From the Cyber Front

This entry is part 2 of 2 in the series DarkBot

Let me break down the L33t (http://en.wikipedia.org/wiki/Leet) for you for you and take the time to officially thank you. Especially, I would like to thank all of the n00bs out there that make my job easy. You know who you are; all those n00bs using your computers without taking the time or understand how to protect your systems from me. You think that it’s not such a big deal to pay your bills, do your banking and send email with all that personal information without taking proper precautions, “Please, keep on doing that”. You are keeping me in hot pockets and all the Red Bull I can drink. read more...

The Dark Underside of the Internet

This entry is part 1 of 2 in the series DarkBot

In this series of articles, you will be exposed to the dark side of the Internet. Through interviews, independent research and real-world examples, you will experience the dangers of the World Wide Web.Travel with me down the “rabbit hole” as we explore the dark side of the Internet. Let me set the stage. Before sharing stories about who is trying to steal your information and how they do it, I need to introduce you to our company of virtual world villains. read more...

Website Disclaimer and Intellectual Property Ownership Statement

This website is owned and operated by Robert C. Whetsel (Owner). The Terms of use (‘Privacy | Copyright | Legal Notice | End User’s License Agreement’) governs your use of this website and it’s content. By using the website, you automatically accept the Terms associated with this site. The Owner may change the Terms at any time and such changes will take immediate effect. By your continued use of the website thereafter, you agree to be bound by such changes. You should visit the Terms of Use page from time to time to review the then-current terms. read more...

Improving Chatbot Accuracy -Draft

This entry is part 5 of 13 in the series The State of Chatbots

Researchers have employed a variety of methods to improve their chatbot’s accuracy. Most current chatbots use a dialog management module and knowledge base and rules, which they use with templates to match user input. Improving chatbot accuracy may be accomplished through expanding the chatbot knowledge base, improving upon the standard rule-based conversation method for chatbots, or using alternative methods to the standard chatbot rule-based conversation method. read more...

Improving Human-Like Qualities of Chatbots -Draft

This entry is part 4 of 13 in the series The State of Chatbots

Some researchers are designing chatbots with the intent of integrating more natural human-like interactions (De Angeli & Brahnam, 2008). Specifically, the developer may program certain responses that would sacrifice accuracy, but confer more human-like traits (Sing et al., 2006). For example, if a chatbot is presented with a math problem it could wait, as if to imitate thinking about the problem, or even give the wrong answer. This could be interpreted by the user as a human trait (failing or making a mistake), but in reality, it is a trick built into the programming. Researchers have attempted to add a sense of humor, through the use of self-contained jokes to their version of the chatbot ALICE (De Boni, Richardson, & Hurling, 2008). One could envision a scenario where a user would ask a question and the chatbot would reply: Jim, I’m not a machine, Im a doctor!”, I’m on my break, come back later.” or Are you sure you want me to check for this, I know its a waste of time”, which gives a jocular feel to the conversation. read more...

Building Better Chatbots -Draft

This entry is part 3 of 13 in the series The State of Chatbots

The major goal of building a better chatbot is to improve its interactions with humans. A wide variety of techniques including developing different architectures, incorporating quirks or tricks, or using different machine learning approaches have been used to improve either chatbot accuracy or human-like qualities. First, some ways that researchers are trying to improve human-like qualities of chatbots will be discussed, followed by some methods that researchers are trying to improve the accuracy of chatbots. read more...

How do Humans View Chatbots? -Draft

This entry is part 2 of 13 in the series The State of Chatbots

Since chatbots have moved into the public realm, there has been interest in evaluating how users interact with chatbots. For example, after analyzing conversations with the chatbot jabberwacky, one study found that the topics and style of conversations were broad (De Angeli & Brahnam, 2008). Users displayed different attitudes ranging from nice to nasty and derogatory. It was often found that users switched style and personalities during conversation with the chatbot. In one experiment, users were found to continue to abuse chatbots longer than they would abuse another human. Some of the reactions did not appear driven by specific reasons. read more...

Rule-Based Conversation -Draft

This entry is part 1 of 13 in the series The State of Chatbots

The majority of current chatbots use a rule-based approach to interact with the user. In order to imitate human conversations, developers use a linguistic model combined with computational algorithms to build chatbots. Depending on the chatbot, rules can be based on simple textual pattern matching or complicated rules based on inference mechanisms (De Angeli & Brahnam, 2008). read more...

Natural Language Processing -Draft

This entry is part 8 of 13 in the series The State of Chatbots

Chatbots need to effectively process natural language. Since starting around the 1960′s, natural language processing (NLP) research has had slow progress until the 1990′s (Lester, Branting, & Mott, 2004). NLP research has been greatly enhanced by development of large corpora of tagged text, as well as by development of better statistical machine learning and other empirical techniques of extracting information from these corpora (Lester et al., 2004). Accordingly, chatbots are now able to more effectively process natural language.

The following are three general steps of chatbot Natural Language Processing according to (Lester et al., 2004):
read more...