’ they will then see more adverts design appear. If the user was then to go and search for something such as ‘iPod’ the search engine will remember that they have previously searched for ‘design’ and therefor it might show results that combine ‘design’ and ‘iPods’
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?
The Internet has made access to information easier. Information is stored efficiently and organized on the Internet. For example, instead of going to our local library, we can use Internet search engines. Simply by doing a search, we get thousands of results. The search engines use a ranking system to help us retrieve the most pertinent results in top order. Just a simple click and we have our information. Therefore, we can learn about anything, immediately. In a matter of moments, we can become an expert.