The Difference Between AI and Conversational AI: Breaking Down The Tech
- Artificial Intelligence
- Posted: 6 months ago
From basic automation to sophisticated dialogues, the difference between AI and conversational AI isn’t always clear. AI itself encompasses a broad spectrum of technologies, from simple rule-based systems to complex neural networks. But what exactly do each of these terms mean and what makes them so different?
What is AI?
AI has transformed numerous industries with things like predictive analysis, automated operations, and much more. The ultimate goal is to create a future that’s smarter and more efficient. At its core, AI refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, self-correction, and even understanding human language. Put simply, AI is about creating machines with the ability to think like humans, but with the work ethic and processing power of machines.
What is Conversational AI?
On the other side of the spectrum is Conversational AI. This is a subfield of AI that focuses on making machines engage in human-like conversation. Interestingly, many of us use conversational AI on a daily basis in the form of voice-powered personal assistants like Siri, and Alexa. It is even in some of those chatbots you see popping up on websites. These are all powered by Conversational AI. It’s a powerful technology that not only understands natural language but can also respond intelligently, and more importantly, humanly. It’s the technology that makes it possible for us to interact with machines more naturally and intuitively.
While both AI and Conversational AI are integral parts of the broader technology landscape, they each have their unique characteristics and applications. So what exactly is the difference between AI and conversational AI? We’re diving into these two fascinating fields of technology, their differences, and why it’s crucial to understand these differences.
Understanding AI
Artificial Intelligence is a vast field with several subsets. To understand AI, we need to grasp its definition and core concept fully. As we touched upon earlier, AI is the technology that allows machines to mimic the problem-solving capabilities and intelligence of a human. It gives machines the power to do things that could usually only be done by humans, like problem-solving or pattern recognition. But it extends beyond that. AI is what allows machines to understand natural language, and similarly, converse in a way that sounds human. It also allows machines tot to learn from past experiences. In a broad sense, there are two main types of AI: Narrow AI and General AI.
The Different Types of AI: Narrow AI vs General AI
Narrow AI
Narrow AI is the type of AI we all interact with most regularly and is also referred to as Weak AI. It operates under limited constraints and can perform one specific task. To do this, developers create a learning algorithm to perform this one task. The learning algorithm then classifies and makes decisions about data depending on how it has been told to interpret this data. Although this is undoubtedly powerful technology, its functionality is limited to its programming.
Narrow AI is embedded into many people’s most used technologies. Voice assistants, like Siri or Alexa, are one type of narrow AI. When you speak to your assistant, AI quickly classifies the data you have given it, interprets it, and responds almost immediately. The process of classifying the inputted data and then responding is the singular, specific task it has been programmed to do.
Another example of narrow AI is recommendation engines, like Netflix or YouTube telling you to watch next. The AI analyses the data of your watch history. It then interprets it according to an algorithm and recommends something for you to watch next that it thinks you will like. Once again, the act of interpreting the data of your history and generating a recommendation is the singular task it has been programmed to do.
General AI
General AI, also known as Strong AI, is on the opposite end of the spectrum in terms of AI capabilities. It is the type of AI that will give machines intelligence that is equal to human intelligence. In essence, General AI will create self-aware, conscious machines with full decision-making skills. Unlike narrow AI, general AI will require no human input to program an action. It is interesting to think of a time when we will have machines with capabilities that rival a human. However, at this point, General AI is only theoretical.
General AI could use the knowledge it has to plan for future events, without a human being prompting it to do so. It would have the ability to reason outside of how a human has taught it to and even exhibit common sense.
Common Uses and Applications of AI
At this stage, AI has made its way into virtually every industry. Professionals from the healthcare to the automotive industry are using it in their daily work. Healthcare professionals have begun using predictive AI analytics to help with early diagnoses and provide better patient care. And thanks to AI, the automotive industry is in the process of testing fully autonomous cars.
The finance industry is also taking advantage of this powerful technology by using AI to analyze credit history and make better underwriting decisions. AI has also helped to make the turnaround time on financial applications quicker since it can process information faster than humans.
The marketing industry is using AI to improve customer segmentation and hone in on their targeted advertising attempts. They are also using AI’s predictive analytics capabilities to get ahead of marketing trends and stay at the forefront of their industry.
Beyond Industry Applications
Outside of industry applications, AI has been a fantastic accessibility tool. With its speech and image recognition abilities, AI is helping ensure that everyone has fair access to technology, regardless of their abilities.
A more exciting application of AI is how people are using it to develop games. AI can be used to simulate opponent and user gameplay. This can help game developers map out strong stories and plots within a game. They can also use AI to develop more complex and interactive worlds, which will enhance the gameplay and user experience dramatically in future.
AI has transformed the way we live and work. Many people may not realize the enormous reach of this technology, but the fact of the matter is that it is quickly assimilating into life as we know it. When it comes to technology, many people fear the unknown. However, the result of incorporating AI into our lives is a more productive and efficient society as we know it.
Understanding Conversational AI
Now let’s get into understanding conversational AI. Conversational AI is simply a type of AI that allows machines to understand and process language that is phrased in a human way. It also enables machines to respond in the same way. This is all made possible by Natural Language Processing (NLP), a branch of artificial intelligence.
Typically, we interact with conversational AI in the form of virtual assistants and chatbots. Natural language processing and AI is what allows these tools to automatically respond to uses and a personalized way. More importantly, it allows this type of communication at scale, since it can respond in this careful way much faster than any human. This has made conversational AI a powerful tool for businesses, especially those with a customer service aspect to their business. Conversational AI can be categorized into three main types: rule-based systems, machine learning-based systems, and hybrid systems.
Rule-Based Conversational AI
Rule-based systems are the most basic of the three. They are preprogrammed to respond to a set of specific commands. This can be done by mapping out a conversation, often in the form of a flowchart or another set of predetermined rules. The benefits of rule-based chatbots are that they take less time to prepare, and their responses are predictable. However, they are quite limited since they have no flexibility and can be a nightmare to maintain.
Machine Learning-Based Conversational AI
Machine learning-based systems are more sophisticated. They learn and improve over time using the data they are given. The obvious advantage is that machine learning-based systems can handle complex, unscripted conversations. This means users can ask entirely open-ended questions and receive a response that is just as human. With every interaction, these systems get smarter and better at responding, since they have more data to learn from. Their major drawback, however, is that they can be unpredictable since each answer is generated differently.
Hybrid System Conversational AI
Then you have the solution that offers the best of both technologies: Hybrid systems. Hybrid systems combine the efficiency of rule-based systems with the flexibility of machine learning systems. The result is a powerful blend of tech that assures a certain level of response and is also not limited in its capabilities.
Common uses and applications of Conversational AI
Now that we understand the fundamentals of conversational AI, it is quite easy to see it has lots of applications. One of its most common uses is in customer service. The majority of businesses nowadays use chatbots and virtual assistants to answer customer queries. These bots and assistants do this in real-time and are available 24/7, making them time and money savers for businesses.
Conversational AI is also an important part of improving a user’s experience when they visit a website or use and app. It does this by tailoring recommendations for users to create a truly personalized experience. Conversational AI can also be the first step in a user getting help when they need it since it can troubleshoot common issues in a natural way.
The e-commerce industry uses conversational AI to guide customers through the buying process. They also use the recommendation abilities of conversational AI to suggest certain products based on their likes and previous purchases.
These are just a handful of ways conversational AI is being used in the world today. The truth is though, that it can be applied across industries in many different ways. However, at the core of it, the result of these different applications is the same: A more personalized and efficient experience for the people it’s assisting.
So What Are The Differences between AI and Conversational AI?
We now understand AI, and we understand conversational AI. And although their underpinnings are similar, there is a difference between the two. These differences can be split into three categories: operational differences, functional differences, and application differences/
Operational Differences
From an operational standpoint, AI involves building systems that can do things that would normally require human intelligence. For example, understanding natural language, recognizing patterns and even interpreting complex data. In essence, they have been designed to do tasks that involve interpreting data from the world around them using algorithms and computing power. The result of this is that AI can make decisions, predict outcomes and understand language just as a human would.
Operationally, conversational AI focuses on using this technology and applying it to human interaction. It understands and interprets language contextually and meaningfully, to create a human-like experience. Although it uses a type of AI to do this, namely natural language processing, it is the activity it is used for that makes it different. Natural language processing is what allows conversational AI to understand and generate text in a way that is both coherent and contextually aware.
Functional Differences
In terms of functionality, AI systems are incredibly versatile. The list of tasks AI can perform is extensive. Primarily though, its functionality comes down to automation, prediction, interpretation, and content. We use AI to automate complex processes which would usually take humans a long time, or use data analysis to predict outcomes. AI can also be used In voice recognition, to interpret images, and even create its own content.
Conversational AI, on the other hand, primarily focuses on improving and automating communication. Its main function is to provide a natural, human-like interface for people to interact with machines. We apply this functionality in ways like using it to answer queries or to provide people with recommendations. Sometimes it is also used to guide users through complicated processes. Conversational AI’s functionality is specialized to focus entirely on communication. Despite its functionality being so specific, its impact is just as significant as that of AI. Without the ability to communicate easily with machines, AI would be entirely inaccessible to most people.
Application Differences
With its broad range of capabilities, AI can be applied in an extensive number of ways across industries. Thanks to its abilities to analyse, predict and automate tasks, AI has found use in industries from healthcare, to finance, to marketing and more. Outside of industry applications, AI is also being used to improve accessibility
Finally, the applications of AI and Conversational AI differ significantly. AI has a broad range of applications, spanning numerous industries. For instance, in healthcare, AI can be used to predict disease outbreaks, aid in diagnosis, and personalize treatment plans. In finance, AI can detect fraudulent transactions, automate trading systems, and provide financial advice.
Conversational AI, on the other hand, is most commonly used to enhance customer engagement and improve customer service. Businesses use Conversational AI to automate customer interactions, provide 24/7 customer support, and deliver personalized recommendations. It’s also used to streamline internal communications, automate routine tasks, and provide employees with easy access to information.
Despite these differences, it’s important to note that both AI and Conversational AI are part of the same technological ecosystem, each serving unique purposes within it.
Why It Is Important To Understand the Difference Between AI and Conversational AI
Understanding the differences between AI and conversational AI is not a frivolous pursuit. Staying up to date on updates in technology ensures that businesses make the right choice when it comes to their specific needs. If businesses don’t understand what’s possible with tech, they risk falling behind their competitors and staying stuck doing things inefficiently. They may also choose the wrong type of technology for their needs, which could be a costly mistake in the long run.
Selecting The Right Technology for Business Needs
Making an informed decision about the right technology for a business’s needs involves understanding the differences between AI and Conversational AI. If a business wants to automate its customer service department, then a conversational AI solution like a chatbot would be the best solution. An AI chatbot could instantly answer their customer’s questions at all times, relieving their customer service department of a large chunk of work.
However, if the company was actually looking to analyse a lot of data so that they can analyse it and make data-driven decisions, they would need a robust AI solution instead. They could use machine learning algorithms to identify patterns and trends in the data and then generate key insights. In this use case, there would be no need to communicate with the machine, rendering conversational AI pointless.
Understanding The Limitations and Potential of Each
Understanding the differences between AI and conversational AI can also highlight the pitfalls and potential of each technology. As an example, machine learning is a powerful AI tool that can learn and become better with each interaction. However, to effectively do this, requires a significant amount of data. If a business was looking to use this technology but had a limited amount of data, this technology may not be that helpful to them. A comprehensive AI solution like this would also need substantial computational power and someone with expertise to implement and maintain it. If a company was not at this point in its AI journey, this may be too much of a commitment to make at that time.
A similar argument could be made about the limitation of conversational AI. Although it is excellent at improving customer engagement and delivering personalized experiences, choosing the wrong type of it could be detrimental. Conversational AI is mainly text or voice-based and operates within a defined set of rules. If a user asks an ambiguous question outside of the model’s understanding, it may not understand or be able to respond appropriately. Although lots of progress has been made in the field of natural language process, it is still not perfect. It may still struggle to understand different accents or colloquial language that some users may speak to it with.
The Future of AI and Conversational AI
There is no single definitive answer to which type of AI and conversational AI solution is right for every business. However, understanding the differences in technology is the best way for businesses to empower themselves to make the right decisions for their needs.
Both AI and conversational AI have their benefits and their limitations, but they are powerful tools earning their spot in society as we know it. They are two entities part of the same technological ecosystem, both contributing to making the world a more efficient place.
AI works on a broad spectrum and is responsible for enabling technology to perform tasks with human-like intelligence. It can interpret complex data, recognize patterns, and help with making decisions that are backed by data. Conversational AI is a type of AI that focuses on getting machines to communicate in a human-like way. It uses Natural Language Processing to understand, interpret and generate language just like a human would.
Each has different applications and functionality, but both have a place in the workplace. The future of AI and conversational AI is very exciting. New advancements in both fields are constantly emerging and in the not-so-distant future, it will become more and more integrated with our daily lives.
The line between human intelligence and artificial intelligence is becoming more and more blurred, which is why it is more important than ever to understand the differences in these technologies so that we can fully take advantage of them.
