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Will Human Teachers Be Replaced by Technology and Computers?

As working and learning from home have become the norm, many of us have become familiar with intelligent tutoring systems.

This begs the question: Can technology like intelligent tutoring systems replace human teachers?

It’s unlikely!

Although incorporating intelligent tutoring systems into your learning platform can help to save time, provide personalised content, and promote self-learning, human teachers are irreplaceable.

Here’s why.

Imagine your sales agent pitching a credit card to a student with no income to pay back! According to a survey, 85% of sales agents have committed mistakes due to incorrect user data.

So, what’s the problem at hand?- Reliable Data!

You can integrate one of the finest Customer Relationship Management(CRM) software; sales goals will look like Everest. The reason being inadequate data of users that interact with your business. Fortunately, there is a solution- “chatbot.”

They can help aggregate valuable user data, enhance the entire customer support system, and offer more extraordinary customer journeys. However, building a chatbot application for your CRM is a business decision, and you need to evaluate the ROI.

So, here are the top reasons you should consider building a chatbot application for your CRM.

#1. Data Quality

CRM systems need high-quality data to strategize engagement with customers and offer support. According to a report, 47% of CRM users believe that high-quality data is essential for customer relations.

Though advanced CRM systems use forms with advanced auto-fill technologies to reduce the manual entering each detail, there are restrictions to the number of fields you can add to a marketing form for data aggregation.

Another essential aspect that hampers CRM adoption in many organizations is the amount of time and resources spent on manual data entry. For example, a salesforce report suggests that about 20% of customer support representatives are spent on manual data entries affecting customer support efficiency.

Chatbot apps uses Natural Language Programming to aggregate data through human language understanding. It allows the algorithm of the chatbot to understand the syntax of human conversations through the co-relation of words and sentences.

Chatbot applications can capture insights, key user details and even identify the search intent. A CRM chatbot can automatically enter details like cost, purchasing power, buyer’s pattern, challenges, and user objections.

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?

#2. Customer Self-Service

Chatbot applications can help you leverage one of the key CRM trends called “Customer Self Service.” However, when the chatbot algorithm comes to customer support, the idea of self-service looks vague, especially in a conventional setting where customer representatives have access to the custom software and not the users.

IVR or Interactive Voice Response is one form of the self-service approach introduced in conventional customer support systems. However, IVR has its limitations, and when the user’s complex issues it is not that effective. For example, reporting a transaction fraud or finding an ideal accident insurance policy for you becomes difficult through IVR.

Chatbot applications, however, have the power of advanced technologies like NLP and Machine Learning algorithms that can guide users to solve issues through self-service. Moreover, these intelligent bots can ask specific questions that are not feasible in the conventional approach.

Source: www.paypal.com

Here, you can see the example of Paypal’s automated customer support system that offers FAQ-type self-service for customers. In addition, it provides tutorials for customers to use its services in response to specific issues.

Apart from customer support, CRM chatbots can help in enhancing the interactions with users through personalizations.

#3. Personalized Chats

Chatbots use powerful AI-based technologies to understand the human language and create personalized interactions. Take an example of an email marketing campaign from Moo. It enhances customers’ overall experience through an upselling technique around specific colors like gold and relates it to January.

Source: https://moosend.com/blog/email-marketing-examples/

Moo creates custom business cards, and personalization are vital to their business. For such personalization, your CRM software and email marketing teams will need interpretation of user interactions. Chatbot apps can offer that data through personalized chats.

Source:https://www.facebook.com/NatGeoGenius/

For example, NatGeo’s Genius series used a messenger bot on Facebook to interact with users by pausing as the featured characters like Einstein, Nikola Tesla, and more.

#4. CRM Efficiency

Chatbot apps can increase CRM efficiency through improved response time, personalization, and enhanced engagement with the customers. However, the most significant benefit of using a chatbot for your CRM is automation. It can help automate,

• Responses
• Interactions
• Customer support
• Data logging
• Product/Service recommendations

Better automation can lead to improved customer relations, which brings the loyalty factor into play. So, chatbots are a great option if your present CRM response is sluggish and dealing with customer attritions.

#5. Optimized Costs

Chatbot applications can offer customer support excellence and manage to automatically input data, reducing the cost of manual logging. Imaging hiring several data entry operators and interpreters that go through transcripts to prepare data insights! Chatbot applications save costs through the automation of several core CRM tasks.

However, the question in your mind will arise what if chatbot application development is costly? What is the ROI?

The answer to this question is not easy as several factors can affect chatbot app development costs. There are several stages of chatbot app development, from ideation to creation of minimum viable product and even deployment.

The cost of development depends on the type of chatbot, core functions, and third-party integrations. However, the average cost of developing a chatbot application is anywhere between $5000 to $150000.

#6. Multi-channel CRM

CRM without multi-channel communication is like Tom Hanks in Cast Away Movie(stranded on an island). For example, 98% of online adults aged 18–34 are more likely to follow a brand on the social media channel. At the same time, your CRM needs to engage with customers across other channels like,

• Email
• Calls
• Live chats
• In-person

Chatbot applications can enhance the multi-channel interactions for CRM. You can integrate chatbots into different channels and feed data to your CRM software to improve customer relations. In addition, AI-based advanced interpretations from chatbots at early interactions make the CRM more efficient. A social media messenger bot is one such example of chatbots integrated into CRM communication channels.

However, you have to deal with regular upgrades of these bots, which is possible through an excellent device management tool. For example, you may have two different chatbot applications for social media messenger and the other on your website for live chat. So, you need to upgrade both of them for efficient CRM.

Signing Off

Chatbot applications are emerging as one of the most feasible options when it comes to CRM automation. This is because they are a marketer’s best friend and an amiable supporter of users. It means that chatbots are creating a win-win scenario for most businesses.

They are offering higher conversions for organizations and at the same time enabling users to have early resolutions. This has helped several businesses improve their CRM efficiency and reduce customer churn.

Don’t forget to give us your 👏 !

Why do you need a Chatbot application for your CRM in 2021? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

What is Natural Language Processing?

NLP is a method or a way in which computer interprets the Human language are perform the task. Alexa, Siri, etc. are some of its example.

Let’s start with the Spam Classifier:

The spam classifier predicts whether received message is a ham or a spam.

Let’s start with the dataset: The dataset consists of 5572 messages and their labels which is either “ham” or “spam”.

import pandas as pd

messages = pd.read_csv(“SMSSpamClassifier”,sep=”t”,names=[‘label’,’message’])

Now the labels needs to be converted in 0 and 1 labels which can be done using get_dummies() method of pandas library.

y = pd.getdummies(messages[‘labels’])

y = y.iloc[:1].values

Here, y wil contain 0 for “ham” labels and 1 for “spam” labels.

Now let’s look at independent data i.e. for x. For that 1st we have to clean the message data i.e. remove stopwords, lower string, group the same type words, etc. For all these we will use WordNetLemmatizer, the main reason of using the lemmatizer instead of stemming, it will provide meaning full words.

Now the code for it is:

import re

import nltk

import nltk.corpus import stopwords

from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()

corpus = []

for i in range(len(messages)):

review = re.sub(‘[^a-zA-Z]’,’ ‘,messages[‘message’][i])

review = review.lower()

review = review.split()

review = [lemmatizer.lemmaatizer(word) for word in review if not word in stopwords.words(‘english’)]

review = ‘ ‘.join(review)

corpus.append(review)

Here, corpus have all the sentences with clear data. The code above removes the stopwords, lowercase them and get all the important words that are required for prediction. Now we use Term Frequency and Inverse Term Frequency i.e. TfidfVectorizer to for the vector of words. The Tf-idf vector provide us with a vector of words and their importance.

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?

from sklearn.feature_extraction.text import TfidfVectorizer

cv = TfidfVectorizer(max_features=5000)

x = cv.fit_transform(corpus).toarray()

The data is prepared in ‘x’ and now we can use it for training our model. Since Naïve Bayes algorithm works better for NLP we will use it for training our model.

from sklearn.model_selection import train_test_split

from sklearn.naive_bayes import MultinomialNB

from sklearn.metrics import accuracy_score

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.20, random_state=0)

spam_detect_model = MultinomialNB().fit(X_train, y_train)

y_pred = spam_detect_model.predict(X_test)

print(accuracy_score(y_test,y_pred))

The model will give of accuracy of around 98%. To predict the new input we can use model.predict(cv.tranform(user_input).toarray()) and get the output for it.

All resources and code is present at:

Darkshadow9799/Sms-Spam-Classifier

To have a look for NLP description click here.

Don’t forget to give us your 👏 !

Spam Classifier: A Natural Language Processing Project was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Making a Rick & Morty Chatbot for Chai

We’re going to be making a chatbot, based on Microsoft’s DialoGPT. I’ve seen lots of other guides on training chatbots, but I haven’t come across one which actually deploys the bot. This tutorial will cover deploying our new bot to Chai, a platform for creating and interacting with conversational AI’s. It will allow us to chat with the bot through a mobile app, from anywhere, at any time. We will also be able to see performance stats and watch our bot climb the Chai bot leaderboard.

By the end of this tutorial you will have your very own chatbot, like the one pictured above 😎

If you would rather start off small, head over to the chai docs for a much simpler and shorter tutorial on creating your first bot!

I made a Google Colab notebook which allows you to run all of the code featured in this blog really easily. You can find it here.

Almost all of the code for training this bot was made by Mohamed Hassan. Their code has been adapted to suit the tutorial better.
The training data has been fetched from this article by Andrada Olteanu on Kaggle

Let’s get started!

Install the Huggingface transformers module

pip -q install transformers

Import DialoGPT

DialoGPT is a chatbot model made by Microsoft. This will be the base for Rick Bot.

Chat with the untrained model

It’s capable of holding a conversation (sort of), but it doesn’t resemble Rick Sanchez at all yet.

Configuring the model

Here we define some config. These can be tweaked to generate a slightly different bot.

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?

Gather the training data

We’re using some Rick and Morty scripts from this article by Andrada Olteanu (the data can be found here)

We want the model to be aware of previous messages from the dialogue to help it decide what to say next. We call this context. The dataset has context from 7 previous messages.

Training

Now, this is quite a hefty chunk of code but don’t worry you don’t need to understand it yet, we can cover this in later tutorials.

Main Runner

Let’s start training!

This should take around 10 minutes so you might as well go grab a cup of coffee ☕️

Chatting with the trained bot

That’s more like it!

Uploading to HuggingFace 🤗

HuggingFace is a platform for hosting machine learning models. Think Github for ML.

apt-get install git-lfs

Git needs an email address:

git config --global user.email <YOUR_EMAIL>

Login with your Huggingface account (if you don’t have one you can sign up here) and then push our new model:

huggingface-cli login

Following the link the code above gives us will take you to your bot’s Huggingface page, it will look something like this:

Great! Now our bot is being hosted on HuggingFace we can deploy to Chai

Deploying to Chai

Chai is a platform for creating, sharing and interacting with conversational AI’s. It allows us to chat with our new bot through a mobile app. This means you can show it off really easily, no need to whip out your laptop and fire up a colab instance, simply open the mobile app and get chatting!

There is also a bot leaderboard to climb. We can see how our new bot compares to others on the platform:

Lets deploy RickBot to Chai!

Install the chaipy package:

pip install --upgrade chaipy

Head over to the Chai Dev Platform to set up your developer account. This allows us to deploy the bot under our own account.
Your developer ID and keys can be found on the dev page.

Setup the notebook:

Chai bot code

Now it’s time to write the code to interact with our Huggingface model.

You can add to the past_user_inputs to change the bot’s starting context. This changes how the bot will act when the conversation starts.

Deploy to Chai

Time to deploy the bot!

You can change the image_url and description to personalise how the bot will appear on the platform.

Success 🎉

deploy.py will generate a QR code, scan it with your phone and you will be taken to a chat screen with your brand new bot. How cool is that?!
(Make sure you have the Chai app installed on your phone)

Conclusion

If you would like to make your own Rick bot, I highly recommend using the Google Colab linked at the start of the blog. I’ve trimmed some of the code snippets on the blog to make it easier to understand, so the code probably wont run properly when copied to your own machine.

Any dialogue can be used to train this bot, it just needs to be in the right format. Try making a bot of your favourite film character, or maybe from a TV show.
You can also try changing the training config. For example, the context length (n) or any of the arguments in the Args class.

Don’t forget to give us your 👏 !

Creating a Rick Sanchez chat bot with Transformers and Chai 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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