1268 Views
In today’s world of customer service, artificial intelligence plays a crucial role. As a result, new technologies such as chatbots, Conversational AI, and machine learning are continually emerging. However, only a small percentage of the population understands what all of these terms signify.
Particularly with phrases like chatbots and Conversational AI, which are increasingly being used interchangeably in the context of artificial intelligence and customer service. The two names, however, have significant distinctions. In this post, you’ll learn more about them.
In 1966, MIT computer scientist Joseph Weizenbaum introduced the world to chatbots in the shape of Eliza, a chatbot based on a restricted, pre-determined flow that could replicate a psychotherapist’s dialogue using a script. Eliza conducted “conversations” by using pattern matching and substitution techniques, which gave users the impression that the software understood them but had no built-in framework for contextualizing events.
However, The irony in this Christmas story is that Weizenbaum designed Eliza to highlight the superficiality of human-machine communication, and in the process, produced a chatbot capable of deceiving Sapiens into thinking it was human. Eliza would eventually confirm her accomplishment by passing a limited Turing test for machine intelligence.
Eight years later, at the Stanford Artificial Intelligence Laboratory, the next significant milestone in conversational engineering would be achieved. Therefore, the developer Kenneth Mark Colby used his previous training as a psychotherapist to create “PARRY,” a natural language software that mimicked the reasoning of a paranoid person. PARRY outperformed expectations by passing the Turing test in its entirety.
Colby devised a complicated system of assumptions, attributions, and “emotional reactions” triggered by varying weights assigned to speech inputs to achieve such astounding results less than a decade after Joseph Weizenbaum’s Eliza. Is this artificial intelligence capable of conversing?
No. Although PARRY had a more controllable structure and a mental model that emulated the bot’s “emotions,” it was still rule-based, which meant it followed a strict (although complicated) if X (condition) then Y (activity) formula.
To our list, we’ll add rule-based. We’ve reminded you to remember the following concepts: restricted pre-determined conversational flow AND rule-based. Let us now proceed.
A.L.I.C.E. was the next great name in the sector (Artificial Linguistic Internet Computer Entity). In other words ALICE was created in 1995 by Richard Wallace and employed an Artificial Intelligence Markup Language (AIML), a version of XML, including tags that allow bots to recursively invoke a pattern matcher to simplify the language. However, In 2000, 2001, and 2004, ALICE received the Loebner Prize three times, an honor given to the most human-like systems.
ALICE was remarkable in every sense, but can it qualify as conversational AI Chatbots? In this case, the answer is once again no. Therefore, ALICE used a large number of “categories” or rules to match input patterns to output templates. However, ALICE makes up for its lack of morphological, syntactic, and semantic NLP modules with a profusion of basic rules; Wallace chose size above intricacy.
ALICE had all the trappings of conversational AI in layman’s terms, but it was essentially simply a pretty huge chatbot.
A bot is defined as “a computer program or character (as in a game) meant to replicate the activities of a person” by the Merriam-Webster Dictionary. Abbot, which derives its name from the word “robot,” is a non-human machine that can mimic certain human characteristics.
A chatbot, often known as a virtual assistant, is a type of robot that can interpret and reply to human language via speech or text. As a result, “chat” comes before “bot.” This is a crucial difference to make since not every bot is a chatbot (e.g. RPA bots, malware bots, etc.). Chatbots can be very simple Q&A bots that are designed to react to predefined questions. A chatbot’s heart is natural language processing (NLP) technology, which allows it to understand user requests and respond appropriately (provided it is trained to do so).
To begin, let’s define what conversational AI isn’t. In contrast to chatbots that follow a predetermined conversational flow, conversational AI is based on dialogue. Conversational AI, unlike chatbots, uses natural language processing, natural language understanding, machine learning, deep learning, and predictive analytics to provide a more dynamic, less limited user experience.
Therefore, An automated speech recognizer (ASR), a spoken language understanding (SLU) module, a dialogue manager (DM), a natural language generator (NLG), and a text-to-speech (TTS) synthesizer are all part of the typical conversational AI architecture. However, ASR receives raw audio and text data, converts it to word hypotheses, and sends them to the SLU. The purpose of the SLU is to capture the basic semantics of a particular word sequence (the utterance). It parses the semantic slots in the user’s utterance and determines the conversation domain and purpose.
The purpose of the DM is to communicate with people and help them achieve their objectives. Moreover, It determines the system’s behavior after determining if the semantic representation is complete. It uses the knowledge database to find the information that the user is looking for. However, the dialogue agent can make more robust judgments with the help of the DM, which includes dialogue state tracking and policy choosing.
Difference between Chatbots and Conversational AI
| Conversational AI | Chatbots |
| Voice and text instructions, inputs, and outputs are all possible. | Text-based instructions, inputs, and outputs are all possible. |
| Websites, voice assistants, smart speakers, and contact centers may all be used as part of the Omni channel strategy. | Only a chat interface is available on a single channel. |
| Understanding and contextualization of natural language | Conversational flow that has been written. |
| Interactions with a broad scope, which are nonlinear and dynamic. | Linear interactions that are canned and based on rules. Tasks that aren’t within the scope of the project aren’t possible to do. |
| Focused discussion | Focused on navigation |
| Continuous learning and quick iteration cycles are essential. | Any change to the predetermined rules and conversational flow necessitates a reconfiguration. |
Early chatbot implementations mostly focused on simple question-and-answer scenarios that NLP engines could handle. In addition many customers viewed these as a convenient way to get answers to frequently asked questions through a digital channel.
These rudimentary chatbots, on the other hand, stopped short of doing more sophisticated tasks, frequently passing off to human agents to continue processing the request, particularly when the client inquiry did not follow the expected path. Chatbots developed a poor image as a result of their failures, which remained during the early stages of the technology adoption wave.
The issue between chatbots and conversational AI has resurfaced in recent years. Bots and conversational AI both have advantages and disadvantages, but which is the better option?
Conversational AI has grown in popularity in recent years as companies seek to enhance customer service. In contrast to a chatbot, which is more functional, a conversational AI may respond to a question in a way that feels more natural to a person.
While conversational AI is the more intelligent of the two, chatbots offer their own benefits. For example, if a consumer wishes to buy anything, conversational AI might direct them to the checkout page and have them finish the transaction there. This benefit is what has sparked the current debate between chatbots and conversational AI.
Conversational AI, on the other hand, is better at anticipating a customer’s demands, while chatbots are better at offering more functional solutions.
Chatbots powered by AI provides a number of benefits, including improved user experience, increased brand loyalty, and increased revenue. However, deploying a AI Chatbot Services with the same degree of experience as a human person is a difficult challenge. To achieve this, it will require regular training and updating.
Therefore, the most difficult aspect of developing and implementing conversational AI systems is convincing people to utilize them. Individuals who are capable and willing to use these services should use them. Even if they want to, individuals aren’t always ready to adopt new technology.
This is why it’s crucial to keep your emphasis on your consumers rather than technology. Always remember that people use these services to solve issues, and they may not be ready for a conversational AI experience just yet.
One of the most significant issues is that chatbots are only effective at one thing: conversing.
Gaining empathy for end-users, knowing the limitations of existing technology, and using a clean and straightforward structure are all part of overcoming these obstacles. Understanding the target users and their behavior is critical when building conversational AI.
Read More: 5 Ways Chatbots will transform the Service Sector
In addition, the first step is to determine who the end-users are and what their needs are. You can accomplish this by creating a persona. However, a persona is a description of a typical end-user in full. It explains the objectives, actions, and motivations of each user. In conclusion, team members can use personas to build human-like characters for use in design, development, and testing.
