Month: October 2021

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A Chatbot Software… And An Online Money Magnet

Conversiobot is a chatbot software that can let you earn good money.

Do you have an online business?… Or maybe, you wish to start a one?… If it’s the case, this blog post is for you.

Conversiobot. It’s an AI-based chatbot software that can make your life easier and happier. It’s not typical software, it’s a money-making machine (Keep reading to know more about this).

And even a newbie can get started right away. No special skills or knowledge are required… You don’t need to know a thing about coding to start creating chatbots.

So let’s dive into the important part: Who is it for? And how can you benefit from it?

Who is Conversiobot for?… How can anyone benefit from it?

1. Bloggers

Are you struggling to get your visitors to engage with your content?… What if you might take advantage of an Artificial Intelligence Technology to engage with your Visitors instantly?

Conversiobot is something that makes your content incredibly interactive. And helps to automatically convert your visitors into Leads & Sales.

You can start with ConversioBot right now, by copying-pasting a line of “Automated Bot Code” to your Blog.

>> Watch this short video to find out how <<

Pause!…

Do you want to learn about some strategies to Generate FREE Traffic to your Blog or Website?… If YES, grab your FREE copy of the “5 Ways to Generate Free Traffic” eBook, here.

2. eCommerce website owners

So many eComm Marketers struggle to convert their Visitors into Leads & Sales. If it’s the case for you, Conversiobot is what you need.

It’s the best solution to engage with your visitors instantly and convert them into Leads & Sales. And you will avoid spending THOUSANDS of dollars on Live Chat Agents. And without hiring expensive coders.

In fact, all you need to do to activate this “AI” Website Tech is to copy & paste a single line of “Website Code”.

>> You can access Conversiobot here <<

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

3. People who want to build an Email list

Building an email list from your Website is getting harder with each passing year. Your Website visitors have probably seen squeeze pages and opt-in forms a million times across the Web. And here is where Conversiobot comes in.

It’s incredible Software for list-building. ConversioBot is an automated Chatbot that engages with your visitors in a really fun and interactive way. It uses sophisticated “Artificial Intelligence” to persuade them to opt-in to your list.

ConversioBot comes with a range of “DONE-FOR-YOU” Chatbots designed to build your list. It’s also fully integrated with major Email marketing services like Aweber, Getresponse, Mailchimp, and Sendlane.

>> You can access Conversiobot here <<

4. Freelancers

Conversiobot is useful for freelancers too. If you’re a freelancer and you struggle to convert your Website Visitors into Clients, this amazing software is all you need.

You could use a Live Chat app on your Website and hire Chat Agents. But only if you’ve got deep pockets and you’re happy to fork out THOUSANDS of dollars for the quality you need. But what if you could save your money? What if you could engage with your visitors instantly, and automatically convert them into Clients? All you need to do to activate this game-changer software is to copy & paste a single line of “Website Code”.

>> take action and access Conversiobot here <<

5. Affiliate marketers

Are you struggling to generate Affiliate Sales? Don’t have a Website? Maybe you do your Affiliate Marketing entirely through Social Media or Videos?

>> Find out how to explode your followers or Subscribers <<

6. Anyone who wants to make money online

How can you make money from Chatbots? Well, most businesses can’t afford to develop their own Chatbots. It’s highly sophisticated technology and it can cost THOUSANDS of dollars to Develop. So they’re looking for a ready-made solution.

ConversioBot is the Internet’s #1 Chatbot for Business Website Owners. It comes with a FULL Commercial License allowing you to sell Bots to hungry businesses. Their highly experienced team will show you exactly where there’s HOT demand for Chatbots.

You can sell their Done-For-You Bots directly to businesses. Think about how many businesses out there have a Website and DON’T have a Chatbot. It’s very common to sell them for $200 to $500 each! Sometimes even more!

They’ll also give you “pitch templates” you can copy and paste. These will do the selling for you. They pitch the benefits of Chatbots as an idea to the business you’re selling them to. So you DON’T need any special skills or experience to build Bots, and you don’t need any selling skills.

>> Watch this short presentation to find out more <<

Plans, Pricing & Money-back Guarantee

Conversiobot offers two plans: Conversiobot LITE and Conversiobot PRO. The PRO version includes some additional features, compared to the LITE one. Some of these PRO features are Unlimited Chatbots, Multi-Site License (which means you can install chatbots on unlimited websites), and FULL Commercial License (You can sell the bots).

The LITE version is for 27$, while the PRO version is for 37$. And it’s a ONE-TIME payment!

You get a full 30 days to put this to the test and make sure this is for you. If for any reason you’re not 100% satisfied, you’ll get a full refund.

If you enjoyed reading this story, then you might find the related below stories worth your time.

• How To Start An Online Business — An Option Explained
• Chatbots to Increase your Conversions and Sales

Disclosure: I will earn an affiliate commission for purchases made through links in this post, with no additional cost to you. Thank you!

Don’t forget to give us your 👏 !

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More Than A Chatbot — A Money-making Machine was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Today we’ll be talking about how to make an AI-powered chatbot using Rasa and Python. It doesn’t matter if you have deep knowledge of python or are just a beginner in the world of coding!

Software to be installed:

This article mainly focuses on the AI framework, Rasa, and a little bit of python. Before getting started, let me tell you the required software to be installed for the project.

1. Visual Studio 2019 C++ Build Tools
2. Anaconda (Conda Package)

I am assuming that you already have Python 3.8 installed in your PC since Python 3.9 version doesn’t work with rasa, it has some issue so I’ll suggest you download version 3.8 if you don’t have it. Here’s the link: https://www.python.org/downloads/. Thank me later 😛

You can download the following two softwares from the link provided below (if you don’t already have them on your PC, or you can continue with the article if you do).

Anaconda installation: https://docs.anaconda.com/anaconda/install/windows/

Visual Studio C++ Build Tools: https://visualstudio.microsoft.com/downloads/

A small piece of advice: While downloading Visual Studio C++ Build Tools, go to the download menu and then scroll to the All Downloads section.

From there you’ll find the Tools for Visual Studio 2019, click on the tab

And there it is! You have to download the last one, Build Tools for Visual Studio 2019. Easy-peasy.

If you are still facing some issue downloading the required tools you can follow up on this video to get it done right:

Rasa Installation on Windows | setup RASA on Windows with Demo | RASA — 2: https://www.youtube.com/watch?v=qmMaGicSFCU

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Getting started with the chatbot:

Now that you have installed the required software, you are ready to go!

Create a folder on your desktop named “chatbot”, since we are making a time-zone bot. Although you can name it whatever you want but I’ll go with a simple name.

After opening the folder, click on chatbot, it will show C:Users<your username>OneDriveDesktopchatbot, type cmd here and press Enter as shown below.

Now that your command prompt is open, type in the following code to install rasa framework:

pip install rasa

and press Enter, it’ll take a while to install rasa on your PC. After the framework is installed, type in

rasa init

if the above code shows error saying ‘rasa’ is not recognized as an internal or external command, operable program or batch file. Type this

py -m rasa init

Now that the framework is up and running, the Command Prompt will ask you to specify a directory, just tap Enter since you have directly opened the Command Prompt using the directory on which you want to build your chatbot, so you don’t need any specification. It will be created as a default at the same location. Let’s check if you are going by the steps uptil now.

Just like in the above gif, type y for yes, when the bot says Do you want to train an initial model?

It will automatically start training the initial model. Now you just have to wait for the bot to train itself. After sometime, the bot will be loaded and display a green message reading: Bot loaded. Type a message and press enter (use ‘/stop’ to exit):

From here on you can talk to the bot, below is a gif showing a conversation with the bot, you can stop the conversation by typing /stop. It will abort the chat immediately.

You can type a “hi” and “I’m good” to check if the mood bot is working fine or not. The bot you are talking to right now is called a mood bot, it greets and checks your mood, if the reply is good and positive it says “bye” (pretty blunt, huh) and if it’s sad, it shares a picture and says “here’s an image to lighten your mood” or something like that and shares a url of an image.

Well, not getting into the intricacies of how a mood bot should understand one’s emotions, all I want to tell you here is that now you have trained your basic rasa mood bot. From here on you have to go to your file again, if you open your chatbot again you’ll find files names “nlu”, “stories”, “domain” etc stored. Open them in your Visual Studio Code. From here on you can make changes to these files as per your requirement and run the bot again using the code

py -m rasa train

After every change you make in your file’s code, try running this code, and with time you’ll understand that the bot is able to answer different questions as per your code adjustment. Make sure to always update your domain file as and when you update your other files. It is like a mother of all the other files.

What about the code, pal? Yeah the code. I’m sharing that too. Don’t worry. I’m not leaving you all astray.

Here’s the link to all the source codes used. This is my GitHub repository, from here on you can easily download all the codes and paste them in your Visual Studio Code as per the requirement. Since, this is a basic chatbot creation code, I suggest you try adding some more features and make your own unique bots!

GitHub Repository for Chatbot Source Code:

GitHub – arya312/Basic-Time-Zone-Predicting-Chatbot-Using-Rasa-And-Python: This is a basic chatbot built using Rasa and Python that predicts the time zone of specific places. It uses very basic python code and AI framework for the same.

I’m sharing my LinkedIn profile and GitHub profile. Hope we can connect there too!

Link to my LinkedIn Profile:

Arya Pandey – Student Fellow – GirlUp DTU | LinkedIn

Link to my GitHub Repository:

arya312 – Overview

Don’t forget to give us your 👏 !

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How to build a chatbot using Rasa and Python 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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With the emergence of chatbots, many workplace roles have changed and in many cases new ones have been born. As sad as it is, but a conversational AI tester is not one of them (yet). Of course there can be several reasons behind this:

• Lack of resources (human, financial)
• Lack of expertise (knowledge in Test Automation)
• The general belief that Testing only comes in the picture after deployment (wrong)
• Management does not recognize the importance of QA

Therefore, Testing remains still in the background, as the “necessary evil”, which consumes resources and money.

It almost does not matter what technical or role background we had, Chatbot Testing will force us to leave our comfort zone, build up extra knowledge and to learn new expressions and methods.

We, at Botium, have decided to make things easier for companies that have already recognized the huge benefit of Testing Conversational AI, but have not yet had the opportunity to appoint a person for this task, or to gather the required expertise.

Although we believe that using Botium is already as easy as possible, (as there is no need to use Selenium or Appium), we have tried to facilitate the initial steps with maximum automation. Our new Go, Botium, Go feature gives you a huge advantage until you really have the necessary information to successfully set up your first test set.

A Quickstart, true to its name

The new Quickstart will not only give you simplicity and speed to get you started, but it will also help you to see what Test Types are possible for your chosen Chatbot Technology. Not all Chatbot engines forward the necessary information to run certain tests. For some, NLP testing is not possible as the Chatbot does not disclose the required information to run these tests, whereas in other cases E2E Testing is disabled, because the Botium connection to the user interface (web widget, mobile app or voice-controlled application) is done with dedicated Botium E2E Connectors.

Once connected to your Chatbot, Botium Box will automatically assign which Test Types are possible, tailored to the selected technology. The first steps remained largely the same. To kick things off you must select the Chatbot technology in the usual way and check the connectivity.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

The biggest difference follows in the third step, where Botium Box automatically offers the Test Types that can be performed.

NLP and Regression Testing will be checked and run by default, unless you intentionally skip it. These Test Types are the starting points for testing Conversational AI. When a Chatbot performs well in those disciplines, you do the next step and add Performance Testing, End-to-End Testing, Security Testing and Monitoring.

The executable Test Types are shown in color, and those that are not supported by the selected Chatbot Technology are shown in gray. For certain Test Types that require some adjustment, Botium Box initially works with predefined values. These can of course be changed at any time with one click. As an example, we can see Performance Testing here, which can be changed to the desired values using the sliders.

Live Monitoring also works similarly. In case you enable this feature, you must enter the corresponding person’s or team’s email address(es) (who will be notified in case of complications) and the frequency of the live Monitoring to be executed.

Once you’re done with the settings, you get an overview of everything that Botium will execute for you. In case you are not happy with the results, you can easily go back to the previous page and change the settings accordingly.

And now, it’s time to make Botium work for you! Press the Go, Botium, Go button, take a sip in your coffee and enjoy the instant results and gratification. The running tests are indicated with a spinning circle and the successfully executed tests are marked with a green checkmark.

By going on the question marks with your cursor, you get a cool tooltip to the executed test to have some deeper insights. In case you come across some unknown expressions, our Wiki is always there to give you a helping hand!

Congratulations, in 2 mins you’ve set up your first Test Suite and run it successfully!

Conclusion

Our latest feature, Go, Botium, Go simplifies the initial steps of Chatbot Test Automation and allows everyone to get some first insights about their Chatbot efficiency. In 4 easy steps everyone will be capable of running tests and interpreting the results.

Please note that this can not replace continuous testing in an organized form, but it offers a huge advantage in terms of simplifying the start of your Chatbot Test Automation journey. The game changer is no longer the ability, but the willingness!

Don’t forget to give us your 👏 !

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How to set up the fundamentals of your Chatbot Testing in 2 minutes was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

These are the problems that conversational AI face.

Conversational artificial intelligence confronts challenges that expect better progressive technology to endure.

Here, we will catch a glimpse of some of the challenges in conversational AI.

Security and Privacy

At the point when users demand a voice assistant, the information sent must be safely prepared and put away. Voice assistance and chatbots must be paid attention in organizations and the high-security norms that organizations characterize for these channels must be conveyed to their clients to make the vital premise of trust. Particularly while performing sensitive individual data analytics that can be taken, Conversational AI applications must be planned with security in mind to guarantee that protection is regarded and all personal details are kept private or redacted depending on the channel being utilized.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Conversations in Native Languages

With only a limited section of the world population speaking English, it is a challenge for a voice assistant to converse in a language other than English. As a result, the choice of chatting to a voice assistant in your mother language is critical to winning more people and building faith more skillfully. The languages of varied regions as well as cultural discrepancies are required to be considered.

Discovery and Adoption

Although conversational AI applications are getting progressively simple to use and standardized for everybody, there are still difficulties that can be defeated to expand the number of individuals who are open to using technology for a more extensive variety of use cases. So, instructing your user based on opportunities can enable the technology to be all the more generally welcomed and make a better experience for the individuals who are not friendly with it.

Language Input

Language input can be a difficult area for conversational AI, regardless of whether the input is text or voice. Dialects and background noises can affect the AI’s comprehension of the raw input. Also, slang and unscripted language can create issues with handling the information. Nevertheless, the greatest challenge for conversational AI is the human factor in language input. Feelings and sarcasm make it hard for conversational AI to understand properly and react appropriately.

Simultaneous Conversations

We all know that most of us keep our smart speakers in our living room and most of us use voice assistance in our smartphones. So, in these situations, we can realize that in these places numerous people could probably have a discussion or deliver instructions. In this situation, the voice assistant might get confused. Hence, it requires skill to differentiate identical voices from each other and not distract user accounts, therefore revealing sensitive user information.

Don’t forget to give us your 👏 !

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Top Challenges Faced by Conversational AI that You Should be Aware of was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

In this blog I’ll be trying to explain in detail the types of chatbots, their development, and how they’re made.

Before we start properly, let’s get a few basics down pat. A chatbot (Conversational AI) is an automated program that simulates human conversation through text messages, voice chats, or both. It learns to do that based on a lot of inputs, and Natural Language Processing (NLP).

Chatbots today are integral in many industries and companies for Social Media, E-Commerce, Customer Services and even Healthcare. Some popular examples are Microsoft’s Zo, IBM Watson or Rasa (A tool used to build chatbots for commercial use).

Let’s get to the real meat then 😤

Table of Contents

• Types of Chatbots (Rule-based, Intent-based, Open-Domain)
• Chatbot Architecture
• Natural Language Processing
• Industry Examples
• Further Research and References

Types of Chatbots

Rule-Based Chatbots

Rule-based chatbots are also referred to as decision-tree bots. As the name suggests, they use a series of defined rules. These rules are the basis for the types of problems the chatbot is familiar with and can deliver solutions for.

Like a flowchart, rule-based chatbots map out conversations. They do this in anticipation of what a customer might ask, and how the chatbot should respond.

Rule-based chatbots can use very simple or complicated rules. They can’t, however, answer any questions outside of the defined rules. These chatbots do not learn through interactions. Also, they only perform and work with the scenarios you train them for. Opting for his type of chatbot comes with several benefits, like:

• The chatbot doesn’t need extensive training which makes the implementation process faster and less complicated.
• By pre-defining the structures and answers, you can better control the behavior and responses of the chatbot.

However, rule-based chatbots have their limitations, with a few of their disadvantages being:

• A rule-based chatbot can’t capture typos which means that in some cases it won’t understand the guest, which can cause frustrations.
• The interactions with a simple chatbot feel robotic rather than conversational.
• They cannot learn on their own which means that any improvements need to be made manually.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Intent-Based Chatbots

In comparison, AI chatbots that use machine learning understand the context and intent of a question before formulating a response.

An AI chatbot is a chatbot powered by Natural Language Processing. So, unlike with a rule-based chatbot, it won’t use keywords to answer, but it will try to understand the intent of the guest, meaning what is it that the guest wants. The more it interacts with guests, the better it will become at understanding the intent, and the better it will become at answering guest requests.

The way they work is that they capture the intent behind a question or response. For example, a user would ask something like ‘Hello! How are you doing?’ or even something like ‘Heyoooo’. Despite these being wildly different, the bot captures the intent as `greeting` so it responds with a comment corresponding to that.

Open-Domain Chatbots

Arguably, Intent-Based chatbots are also AI-Based Bots. These chatbots operate on a semi or completely Artificial Intelligence backed algorithm. A bot built on GPT-3 would be a perfect example of this, where it understands the context and responds completely on it’s own.

This type of bot is much more suited for complex kinds and a larger number of queries. Choosing an AI chatbot comes with several advantages, like:
The chatbot can understand typos and grammatical mistakes, so it is still able to respond to the question.
It will continuously improve without much further assistance.
Conversing with an AI chatbot feels much more natural and human-like.

Although Artificial Intelligence is an advanced technology, this bot also has its limitations:

• AI chatbots go through a learning process, which makes their implementation process more complicated and longer.
• By not using pre-defined structures, the conversations led by an AI chatbot are less predictable.
• When a chatbot was wrongly taught something, it takes a while before it “unlearns” and can learn the correct behavior.

Chatbot Architecture

NLP Engine

The Engine is the core component that interprets what users say at any given time and converts the language to structured inputs that the system can further process. Even if a chatbot is doamin specific, it needs to contain and harness a vast amount of information. The NLP Engine helps with that.

It contains advanced machine learning algorithms to identify the user’s intent and further matches them to the list of available intents the bot supports. It can be understood to have two components:

• Intent Classifier: Intent classifier takes user’s input identifies its meaning and relates back to one of the intents that the chatbot supports.
• Entity Extractor: Entity extractor is what extracts key information from the user’s query.

Knowledge Base

This is the key component in answering users’ frequently asked questions. Q&A systems interpret the question and respond with relevant answers from the knowledge base. It can be trained manually or trained to do so itself

• Manual Training involves the domain expert creating the list of frequently asked users queries and map its answers. This helps the bot quickly identify the answers to the most important questions.
• Automated Training involves submitting the company’s documents like policy documents and other Q&A type of documents to the bot and asking it to train itself. The engine comes up with a list of questions and answers from these documents. The bot then can answer with confidence.

Data Storage

The data storage is simply a store of previous interactions to feed to the nlp engine so that the bot retains a semblance of context during a chat. This is especially critical to remember knowledge about the user for further interactions.

Natural Language Processing

Natural Language Processing in Chatbots finds a way to convert the user’s speech or text into structured data. Which is then utilized to choose a relevant answer. Natural Language Processing includes the following steps;

• Tokenization: The NLP separates a series of words into tokens or pieces that are linguistically representative, with a different value in the application.
• Sentiment Analysis: It will study and learn the user’s experience, and transfer the inquiry to a human when necessary
• Normalization: This program model processes the text to find out the typographical errors and common spelling mistakes that might alter the intended meaning of the user’s request.
• Named Entity Recognition: The program model of chatbot looks for different categories of words, similar to the name of the particular product, the user’s address or name, whichever information is required.
• Dependency Parsing: The Chatbot searches for the subjects, verbs, objects, common phrases and nouns in the user’s text to discover related phrases that what users want to convey.

Industry Examples

Meena by Google

Meena is an end-to-end, neural conversational model that learns to respond sensibly to a given conversational context. The training objective is to minimize perplexity, the uncertainty of predicting the next token (in this case, the next word in a conversation).

At its heart lies the Evolved Transformer seq2seq architecture, a Transformer architecture discovered by evolutionary neural architecture search to improve perplexity. Find out more about it in Google’s AI blog.

Replika

Replika was founded by Eugenia Kuyda with the idea to create a personal AI that would help you express and witness yourself by offering a helpful conversation. It’s a space where you can safely share your thoughts, feelings, beliefs, experiences, memories, dreams — your “private perceptual world.”

Essentially, Replika is a chatbot that while talking to you, gradually learns to imitate you till it becomes you.

Tay by Microsoft

Tay was an artificial intelligence chatter bot that was originally released by Microsoft Corporation via Twitter on March 23, 2016; it caused subsequent controversy when the bot began to post inflammatory and offensive tweets through its Twitter account, causing Microsoft to shut down the service only 16 hours after its launch. It has since been seen as a case study on how user interaction can ruin chatbots.

Further Research and References

For the purpose of creating this blog, we referred to several sources which can be found below. Apart from those, if you are interested in learning about or developing chatbots, we’d encourage you to check out Rasa, a popular open-source library used to develop chatbots. And there’s also the Chatbotslife Magazine right here on Medium!

And here are our sources:

• “Towards a Conversational Agent that Can Chat About…Anything”, Google Research: Brain Team, 2020
• “Understanding the Architecture of Conversational Chatbots”, VSoftConsulting Blog, 2019
• “The Rise of Social Bots”, Ferrara Emilio, Varol Onur, Davis Clayton, Communications of the ACM, July 2016
• “Why Microsoft’s ‘Tay’ AI bot went wrong”, Hope Reese, Tech Republic, March 2016

Don’t forget to give us your 👏 !

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Machine Learning in Chatbot Development was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.