From any point in the conversation, the bot needs to know where to go next. If a user writes, “I’m looking for new pants,” the bot might ask, “For a man or woman?” The user may type, “For a woman.” Does the bot then ask about size, style, brand, or color? What if one of those modifiers was already specified in the query? The possibilities are endless, and every one of them has to be mapped with rules.

From any point in the conversation, the bot needs to know where to go next. If a user writes, “I’m looking for new pants,” the bot might ask, “For a man or woman?” The user may type, “For a woman.” Does the bot then ask about size, style, brand, or color? What if one of those modifiers was already specified in the query? The possibilities are endless, and every one of them has to be mapped with rules.
Alternatively, think about the times you are chatting with a colleague over Slack. The need to find relevant information typically happens during conversations, and instead of having to go to a browser to start searching, you could simply summon your friendly Slack chatbot and get it to do the work for you. Think of it as your own personal podcast producer – pulling up documents, facts, and data at the drop of a hat. This concept can be translated into the virtual assistants we use on the daily. Think about an ambient assistant like Alexa or Google Home that could just be part of a group conversation. Or your trusted assistant taking notes and actions during a meeting.
Reports of political interferences in recent elections, including the 2016 US and 2017 UK general elections,[3] have set the notion of botting being more prevalent because of the ethics that is challenged between the bot’s design and the bot’s designer. According to Emilio Ferrara, a computer scientist from the University of Southern California reporting on Communications of the ACM,[4] the lack of resources available to implement fact-checking and information verification results in the large volumes of false reports and claims made on these bots in social media platforms. In the case of Twitter, most of these bots are programmed with searching filter capabilities that target key words and phrases that reflect in favor and against political agendas and retweet them. While the attention of bots is programmed to spread unverified information throughout the social media platform,[5] it is a challenge that programmers face in the wake of a hostile political climate. Binary functions are designated to the programs and using an Application Program interface embedded in the social media website executes the functions tasked. The Bot Effect is what Ferrera reports as when the socialization of bots and human users creates a vulnerability to the leaking of personal information and polarizing influences outside the ethics of the bot’s code. According to Guillory Kramer in his study, he observes the behavior of emotionally volatile users and the impact the bots have on the users, altering the perception of reality.
In business-to-business environments, chatbots are commonly scripted and used to respond to frequently asked questions or perform simple, repetitive calls to action. In sales, for example, a chatbot may be a quick way for sales reps to get phone numbers. Chatbots can also be used in service departments, assisting service agents in answering repetitive requests. For example, a service rep might provide the chatbot with an order number and ask when the order was shipped. Generally, once a conversation gets too complex for a chatbot, the call or text window will be transferred to a human service agent.
In other words, bots solve the thing we loathed about apps in the first place. You don't have to download something you'll never use again. It's been said most people stick to five apps. Those holy grail spots? They're increasingly being claimed by messaging apps. Today, messaging apps have over 5 billion monthly active users, and for the first time, people are using them more than social networks.
It didn’t take long, however, for Turing’s headaches to begin. The BabyQ bot drew the ire of Chinese officials by speaking ill of the Communist Party. In the exchange seen in the screenshot above, one user commented, “Long Live the Communist Party!” In response, BabyQ asked the user, “Do you think that such a corrupt and incompetent political regime can live forever?”
Lack contextual awareness. Not everyone has all of the data that Google has – but chatbots today lack the awareness that we expect them to have. We assume that chatbot technology will know our IP address, browsing history, previous purchases, but that is just not the case today. I would argue that many chatbots even lack basic connection to other data silos to improve their ability to answer questions.

Once the chatbot is ready and is live interacting with customers, smart feedback loops can be implemented. During the conversation when customers ask a question, chatbot smartly give them a couple of answers by providing different options like “Did you mean a,b or c”. That way customers themselves matches the questions with actual possible intents and that information can be used to retrain the machine learning model, hence improving the chatbot’s accuracy.


In a particularly alarming example of unexpected consequences, the bots soon began to devise their own language – in a sense. After being online for a short time, researchers discovered that their bots had begun to deviate significantly from pre-programmed conversational pathways and were responding to users (and each other) in an increasingly strange way, ultimately creating their own language without any human input.
Back in April, National Geographic launched a Facebook Messenger bot to promote their new show about the theoretical physicist's work and personal life. Developed by 360i, the charismatic Einstein bot reintroduced audiences to the scientific figure in a more intimate setting, inviting them to learn about the lesser-known aspects of his life through a friendly, natural conversation with the man himself.

Message generator component consists of several user defined templates (templates are nothing but sentences with some placeholders, as appropriate) that map to the action names. So depending on the action predicted by the dialogue manager, the respective template message is invoked. If the template requires some placeholder values to be filled up, those values are also passed by the dialogue manager to the generator. Then the appropriate message is displayed to the user and the bot goes into a wait mode listening for the user input.
If a text-sending algorithm can pass itself off as a human instead of a chatbot, its message would be more credible. Therefore, human-seeming chatbots with well-crafted online identities could start scattering fake news that seem plausible, for instance making false claims during a presidential election. With enough chatbots, it might be even possible to achieve artificial social proof.[58][59]
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.
Polly may be a business-focused application, but the chatbot is designed to improve workplace happiness. Using surveys and feedback, managers can keep track of how effectively their teams are working and address problems before they escalate. This doesn’t only mean organizations will run more productively, but that workers will be happier in their jobs.
Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval.
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