Algorithm Essay

This paper will give a brief definition of the term Artificial Intelligence (AI). It will take an in-depth look at the origins and purpose of this exciting field in computer science. In particular, this paper will discuss a few of the many subcategories of research, applications and current technological obstacles that scientist face when developing AI. In addition, the author will look at AI’s various military specific applications for the purpose of training, target acquisition and command and control capabilities.

The computer program RIP uses statistical probability to analyze the data from a certain patient in order to create a prognosis on that patient’s chance of survival. This program is designed to help doctors from around the globe make informed decisions about whether or not to administer treatments. It shows the likelihood of a patient’s survival and it speeds up the process, giving doctors all of the information needed on whether or not to put their effort towards a certain patient. This program doesn’t only give doctors the likelihood of a patient’s survival but it’s also able to print out recommended treatment plans for said patients. All of this analyzing happens within milliseconds saving doctors valuable time, time that could mean life or death for a patient.

a network is represented by the vector v, which has a length of n. The concentrations

Have you ever looked at some Python code that included *args and **kwargs and wondered what in the world are these? Python is supposed to be immediately readable to someone either new to programming or someone coming from another programming language. What gives?

Therefore consider the Actor -- Leading Role, Actress -- Leading Role, Best Picture and Directing in the years of 1973 and 1987.

mine the most relevant results in the index. Although the precise workings of these algorithms are kept at least as secret as Coca-Cola’s formula they are usually based on two main

This index can only determine whether a word exists within a particular document, since it stores no information regarding the frequency and position of the word; it is therefore considered to be a Boolean index. Such an index determines which documents match a query but does not rank matched documents. In some designs the index includes additional information such as the frequency of each word in each document or the positions of a word in each document [1]. Position information enables the search algorithm to identify word proximity to support searching for phrases; frequency can be used to help in ranking the relevance of documents to the query. Such topics are the central research focus of information retrieval.

Free trade kills jobs. You hear it everywhere, but is this statement true? It’s far from that. The real job killer is automation. Just as the automobiles made horses obsolete, newer and better technology will do the same to humans. But some argue this fact, saying “we aren’t horses”. They believe humans to be special snowflakes whose complexity machines can never match. Or they say it will only be low paying jobs that machines take over. They are wrong. Whether you have the intelligence that’s on par with Einstein or artistic ability of Da Vinci, you can be replaced. Nobody is irreplaceable. Sooner or later every job in the world is going be automated. But why are robots a better alternative to humans? One of the

ne. Most of the time, explicit feedback corresponds to a preferential vote assigned to a subset of the retr ieved results. This technique, allows the system to build a r ich representation of user needs. Deliver ing relevant resources based on previous ratings by users with similar preferences is a for m of personalized recommendation that can also be applied to web search, following a collaborative approach. Another idea to help users dur ing their search is to group the quer y results into several clus- ters, each one containing all the pages related to a topic. The clusters are matched against a quer y, and, the best results are retur ned. This kind of approach is called adaptive result cluster ing. Some search engines include

The K-means algorithm is an unsupervised clustering algorithm which partitions a set of data, usually termed dataset into a certain number of clusters. Minimization of a performance index is the primary basis of K-means Algorithm, which is defined as the sum of the squared distances from all points in a cluster domain in the cluster center. Initially K random cluster centers are chosen. Then, each sample in the sample space is assigned to a cluster based on the minimum distance to the center of the cluster. Finally the cluster centers are updated to the average of the values in each cluster. This is repeated until cluster centers no longer change. Steps in the K-means algorithm are [K.M. Murugesan and S.

This technique is based on the popular k-means clustering algorithm. The clustering algorithm can work on data with many dimensions and target to reduce the distance within clusters. In the same time, it increases the distance between clusters. Initiating with K centers, the method iteratively assign each point to its nearest center dependent on

Content-based: In this method, the system learns to recommend items that are similar to the ones that the user liked in the past. The similarity of items is calculated based on the features associated with the compared items. For example, if a user has highly rated a movie that belongs to the comedy genre, then the system can learn to recommend other movies from this genre. Some of the advantages of content-based systems include their ability to exploit just the ratings provided by the active user to build his own profile. On the other hand, collaborative filtering methods need ratings from different users. Explanations on how the recommender system works can be provided by explicitly listing content features that caused an item to occur in the list of recommendations. Also, content-based recommenders are capable of recommending items not yet rated by any user. Thus, they do not suffer from the first-rater problem, which affects collaborative recommenders. One of the disadvantages of the content-based systems are that they need domain knowledge to generate recommendations.

The K-Means clustering method is a technique of clustering which is commonly used. This algorithm is one of the most widespread

The data is sorted using the average of the Google and Yelp rankings for a location. In addition, if the Naïve Bayes algorithm categorizes a location as interesting to the user, an additional two points plus the calculated Naïve Bayes probability (a number between 0 and 1) that the place is positive are added to the average Internet user rating. Similarly, if the Naïve Bayes algorithm categorizes the location as something the user would not want to visit, two points and the Naïve Bayes probability are subtracted from the rating. The data is then

WordNet In (Bouras and Tsogkas, 2012), the importance of WordNet hypernymy relationships is highlighted in enhancing K-means clustering algorithm. Similar to the procedure prior to clustering process, an aggregate hypernym graph is generated to label a resulting cluster. The effect of other relationships, on the clustering performance, is not studied. Another Word-Net-based clustering method is presented in (Fodeh et al., 2011), where the role of nouns, especially polysemous and synonymous nouns in document clustering is investigated. A subset of core semantic features is chosen from disambiguated nouns through an unsupervised information gain measure. These core semantic features lead to admissible clustering results. The effect of