Month: August 2021

Welcome back to “Lessons from Our Voice Engine,” where members of our Engineering and Speech Tech teams offer high level insights into how our voice engine works.

Lesson 2 is from Lora Lynn Asvos, a Computational Linguist on our Speech Tech team.

What are CLMs?

CLM stands for “custom language model.” As mentioned in Lesson 1, language models are statistical models of language that can predict the next word based on the context.

CLMs are language models, as the name implies, but they have a little something extra. Instead of focusing generically on a given language, a CLM focuses on a specific domain of that language. This domain could be fairy tales, fables, scientific texts, cooking recipes, knitting patterns, you name it.

Even though CLMs specialize in a particular domain, they are still bolstered by general language knowledge. This allows CLMs to cope if the user goes outside the intended domain, which is particularly useful with children — they excel at saying the unexpected!Why are CLMs important for our kid-specific voice engine?

We often get this question from clients in conjunction with, “Why is a CLM better than a generic LM?” Generic LMs cover many topics and contain lots of data. For general knowledge applications, they can be useful. However, generic LMs are trained on adult words, use cases, and sentence structures. Their strength is also their weakness. As the old adage goes, a jack-of-all-trades is a master of none. Or in this case, a jack-of-all-domains.

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?

When a child says “the train went choo-choo,” a generic LM might interpret “choo-choo” as “to you” or “chew chew,” similar-sounding but more standard words. Children’s texts are also full of fun and unique character names, places, and objects. With a generic LM, the unique word won’t be understood, leading to a disappointing reading experience.

Since our focus is children’s speech, our CLMs are trained on kid-centric data, which means words like “choo-choo” are correctly understood. Our CLMs also allow for phrases with unique words like “the alien smork of planet Terratow” to be recognized with exceptional accuracy. This keeps the experience of reading engaging, educational, and enjoyable.

Are you interested in natural language processing (NLP) and voice technology for kids? Check out our first “Lesson from Our Voice Engine” on NLP.

Don’t forget to give us your 👏 !

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Why Custom Language Models (CLMs) are Needed in Speech Recognition for Kids was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Shopify is one of the most widely used e-commerce platforms that have helped many businesses globally to go online by building their marketplace. It’s used by more than 800,000 vendors worldwide. While the customer is the pivot around which the eCommerce business works, it is essential to have effective customer engagement to enhance their experience and improve overall sales. This is where the Shopify bot comes in handy.

Bots have played an instrumental role in addressing the customer’s queries and converting them into active sales. Due to limited person power and the ever-increasing volume of customers on e-commerce stores, it is impossible to keep up without automation, AI and chatbots.

Benefits of Shopify ChatBot

In the e-commerce world, bots are bringing out a transformation. They are recreating the user experience with businesses and replacing it with an experience they have with friends. Some of the benefits of Shopify chatbots include,

• 24×7X365 support, they do not go on holidays
• Resolving queries
• Personalization
• Reduced costs
• Product guidance and onboarding
• Generating leads
• Driving sales

Now that we have concluded e-commerce stores need chatbots, let’s jump into making AI bots and deploying them on Shopify websites.

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?

Setup & Activate Shopify ChatBot

Step 1: Setup an account in Kommunicate

We will be using Kommunicate’s bot builder and chat UI plugin for this example. First, login to your Kommunicate dashboard and navigate to the Bot Integration section.

If you do not have an account, you can create one here for free.

Locate the Kompose section and click on Integrate Bot. Kompose is Kommunicate’s AI-powered bot builder.

Now, set up your bot by providing bot name, bot language, human handoff setting, and finish the bot setup.

Additionally, Kompose has an in-built e-commerce bot template that you can use. It has multiple intents related to Shopify store operations such as order status, product catalogs, recommendations, FAQs, etc.

You can check your newly created bot in the below section:

• Dashboard →Bot Integration → Manage Bots.

Step 2: Create welcome messages & answers for your Shopify Chatbot

Go to the ‘Kompose — Bot Builder’ section and select the bot you created.

First, set the welcome message for your bot. The welcome message is the first message that the bot sends to the user who initiates a chat.

• Click the “Welcome Message” section. In the “Enter Welcome message — Bot’s Message” box, provide the message your bot should be shown to the users when they open the chat and then save the welcome intent.

• If you are using the template, feel free to edit the intents as you wish.

After creating the welcome message, the next step is to create possible questions and answers for your Shopify bot.

The answers section is where you’ve to add all the user’s messages and the bot responses.

• Go to the “Answer” section, click +Add. Give a name to your Intent.

In the Configure user’s message section — you need to mention the phrases that you expect from the users that will trigger.

Configure bot’s reply section — you need to mention the responses (Text or as Rich messages) the bot will deliver to the users for the particular user’s message. You can add any number of answers and follow-up responses for the bot.

You can also refer to this blog or the video below for Kompose bot integration with Kommunicate.

Step 3: How to activate the chatbot?

Once you create a bot, then you can set it as a default bot in the conversation routing rules section, as shown below.

• Click on ⚙️Settings >> Conversation rules >> Routing rules for bots >> Then click on bot like below and select your bot.

We have completed the bot setup. Now, this bot will reply in all the conversations. Next, let’s jump into deploying the bit on your Shopify store.

Add ChatBot to Shopify Websites

Step 4: Install Kompose bot into Shopify

Login to the Kommunicate dashboard. Navigate to the Install section under ⚙️Settings, where you will get the live chat plugin script. Copy the script.

Step 5: Log in to Shopify

Log in to your Shopify account. Click on Online Store.

Step 6: Edit code

Navigate to the Themes section. Click on the Actions button and then click the Edit Code option.

Step 7: Add Live-chat script

In the Edit Code section, go to the left side Edit Code for Debut panel and click on Sections.

Open the footer.liquid section and paste the copied live-chat plugin script just below </footer> tag. Click on Save.

Step 8: Preview & Publish

After saving, click on Preview to see the changes. You will see the chat widget on the bottom right corner of the Shopify website.

Here’s a video to walk you through the Shopify integration with Kommunicate:

Wrapping Up

That’s all! In these easy steps, you can install the chatbot on your Shopify website. You can use your newly created Shopify chatbot to improve customer support, customer experience, and lead generation.

This article was originally published here.

Don’t forget to give us your 👏 !

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Shopify Chatbot: How to Create & Automate Your Customer Support? 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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Chatbot Developers Need To Know — A Study

Today, we’ll look at how chatbots or virtual assistants evolved, how to build chatbots for different cases, what aspects to consider when selecting the right use-cases and while building them, and what are the common challenges and solutions that most enterprises encounter.

Anthropology

How many of you guys have heard about this term?

Do you agree that earlier the culture was just meant to be about religion?
But, as time passes, things are changing. There is a development in the human lifestyle as humans evolving, it can be about anything, including art, devotion, politics, economics, psychology, and so on.

A study has been going on for a long time, but to better comprehend, it is a scientific study of humanity, concerned with human behavior, human biology, cultures, and societies. There are four major areas to consider in it:

1. Archaeological — Which is a study of human activities through the investigation of physical evidence.
2. Biological — This is focused on the study of human and non-human evolution. How different climates or temperatures, affect human evolution in different regions.
3. Linguistic — About understanding the process of human communications, verbal and non-verbal.
4. Cultural — This is the study of human similarities and differences in the community or between communities.

So now, these findings are no longer confined to textbooks or geographic channels. Researchers are attempting to understand how this data might be properly applied to assist humans by giving exactly what they want.
This is how organizations are understanding their customer’s emotions to give them better comfort and meet their demands.

People’s modes of communication have shifted from telegrams to telephones, cell phones, and apps as technology advanced.
They figure out the quickest, smartest, and most cost-effective method to communicate, purchase something, transfer money, and make deliveries, etc…
In fact, people are treasuring their time for better work and spending time for themselves and their families.

And,,,,, That is how “chatbots and virtual assistants” became a reality!

People don’t like waiting for support on call forever. However, some people are hesitant or shy to speak. This technology has made their lives much easier.

Some may argue that virtual assistants such as Siri, Ok Google, and Alexa don’t always answer my questions, but always says I’m still learning.
They actually mean it. They’re trying to understand you, your likes and dislikes. That’s how everything gets off to a good start.
For example, when you adopt a pet, it takes time for the animal to get to know you and the environment. The same goes for these bots.

So, what are chatbots?

In simple language –

Any device that takes our text or voice command and performs our tasks is a chatbot.

A Textbook Definition –

A chatbot is a computer program that simulates and processes human conversation (either written or spoken), allowing humans to interact with digital devices as if they were communicating with a real person.

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?

Different types of chatbots?

Any method of building a chatbot depends on the use case.

1. Rule-based chatbots are mainly used for tasks that have a lower scope, limited and fixed range of transactions.

For example, chatbots for McDonald’s, Dominos, Bus or Cab booking, Real Estate etc…

2. Smart Chatbots are highly backed by AI. There will be an NLP engine to identify the use context. The response provided by the bot would be more precise and no extra information is shown. The use cases chosen here would be wider in scope.

Few examples like — Alexa, Ok Google, Siri. And few Chatbots were created for customer support, IT team, Insurance Bots etc…

3. Hybrid Chatbots — This method is flexible to use and can adopt wider scope and wider users.

So, are Chatbots fit for any use-cases? The answer is Yes and No.

Yes, because the possibility is at the sky level. But, we need to look through some important factors before we decide to build the chatbot.

How to choose the chatbot use-case?

1. Need — First and foremost is, do users really need a chatbot? Obviously, we do not want to waste our human and infrastructure resources for nothing.
2. Users — Do we have a bigger user base for this? It shouldn’t be like we developed and nobody is there to use.
3. Cost — Does the use-case minimize the cost? like, if a particular task was performed by 3–4 associates regularly, and by building a chatbot can save associates from doing the boring job? That saves multiple resources cost.
4. Time — Can this innovation save your time? Can you get the answers quickly? For example, quickly book a cab, find the nearest gas station, or the best example is a ticket created to fix the software installation issue that takes 2–3 days SLA, can be solved in seconds if the same or similar queries are already answered by ADE associates before. The bot can pick that answer to show users.
5. Trend — If the current approach or the technology used to resolve the issue or to perform a task is old, and, a chatbot is an easy access for users to get that task done.

Things to note while building chatbots

1. Convenience:

• Choose a place (or omnichannel) that is most convenient for users to access the chatbot.
• Ability to understand the language user like to chat with and respond accordingly.

2. Reliability (Dependable):

• Handle the user queries accurately with a minimum 80% of accuracy.
• Provides the relevant information.
• Handle the escalations smoothly.
• Having alternate measures to answer the queries (Live Support).
• Avoid users from going to other sources to search for data.

3. Availability: To make it available 24/7. Should be available even during emergencies.

4. Scalable: Ability to accommodate new features without much effort.

5. Better UI/UX: Giving a better experience by look and feel brings a smile to the user’s face.

6. Handle Requests Load: Ability to handle millions of requests without breaking the conversation

7. Logging:

• Logging the flow of data and the user conversations for analytics
• To provide a better user experience from the past experience
• For personalization

8. Monitoring: Having a dashboard or any tool to monitor post-production deployments

If you read the points again from the top, these also answer our point “why should we use chatbots?” isn’t it?

But, it is not over yet, there are other important points that benefit the business

How does it benefit the business?

1. Improves customer retention: by keeping the ongoing conversation. Like how Instagram and Facebook keep us engaged in the app by giving more relevant and similar information.
2. Increase Sales: How many of you agree that pandemic has increased sales? Bots encourage the consumer to purchase more relevant items.
3. Promote Products: By providing offers and gifts, and Highlighting the product by comparison, demoing new items etc…
4. One-Stop-Shop: Book tickets, events and perform other actions like tracking, cancellation, modify etc.
5. Better UI/UX: show nice carousels, images, etc… to give a good feeling about the products
6. User Feedback and Personalization: Asking user’s feedback and observing user shopping patterns helps in personalization.

• Who are they?
• What do they expect? What are their preferences?
• What are their pain points?
• How is their experience with AI chatbots?

Let me show you the trending elements that a customer expects in his/her shopping experience.

E-Commerce customer experience trends 2021

What decides your chatbot is successful?

In short, it must fulfill all the business expectations.

1. First of all, the percentage of happy customers

• From their feedback — +ve or -ve
• Their mood or sentiment analysis

2. By Chatbot accuracy — Above or equal to some threshold. Like above 85% or 90%

3. By the containment ratio

Even when we are good at considering all the above factors, there are some common challenges that most enterprises face.

Common Challenges

1. Multiple Chatbots

• When enterprises create multiple chatbots, users cannot know which chatbot to use for which problem?
• If I am done with one query, for another I need to navigate to another place.

2. User spelling mistakes are hard to understand by chatbots, due to which the bots accuracy gradually drops

3. Engage live support when the chatbot escalates certain queries.

4. Complex Infra Setup: Every time it takes a while to build a new chatbot from scratch, test, retune, deploy and monitor.

Possible Solutions

So, how do we overcome these challenges?

1. Collaboration

• Teams who are building the chatbots need to collaborate. Define standard rules of building chatbots
• Every chatbot deployment in the enterprise must be reviewed and deployed

2. Build a concierge bot to handle multiple chatbots from a common screen. I have explained it in detail in my previous article.

3. Set up a common monitoring platform for all the chatbots across the enterprise.

4. Setup platform to reuse the architectural components so that no other development team needs to spend time again in rewriting. That saves a lot of time.

5. Automate chatbot building process. Leverage AI techniques to fulfill the need. For more — check this article.

Don’t forget to give us your 👏 !

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Why Many Enterprise Chatbots Fail — A Study was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

All chatbot testers are dreaming of two buttons. One for detecting all problems of a chatbot, and another one for fixing them all.

With OpenAI we were able to add some nice features to Botium Box which are going in that direction.

• We are using Open AI to guess what can be the next message in conversation
• We integrated a new, multilingual paraphraser which takes the risk while generating new alternatives.

Here are some tips and usecases that we learned while developing those features.

Normalize text

Documentation of OpenAI says that it is better to check spelling mistakes in text. But we can go on. Text has to be as strict as possible. Some samples:

• Avoid using enters. For example we can confuse OpenAI if we allow new line in the “human” section of a chat.
• Terminate the sentences with dots. (OpenAI will deal with it as a sentence, won’t continue it for example)
• Avoid unnecessary information. For example we distinguish between user writes the text “pizza”, or just pushes the “pizza” button. Second one is something like this: “#user button:pizza” for us. But this information can be confusing for OpenAI, it’s better to use just “#user pizza”.

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?

Use enter on the end of the prompt correctly

This:

"Human: Hello, who are you?
AI: I am an AI created by OpenAI. How can I help you today?
Human: Can I ask you"

and this:

"Human: Hello, who are you?
AI: I am an AI created by OpenAI. How can I help you today?
Human: Can I ask you
"

is a big difference for OpenAi, but it is just an extra enter. In the first case OpenAI continues the human message, in the second it generates new AI message.

Multilingual translator

Building a multilingual translator is not difficult. There is a sample for translator in Open AI playground. We can use the same parameters and datastructure, but with multilingual samples:

English: See you later!
French: À tout à l'heure!

German: Ich möchte Geld überweisen.
English: I want to transfer money.

Russian: Я хотел бы заказать пиццу.
German:

That works well, but we have to define explicitly the language of the source text. We will overcome this restriction in the next step.

Multilingual translator, basic language is not specified

In order to do it, we have to restructure the prompt a bit:

Text: See you later!
LanguageOfResult: French
Result: À tout à l'heure!

Text: Ich möchte Geld überweisen.
LanguageOfResult: English
Result: I want to transfer money.

Text: Я хотел бы заказать пиццу.
LanguageOfResult: German
Result:

But lets play with it a little bit more

Multilingual translator and language detector

If we add a new field, then we can ask OpenAI for language detection:

Text: See you later!
LanguageOfResult: French
Result: À tout à l'heure!
LanguageOfText: English

Text: Ich möchte Geld überweisen.
LanguageOfResult: English
Result: I want to transfer money.
LanguageOfText: German

Text: Я хотел бы заказать пиццу.
LanguageOfResult: German
Result:

It is a nice experiment. Sadly it does not recognize language always good, but reveals us some facts:

• It is possible to teach OpenAI parameters (Text, LanguageOfResult), and return values (Result, LanguageOfText) Of course they are not parameters, and return values for OpenAI, but for us.
• OpenAI can solve more complex tasks. What is a two step solution for us, it is maybe not for OpenAI.

Paraphraser

Sure, there are more ways to create a prompt for a parapraser. There is no best solution, each has its own pros, and cons. Our paraphraser is very simple, but it does what we need.

Pros:

• It is open, it finds really nice new alternatives.
• Prompt is just 2 rows (less cost)
• It is not stuck to any language. (Some prompts, like our translator have a training section, and a request section. If we create some ‘hard coded’ training sections to paraphrase in english, then we get a request to paraphrase german sentences, then we will have a prompt in multiple languages. It can be misleading for OpenAI)

Cons:

• About half of the result is not a good paraphrasing. But it does not mean that it’s worthless! If we got “Can I replace my card if it is lost or stolen?” for “Someone took my card!” and “What is the procedure to report stolen card?” then we see that it does not fit. But in our case for a Banking chatbot it can indicate a new use case.

Don’t forget to give us your 👏 !

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OpenAI GPT-3 tricks and tips was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

When we are working with computer vision tasks, there are some scenarios where the amount of data (images) is small or not enough to reach acceptable performance. In addition, dealing with image data and Convolutional Neural Networks (CNN) is expensive in terms of computational power.

Due to the issues aforementioned, in most cases it is convenient to use a technique called Transfer Learning, which consists of using models trained with millions of images, to improve the performance during the training process. We can implement this technique with Natural Language Processing (NLP) tasks, but instead of using pre-trained CNN models, for text classification, we are going to use pre-trained Word Embeddings.

Using Pretrained Word Embeddings

When we have so little data available to learn an appropriate task-specific embedding of your vocabulary, instead of learning word embeddings jointly with the problem, we can load embedding vectors from a precomputed embedding space that you know is highly structured and exhibits useful properties, that capture generic aspects of language structure.

Using Word Embeddings with TensorFlow for Movie Review Text Classification.

In this post, I am going to build a tweet classifier to show how we can implement transfer learning with Embedding Layers so we can improve the learning process.

Use case: Tweet Classification

The problem we are trying to solve is related to text classification and sentiment analysis, in this case we have a dataset that contains tweets labeled according to the sentiment they express.

In the figure shown above, we can see the columns in our dataframe, for this specific task we are just going to use the columns OriginalTweet and Sentiment

Clean tweets.

Texts coming from tweets are usually “noise” in terms of language use, in tweets we might encounter things like links, abbreviations, hashtags, HTML code, emojis, and other things that are made just for human communication so we need to pay special attention to the cleaning process. The cleaning process consists of the following steps:

• Remove links
• Replace abbreviations.
• Replace emojis.
• Turning lower all the words.
• Remove symbols and pictographs.
• Remove punctuation signs.

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?

After applying these steps we obtain text data we can implement the rest of the text processing tasks that are usual when we are dealing with this kind of problem.

Above, we can see the same tweet before and after the cleaning process. This process helps to improve the learning process of word embeddings and it is necessary for every NLP task.

Sentiment Analysis.

The tweets in our dataset were tagged manually according to 5 categories. The categories are:

• Positive
• Extremely Positive
• Neutral
• Negative
• Extremely Negative

The data distribution looks like:

To facilitate the process, I am going to simplify the problem and not worry about the intensity of the sentiment, so I will put together the instances that belong to the classes Positive and Extremely Positive and do the same with the tweets with labels Negative and Extremely Negative resulting just three classes:

• Positive
• Neutral
• Negative

In the figure above we can see the data distribution of the new labels. The labels 0, 1, and 2 correspond to the categories negative, positive, and neutral. It can be seen that the problem we are dealing with is represented by an unbalanced dataset. This is, the number of instances belonging to each class is different.

Movie Review Text Classification Using scikit-learn

Text Processing.

The first step I am going to perform over the data is called Tokenization, This process is about reducing each tweet up to the minimum unit of information, these units of information are called Tokens, we can Tokenize or tweets are character level or word level, in this case, I will Tokenize the tweets at the word level, TensorFlow brings a tool to Tokenize text data automatically.

After the Tokenization process, we need to convert the tweets into sequences, we can do this using a method from the class Tokenizer and become the text data into numeric sequences. Let’s see an example to be clear.

The Tokenizer generates an index for the words in the text, the code shown above generates the next word index:

{'to': 1,
 'data': 2,
 'i': 3,
 'am': 4,
 'trying': 5,
 'show': 6,
 'how': 7,
 'implement': 8,
 'transfer': 9,
 'learning': 10,
 'with': 11,
 'word': 12,
 'embeddings': 13,
 'but': 14,
 'every': 15,
 'project': 16,
 'that': 17,
 'involves': 18,
 'requires': 19,
 'some': 20,
 'pre': 21,
 'processing': 22,
 'tasks': 23,
 'for': 24,
 'example': 25,
 'we': 26,
 'need': 27,
 'clean': 28,
 'the': 29,
 'text': 30,
 'and': 31,
 'perform': 32,
 'other': 33,
 'processes': 34,
 'like': 35,
 'tokenization': 36}

The Tokenizer class allows us to know other aspects related to the data like the word count, but for know let’s pay attention to the fact that now each word can be represented with a number (index). Thanks to this we can represent every line in our little dataset as a sequence, to do that we just use the method texts_to_sequences

In the figure shown above, we can see how each line of text in the list is represented by a numeric sequence. But there is a problem with this approach, neural networks can only receive as input, sequences with equal lengths, to solve this problem we can use TensorFlow to pad or truncate the sequences in such a way all the sequences have the same length.

By default, the method pad_sequences implement “pre-padding” and “pre-truncating”, this is, if one sequence is longer than the maximum length allowed (maxlen), the sequence will be truncated removing values from the beginning of the sequence. On the other hand, if the sequence is shorter than the maximum length, then it will be padded by putting values at the beginning of the sequence. The default value for padding sequences is 0.

Building the model

There are various precomputed databases of word embeddings that we might use in a Keras Embedding layer. One of them is Global Vectors for Word Representation (GloVe), which was developed by Stanford researchers in 2014. Its developers have made available precomputed embeddings for millions of English Tokens, obtained from Wikipedia data and Common Crawl data. In this example, I am going to use a precomputed embedding from 2014 English Wikipedia. It’s an 822 MB zip file called glove.6B.zip

The first thing that we need to do is parse the text file, we can reach this using the code shown below.

The code shown above will print the following results:

Found 400000 word vectors.

Now we have a dictionary where each key is the word and each value is the vector coefficients. The next step is to create a word embedding matrix adapted to our dataset (tweet dataset). This means, find the vector that corresponds to each word in our vocabulary after the cleaning and Tokenization processes. This will be a matrix of shape (max_words,embedding_dim) The next code is in charge of that.

Let’s create a sequential model using Keras, the first layer of this model will be the embedding layer, then I am going to use a LSTM layer followed by a GlobalMaxPool1D, which downsample the input representation (LSTM output) by taking the maximum value over the time dimension. Finally, we have two dense layers.

Up to this point, we haven’t used the transfer learning technique yet. The next step is to set the weights of our embedding layers, which corresponds with the embedding matrix created before. In addition, we mustn’t change these weights during the learning process, so we must freeze the embedding layer to avoid changing the weights during the training stage.

To train the model, I am going to implement two callbacks to improve the training process, “reducing on plateau” will reduce the learning rate by a 0.2 factor when there is no improvement. I will also implement early stopping to avoid overfitting due to over training.

How to Use Callbacks with TensorFlow and Keras

Using class weights.

In addition, we can use weights proportional to the number of instances in each class to letting the model know what classes need “more attention” reducing the problems due to the unbalanced dataset that we are using. We can use the parameter called “class_weight” to do this.

Model evaluation

In the report shown above, the labels 0, 1, and 2 correspond to the categories negative, positive and, neutral, respectively. The model has an accuracy of around 90 %, as we can see, the label 2 or neutral category has the worst performance.

Conclusion

In this article, we have learned how to implement transfer learning in NLP tasks, as we have seen, TensorFlow and Keras allow us to use this technique easily. You can see more details related to the code in my GitHub repository.

I am passionate about data science and like to explain how these concepts can be used to solve problems in a simple way. If you have any questions or just want to connect, you can find me on Linkedin or email me at manuelgilsitio@gmail.com

References.

• TensorFlow for text processing.
• Francois Chollet, Deep Learning with Python. New York:Manning Publications Co. 2018
• Kaggle: Coronavirus Tweets NLP Text Classification

Don’t forget to give us your 👏 !

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Using Transfer Learning with Word Embeddings for Text Classification Tasks was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Released in April 2020 and created by Facebook, BlenderBot makes a super-chatbot. In other words, it is able to speak on open questions

Recently, version 2 has been released but this article remains on version 1 for simplicity

Our goal, create a server with Flask that will allow to have an API to communicate with the chatbot

1. We will be able to reset the conversation (remove the context)
2. Put a first context, which would allow to give a personality to the chatbot
3. Send a message and get the chatbot’s answer

Prerequisites

1. Have Python 3 and Pip installed on your machine
2. Have some knowledge of the Python language

Note that a good computer is necessary. The worse your machine’s capabilities are, the slower the chatbot will respond.

Installation

1. Create a file named main.py

2. Go ahead, install the packages

pip install flask transfomers torch

Remember to create a virtual environment to initialize a Python project

Get Started

We import the library:

1. transformers and classes for the conversation
2. flask for the server and jsonify because we will return the JSON format to the client

We get the pre-trained model (line 5) and the tokenizer (line 6)

A tokenizer is a tool based on an algorithm based on a set of rules or on learning from a manually tagged corpus. It allows to split the text into words.

The model will be downloaded at the first start of our application

We take the model with 400M parameters because the results are rather correct. You can take larger models but it will consume more resources on your machine

We create a variable “nlp” which will allow, later, to have a generated text.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

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3. Chatbots As Medical Assistants In COVID-19 Pandemic

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

Endpoint to process a message with AI

Our enpoint will be /add_input which we can call with the POST method

* (Line 3) We retrieve the text in the corpt of the request
* (Line 4) Add the user’s input to the conversation
* (Line 5) Process the message to get a response from the chatbot
* (Line 7 to 11) We browse the result to form a list of messages (and form our dictionary to return)

Just with the two code blocks above, you can already test:

1. Start the server:

By default, the server runs on port 5000

2. Test

On my end, here is the return:

Fun 🙂

Endpoint to reboot

Why? Because if you continue the conversation, it will keep the context. Ideally, we’d like to start from scratch.

We add an endpoint for that, which I call /reset:

Endpoint to give a personality

What can be interesting is to give a personality. How, by adding our own context.

* (Line 4) — We add a default text (we deduce that the user says hello at the beginning)
* (Line 5) — We give a default text, which represents the personality of the chatbot
* (Line 7) — We “archive” the previous messages and consider them as a context
*
And if we test with the commands

Here is the return

Bonus

Now that we have the API, we can create the frontend.

I’m not going to explain it here (because I focus on BlenderBot) but if you want to have the explanation, tell me in comment

Giving a personality:

And this is the beginning of a conversation:

Done 🙂

Don’t forget to give us your 👏 !

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Create a very smart chatbot with BlenderBot was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Chatbots are digitizing various business processes across industries. To stay ahead in the market competition, it is crucial to adapt technology to speed up business processes. The manufacturing industry is one such industry that can tremendously benefit from AI-enabled digital applications. Workbots can help address and resolve the common challenges faced by the manufacturing industry such as forecasting demand, inventory management, managing labor, improving efficiency, and monitoring daily activities, creating solutions with conversational AI-powered Workbots will ensure workers are free from mundane tasks and focus more on high-volume tasks.

29% of AI implementations in manufacturing are for maintaining machinery and production assets.

Workbots by Botspice, are powered by Artificial Intelligence, Natural Language Processing, and Machine Learning technologies. They can streamline manufacturing processes by providing information on the go. Deploying Workbots can help overcome the challenges mentioned earlier without any major human intervention, thus paving the way to create greater profits and a competitive edge in the global marketplace.

The manufacturing industry will be able to benefit from AI-powered Workbots in the following ways:

· Streamline and synchronize processes between roles and departments.

Supply chain management systems can now function smartly, thanks to AI. Augmenting Workbots for supply chain management ensures efficiency in the process and also saves a lot of time. Manufacturers are also often faced with aggressive deadlines and inter-communicating between roles and departments becomes pivotal. Workbots can be integrated with existing ERP and CRM solutions, thus making information available to the user in seconds. This not only saves time but also ensures maximum efficiency while reaping greater returns.

· Manage supply chains and improve communication with stakeholders.

Many manufacturers tend to treat every lead in the same way. Prioritizing leads could benefit manufacturers tremendously by improving communication and maintaining a long-term relationship with stakeholders. Workbots can help by creating a system that is easy to manage and track prospect’s information.

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?

· Monitor safety and maintenance inquiries.

For the smooth operation of any manufacturing plant, the workspace environment and processes need to be well organized. Workbots provide real-time insights that help to keep track of targeted processes. If there is a fault in any machine workers can immediately raise a ticket without wasting time instead of going through a formal email channel that could take time and hamper immediate resolution of the problem. Equipment metrics can be accessed and any possible asset failure can also be predicted. This information can be accessed without having to log in to any other software system, thus improving overall efficiency.

A recent study showed that unplanned downtime caused by machinery costs manufacturers an estimated $USD 50 billion annually, and that asset failure is the cause of 42 percent of this unplanned downtime.

· Manage HR activities.

People are a major factor in a manufacturing unit. Managing labor is a task considering the nature of the worker’s jobs. Unlike office environments, factory workers are unionized which means HR in manufacturing units must be experts on union contract issues, such as negotiation and compliance. Workbots can bridge the gap between the union workers and the HR team. This helps to create an amicable manufacturing environment, where workers can raise their issues through intelligent conversations for timely resolution.

· Stay up to date on inventory status.

Workbots powered by AI can predict demand patterns to get accurate forecasting results through the analytics tool. With these analytic tool’s manufacturers can manage their inventory better by preventing “material-in-stock and out-of-stock” scenarios from happening. Inventory management is crucial as it can predict any delays or hiccups in the manufacturing flow.

· Identify demand patterns through customer service Workbots.

Customer service workbots are used widely to understand customer buying behaviour and trend analysis. The manufacturing industry is no different. Manufacturers can predict demand, trends, and customers behaviour to facilitate adjustments in manufacturing processes based on these insights. This would allow manufacturers to make smarter and more informed decisions that can save time and money.

In Conclusion

There is no doubt that Workbots can bring tremendous benefits to the manufacturing sector. We have simply mentioned a few use cases here. With humans and technology working in unison, transparency, and trust, the best time to exploit and explore Artificial Intelligence is now! Workbots by Botspice, provide personalized augmented operations that are easy to implement and cost-effective.

Prepare to rethink manufacturing processes with Botspice!

Don’t forget to give us your 👏 !

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‘Workbots’ to boost manufacturing process efficiency was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Whitelabel Chatbot Partnership — How to invest in automation?

It’s the start of a new decade bringing on a multitude of changes in the way our daily lives function. One major aspect that has been evolving at a rapid pace is the way we communicate with each other. Be it B2B, B2C, or C2C, all our conversations are evolving into a newer dimension of AI.

So, before we talk about the benefits of a white label chatbot and live chat platform, let’s look at some of the ways in which AI and chatbot technology is transforming the way we work and live.‍

First, what is Conversational AI all about?

Conversational AI refers to the use of messaging apps, speech-based assistants, and chatbots to automate communication and create personalized customer experiences at scale.

Chatbot tech, a division in conversational AI, is also rising into popularity swiftly. To an extent where it’s predicted that 80% of the businesses will incorporate chatbots into their functional system to carry out seamless conversations across various channels.

Businesses are using Conversational AI to automate customer-facing touchpoints everywhere. They are targeting social media platforms — Facebook, Instagram, WhatsApp, Twitter, etc, and are introducing voice assistants like Google Home.

Conversational AI systems offer a more straightforward and direct pipeline for customers. The technology helps them sort out their problems, address concerns and provide seamless support.‍

The future of AI tech

Artificial intelligence is everywhere, and so are chatbots. These are not only convenient tools used to distribute mundane tasks and cut costs, but they are also an abundant multiplier creating new quality. Whether it’s a virtual assistant like Alexa, Siri, or Google Assistant, or a charming chatbot assistant like Enbo, an AI-powered chatbot, they support us in all kinds of situations, answering any of our questions instantly.

Chatbots are here to stay. While we’re bound by gradual biological evolution, the sky is the limit with artificial intelligence. Chatbots have fewer limitations on what they can accomplish as AI technology gets stronger and more powerful each year. As a result, people will communicate with chatbots presently, as they would interact with people.

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?

Most companies are very close to integrating AI chatbots into their day-to-day operations but have not made the transition yet. On average, three out of four executives believe that artificial intelligence will allow them to open up new business ventures, and 85 percent believe that applying AI technology offers a competitive market advantage. The same survey discovers that just one out of 20 companies has integrated chatbots in its processes or offerings.

Moreover, artificial intelligence has made its way into many homes. Technology will soon be indispensable in most households. With more technical advancements, AI and related applications will fulfill the promise that computers would make our lives easier. AI technology will help us live happier and healthier life. In many ways, it will also help us conserve time, energy, and money.

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Build your chatbot brand on our platform‍

What is white labeling?

White labeling is what happens when a company creates a product or service, removes its own branding from it, and adds the branding of the reseller.

The product appears to be made by the reseller. Essentially, one company makes the product and allows other companies to sell it under their brand names.

It allows the purchasing company to get into the industry without having the capacity to make the product in the first place. It also saves them a lot of time and resources that they’d otherwise send making the product.

Another benefit to the purchasing company is that they can expand their current offerings with white label products. They could offer their customers a more comprehensive experience in this manner.

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Why invest in a white label chatbot and live chat solution?

As of 2018, the global chatbot market was valued at USD 1.17 billion. By 2026, it is expected to go up to USD 10.08 billion.‍

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But, with the COVID-19 crisis and the lockdowns, the demand for conversational intelligence is going up. Businesses are investing heavily in conversational intelligence. They’re looking for ways to engage their customers well, without endangering the health of their employees.

Businesses know that they can’t handle all their customers’ queries over the phone, because their customer service representatives are working from home. They realize that chatbots are a great initial point of contact. They understand that bots improve the agents’ productivity and efficiency by handling monotonous queries and only sending the complicated ones their way.

They’re also starting to realize that customers are now spoilt. Customers have no intention of waiting on hold for a human agent to pick up their call. They want to be served fast and then go about their day.

Hubspot reported that around 80% of their respondents in a study stopped doing business with a company because of subpar customer experience. A chatbot reduces the average waiting time to be served. It drops the waiting time from 11 minutes (on a call) to a few seconds, thus increasing customer satisfaction.

1. White labeling can scale your services

First and foremost, as mentioned above, the benefit of using the services of white labeling is to expand your current offerings. You don’t have to specialize in every service you’re providing. And by outsourcing them to a white label service provider, you can provide your customer base with a wide range of services. Here’s an article on why outsourcing software development could be a better choice!‍

2. It can help you reduce expenditure

If you’re a small-scale firm on a tight budget and want to include a chatbot platform in your service line, white labeling a chatbot platform provider would be ideal. By outsourcing services to a white label chatbot and live chat platform, you can escape from maintaining a full-time team of specialists. You can avoid or reduce expenses such as salaries for full-time employees, overhead costs, training costs, development costs, licensing, and much more.‍

3. Add another revenue source to your business

Each additional service you provide to your customers will create an added income stream for your business. The key, of course, is to resell these services at a profit margin in order to maintain healthy profitability of revenue.‍

4. Expand your customer base

By increasing your service line, you will also attract a new set of audiences. White labeling will enable you to have a customer base belonging to a multitude of sectors of the industries. The more service offerings that you have, the more clients you can pitch your services to. If you have an industry reputation for providing high-quality service, it will be easier for you to attract clients.‍

5.It improves your brand value

Using white label chatbot services, you can ensure that you deliver on your promises to your clients and build a good reputation in the AI industry. By being able to provide a broad range of services, you can position yourself to be the go-to agency for all sorts of services.‍

‍How do I get started with a white label chatbot and live chat partnership?

Your best bet would be to partner up with Engati.

You’d get to build your brand with a strong, reliable platform that has proved itself and is used in 186 different countries (for context, there are 193 member countries in the United Nations).

You also get access to Engati’s Bot Experts. The experts will train your entire team extensively on the platform and even help you out with customizations and bot-building at a negligible cost.

‍Your customers would have a smooth experience, building intelligent and multilingual chatbots that run on our powerful NLP engine, with minimal effort. They won’t even need to code their bots thanks to our conversation builder.

You’ll be in a position to offer them the best of both worlds; combining Artificial Intelligence and human efforts to create better experiences for their customers. With live chat, the bots will be able to handle the repetitive queries and only pass the complicated ones over to the live agents.

And to make it better, you’ll be able to monitor interactions through the admin portal and assign trials to your clients, among many other things.

Essentially, you’ll be able to build and strengthen your business on the back of a powerful platform that has been used to build over 30,000 chatbots across 186 countries, in pretty much every domain and use case.‍

Preferred partnership

There is also the preferred partnership model. It helps you ride the automation wave with hassle-free flexibility. It’s like becoming a White Label partner- no profit-sharing arrangements, and you still have control over your pricing model. You’ll also reap the same features, like omnichannel support, all 54 languages, and our powerful e.Sense proprietary NLP engines, but with an Engati Partner portal instead.

You’ll receive the same extensive training from the same bot experts. We’ll also provide you with comprehensive pre-sales support to help you pitch our services to your clients. In addition, as a strategic partner, you’ll also have access to our lead directory thus powering you to become a strategic solution expert in your region. Get featured in our partnership directly, and our leads will come flying to you.

Along with a lighter monthly subscription fee, you’ll get access to Engati’s plans where you can upsell at a price of your choice. What’s more? You’ll get 1 free WhatsApp number, and an additional discount on the first 3 subscriptions you sell to your customers. Interested?

Our solutions allow you to make the chatbot and live chat market more accessible to your local market, so get in on the action, and register with Engati to get started.‍

Why choose Engati?

Engati was built to make personalized conversations at scale easy. Our focus? Power and ease of use.

With the Engati white label chatbot and live chat partnership, you get access to this entire platform along with focused training, exclusive help, and bot specialists who have created 1000s of bots for companies of all scales and use cases.

As an extension of your current offerings, Engati provides you the ability to create or expand your company in your local market. As a White Labeled Partner, you also have the ability to control your brand and, most importantly, the ability to make business deals as you find fit for rates accepted in your local market.

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The conversational virtual assistants. We have now taken from just instant message to functionality and productivity enhancers.
 — Graham Chee, Engati White Label partner

‍

Here are some of the fantastic features you get with Engati:

Supports 54 languages

With only 25% of netizens understanding English (and most of them as a second language), we realized that businesses need to provide support in the language their customers are most comfortable with.

So, our AI-powered chatbots can converse with users in 54 languages; including Right-to-Left (RTL) languages like Arabic, Persian, Urdu, and Hebrew.‍

14 deployment channels

An Engati chatbot and live chat solution can be used to serve customers on the channel they prefer. They can be deployed to serve and engage customers over 14 channels, including WhatsApp, Facebook Messenger, and Twitter.
‍

Conversation flow builder

We wanted to make it easy to build and deploy intelligent chatbots. So, our no-code chatbot platform has a drag and drop UI that makes building conversation flows a breeze.
‍

Voice support

Customers can even have vocal conversations with Engati chatbots. Now customers can seek support even while multitasking.
‍

DocuSense

We made it easier for customers to train their bots. They don’t need to manually upload FAQs any more. All they have to do is upload a document to their bot and our DocuSense technology will parse through it and pull relevant answers to the questions.
‍

Rich analytics

Our deep analytics help you understand your customers’ needs to a greater extent.

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Intent & Entity recognition

Our conversational AI pick recognized a variety of standard intents from date, location, time, and more and provides custom entity support

‍
Extensive integrations

Engati has an advanced integration framework for JSON rest APIs with many OOB standard integrations.
‍

Human takeover

You can transition seamlessly from bots to live agents handling conversations.

‍

Here’s what you get by investing in Engati White Label

We built the platform. You get to brand it your way. Your logo, your colours, your name. We replace all our branding elements with yours.
‍

Pricing control

You get to decide the pricing plans for your customers. You decide how much each plan costs and what features you wish to provide under that plan.

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Grow your business

Use the Platform that is deployed in over 180 countries and used by many businesses around the globe. With white-label, the product is as much your product as is any other product that you design or build in-house. This product will meet the same specifications and perform tasks as well as your in-house team’s work would. The only difference is that it will go under your own brand name and you will be able to grow your business with more offerings.
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Improve customer satisfaction

Deploy to your customer base to better handle customer queries. Use the Partner Portal to manage them. Moreover, with the increasing competition the only differentiating factor today is how well you serve your customers. Therefore, customer satisfaction is one of the major factors that will help you stand out in the market.
‍

Exclusive support

You get exclusive access to our white label chatbot and live chat experts who support and guide you on your journey with Engati White Label.

Here’s what one of our White Label partners has to say about our support:

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“Your team that I have been dealing with at Engati has been outstanding. They have made my job so much easier and the ability to think outside of the box in the creation of approaches is outstanding.”
 — Graham Chee
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Personalized training

Our experts even train your entire team on handling the chatbot and live chat platform.

‍

Engati white label chatbot and live chat platform for agencies

Now, you may be an agency with a customer base, a company with a software solution that wants to integrate chatbots and live chat, a start-up, or a working idea individual. Develop a cross-channel communication process with deep CRM and other business systems integration without increasing the technical assistance needed for API upkeep.

Engati has dozens of partners across a variety of firms and industries. Our chatbots in eCommerce, Healthcare, Customer Service, and as part of Marketing Automation Software are ready to help with customer support and CRM systems.

Our platform acts as a foundation for various messaging-based business areas and allows our partners to overcome challenges successfully. Use our innovative technology to provide your customers with state-of-art products and make them market leaders. Our professionals are going to assist you in this whole process.

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How financially viable is Engati White Label?

Engati White Label is a powerful and rewarding solution. But numbers speak better than words, so we’ll let you know something we’re particularly proud about.

One of our white label partners just earned a 3403% return on investment within a year of signing up with us.

And here’s the best thing:

Your profits stay with you. We don’t take a cut on them.

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What did you get with the white label?

• Platform Licensing
• NLP
• Conversational Builder
• Live Chat
• Bot Management
• Campaigns and Broadcast
• Extensive Integrations
• Partner Portal
• Support Via
• Knowledge Base
• Community Form
• Email
• Bot Experts Access
• Exclusive Training
• Branding
• Logo
• Theme Color
• Domain‍

Thanks for reading. Try the platform today!

This article about the “Whitelabel Chatbot Partnership — How to invest in automation?” was originally published in Engati blogs.

Don’t forget to give us your 👏 !

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Whitelabel Chatbot Partnership — How to invest in automation? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

How to Design a Logo for Chatbots

A logo is the first face of the chatbot. Its main power lies in its visual nature: it is a leading channel of receiving information about the world around them for the vast majority of people. Accordingly, correctly chosen logos create solid images in the mind and form a stable positive associative series with the chatbot.

A stylish and up-to-date logo is an excellent tool for chatbot promotion. Many businesses make a logo for chatbots incorrectly. As a result, people don’t use them because chatbots` logos don’t attract their attention.

Logo allows you to convey to chatbot users everything that you consider necessary. The creation of a chatbot should be approached responsibly and carefully. A well-designed logo will improve the chatbot’s popularity, while a low-quality logo will only harm the chatbot.

The principles of chatbot logo

Is it possible to make a visually impressive and effective logo for the chatbot by yourself? Yes, to design a quality chatbot logo is more than realistic, but for this, you need to know some rules of composition, the choice of shades, and other details.

Uniqueness

A lot has already been invented in today’s world, but this is not an excuse for copying elements used by other chatbots. At the same time, no one forbids to be inspired by successful options and create something of your own: original, exceptional.

Conformity with the direction of the chatbot

Even the most stylish, beautiful, neat, relevant logo will not promote the chatbot if it does not reflect its field of activity. Users may simply not understand what the chatbot does.

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?

Simplicity and memorability

As a rule, both of these qualities lie on the same plane. Overly complex and intricate logos are remembered by the users worse than minimalist ones.

Tips for choosing a typeface

There are a huge variety of different fonts that can be used in the chatbot logo. But how do you decide which one to use? To do this, use the top tips for choosing a font for your chatbot logo.

Keep in mind your chatbot’s line of business

Logo font should fit your chatbot. It can be used to convey the chatbot’s strengths and characteristics. For example, for a watch store chatbot, it’s best to use a serif font with smooth lines.

You can also analyze your competitors. Look at what fonts they use in their chatbot logos. You don’t have to choose the same style, but tracing the general direction is a good idea.

Strive for harmony

All elements of the logo, including font style, should blend with each other. For example, a large and heavy inscription will not work for a minimalist knowledge management chatbot logo.

Use 1–2 fonts

When a chatbot logo displays several font styles at once, it looks absurd. It’s important not to go overboard. Optimal to use a chatbot logo of 1–2 different fonts (although it’s better to stop at one type).

Watch for clarity

There should be enough spacing between letters. This will allow the clients to more easily perceive what is written. Merging elements of the inscription should be avoided.

Get creative

Don’t just do what everyone else is doing. You don’t have to choose a font with oriental motifs for a Chinese food restaurant chatbot. You can use a more standard style, and reflect the chatbot’s specialization with icons and colors.

How to choose a logo icon for a chatbot

An icon is an image on a logo. With the help of this element, you can tell clients about the purpose of the chatbot without words. The icon is the first thing customers pay attention to when they see your logo. Therefore, you should take care of the proper selection of images for the emblem.

Below are basic tips for choosing an icon for a chatbot logo of any direction.

Reflect the theme

A themed icon should tell you as clearly as possible about your chatbot’s field of work. For example, for a coffee shop, it is optimal to use images of coffee beans, mugs, coffee machines, etc. on the chatbot logo. Thus, the client, just once looking at the logo, will learn about the purpose of the chatbot.

You can also use an abstract icon. This is quite a bold step. But more and more companies tend to choose such images for their logo.

Watch for compatibility

The icon should be in harmony with the font of the inscription and the color. For a heavy title and dark colors it is better to use a weighty picture. Thus, you will get a seamless brand, made in the same style.

Do not overload the logo

If all the elements of the logo look loaded and worked out, the realistic and large icons will completely overload the design. Therefore, it is important to strive to keep the logo light and simple.

How to choose the color of the chatbot logo

The color of a logo plays an important role in the attractiveness of the user. In addition, colors can tell a lot of information about the chatbot. To choose a color scheme, it is advisable to study the psychology of colors in a logo. Each shade evokes certain associations in customers. And the wrong color can create a false impression of the chatbot.

I have collected the main recommendations on the selection of colors. Based on these tips, it will be easier for you to decide on the color of the chatbot logo.

Study your competitors

It is always useful to analyze the work of your main “competitors” in the market. It allows you to determine the strengths and weaknesses of your competitors, so you can use this to the benefit of your brand. The same goes for colors. You should look at what logo colors similar brands are using. You don’t have to use the same colors. But this information will help you move in the right direction.

Stop at one or two colors

In the chatbot logo, it’s important not to go overboard. To make the logo look beautiful and harmonious, it is better not to use more than 2 colors. Optimal to paint in one shade background, and in the second — the main elements. You can use additional tones, but they should be derived from the primary colors. Ready-made color schemes can help in choosing colors.

Don’t forget to give us your 👏 !

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How to Design a Logo for Chatbots was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.