Conversational AI and chatbots: what are the differentiators between conversational AI and traditional rule-based chatbots? The answer lies within the implementation of Natural Language Understanding, Machine learning and other human-like behaviours.
AI explained - Artificial intelligence mimics human intelligence in areas such as decision making, object detection, and solving complex problems.
4 types of AI - Different in their limitations and functionality, the 4 types of AI marks the stages in AI development.
Conversational AI - Primarily taken in the form of advanced chatbots or AI chatbots, conversational AI interacts with its users in a natural way.
5 levels of conversational AI - The 5 levels for both user and developer experience categorise conversational AI based on its complexity.
The key differentiator of conversational AI - Conversational AI is different from chatbots in its ability to use machine learning and conduct natural language processing.
For businesses - Conversational AI unlocks many opportunities for businesses - from developing personal and customer assistance to workplace assistants.
Value of conversational AI - Conversational AI also benefits businesses in minimising cost and time efficiency as well as increasing sales and better employee experience.
How conversational AI works - Conversational AI improves as its database increases; it processes and understands questions, then generates responses.
Conversational AI platforms - A list of the best applications in the market for building your own conversational AI.
Our take - Despite the merits of using conversational AI, the long-standing problem of fragmentation is still holding it back.
After making headlines for revealing Google's AI chatbot LaMDA was concerned about "being turned off", Blake Lemoine - the Google engineer and mystic Christian priest - has now been fired.
Perhaps LaMDA embodies the most classic fear of AI embedded in countless sci-fi movies like I, Robot, or Ex Machina.
Like Google, many companies are investing a lump sum of money in conversational AI development. And it’s not surprising why. The global conversational market is expected to reach USD 41.39 billion by 2030. The market is also expected to expand at a CAGR of 23.6% from 2022 to 2030.
Moreover, its ability to continuously self-evolve makes conversational AI a key trend in the future of work. Conversational AI is becoming more indispensable to industries such as health care, real estate, eCommerce, customer support, and countless others.
In brief, this blog will provide a crash course on AI and more specifically conversational AI. We will look at its development over the years, and the different types of AI we use in our daily life.
Meanwhile, analyse the pros and cons of implementing conversational AI along with how businesses can benefit from the technology.
A simple definition of AI - short for artificial intelligence - “is the simulation of human intelligence processes by machines, especially computer systems.”
AI makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.
Some examples of the tasks performed by an AI include decision-making, object detection, solving complex problems, and so on.
According to Government Technology, there are four distinct types of AI with some more advanced than others.
Below are the four types of Artificial intelligence listed from least to most advanced: reactive machines, limited memory, theory of mind, and self-awareness.
1. Reactive machines
The most basic type of AI system is purely reactive with the ability neither to form memories nor to use past experiences to inform current decisions.
2. Limited memory
This is the stage of AI we are currently in. With limited memory AI, development teams continuously train the model in how to analyse data.
These AIs will then have the ability to store previous data and make predictions when gathering information and weighing potential decisions.
3. Theory of mind
AIs at this stage and beyond are theoretical. Theory of mind AI has thoughts and emotions that affect behaviour of one’s self.
Self-aware AI possesses human-level consciousness similar to what Hollywood envisions AI dystopia science fiction.
“Worrying about evil-killer AI today is like worrying about overpopulation on the planet Mars. Perhaps it'll be a problem someday, but we haven't even landed on the planet yet.” - Andrew NG, VP and chief scientist of Baidu; co-chair and co-founder of Coursera; adjunct professor, Stanford University
Conversational AI is a type of artificial intelligence that enables humans to interact with computer applications the way we would with other humans.
Conversational AI uses machine learning, deep learning, and natural language processing to digest large amounts of data and respond to a given query.
Released by Apple in 2011, Siri is a conversational AI intended to help Apple users. Siri is equipped with functionality from translation to calculations and from fact-checking to payments, navigation, handling settings, and scheduling reminders.
Alexa is another conversational AI that Amazon developed in 2014 for keeping users informed and connected. The AI can manage tasks such as controlling smart devices, providing traffic information, creating lists, and streaming podcasts.
Released in 2016, Google home is another great example of conversational AI. It allows users to access services through Google Assistant, including playing music and podcasts and setting reminders.
With the development of conversational AI, opportunities for developers to create user-friendly AI assistance applications are also becoming possible.
It’s difficult, however, to use and develop conversational AI - for both the developer and users. This is why RASA has developed the 5 levels of user and developer experience.
Level 1 assistants provide some level of convenience, but it puts all of the work onto the end user. These can take the form of notification assistance in chatbots. When asked a query, nothing happens or they direct you to a human. Another example would be static web, where the assistant requires the user to use command lines and provide input.
Level 2 assistants take the form of basic chatbots. Although these chatbots can answer questions in natural language, the users would have to follow the path and provide the information the bot requires. This form of assistance can find the intent of the user and will provide websites and directions - but cannot achieve the result in one step.
Level 3 assistants are contextual assistants. At this level, the user can now ask for clarification on previous responses without derailing and breaking the conversation.
Level 4 is a consultative assistant. This consultative assistant enables the use of “ambiguous input” where the assistant will find out how they can help. At this level, the assistant will be able to directly answer questions given the aid of several follow-up questions for specification.
Level 5 is an adaptive assistant. The assistant knows the level of detail that the user is asking for at that moment. It will be able to automatically understand whether the request is a clarification on a single detail, or whether the topics need more analysis. A level 5 assistant picks up cues and adjusts its behaviour.
Level 1 is when it is easy for the developer to add in new functions and features and it leaves the issue of learning how to use the features to the users.
Level 2 assistants are built-in with a fixed set of intents and statements for a response. Therefore, making it harder for developers to add new functionality as the assistant evolves.
Level 3 is when the developer accounts for the user experience and hence separates larger problems into separate components to serve the user’s intent.
Level 4 assistance is when the developers start to automate parts of the CDD - Conversation-Driven Development - process. This allows the assistant to decipher if the conversation was successful or not; which pinpoints areas of improvement for developers.
Level 5 assistant is when the developers fully automate CDD. At this level, the assistant can effectively complete new and established tasks while carrying over context.
Currently, we often see conversational AI as a form of advanced chatbots, or we see it as a form of AI chatbots that contrast with conventional chatbots.
However, this is not the case. Below we explain the development of both rule-based chatbots and conversational AI as well as their differences.
Chatbot - short for chatterbot - can be embedded through any major messaging application.
A chatbot is a computer program that simulates human conversation through voice commands or text chats.
A rule-based chatbot follows the pre-defined tree-like structure. The chatbot asks the guest follow-up questions until it reaches the correct resolution.
Developed by Joseph Weizenbaum at the Massachusetts Institute of Technology, ELIZA is considered to be the first chatbot in the history of computer science.
Conversational AI is a further development of conventional chatbots that enable authentic conversations between a human and a virtual assistant.
Different from rule-based chatbots, machine learning and in-built memory in conversation AI help to provide a personalised service and solutions.
Conversational AI is used in marketing, retail, and banking to increase efficiency and enhance the customer experience.
Powered by conversational AI, AI chatbots are also increasingly used in the healthcare sector to help improve the quality of care and reduce clinical workload.
In short, AI chatbots are a type of conversational AI, but not all chatbots are conversational AI.
Rule-based chatbots use keywords and other language identifiers to trigger pre-written responses. The ultimate differentiator for conversational AIs is the built-in technology that enables machine learning and natural language processing.
Not only can AI chatbot software continuously improve without further assistance, it can also simulate human conversation.
Conversational AI leverages natural language processing (NLP) and natural language understanding (NLU). With training, conversational AI can recognise text or speech and understand intent.
With these features, conversational AI can understand typos and grammatical mistakes - allowing conversing with an AI chatbot to feel more human-like.
Conversational AI needs to go through a learning process, making the implementation process more complicated and longer.
Although not having predefined structures makes conversations more natural, the conversations led by the AI may also be unpredictable.
When conversational AI is wrongly taught, it takes a while before it “unlearns” and learns the correct behaviour.
An alternative: the hybrid model
The implementation of hybrid models isn’t as long and complicated as with AI since it uses predefined structures and responses.
In addition, since it is powered by AI, the chatbot is continuously improving to understand the intent of the guest. And conversing with a hybrid model will still feel conversational and natural.
With conversational AI being an innovative and improved version of rule-based chatbots, what are the different business opportunities?
Mobile assistants like Siri, Google Now are much like Home Voice Assistants like Amazon Alexa, Google Home and Apple HomePod.
With these products, consumers are using mobile assistants to perform the functions that need to be done quickly when their hands are full.
Found on websites, built into smartphones, and on apps to order services, like food delivery, conversational AI assists users with a better user experience.
According to the latest data, AI chatbots were able to handle 68.9% of chats from start to finish on average in 2019. This represents an increase of 260% in end-to-end resolution compared to 2017 when only 20% of chats could be handled from start to finish without an agent's help.
In most of these circumstances they’re responding to more than just support questions – they are actually allowing people to discover the products they like and want to buy.
Voice assistants are similar to chatbots where users can speak aloud to communicate with the AI. This feature allows consumers to ask branded questions and have on-boarding experiences.
Conversational AI also has an impact on the workplace, with this set to grow exponentially in the near future.
Gartner predicts that by 2025, 50% of knowledge workers will use a IVA - up from 2% in 2019.
Today, there are a multitude of assistants that enable automatic minutes of meetings along with other automated functions.
Furthermore, with the aid of conversational AI, the efficiency of HR can also be greatly improved. AI-powered workplace assistants can provide solutions for streamlining and simplifying the recruitment process.
These tools can help recruiters automate repetitive processes in recruiting. It has been proven that conversational AI can reduce HR administrative costs by 30% by decreasing dependency on HR representatives to solve redundant queries.
In terms of employees, conversational AI creates an opportunity for high efficiency in companies.
A well-designed conversational AI solution uses a central access point for all other employee channels and applications. This way, no matter the case, geographic region, language, or department, all resources and information can be discovered from one touchpoint.
Customers nowadays seek 24/7 support from companies. With such service, companies would have to sustain a costly customer service team.
While conversational AI can’t currently entirely substitute human agents, it can take care of most of the basic interactions, helping companies reduce the cost of hiring and training a large workforce.
Conversations with clients can be very time-consuming with repetitive queries. Using conversational AI then creates a win-win scenario; where the customers get quick answers to their questions, and support specialists can optimize their time for complex questions.
In any industry, competition is inevitable. When users stumble upon minor problems, instead of taking the time to call customer support, going to another competitor is much easier.
A friendly conversational AI assistant that’s always ready to help users solve issues regardless of the time or date will prompt potential customers to stick with your brand rather than turn to a competitor.
Not only can conversational AI increase retention, it can also recommend products or services users might be interested in. In turn, this increases the likelihood of a purchase.
Conversational AI-based solutions can help organisations converge their current tech suite and resolve employee queries within seconds.
This can be particularly useful for having faster onboarding of employees, and for finding relevant information.
At Omnifia, we are developing an integrated workplace assistant, radically transforming workplace communication and collaboration. Interested in finding out more? Feel free to reach out to one of the team.
Conversational AI has principal components that allow it to process, understand, and generate responses in a natural way.
Machine Learning is a subfield of artificial intelligence. It is made up of a set of algorithms, features, and data sets that continuously improve themselves with experience.
As the input grows, the AI gets better at recognising patterns and uses it to make predictions - this is also one of the biggest differentiators between conversational AI and other rule-based chatbots.
Given one of the biggest differentiators of conversational AI is its natural language processing, below the four steps of using NLP will be explained.
NLP consists of four steps: Input generation, input analysis, output generation, and reinforcement learning.
Input generation: Users provide input through a website or an app; the format of the input can either be voice or text.
Input analysis: In text-based input, the conversational AI solution app will use natural language understanding (NLU) to decipher the meaning of the input and derive its intention.
Dialogue management: During this stage, Natural Language Generation (NLG) formulates a response.
Reinforcement learning: Finally, machine learning algorithms refine responses over time to ensure accuracy.
Conversational AI platforms enable companies to develop chatbots and voice-based assistants to improve your customer service and best serve your company.
Rasa Open Source supplies the building blocks for creating virtual assistants. Use Rasa to automate human-to-computer interactions anywhere from websites to social media platforms.
This platform uses Natural Language understanding, machine learning-powered dialogue management and has many built-in integrations.
Amelia is a Conversational AI solution that takes on high volumes of HR requests simultaneously, ensuring that every employee receives the help they need, when they need it.
ChatBot offers templates and ready-to-use AI powered chatbots for businesses to build without using a single line of code.
SAP Conversational AI automates your business processes and improves customer support with AI chatbots.
The companies can leverage the power of SAP’s highly performing NLP technology capable of building human-like AI chatbots in any language.
is an audio and speech recognition company founded in 2005. It develops speech recognition, natural language understanding, sound recognition and search technologies.
There is no doubt that conversational AI is here to stay. We are still in the beginnings of this industry, but the next few years will see seismic growth. Gartner has predicted that by 2025, 50% of knowledge workers will use a IVA - up from 2% in 2019.
Like many new innovations, conversational AI has accelerated first in consumer applications. Most of us would have experienced talking to an AI for customer service, or perhaps we might have tried Siri or Google Assistant.
The same can’t be said for the workplace. The thing that’s been holding conversational AI back is a long standing problem: application fragmentation. There will not be a transformative AI assistant until this problem with fragmentation is alleviated. Currently, fragmentation is at its worst, with this only accelerating in the best of breed era.
The solution lies within integration. This is our area of expertise, and we’re incredibly excited to see how this industry evolves and plays out. There will be huge benefits for workplace teams worldwide. Those that innovate will have a clear competitive advantage.