Chatbots could be used as weapons on the social networks such as Twitter or Facebook. An entity or individuals could design create a countless number of chatbots to harass people. They could even try to track how successful their harassment is by using machine-learning-based methods to sharpen their strategies and counteract harassment detection tools.
“Utility gets something done following a prompt. At a higher level the more entertainment-related chatbots are able to answer all questions and get things done. Siri and Cortana you can have small talk with, as well as getting things done, so they are much harder to build. They took years and years of giant company’s efforts. Different companies that don’t have those resources, like Facebook, will build more constrained utility bots.”
Today, consumers are more aware of technology than ever. While some marketers may be worried about overusing automation and chat tools because their tech-savvy audience might notice. Others are embracing the bots and using them to improve the user journey by providing a more personalized experience. Ironically, sometimes bots are the key to adding a human touch to your marketing communications.
Now, with the rise of website chatbots, this trend of two-way conversations can be taken to a whole new level. Conversational marketing can be done across many channels, such as over the phone or via SMS. However, an increasing number of companies are leveraging social media to drive their conversational marketing strategy to distinguish their brand and solidify their brand’s voice and values. When most people refer to conversational marketing, they’re talking about interactions started using chatbots and live chat – that move to personal conversations.
In a new report from Business Insider Intelligence, we explore the growing and disruptive bot landscape by investigating what bots are, how businesses are leveraging them, and where they will have the biggest impact. We outline the burgeoning bot ecosystem by segment, look at companies that offer bot-enabling technology, distribution channels, and some of the key third-party bots already on offer.
Say you want to build a bot that tells the current temperature. The dialog for the bot only needs coding to recognize and report the requested location and temperature. To do this, the bot needs to pull data from the API of the local weather service, based on the user’s location, and to send that data back to the user—basically, a few lines of templatable code and you’re done.
The sentiment analysis in machine learning uses language analytics to determine the attitude or emotional state of whom they are speaking to in any given situation. This has proven to be difficult for even the most advanced chatbot due to an inability to detect certain questions and comments from context. Developers are creating these bots to automate a wider range of processes in an increasingly human-like way and to continue to develop and learn over time.
The process of building, testing and deploying chatbots can be done on cloud-based chatbot development platforms offered by cloud Platform as a Service (PaaS) providers such as Oracle Cloud Platform Yekaliva and IBM Watson. These cloud platforms provide Natural Language Processing, Artificial Intelligence and Mobile Backend as a Service for chatbot development.
"From Russia With Love" (PDF). Retrieved 2007-12-09. Psychologist and Scientific American: Mind contributing editor Robert Epstein reports how he was initially fooled by a chatterbot posing as an attractive girl in a personal ad he answered on a dating website. In the ad, the girl portrayed herself as being in Southern California and then soon revealed, in poor English, that she was actually in Russia. He became suspicious after a couple of months of email exchanges, sent her an email test of gibberish, and she still replied in general terms. The dating website is not named. Scientific American: Mind, October–November 2007, page 16–17, "From Russia With Love: How I got fooled (and somewhat humiliated) by a computer". Also available online.
Ultimately, only time will tell how effective the likes of Facebook Messenger will become in the long term. As more and more companies look to use chatbots within the platform, the greater the frequency of messages that individual users will receive. This could result in Facebook (and other messaging platforms) placing stricter restrictions on usage, but until then I'd recommend testing as much as possible.
Since Facebook Messenger, WhatsApp, Kik, Slack, and a growing number of bot-creation platforms came online, developers have been churning out chatbots across industries, with Facebook’s most recent bot count at over 33,000. At a CRM technologies conference in 2011, Gartner predicted that 85 percent of customer engagement would be fielded without human intervention. Though a seeming natural fit for retail and purchasing-related decisions, it doesn’t appear that chatbot technology will play favorites in the coming few years, with uses cases being promoted in finance, human resources, and even legal services.
Smart chatbots rely on artificial intelligence when they communicate with users. Instead of pre-prepared answers, the robot responds with adequate suggestions on the topic. In addition, all the words said by the customers are recorded for later processing. However, the Forrester report “The State of Chatbots” points out that artificial intelligence is not a magic and is not yet ready to produce marvelous experiences for users on its own. On the contrary, it requires a huge work:
In 1950, Alan Turing's famous article "Computing Machinery and Intelligence" was published, which proposed what is now called the Turing test as a criterion of intelligence. This criterion depends on the ability of a computer program to impersonate a human in a real-time written conversation with a human judge, sufficiently well that the judge is unable to distinguish reliably—on the basis of the conversational content alone—between the program and a real human. The notoriety of Turing's proposed test stimulated great interest in Joseph Weizenbaum's program ELIZA, published in 1966, which seemed to be able to fool users into believing that they were conversing with a real human. However Weizenbaum himself did not claim that ELIZA was genuinely intelligent, and the introduction to his paper presented it more as a debunking exercise: