As you may well know, Artificial Intelligence (AI) is a hot topic these days due to its prominent branches. Generative and Conversational AI. They've leveraged Deep Learning algorithms with billions of words in training data. We agree that they've revolutionized the world with millions of users in less than a year. And if you're wondering, this is far from over. Gartner predicts that 30% of outbound marketing messages from big corporations will come from Generative AI.
If you think that's not impressive, let me tell you that the current market value for Generative AI is over 40 billion USD. Plus, its projected value by 2030 is 667.96 billion USD. While Generative and Conversational AI tend to go hand in hand, they have key differences. In this manner, they also have specific use cases that depend on various factors. Let's dive a bit deeper into that.
What is Conversational AI?
It's common for some to think of Conversational AI as an improved version of chatbots. When it comes to their main applications, they do have quite a few things in common. They both operate heavily in the customer service sector. Yet, Conversational AI uses advanced Machine Learning techniques and Natural Language Processing (NLP) to extract meaningful insights from training data. That's how it knows how to give better and more intelligent responses to user queries, allowing the models to go above and beyond. We can define it as a type of AI that aims to replicate human-like interactions. The goal is to make human conversations and chats with users more dynamic and enjoyable, expanding the possibilities. Chatbots work using predefined scripts and limited rules. In terms of programming, you can picture them as a sequence of "if statements." If the human agent asks this, then give them this or that answer.
Unlike chatbots, Conversational AI can recognize speech and text messages to determine the user's intent. It's like a smart chatbot expert in human language. As if that isn't impressive enough, Deep Learning algorithms also allow for continuous learning. That means your Conversational AI tool will use previous knowledge from user interactions in future conversations. This way, its answers will become more and more relevant. There's a whole fancy term for that, actually: Reinforcement Learning. All this translates into machines capable of working on various tasks and solving multiple user problems. To some extent, It's like having a virtual assistant. For all these reasons, compared to regular chatbots, Conversational AI has a strong impact on User Experience (UX) and overall customer satisfaction.
What is Generative AI?
Generative AI also involves NLP and Deep Learning models but uses complex neural networks to identify patterns. In this manner, its responses often include predictions from these patterns and allow for much more creativity. As you probably know, the main goal of Generative AI is to create content from scratch out of user inputs, also known as prompts. So, apart from replicating human-like text, users can use Generative AI to solve many more problems. That includes any problem they can solve by creating fresh content. Common formats include text, sound, images, animations, videos, 3D models, and code. That helps explain why Andrej Karpathy, Sr. Director of AI at Tesla, said English is now the hottest programming language. He didn't overreact; the content generation capabilities of generative AI models rival sci-fi.
Creating innovative content comes in handy for many fields other than customer service. Generative models have become extremely popular in marketing, entertainment, education, communications, healthcare, and engineering industries. As you can see, it isn't just marketing content. Since they can also write, rewrite, debug, and test code, they've become remarkably popular in Software Development. Generative AI uses a combination of Deep Learning techniques to perform all the tasks it can. That includes Generative Adversarial Networks (GANs), Variational Autoencoders, Recurrent Neural Networks, and Transformers.
What are Transformers in Generative AI?
Transformers deserve an honorable mention because they represent the leading architecture for language models. As you can imagine, we owe them a lot. Transformers are an artificial neural network focusing on text generation, translations, and summarizations. In other words, anything related to language. Transformers lay the foundation for the most cutting-edge Large Language Models (LLMs) available today. That includes PaLM, Lambda, BERT, and GPT-4 (Generative pre-trained transformer).
One of the main reasons LLMs have defied what we imagine is that they use a vast ocean of training data. The extremely sophisticated training used for Transformers and LLMs made GPT-3 capable of writing an essay in the blink of an eye on why we shouldn't fear AI. It even included the sassy title, "Are you scared yet, human?" The bottom line is that Transformers are one of the main reasons AI has become so popular and influential.
Differences Between Generative AI and Conversational AI
As mentioned before, Generative and Conversational AI typically go hand in hand. Some of the most popular AI chatbots involve both. That can make it somewhat difficult for some to catch their differences. Let's explore them in more detail.
The most obvious difference you must know by now is that Generative and Conversational AI serve different purposes. Conversational AI strongly focuses on customer service experience and assistance by engaging in human-like conversations. Conversely, Generative AI aims to create brand-new original content in various formats. Think of Generative AI models as content generators. In this manner, inputs and outputs can be significantly different. Conversational AI bots may receive questions or user queries, whereas Generative AI tools receive a text prompt to create a text such as a definition, an essay, or a poem.
From a technical point of view, Conversational and Generative AI are also slightly different. The latter requires Deep Learning algorithms to analyze patterns in neural networks, relying heavily on Transformers for most cases. Conversational AI also uses much training data but focuses more on NLP and dialogue management techniques. Remember, the primary function of Conversational AI involves recognizing intent and analyzing sentiment. Thus, the learning and training process centers around the necessary aspects to provide polite and meaningful responses.
Having different purposes and goals, they also have other challenges and roadblocks. As you may have guessed, Generative AI faces several more pitfalls relative to creative content generation. Conversational AI tools may have trouble recognizing sarcasm, tone, intent, slang, and bias. On the other hand, Generative AI has to deal with ethical concerns, plagiarism, copyright, and other legal issues. Some challenges they have in common include the well-known hallucinations, privacy, and security.
Final Thoughts
Despite differences, Generative AI and Conversational AI often go together to provide users with the most value. Every day, it becomes increasingly common to see tools that incorporate both to deliver enhanced customer satisfaction. They have become highly influential in almost any business field. To a large extent, it's fair to say that AI is a powerful tool for businesses looking to streamline their processes. But more than just business operations, They help avoid repetitive tasks and enhance customer experiences with intuitive interactions.
Thus, we definitely shouldn't fear it. We believe the best approach to get the most out of AI is to combine it with the specific aspects of your business. In this way, Funes aims to increase efficiency and creativity in the workplace based on your special needs. Feel free to reach out to learn more about how Funes can help you streamline internal business processes and drive growth.
