“80% of business to customer communication is going to be done through bot messengers within the next three to five years” – ManyChat CEO Mikael Yang
The fact that we’ve grown accustomed to texting and messaging over the last 20 years has actually primed us for interactions with conversational bots. We’ve grown comfortable with the texting interfaces. In fact, most of us even prefer to ignore inbound phone calls and text the caller back.
When was the last time you had a conversation online with a customer service agent? You were probably complaining about receiving an order late, or needing to make a return. And there is a high chance that the person on the other end was not even human, but an artificially intelligent conversational bot.
A chatbot (also known as a smartbots, talkbot, chatterbot, Bot, IM bot, interactive agent, Conversational interface or Artificial Conversational Entity) is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods.
Conversational bots are programs that imitate human conversation through text chats or voice commands. They are computer programs that enable the interaction between a user and a service by using a conversational user interface (UI) as its means of reaching from inside its world out into ours.
But beyond a computer program, conversational bots are the present and future of customer engagement. They provide a suitable and interactive way for companies to interact with customers.
Conversational bots “live” online and give customers a familiar experience, similar to engaging an employee or a live agent, and they can offer that experience in higher volumes. Conversational bots offer scaling—or the capability to perform equally well under an expanding workload—in ways that human can’t, assisting businesses to reach customers in a way they couldn’t before. For one, businesses have created 24/7/365 online presence through conversational bots.
Would you like to learn more about commonly used words in Chatbot Terminology? Visit our Chatbot Glossary to discover must know industry terms.
Chatbot Terminology
What are Terminology of Chatbots?
Artificial Intelligence (AI): The study and programming of computer systems that can perform tasks normally associated with human intelligence (including perception and discovery, decision-making, and speech recognition).
Algorithms: An equation or set of rules that a computer follows, coupled with AI decision-making capabilities.
Artificial Neural Networks: These are considered the “foundation of AI,” and refer to the pieces of the processing system that computers use to perform human-like, intelligent problem solving.
Natural Language Processing (NLP): This is the area of AI that revolves around the study of machines interacting with humans through “natural” (i.e., human) language.
Machine Learning: This is where AI equips a machine to learn and improve its processes automatically without human intervention.
Interactive Voice Response (IVR): This is the technical name for the interactive telephone systems that can respond to voice and dial pad commands.
User Interface (UI): This is where the program “comes to the surface” where it can interact with a user. UI refers to all the means a system uses to interact with humans, including a desktop, display screens, keyboards and mouse.
Conversational Interface: The conversational interface is where the system intakes natural language, whether written or spoken.
Deep Learning: This is a subset of machine learning, which is a subset of AI. It’s considered “deep” because it has the ability to machine-learn from unstructured, unlabeled data on a completely unsupervised basis.
How did we reach this level of sophisticated conversational interfaces?
Conversational bots date back to the mid-20th century—can you believe it? Below is a list of some of the most influential conversational agents.
1966 ELIZA: Eliza simulated human conversations by matching user prompts to scripted responses. However, it could not learn or contextualize through interaction.
1972 PARRY: Parry was programmed to mimick a person with paranoid schizophrenia.
1995 ALICE: “ALICE” is an abbreviation for “Artificial Linguistic Internet Computer Entity.” In order to engage in a conversation, ALICE used pattern-matching heuristics (learning by experimental trial and error) to the input received from a human user in “conversation.”
2010 SIRI: Though Siri is considered colloquially to be a virtual assistant rather than a conversational bot, it was built off the same technologies and paved the way for all later AI bots and PAs. Siri is an intelligent personal assistant with a natural language UI to respond to questions and perform web-based service requests. Siri was part of apples IOS.
2015 ALEXA: Like Siri, Alexa is more of a virtual assistant. Alexa is Amazon’s voice service that uses natural language to receive, interpret and respond to voice commands. And the interface can be used across multiple connected devices, primarily in the home.
2016 CORTANA: Cortana is an intelligent personal assistant created by Microsoft. Cortana recognizes natural voice commands, sets reminders and responds to question using the Bing search engine.
2016 BOTS FOR MESSENGER: In April 2016, Facebook launched a platform where they allowed business pages to create bots that can interact with users.
Chatbot News
Latest Developments in Chatbot news
The field of chatbots is continually growing with new technology advancements and software improvements. Staying up to date with the latest chatbot news is important to stay on top of this rapidly growing industry. We cover the latest in artificial intelligence news, chatbot news, computer vision news, machine learning news, and natural language processing news, speech recognition news, and more.
Today, Alexa allows us to turn on or off the lights with voice; Cortana and Siri live inside our phones and respond to our commands; Google Assistant makes suggestions on where we might want to visit while discussing holiday plans with our family; and Tesla can drive on our behalf.
Screenless conversations are expected to dominate even more as internet connectivity and social media is poised to expand. From the era of Eliza to Alice to today’s conversational bots, we have come a long way. Conversational bots are changing the way businesses and programs interact with us. They have simplified many aspects of device use and the daily grind, and made interactions between customers and businesses more efficient.
For example, say you want to purchase a pair of shoes online from Nordstrom. You would have to browse their site and look around until you find the pair you wanted. Then you would add the pair to your cart to go through the motions of checking out. But in the case Nordstrom had a conversational bot, you would simply tell the bot what you’re looking for and get an instant answer. You would be able to search within an interface that actually learns what you like, even when you can’t coherently articulate it. And in the not-so-distant future, we’ll even have similar experiences when we visit the retail stores.
Chatbot Categories
What are the different categories of Chatbots?
Several different categories of conversational bots exist, in which each type is based on the desired response and use case. Chatbots can be classified and categorized using the following parameters:
1. Response Generation Method
Here, conversational bots are classified depending on whether their predominant feature is the use of AI or of scripted questions and answers.
A. Scripted and Structured Conversational Bots
Conversational bots that work on hard-coded questions and answers have a narrower knowledge base and skill set. They only provide the right responses to specific instructions. This implies that our instructions or questions have to fit the programming of the conversational bot.
Let’s take a weather chat bot as an example to examine the capabilities of Scripted and Structured chatbots. The question “Will it rain on Sunday?” can be easily answered. However, if there is no programming for the question “Will I need an umbrella on Sunday?” then the query will not be understood by the chat bot. This is the common limitation with scripted and structured chatbots. However, in all cases, a conversational bot can only be as intelligent as the programming it has been given.
If your interaction with a conversational bot is through a specific menu (where you interact through buttons but the bot does not understand natural language input), chances are you are talking to a bot with structured questions and responses. This type of bot is usually applied on messenger platforms for marketing purposes. They are great at conducting surveys, generating leads, and sending daily content pieces or newsletters.
B. NLP-Based Conversational Bots
If you have any intentions of offering a customer service conversational bot for your enterprise, then this is probably the right type of bot.
Natural Language Processing (NLP) is the technological process in which computers derive meaning from natural human inputs. NLP-Based Conversational Bots are machine learning bots that exploit the power of artificial intelligence, which gives them a “learning brain.” These types of conversational bots have the ability to understand natural language, and do not require specific instructions to respond to questions as observed in types of chatbots such as Scripted and Structured Conversational Bots.
In the case of the weather bot example in our previous segment, the question “Will I need an umbrella on Sunday?” would have been understood by an NPL-Based Conversational Bot. Natural Language Processing (NLP) allows them to understand the input of natural language.
2. Knowledge Domain
This is a classification based on the knowledge conversational bots have or the type of information they are expected to provide. In this parameter, we have an open domain and closed domain categories of conversational bots.
A. Open Domain
Open domain chatbots tends to talk about general topics and give appropriate responses. In other words, the knowledge domain is receptive to a wider pool of knowledge. However, these bots are difficult to perfect because language is so versatile. Conversations on social media sites such as Twitter and Reddit are typically considered open domain — they can go in virtually any direction. Furthermore, the whole context around a query requires common sense to understand many new topics properly, which is even harder for computers to grasp.
B. Closed Domain
Closed domain chatbots focus on a specific knowledge domain, and these bots may fail to answer questions in other knowledge domains. For example, a restaurant booking conversational bot will be able to take your reservation, but may not respond to a question about the price of an air ticket. A user could hypothetically attempt to take the conversation elsewhere, however, closed domain chatbots are not required, nor often programmed to handle such cases.
3. Service Provided
This parameter is based on the sentimental proximity of the conversational bot to the user. It is dependent on the intimate interaction that happens and is also dependent on the task being performed by the bot.
A. Interpersonal
These are conversational bots that exist and operate within a personal range of the user. They perform services such as flight and restaurant booking. These types of bots get information and pass it on to the user, too. They are enablers.
B. Intrapersonal
These lie within the personal domain of a user but perform personalized services like keeping the user’s opinions stored and tagged or managing a calendar.
C. Inter-agent
These are mostly found in IoT-dominant areas. Bots communicate with each other in these two systems to perform a task. An example of an inter-agent conversational bot is the Alexa-Cortana integration.
How Do Chatbots Work?
Explain how a Chatbot works?
Conversational bots work in a similar way as an employee manning a customer care desk. When a customer asks for assistance, the conversational bot is the medium responding. If a customer asks the question, “What time does your store close on Friday?” the conversational bot would respond the same as a human would, based on the information available. “Our store closes at 5pm on Friday.”
So, how do they actually work?
Conversational bots work through three key technologies:
Pattern Matchers
Conversational bots apply pattern matching to classify the speech or the text and give an appropriate response to the user.
Algorithms
For every question or instruction input to the conversational bot, there must exist a specific pattern in the database to provide a suitable response. Where there are several combinations of patterns available, and a hierarchical pattern is created. In these cases, algorithms are used to reduce the classifiers and generate a structure that is more manageable. This is the “reductionist” approach—or, in other words, to have a simplified solution, it reduces the problem.
Artificial Neural Networks
These are one of the major tools applied in machine learning. They are brain-inspired processing tools that actually replicate how humans learn. And now that we’ve successfully replicated the way we learn, these systems are capable of taking that processing power to a level where even greater volumes of more complex data can be understood by the machine.
Chatbot Companies
Discover innovative Chatbot startups and companies
It takes bold visionaries and risk-takers to build future technologies into realities. In the field of chatbots, there are many companies across the globe working on this mission. Our mega list of artificial intelligence, machine learning, natural language processing, and chatbot companies, covers the top companies and startups who are innovating in this space.
NLP is the module through which user requests are analyzed. NLP takes unstructured phrases from the user and processes them into structured phrases. NLP may be built in-house or acquired from a third party.
Dialogue Manager
Dialogue manager is the component that determines what to reply to the user, depending not only on the input but on past interactions with the user and any other information derived from other sources. Dialogue managers may be built in-house or may be obtained from a third party.
Content
Content is the third component to conversational bots. It is basically a template of what the conversational bot is going to reply after analyzing the input and other data. Content is very custom and can change completely depending on the project. It may also be built in-house or may be obtained from third parties.
Health Monitor
This is a usually-assumed but very necessary component of chat bots. A health monitor dashboard is created to give analytics and insights into the conversational bot’s performance and learning.
Chatbot Applications
What are applications of Chatbots?
Conversational bots have revolutionized the way humans interact with computers and smartphones, and now all other smart devices. Having blended artificial intelligence into human conversations, conversational bots can be applied in many areas. In fact, their applicability is only limited to your creativity.
Customer relationship management
Conversational bots can help a business’s customers with difficult transactions, plus collect data and give recommendations. For example, a conversational bot integrated to an airline’s website can answer questions regarding flight availability, rebook tickets, fees and suggest add-ons like hotels. Though a conversational bot may not be able to finish the exchanges, it could still be able to gather preliminary data and pass it on to the next available customer care agent. In both cases, the airline will save considerable time in its call center.
Productivity
Specialized conversational bots can be used to make professional tasks easier. For example, a conversational bot could be used to retrieve information faster compared to a manual lookup; simply ask, “What was the patient’s blood pressure in her May visit?” The conversational bot will answer instantly instead of the user perusing through manual or electronic records.
Retail and food service
Conversational bots enable businesses to maintain a 24/7/365 online presence and enable customers to place orders or ask questions with faster response. There is no waiting; customer concerns are addressed instantly, regardless of the time or date.
These are just a few conversational bots applications. The list is endless and includes entertainment, wellness coaching, consumer banking, and finance, among others.
Chatbot Tools
What are examples of Chatbot tools?
These are some of the endless list of tools, platforms, and resources that assist or make the process of creating conversational bots easier.
Automat gives a platform and a set of WYSIWYG (what you see is what you get) tools for creating bots that learn over time. It integrates the understanding of the whole conversation, not just a single input, and can be improved through simple intervention. Automat also contains mechanisms of bringing a human into the loop.
This company offers development tools to continue augmenting the study and innovation of conversational bots. It provides solutions through an AI-as-a-service API and hosting service.
There will be over 1.8 billion active conversational bots or virtual assistant users by 2021. It is wise for businesses big and small, no matter the industry, to consider investing in conversational bots now and get ahead of the curve to capitalize on future opportunities.
When was the last time you had a conversation online with a customer service agent? You were probably complaining about receiving an order late, or needing to make a return. And there is a high chance that the person on the other end was not even human, but an artificially intelligent conversational bot.
A chatbot (also known as a smartbots, talkbot, chatterbot, Bot, IM bot, interactive agent, Conversational interface or Artificial Conversational Entity) is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods.
Screenless conversations are expected to dominate even more as internet connectivity and social media is poised to expand. From the era of Eliza to Alice to today’s conversational bots, we have come a long way. Conversational bots are changing the way businesses and programs interact with us. They have simplified many aspects of device use and the daily grind, and made interactions between customers and businesses more efficient.
Let’s take a weather chat bot as an example to examine the capabilities of Scripted and Structured chatbots. The question “Will it rain on Sunday?” can be easily answered. However, if there is no programming for the question “Will I need an umbrella on Sunday?” then the query will not be understood by the chat bot. This is the common limitation with scripted and structured chatbots. However, in all cases, a conversational bot can only be as intelligent as the programming it has been given.
Natural Language Processing (NLP) is the technological process in which computers derive meaning from natural human inputs. NLP-Based Conversational Bots are machine learning bots that exploit the power of artificial intelligence, which gives them a “learning brain.” These types of conversational bots have the ability to understand natural language, and do not require specific instructions to respond to questions as observed in types of chatbots such as Scripted and Structured Conversational Bots.
Open domain chatbots tends to talk about general topics and give appropriate responses. In other words, the knowledge domain is receptive to a wider pool of knowledge. However, these bots are difficult to perfect because language is so versatile. Conversations on social media sites such as Twitter and Reddit are typically considered open domain — they can go in virtually any direction. Furthermore, the whole context around a query requires common sense to understand many new topics properly, which is even harder for computers to grasp.
Closed domain chatbots focus on a specific knowledge domain, and these bots may fail to answer questions in other knowledge domains. For example, a restaurant booking conversational bot will be able to take your reservation, but may not respond to a question about the price of an air ticket. A user could hypothetically attempt to take the conversation elsewhere, however, closed domain chatbots are not required, nor often programmed to handle such cases.
Conversational bots apply pattern matching to classify the speech or the text and give an appropriate response to the user.
For every question or instruction input to the conversational bot, there must exist a specific pattern in the database to provide a suitable response. Where there are several combinations of patterns available, and a hierarchical pattern is created. In these cases, algorithms are used to reduce the classifiers and generate a structure that is more manageable. This is the “reductionist” approach—or, in other words, to have a simplified solution, it reduces the problem.
These are one of the major tools applied in machine learning. They are brain-inspired processing tools that actually replicate how humans learn. And now that we’ve successfully replicated the way we learn, these systems are capable of taking that processing power to a level where even greater volumes of more complex data can be understood by the machine.
