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Category: Chat
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How to answer investor question about accuracy of a chatbot?
I was once asked by a investor in a pitching competition how accurate is my chatbot.
Since my chatbot related to medical question Q&A, I’m thinking to include a feedback from user whether the bot answered their question correctly and count percentage of accuracy by total accurate answers divide total answers
What do you guys think?
submitted by /u/wuboh
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AI-powered Customer Service Solutions Are Driving Better experience in the face of COVID-19
Amid the ongoing Coronavirus pandemic and the demand for digital services reaching an all-time high, AI-powered Conversational Solutions…
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10 wrong ways to use conversational AI for chatbots
Artificial intelligence algorithms are widely used in e-commerce. For example, when creating chatbots. Technologies provide automated, prompt, and high-quality solutions to customer problems. Accordingly, they help to increase sales.
However, this is only relevant if chatbots are used correctly. Some mistakes completely negate the possibilities of technology. Today we will tell you about them. Find out 10 common mistakes companies make and how to fix them.
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10 wrong ways to use conversational AI for chatbots
Inappropriate conversations
An improperly programmed chatbot is capable of maintaining a conversation that is best terminated. For example, of an offensive nature. This happens when a chatbot is programmed to give standard “yes/no” answers without understanding the question. The implications of setting up to blindly repeat the user’s request are similar. Avoid this.
Internet trolls often entertain themselves by asking inappropriate, incorrect, or stupid questions. The chatbot must be able to identify this. And in such a way that the conversation ends. Programming to repeat the question, or send yes / no answers if the request is not understood, does not improve the quality of service. It damages the brand.
Misunderstanding the basics
Chatbots require prior programming by learning the appropriate words and expressions. With insufficient knowledge of the latter, a misunderstanding of the issues is possible. For example, Messenger had a Poncho chatbot that tells users the weather based on their location. He did the task well. However, the word “weekend” confused the program.
Chatbots answer user questions based on specific previously learned prompts. These are the given statements and words. Also, semantics can be used by spoken artificial intelligence agents. It allows you to understand the context without learning a specific formulation.
To eliminate misunderstanding, teach the chatbot the maximum number of words, situations, and expressions. Use crowdsources to collect additional data. Track engagement and continue regular training for the program.
Nonadaptation to the channel
Long messages containing more than 10 sentences are relevant exclusively on the site. The long answer is good. However, there is a high probability that the user will turn over the key information contained somewhere among the lines.
It’s easier for people to process small messages. Therefore, instead of several large paragraphs of text, limit the chatbot’s responses to a couple of sentences, or even one.
Lack of context awareness
Human communication involves many influencing variables. The meaning of a particular word changes depending on the situation, context, and subject of communication. This also needs to be taught to the chatbot. Otherwise, the program can forget everything previously written by the client and lose the thread of dialogue.
You can fix this by mapping out detailed conversation trees. Just take the time to do this. Managing expectations can also improve the quality of customer service by informing users about the limitations of chatbots.
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Lack of investment in a supporting ecosystem
Artificial intelligence is at the heart of any communication platform. However, this does not exclude the importance of the ecosystem. For artificial intelligence, the quality of the data used in training/adaptation is critical. The infrastructure used to build is also important.
Therefore, machine learning programs must be dealt with. It is part of the supporting ecosystem. By investing in them, the company is accelerating the training of artificial intelligence.
Lack of effort to improve chatbots’ ability to communicate
The secret of the intelligence of chatbots is the use of artificial intelligence and machine learning technologies. However, the foundation is laid by man. Chatbots should be taught basic things, expressions, and words. Only then do the programs gain the ability to recognize and understand users’ questions in the process of communication.
The difficulty is that people express thoughts in different ways. It is necessary to systematically train chatbots using the conversation history. The more examples the program learns, the faster and more accurately it will be able to determine the client’s intent.
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No effort to improve the chatbot’s conversation skill
Simulating human conversation is not an easy task. However, it is possible to help artificial intelligence do it. To do this, you need to provide basic knowledge.
Provide the chatbot with answers to all possible customer questions. For example, after analyzing the previously set. Try to expand the list of possible answers by providing examples. Remember that a chatbot must communicate not only efficiently, but also competently.
Grammar errors in messages can spoil the support experience. To prevent this from happening, use services like Ivory Research. This guarantees literacy, readability, and consistency of answers.
Also, introduce the AI to customer intent. This will help the program understand exactly where it needs help. Consequently, the chatbot will be able to determine how this can be done.
Inappropriate use of conversational artificial intelligence
Technology opens up many possibilities. However, there are situations where the use of artificial intelligence is impractical. For example, if the client has a clear understanding of what he needs. Then the technology only complicates the execution of the action.
Conversational AI is great for situations where the task is time-consuming. And so much so that the use of a tone menu or graphical interface will make it unnecessarily cumbersome.
Remember one rule: if a task cannot be completed using a fixed set of inputs, then conversational AI is needed. Most often these are nonlinear processes.
Making chatbots more complicated
Remember that chatbots do not replace people. They are designed to be supportive. Chatbots work great when you need to reduce the time and labor costs of performing repetitive tasks.
However, too many functions confuse them. Optimally, there should be 3–4 of them. Highlight the key tasks of chatbots and then they will perfectly cope with their duties.
Focusing chatbots solely on data collection
It’s a bad idea to build chatbots to collect data from users. They are tailored for other tasks. Chatbots are capable of providing automated, instant, and professional user support. Therefore, the key task is to improve the quality of communication.
The connecting function of chatbots in business communication with customers is the ability to understand questions and provide accurate answers. This creates sustainable values. Only after getting a positive impression of the company can the data from users be used to achieve better results.
Conclusion
For a client, interaction with a chatbot differs little from communication with a manager. It can also create a great user experience or ruin the experience of a company. Therefore, it is important to improve the work of chatbots. Avoid the above mistakes, help AI train, and provide excellent customer service.
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10 wrong ways to use conversational AI for chatbots was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.
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Remote Working – How to Manage Virtual Teams
“One day, offices will be a thing of the past”,
Richard Branson, Entrepreneur and Founder of Virgin Airlines
Today’s offices go beyond the boundaries of physical offices. We are on the fourth industrial revolution and the business world is constantly being disrupted by digital transformation. For businesses to thrive in this ever-changing environment, many are currently setting up or have set up virtual teams for different aspects of their businesses.
What are virtual teams? Virtual teams consist of working professionals who operate remotely from home or other locations full- or part-time.
With all the benefits going around about the strengths of remote working, the challenges associated with virtual teams include
- How do we build an effective virtual team?
- How should we manage teams that are not physically in the same timezone or geographic location?
- How do we make sure that teams are independently accountable for their tasks? How do we maintain work standards while remote working?
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Chatbots and Policy manuals for employees
Greetings. First, let me apologize for what may be simple questions, but everything can be answered on Reddit…. right?
I work in a heavily regulated industry and as a result, we have large policy and procedure manuals for our people. The challenge here is navigating them and making them user friendly for the end users.
Currently our manuals are written in HTML5. We have the ability to view them in either a PDF manual or online. The subsections of the manual are revised as often as monthly if needed.
What we would like is something like what you would get with TurboTax.
Ideally it would allow you to ask questions, and it would come back with potential answers. So if we were transportation for example, you might have a question about damage to the structure. The bot would come back with choices from the regulation governing damage, allowable limits reference, engineering group responsible for repair development, reporting procedures, how to measure damage, training topics, etc.
Again, we capture this in HTML5, but how do we make this easy to find for the end user.
Appreciate your reading this.
submitted by /u/6thlott
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