Month: July 2021

How to Create a Chatbot Using DialogFlow ?

What is DialogFlow ?

DialogFlow is a development platform created by Google that can help us to create Chatbots. It’s based on NLP (Natural Language Processing) which offers our chatbots the possibility to be very powerful.

What is the ChatBot ?

A chatBot is an intelligent program that can interact with people like a human and serves them in the specific domain where it has been created. The chatbot analyzes the intention of the client and researches the response that will be more adapted.

Now you know what DialogFlow and chatbot are, let’s see how we can create a chatbot using Dialogflow.

Note: You should have a google account and login in to the Dialogflow platform before following these steps.

In this article, we will create a chatbot that can serve clients who will want to do a reservation for a bedroom in a Hotel.

Step 1. Create an Agent

An Agent is an intelligent program inside the chatbot, it’s that program that interacts with the clients or users.

To create an Agent, go to the left section of your screen and click on the first button below the Dialogflow logo and go down to the create new agent button.

After that, the new screen will be loaded, and you will be ask to specify the name of the Agent, the language that it should be speak and the time zone. For me, I type reservation-bot for the name and the rest, i leave the default values. After that, you must click on the CREATE button and DialogFlow will create an agent for your chatbot.

Step 2. Create intents

Intents is use by the chatbot to understand what the clients or users want. It’s inside the intents that we should provide to the chatbot the examples of phrases that the clients may ask and some responses that the chatbot should use to answer to the clients. Let’s show how we can do it.

Note: When we create a new agent, it comes with two defaults intents named Default Fallback Intent and Default Welcome Intent

For create a new Intent, click on the Create Intent button

After that, you must give the name of your intent. Then go to the Training Phrases section and click on add training phrases. This section concerns the way where you should give the example of the phrase which represents the different questions that clients may ask to the chatbot. we recommend giving many examples to make your chatbot very powerful.

For this example, you could take the same phrases as me.

We have added some phrases that clients may ask to our chatbot, for your own chatbot, feel free to add another phrase to improve the power of your chatbot

In this image, we can see that two expressions are overlined. In fact, DialogFlow has identified these expressions as an entity. DialogFlow recognizes three types of entities such as systems entities, developer entities, and session entities. this night and today are recognized as systems entities, it refers to the date or period of time, this type of entity is already set in Dialogflow. Later we will create our own entities which will recognize by DialogFlow as Developer entities. For more information, check out this documentation

Now, let’s define some Responses that the agent may use to answer to clients. Go down to the Response section and click on the Add response button, and add some responses statements.

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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?

You can see that inside these responses examples there are some expressions that start with the $ symbol, these expressions are considered as variables that will contain the values that clients will mention in their questions, and that DialogFlow will have recognized as a certain entity. On the image above, we have three variables such as $time-period, $date-time, and $reservation-type. $time-period and $date-time are systems entities variables and $reservation-type is a Developer entity variable, which means $reservation-type should be created by the developer, before that DialogFLow may recognize it. After added some responses that the agent should use, click on the Save button, we will come back hereafter.

Step 3. Creation of entities

In reality, entities are keywords that help the Agent to recognize what the client wants. To create it, just follow me.

Click on the Entities button

After click on the Create Entity button

After, specify the name of the entity (you should give reservation-type as name of your entity, because you have use it as variable when you gave some responses to the agent). Then, add an entity bed-room and some synonyms like below.

make sure to check the case Define synonyms before, and then click on Save button.

The role of synonyms is that, when clients should talk about bed-room, bed or room, all of this should refer to the bed-room.

Do the same with the entity reservation-action and save it.

Now, we have two entities ready to be used.

Step 4. add our entities inside training phrases expressions

back to the reservation intent interface and go to the training phrases section.

When you are there, select an expression, and inside this expression select the word bed-room like this

Then, research for @reservation-type

And click on this, and the color of bed-room will change.

Do the same thing to all the bed-room inside all expressions.

For the words booking, reservation, and reserve, do the same things but instead of research @reservation-type you will research @reservation-action.

Step 5. Definition of parameters and actions

It’s not required, but in some cases, it will be very important to obligate the user to give to the chatbot, some information.

Go down to the Actions and parameters section, always inside the reservation intent interface. you should have this image below.

For our chatbot, we want that clients provide the reservation type and the date of the reservation. Make sure to check it.

After that, we should specify the prompt text that the Agent should display to the client when they haven’t specified the required parameters. You need to click on the Define prompts… space on the right place of this section, after defining prompt text, close the box dialog.

for the date-time parameter

for the reservation type parameter

After this, save the intent.

Now you can test your chatbot.

You can test your chatbot here.

Step 6. Integration on the web platform

Click on the integrations button

You can integrate your chatbot inside of many platforms, like Facebook messenger, WhatsApp, telegram, and so on.

For this article, we are going to choose the Web Demo

click on the link, and test it again.

My Demo on my phone.

reservation-bot-dialogflow.mp4

Thank you for reading…

Don’t forget to give us your 👏 !

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How to create ChatBot using DialogFlow ? 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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So far we have discussed the idea of dialogue systems and the various types of architectures around which to model our chatbots which you can read here. In this post, I would be talking more about the techniques and algorithms that are widely being used to implement the architecture and touch upon the idea of social and emotional awareness in chatbots.

Techniques and Algorithm

To design a chatbot, there exist several techniques and algorithms that are useful to a programmer. Some of these are based on natural language processing techniques and were quite common in the earlier versions of the dialog systems, while others make use of deep neural network techniques. Bradesko and Mladenic [1] and Abdul-Kader and Woods [2] investigated some of the design techniques and algorithms used in Loebner Prize Competition conversational chatbots, some of which are mentioned below:

1.Pattern Matching: One of the most widely used techniques for designing chatbots that match the input message with the patterns present in the knowledge database using sentence measurement techniques like bigram methods [3].

2. Parsing: This technique takes the user’s message as input and looks for objects and subjects like verbs, nouns, and common phrases in the message to find dependent and related phrases and to determine the grammatical structure which can then be analyzed to check if it forms a valid expression. By this method, a chatbot can cover varied input sentences with similar keywords using a limited set of patterns.

3. AIML: A universal, easy-to-understand derivative of XML which consists of data objects called AIML objects which contain two units topics, and categories, consisting of either parsed or unparsed data. Topic includes a name attribute and set of categories related to the topic, wherein each category consists of patterns which represent user input and a template implies chatbot response. Categories can be atomic, default, or recursive [4].

4. Markov Chain Models: The idea behind Markov Chains is that there is a fixed probability with which each letter or a word appears in a dataset. This idea is used to generate responses that are probabilistically more applicable. As explained by Serban et al. a two-step procedure is followed. Firstly a seed reply is created using a sequence of keywords extracted from the message and secondly, two separate Markov chains generate the words following and succeeding the seed keywords starting from the seed reply. In this way, many candidate responses are produced and one with the highest entropy is returned as the response from the chatbot [5].

5. Ontologies: Modern task-based dialogue systems are based on domain ontologies which contain the knowledge as a set of concepts that are hierarchically and relationally interconnected. Ontologies contain concepts that are interconnected into graphs and this structure can be used to represent the intentions that the system can extract from messages. An ontology defines one or more frames with each frame having a group of slots and defines the values that each slot can take. Milward and Beveridge examined the use of ontological domain knowledge for dialogue based breast cancer referrals and control of networked home appliances [6].

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?

6. Word Embeddings: Bengio et al. proposed a distributed vector representation of words called word embeddings [7]. Word embeddings are words converted into numbers that capture the meanings, semantic relationships, and contexts and may have different numerical representations for the same word. Different types of word embeddings include frequency-based embeddings and prediction based embeddings.

7. TF-IDF: It intends to capture the importance of a particular word to some documents present in the corpora [8]. The ‘term-frequency’ term simply denotes to the contribution of a word in a document or the count of the number of times a word appears in a corpus and the term ‘inverse document frequency’ is used to penalize the words that appear more often in the corpora. The final score is calculated as the product of these two terms.

8. Recurrent Neural Networks: A variant of neural networks that stores the state of previous input and combines with the current input which helps preserve some of the relationships between the current state and the final state. This leads to the formation of an internal state which models the time-dependent data and the internal state is updated at each time step [8].

9. LSTM: To model longer dependencies the hidden units in Recurrent Neural Networks are changed to Long-Short Term Memory units [9]. It maintains two different memories- a cell state which is the long-term memory and a hidden state which is the short-term memory. A series of gates is used to determine if a new input is to be remembered, forgotten, or used as an output.

Social Chatbots

One of the primary objectives of artificial intelligence is to make machines act like humans. As suggested by, H. N. lo et al. research in the field of chatbots and conversational agents has seen a sudden increase from 2015 but more attention should be given to the new technologies like deep learning and new trends like mobile chatting apps for further research in this topic [10]. Keynar et al. investigated the importance of Open Data as a new trend that enables government transparency and citizen participation. They have used machine learning models for entity recognition and intent classification and a neural network that selects the response from a predefined set of actions [11].

Young, Cambria et al. made the first attempt to augment a large common sense knowledge base into an end-to-end conversational model [12]. Common sense knowledge in artificial intelligence research refers to facts about the everyday world that humans are assumed to know. Commonsense knowledge has to be integrated into a conversational model in order to make human-computer interaction more interesting and engaging and since this kind of knowledge is very vast in itself the model in this paper uses an external memory module which is better than forcing the system to encode it in model parameters. This model takes into account both the message content and related commonsense for selecting appropriate response and employs retrieval-based methods.

One other important issue that needs to be taken care of is the emotional intelligence of these dialogue systems or chatbots. Cambria et al. study the use of common sense knowledge for developing emotionally sensitive systems [13]. Such systems or social chatbots must be designed in such a way so as to enhance user engagement while taking both intellectual quotient (IQ) and emotional quotient (EQ) into account [14]. Social chatbots are designed in such a way that their primary task is not solving all the problems but acting as a virtual companion to the user. They need to have an emotional connection with the user and can interact through a number of modalities including text, speech, and vision.

Conclusion

We studied and analyzed various types of dialogue systems that exist including rule-based and corpus-based systems. From using simple natural language processing techniques, including pattern matching, parsing, and AIML for designing chatbots, dialogue systems have come a long way and nowadays implements complex neural network architectures for response generation. From rule-based approaches such as ELIZA to data-driven approaches which can either be based on information retrieval methods such as DBpedia Chatbot or generation based methods implementing recurrent neural networks or hierarchical neural networks to model short term dependencies. The architecture was further improved with the introduction of long-short term memory units which made dialogue systems more engaging and helped produce more natural and humanly responses. Use of common sense knowledge and other techniques are being worked upon to develop intellectual and emotional chatbots like XiaoIce. We can certainly say that there has been a lot of research and improvement going on in the field of designing a dialogue system.

References

[1] Bradeško, L., & Mladenić, D. (2012, October). A survey of chatbot systems through a loebner prize competition. In Proceedings of Slovenian Language Technologies Society Eighth Conference of Language Technologies (pp. 34–37).

[2] Abdul-Kader, S. A., & Woods, J. C. (2015). Survey on chatbot design techniques in speech conversation systems. International Journal of Advanced Computer Science and Applications, 6(7).

[3] Setiaji, B., & Wibowo, F. W. (2016, January). Chatbot using a knowledge in database: human-to-machine conversation modeling. In 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS) (pp. 72–77). IEEE.

[4] Shawar, B. A., & Atwell, E. (2002). A comparison between Alice and Elizabeth chatbot systems. University of Leeds, School of Computing research report 2002.19.

[5] Serban, I. V., Lowe, R., Henderson, P., Charlin, L., & Pineau, J. (2015). A survey of available corpora for building data-driven dialogue systems. arXiv preprint arXiv:1512.05742.

[6] Milward, D., & Beveridge, M. (2003, August). Ontology-based dialogue systems. In Proc. 3rd Workshop on Knowledge and reasoning in practical dialogue systems (IJCAI03) (pp. 9–18).

[7] Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of machine learning research, 3(Feb), 1137–1155.

[8] Lowe, R., Pow, N., Serban, I., & Pineau, J. (2015). The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. arXiv preprint arXiv:1506.08909.

[9] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.

[10] Io, H. N., & Lee, C. B. (2017, December). Chatbots and conversational agents: A bibliometric analysis. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 215–219). IEEE.

[11] Keyner, S., Savenkov, V., & Vakulenko, S. (2019). Open Data Chatbot. arXiv preprint arXiv:1909.03653.

[12] Young, T., Cambria, E., Chaturvedi, I., Zhou, H., Biswas, S., & Huang, M. (2018, April). Augmenting end-to-end dialogue systems with commonsense knowledge. In Thirty-Second AAAI Conference on Artificial Intelligence.

[13] Cambria, E., Hussain, A., Havasi, C., & Eckl, C. (2010). Sentic computing: Exploitation of common sense for the development of emotion-sensitive systems. In Development of Multimodal Interfaces: Active Listening and Synchrony (pp. 148–156). Springer, Berlin, Heidelberg.

[14] Shum, H. Y., He, X. D., & Li, D. (2018). From Eliza to XiaoIce: challenges and opportunities with social chatbots. Frontiers of Information Technology & Electronic Engineering, 19(1), 10–26.

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A Comprehensive Survey of Existing Chatbot Architectures and Techniques(Part-2) was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Are you a small business owner? 24 hours just don’t seem enough. Getting tangled in between endless To-Do lists and wishing everything could be automated with a click of a button…all this while saving costs?

According to a recent Salesforce study, more than a third of small business owners worry about not having enough time in the day.

A major problem small business owners face is that most mundane daily tasks get divided attention; leading to quality being compromised or even tasks being abandoned before completion.

Enter Artificial Intelligence.

AI allows companies to automate numerous steps in both core and support functions thus increasing efficiency and reducing costs. With Artificial Intelligence one can assure consistency in business processes, especially for those mundane and repetitive tasks.

For example, you are a real estate agent, the job demands you to be in and out of the office throughout the day. As such, there are chances you could lose a potential customer while you are out of the office. But certain answers to clients’ questions can be delivered only by you. Therefore, cloning your expertise with an AI-enabled chatbot will ensure consistent dispensation of knowledge and commoditized expertise.

As a small business owner, there would be several doubts about applying “AI for my small business”. Will the investment give good returns? Do I need this cutting-edge technology? Botspice provides an easy customizable Workbot model that is easy to implement and grows with time.

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?

Here’s are some benefits to cloning yourself using Artificial Intelligence,

· WORKBOTS NEVER TAKE A DAY OFF!

Workbots are available 24 hours a day 7 days a week and can often answer customers’ questions more quickly and efficiently than humans. Cloning your expertise with a Workbot will not only allow consistent answers to queries but also make your expertise available during non-business hours.

· NO ADVANCED I.T. SKILLS REQUIRED

83 percent of the small businesses surveyed don’t have IT staff to help them when technical difficulties arise.

With a Workbot business can grow without any complexities of adding technical knowledge. Workbots are future proof and it does not need much supervision once deployed. Botspice provides a variety of activity nodes to support a multi-lingual and multi-dimensional conversational flow. And it is easy to create and maintain a Workbot.

· VALUABLE CUSTOMER DATA

Gone are those days where one would fill a form and call it a lead. Replacing lead-generating forms with an intelligent conversationalist will enable a business to understand customer behavior. This valuable data can be used to identify the overall demographics and identify how they can improve the customer experience through these conversational funnels.

· GENERATE LEADS WITH AI-POWERED INSIGHTS

Businesses can analyze the statistical data and record market behavior over time to generate valuable insights for strategizing and forecasting future business plans. Through an analytics dashboard, one will be able to obtain a 360-degree view of customers. It also gives an idea of which lead is likely to convert into a sale.

It is benefits like these that make Artificial Intelligence the best fit for small businesses and business owners. Cloning expertise helps automate time-consuming and costly processes. While you may not be able to biologically clone yourself anytime soon, an AI-enabled ecosystem could be the answer to digitize and automate your business processes.

Don’t forget to give us your 👏 !

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Clone your Expertise with AI-Enabled Workbots was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

As a tool to build and deploy interactive chatbots, AI-based bot platforms are a rage today. Organizations utilize these tools to create intelligent and conversational bots to interact with their website visitors, offer them the required information, and improve customer satisfaction.

In this blog, we explore chatbots and their types along with the best chatbot builder in 2021 to help you build an interactive chatbot.

Building a Chatbot — Use Cases, Requirements, and Types of Chatbot

Building a chatbot is a complex process that requires everything from a detailed strategy, tools, conversational process flow, reporting, and much more. Among the key things that you need here include-

• Problems you are trying to solve with chatbot or use cases
• Time required to address the issue to figure out the ROI
• Functionality-Manual, automated or both
• Integrations

Types of Chatbots

There are mainly three different types of bots that you can build here-

• Rule-Based Chatbots

As the name suggests, rule-based bots work on predefined rules. These rules form the basis for the types of issues the chatbot is familiar with and can deliver solutions for.

• AI Chatbots

AI chatbots leverage NLP to understand the intent, followed by processing the information and offering relevant/contextual answers instead of simply relying on a predetermined output.

• Hybrid Chatbots

Hybrid chatbot combines the power of both rule-based and AI-powered bots to offer a superior customer experience.

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?

14 Best Chatbot Development Platforms

Some of the best AI-chatbot builder platforms include-

1. WotNot

One of the best ai based chatbot builder platforms available, WotNot helps you build intelligent chatbots and also offers a complete range of conversational marketing solutions for multiple industries.

2. Landbot.io

Landbot.io is an intuitive tool that allows you to build both rule-based chatbots and AI-powered bots. The platform enables you to interact with your prospective customers and generate high-quality dialogues.

3. Bold360

As one of the most popular chatbot development tools, Bot360 leverages NLP technology to help customer service executives be more efficient and effortlessly take over conversations.

4. Intercom

Intercom is a well-known name that offers custom chatbots for use cases around multiple fields, including marketing, sales, and support. This AI bot builder can also be integrated with social media platforms and e-commerce websites and have live chat options.

5. Drift chatbot

If you want to build a bot to book meetings for sales teams and seamlessly facilitate conversations with leads, Drift is a great choice. Besides identifying the right sales executive and scheduling an appointment, it also qualifies website visitors without using any cumbersome forms.

6. LivePerson

An excellent platform to help you comfortably build, deploy, and optimize AI-powered chatbots, LivePerson is an ai based chatbot builder that enables you to use advanced analytics for real-time intent detection.

7. Flow XO

Flow XO is a platform that lets you build bots without any coding. You can use the platform across multiple platforms while also integrating them with other 3rd party platforms.

8. Botsify

One of the best chatbot builders, Botsify, helps you create bots for websites, Messenger, and Slack with various ready-to-use templates. The platform also allows you to hand over the chat to a human agent seamlessly.

9. Aivo

Offering full customer service and the ability to respond to customers in real-time, Aivo bots can be easily programmed under different rules and conditions across channels to interact with customers.

10. Chatfuel

Chatfuel bot builder allows you to leverage NLP to identify intents and utterances, followed by sharing predefined answers.

11. BotsCrew

A popular choice for SMBs and SMEs, BotsCrew is among some excellent chatbot development tools that provide a managed service.

12. Pandorabots

Pandorabots is an excellent AI-based chatbot platform that offers comprehensive solutions for chatbot development. As one of the oldest and largest chatbot builders, it is also a multilingual chatbot.

13. Manychat

Manychat allows you to build and deploy bots on Messenger for use cases ranging from sales, marketing, and customer service.

14. Octane AI

Octane AI is an excellent choice if you wish to integrate a chatbot with a Shopify store via Facebook Messenger, answer customer questions automatically, and send shipping information.

In Conclusion

To pick the best chatbot builder, it is essential to develop a thorough understanding of your specific use case to help you determine what exactly you want out of your chatbot.

Once you do thorough research based on features, pricing, and integrations, it becomes easier to pick the platform that suits your individual needs. Want to know more about the topmost chatbot platforms? Read the detailed blog here.

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A Comprehensive List Of The Best Chatbot Platforms 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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By Jeff Betts, Head of Product

Conversational AI experiences have made great strides in recent years to support a growing number of use cases with an often unnoticed efficacy. From ordering a pizza to more serious matters like securing COVID-19 vaccine appointments and locating where people can register to vote, chat experiences have helped connect users to an incredible amount of resources. Despite this growth, one thing many conversational AI experiences have lacked is a unique voice.

Not a literal voice, but rather a personality and tone that communicates to us as an extension of a brand always available to help solve a problem or accomplish a goal. There are many reasons a brand might opt to include a personalized voice in a chat experience like leveraging a consumer connection with a team through a mascot, highlighting a marquee product, or even as straightforward as softening or brightening the tone of an important message so it’s more relatable to audiences that are used to communicating on social media apps.

Personification: Extending your brand

GameOn Technology has helped evolve the chat app industry by having our products match the tone of real-world brands, such as our recent work with Mike Tyson, the Atlanta Hawks, and Arsenal FC. Yes — these conversational applications give fans the information they’re looking for like Iron Mike’s favorite knockout and video of Trae Young’s latest 3-pointer from the Hawks’ center court logo — but they also provide a personality-driven chat experience that keeps users coming back for more.

In working with Mike Tyson on his custom experience, the GameOn team helped translate Mike’s story and voice to messaging channels with unique quotes, images, custom artwork, and Deep Thoughts from Mike. Upon launching, we soon observed frequently asked questions such as “when is your next fight?” or “what was your favorite knockout?” The Tyson team was able to use GameOn’s Q&A feature to pair select questions to answers that came from Iron Mike himself.

The Atlanta Hawks are a great example of utilizing a mascot to personify an experience. The Hawks made an incredible 2021 NBA Playoff run which has helped fuel a growth of over 250% in new users month over month for their new application! Fans are asking everything from “what’s the current score?” to “where can I buy the red playoff shirts?” and “how many kids does Harry the Mascot have?” Harry’s personality, and immense Atlanta Hawks knowledge from his 35 years with the team, helps power the bot and keeps fans engaged.

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?

Data Driven Results

Many industries that would reap the benefits of a personality driven experience include customer service, brands with well-known mascots, celebrities, sports organizations, video games, sales organizations, and even retail brands. In the case of Arsenal, GameOn worked directly with the team to base their application on football legend Robert Pires. This resulted in an authentic 1:1 relationship between fans and the sports team, making the experience a huge success. Gunners fans are delighted with, and appreciate, the Easter eggs and remarks from “Robot Pires” sprinkled throughout the experience such as his slide into victory when a user asks about a match Arsenal won! The Arsenal FC conversational application saw an increase of engagement on match days by over 3 times and 90% of fans become weekly-active users (WAUs) averaging over 1M messages per week within the first 6 months.

This is only the beginning for GameOn’s ChatOS platform. We will continue to push the industry forward by creating conversational applications that better deliver the answers consumers seek from brands they follow. The closer we are able to secure that 1:1 connection, the more rewarding the experience will be for everyone.

Don’t forget to give us your 👏 !

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A.I. with Attitude: Building a chatbot with personality was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Artificial Intelligence (AI) is one of the most pervasive technologies in use today. With the human language being the medium to how we communicate, it is no surprise that Conversational AI (CAI) is becoming the most prominent frontier of this technology. Many businesses are enlisting the help of this technology to stay competitive.

According to Markets and Markets, the expected global Conversational AI market size is set to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 21.9%.

Therefore, companies, industry leaders, and employees need to understand precisely what Conversational AI is, why it’s essential, and the many use cases of this AI application disrupting Healthcare, IoT Devices, Retail, HR, and Finance and Banking Industry.

What is Conversational AI?

Conversational AI is a subset of AI that focuses on imitating conversations with humans to deliver a human-like conversational experience. The goal of these types of AI is to replicate the human experience as closely as possible. Through Machine Learning technologies like Natural Language Processing (NLP) and Natural Language Understanding (NLU) — Conversational AI aims to process language data (what you say) and understand it (what you mean).

Other technologies like speech recognition, sentiment analysis, and dialogue management are also used to provide Conversational AI with the ability to respond accordingly. To do so successfully, Conversational AI needs input data that humans curate to learn from how we communicate and understand one another naturally.

Conversational AI is seen as a successor to chatbots, one of AI’s first applications. While chatbots strictly follow a script, Conversational AI allows for a more contextualized conversation. The application of Conversational AI is far more complicated and nuanced than chatbots simply because of its ability to understand language on a deeper level.

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?

Why is Conversational AI important?

As the adage goes — it’s not a matter of if, but when.

And with Chatbots, it’s not hard to see why. ACCORDING TO JUNIPER RESEARCH, Conversational AI solutions can scale, provide 24/7 service and asynchronous conversations, and are forecasted to have operational cost savings in industries like banking of $7.3 billion globally by 2023.

However, it is not enough to have a chatbot on your site in 2021. Businesses need to have intelligent chatbots with natural language processing and understanding for the best customer support experience. In fact, with the emergence of Conversational AI, more and more people expect chatbots to understand them and assist them beyond what they can find on a FAQ. Conversational AI can deliver a customer experience equal to or better than a live chat when done correctly.

In addition, One cannot overlook the importance of the handshake between a bot and a website. A bot cannot replace or compete with a website: the best chatbot designs are ones where the site and bot work in tandem. The trend for Conversational AI bots is now increasingly beyond solutions for just reducing operation costs of call centers, but instead adding to the customer experience and providing better engagements.

Armed with the machine learning technologies, it is not surprising that Conversational AI applications are behind many chatbots and devices that exist in the market today, proving to be a core component to social success.

What are the 5 Industry Use cases for Conversational AI?

The Conversational AI industry is currently undergoing explosive growth as it becomes increasingly applicable for more use cases. Here are the top five sectors and their use case of CAI technologies to improve user experience and interactions and provide excellent customer support.

1. Healthcare usage cases for Conversational AI

The healthcare industry is undergoing a paradigm shift as it becomes increasingly apparent that Conversational AI can help cut costs and streamline the patient experience. Patient care has not been immune to this new age of technology, with chatbot ai solution now playing an essential role in cutting down on unnecessary time from human medical assistants to helping patients understand their treatment plans through natural dialogue — albeit digitally enhanced.

Use cases for Conversational AI for the healthcare:

• Diagnosis: Conversational AI can help diagnose conditions online by asking the patient a series of questions and learning based on their responses to provide insight into any potential health issues they may be experiencing.
• Medical scheduling: Conversational AI can streamline a patient’s medical appointment by providing them with general information about their next visit before they even arrive at the hospital. It can also handle the patient’s paperwork and help them schedule appointments.
• CBT: Cognitive Behavioral Therapy is an effective way to treat mental health problems like anxiety. Conversational AI can provide a completely immersive CBT experience with the help of NLP and NLU.
• Therapy: Conversational AI can help fill the gaps in care that mental health patients receive from human clinicians. Conversational AI can provide a 24/hour service, which means it can be provided for as long as necessary without needing breaks or days off. Additionally, bots have no judgment and won’t stigmatize patients when engaging in a conversation- something significant for mental health.
• Mental Health: Bots like Replika, which are conversational chit-chat bots, can help with emotional counseling, providing a safe and private space for people to share their feelings. Conversational AI can also aid in therapy sessions themselves- such as assisting a therapist by taking notes or summarizing the session.
• Medical assistant: Conversational AI can be a virtual assistant to support patients and their carers, helping them understand health-related topics. This tactic often helps reduce the stress levels associated with healthcare services by freeing up human medical assistants for more high-level work that is best left in their hands.
• Data Collection: Conversational AI is also being used by pharmaceutical companies as a method for gathering user feedback on their products via surveys or focus groups — all without the need for an interviewer. This saves both time and money spent on hiring human data collectors while still collecting valuable information from consumers that can be analyzed using Conversational AI’s Natural Language Processing capabilities.

2. Internet of things (IoT) devices

Perhaps becoming the more useful helper in the household in this day and age are Conversational AI-enabled devices that use automatic speech recognition to engage with users. These include voice assistants such as Amazon’s Echo and Google Home and mobile, smartwatch, and desktop assistants like Apple Siri and Cortana. Conversational AI can bridge the gap between humans and non-human interfaces by understanding natural speech patterns and allowing context without a rigid conversation structure.

Many of these devices use unsupervised machine learning — meaning Conversational AI’s abilities are self-learned through trial and error in response to user input.

Some of the use cases for this industry include:

• It can control home appliances through google or Alexa.
• We are getting any devices to “dial” phone numbers and send messages on the user’s behalf.
• Ordering food or grocery items through Conversational AI-enabled devices and apps like Amazon’s Alexa while simultaneously learning what the user likes to suggest better products that they may be interested in.
• It is remotely actioning tasks such as turning on the lights or air conditioner.

3. Retail use cases for Conversational AI

Conversational AI in the retail space is an emerging trend: lead generation, lead qualification, lead nurturing to 24/7 concierge services, faster order fulfillment, and amplifying marketing messages.

Through smarts like API integrations, other use cases for retailers with conversationally enabled applications include:

• Product Recommendations: Conversational AI is leveraged by retailers as a customer service chatbot to provide product recommendations based on user interactions and search queries.
• Customer Data Insights: Conversations with customers are recorded digitally, eliminating the need for humans to manually input every word spoken during an interaction or call center conversation. A simple data analysis into the type of search queries asked can provide businesses with further insights into their products and services.
• Scalability and Multi-Channel Integrations: Conversational AI can scale conversations across different channels simultaneously (i.e., email to web assistance to Facebook) without human intervention. This provides increased opportunity for conversions and sales while at the same time reducing costs associated with traditional methods of communication that require human involvement, such as phone calls.
• Better User Experience and Engagement: Conversational AI can be used in retail settings not just as one-off ‘conversations’ but as ongoing conversations. Maintaining context and holding data from previous conversations will translate to better customer experience, engagement, and conversion rates.
• Inventory tracking: Conversational AI provides the ability to track inventory and offer availability to customers.

Through all these use cases, Conversational AI provides the foundation to excel in online retailing in 2021 — not only by providing the information that customers need immediately and 24/7 but gives businesses the data from their customers to provide better and more optimized products and services.

4. Human Resources

Companies are also leveraging conversational AI for HR purposes — from recruitment to documentation directory. Companies can find that their resources can be allocated more efficiently, boosting productivity, maintaining staff satisfaction, and saving time and money.

The most common use cases for an HR bot are:

• Recruitment: Conversational AI can be used to sift through CVs and job applications, eliminating a need for HR staff to go through every application manually. The ability to analyze data such as keywords found on the CV compared to other applicants’ search terms means that these AI can provide companies with a more accurate list of possible candidates than any HR could.
• Onboarding: Conversational AI can be used to automate onboarding and orientation processes, including advising new employees with any necessary information they need about their work before they start work- such as where bathrooms are located. AI chatbots also have a user interface that is more intuitive than any HR staff member will ever have and can remember every conversation with an employee.
• Documentation: AI can automate documentation processes, which means that HR staff will not constantly update their records. Conversational AI’s memory function also ensures all employees’ documents are up to date in real-time.
• General staff advisory: Acting as a concierge service or a help desk, the use of Conversational AI extends to answering any questions, filling out leave applications for employees, and automated shift date and reminders.

5. Finance and Banking

According to a recent study by Juniper Research, the success rate of bot interactions in the banking sector will reach 90% in 2022. This means that the most robust way for financial institutions to stay competitive is by embracing digitalized customer experience strategies such as Conversational AI.

Conversational AI is currently making waves in the world of finance and banking, with use cases including:

• Banking virtual assistant bots can check user balances or process a transaction across any bank accounts.
• It prevents fraud with automatic speech recognition, detecting any keywords or phrases that could signify fraudulent activity on the user’s account. Conversational AI also can detect any anomalies from normal behavior, which could be indicative of fraud.
• Finance bots can process any transactions to provide you with an accurate picture of your finances. Conversational AI will help access and analyze data, such as trends in spending habits or bank accounts, to recommend how best to spend money.

The Bottom Line

Conversational AI promises a lot of potential in 2021. There are many industries with excellent use cases for Conversational AI: Healthcare, Public Sector, HR, IoT devices, Retail and Banking, and Finance — each one promising to change how that industry operates. Conversationally enabled applications have the potential to take customer service from good to great — chatbots can assist companies in saving up to 30% customer support costs and answering up to 80% of routine questions.

Let’s take a look at how far AI has come. Enjoy the video below — it might change the way you think.

What It’s Like To be a Computer: An Interview with GPT-3

Now back to the topic. While it may seem that Conversational AI is an easy to implement, all-encompassing force to be reckoned with, it is worth noting that a chatbot is only as good as its solution and conversation design and the platform that facilitates it.

Ultimately, Conversational AI chatbots still need to be human-curated simply because humans understand how humans communicate best. While Conversational AI can never truly replace human-to-human conversations and interactions, it’s certainly getting close.

Originally posted at RuhaniRabin.com. Click here to subscribe now

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The Top 5 Industry Use Cases for Conversational AI was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

What is “conversational”? By the Merriam-Webster dictionary, conversation is defined as an “oral exchange of sentiments, observations, opinions, or ideas”. Because of the exchange, one-on-one conversations are often considered the most effective way for humans to engage with one another.

However, human-driven conversations don’t scale. From our conversations with universities, we learn that it is not unusual for an admissions office to have only four to five staff members, who might need to handle over 30,000 to 40,0000 incoming student inquiries, not to mention that many such inquiries are repetitive on the similar topic , e.g., program requirements, financial aid options, and application process. Additionally, universities wish to learn more about prospective students who made such inquiries, such as their interest and expectations, which will enable university staff to better follow up with the students (e.g., inviting them to apply for a program or scheduling an interview). So what capabilities must a chatbot have in order to help universities automate such engagements but without losing a human touch?

What is a Conversational Chatbot

To scale out human-driven conversations while containing cost, more and more universities now are enlisting the help of chatbots. Now you might be puzzled: how could a chatbot be not conversational? From the literal sense, it makes absolutely no sense if a chatbot is not conversational. In reality, however most chatbots out there unfortunately cannot converse with their users, hence are not conversational.

How can chatbots be made conversational? To possess human conversation skills, conversational chatbots must be powered by cognitive intelligence — an advanced, human-like Artificial Intelligence (AI). Such a chatbot, also known as a cognitive AI assistant or a cognitive AI chatbot, should demonstrate three key conversation capabilities.

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A Conversational Chatbot Supports Two-Way Communication

We’ve all had the experience with a chatbot where we are clicking buttons and being forced to follow a path that leads nowhere. Frustrated, we give up and try to ask a question directly in the chat window. The chatbot, however confused, responds that it does not understand our input. This is a typical one-way exchange, where a chatbot tries to dictate the whole conversation by following a rigid, pre-defined chat flow, which often leaves users frustrated and unhelped.

In another scenario, a chatbot might open up a conversation with a phrase like “How may I help you?” What happens next is also quite familiar: after texting a question, we receive a chatbot response “Sorry I’m not sure I understand, would you mind rephrasing your question?” Left clueless, we don’t know how to rephrase the question and are also afraid no matter how we rephrase the question, the chatbot still could not understand. So we give up. This is another typical one-way exchange, where a chabot tries to let users drive the conversation without providing any guidance, which often results in failed conversations because of today’s technology limitations.

In contrast, conversational chatbots support two-way exchanges, delivering more human-like conversational experiences. Specifically, conversational chatbots can take initiatives and guide a conversation while actively listening to users and allowing users to take initiatives during the conversation. The example below shows a two-way conversation between a chatbot and a student who is inquiring about financial aid options.

A Conversational Chatbot Understands and Maintains Conversation Context

Continuing our above example, a typical chatbot might not recognize and respond to the user’s question, even if it did, it would get lost by the user interruption or take us back to the beginning of the conversation. Either way would derail the conversation and fail to complete the intended task (in this case, recommending financial aid programs).

Just like humans in a conversation, conversational chatbots are able to understand the focus of attention and remember the conversation context. As shown below, while a user diverts from the initial flow of the conversation, the chatbot answers the question, and then naturally resumes the flow where it was left off. This conversation capability enables a chatbot to complete assigned tasks while helping users whenever possible.

A Conversational Chatbot Can Read People and Personalize Each Engagement

In addition to language abilities, great human conversationalists have high social-emotional intelligence, which enables them to deeply understand and empathize with their conversation partners and foster effective and pleasant conversations.

Unlike a typical chatbot that often offers one-size-fits-all responses and knows little about its users, a conversational chatbot can actively listen to its users, infer the users’ unspoken needs and wants from a conversation, and personalize each conversation. Not only does this capability allow a conversational chatbot to behave more human-like, but also enables it to better help its users based on the users’ needs and wants. The conversation example below shows that such a chatbot provides personalized career information to students based on the students’ passions, interests, and personality.

Why Adopt a Conversational Chatbot Now?

The demand for personalized, conversational interactions is a growing expectation by today’s university students. Multiple university studies show that students are willing to and wish to interact with a chatbot and can also benefit from such interactions. Higher education needs to cut through the clutter and create a better connection with their audience, e.g., current and potential students and alumni. Additionally, university studies indicate that technologies already exist to support rapid, no-coding setup and adoption of cognitive AI assistants, which not only deliver a higher engagement rate, but also continually improve from users’ interactions.

In other words, conversational chatbot solutions are no longer a luxury item for only certain universities. Without requiring additional technical resources, EVERY university now can easily adopt cognitive AI technology and their human-like conversational capabilities to improve student/alumni engagements, reduce administrative burdens, and elevate workplace morale, as the university hopes to achieve.

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Why Conversational is an Essential Component of a Successful Chatbot in Higher Education 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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