The artificial intelligence technology is not new. It has existed for quite a long time now and it is only growing. Also, there are huge chances that you might have often heard about the term Generative AI. It’s currently a buzzword in the tech world. Isn’t it? And so does the term “conversational AI”. Often both these subsets of AI are used interchangeably but are quite different in their characteristics, features, as well as applications.
Generative AI and conversational AI have been revolutionizing how we interact with machines. Generative AI’s widespread adoption is leading to its huge market size growth which is expected to reach $967.65 billion by 2032 (Fortune Business Insights), growing at a CAGR of 39.6%. At the same time, conversational AI is expected to grow up to $49.9 billion, as reported by Markets and Markets.
Whether you want to get into a career in AI, or are a regular user of these AI technologies, you must be aware of their differences, as well as their features like available conversational AI tools, applications of conversational AI and generative AI, etc. so that you can take maximum advantage of them.
Conversational AI
“Designed to understand human language and respond to them in their own language”
Let us first start with conversational AI and explore in depth about its components.
Definition: Conversational AI is a form of artificial intelligence technology that facilitates easy two-way conversations between humans and machines via conversational AI chatbots.
Conversational AI platforms use the power of ‘Natural Language Processing (NLP)’ to understand the inputs (in the form of texts or voice notes) provided by humans, and then leverage sentiment analysis technique to understand the sentiment or context, and finally the ‘Natural Language Generation (NLG)’ helps to generate output similar to that of humans.
Important Elements of Conversational AI
There are 4 important elements that make conversational AI technology work with maximum efficiency without any error.
- Machine Learning
This is the core element that powers up conversational AI tools with intricate algorithms designed to identify patterns within huge data sets and learn continuously. This helps the conversational AI chatbots to understand and respond to queries properly.
- Natural Language Processing
As discussed above, it helps the conversational AI to understand and process human text and provide corresponding output.
- Data Mining
Conversational AI technology doesn’t work on identifying patterns and providing contextual responses. Data mining can help extract valuable insights out of the conversational data for further refinement of the conversational AI models.
- Automatic Speech Recognition (ASR)
This is particularly important in conversational AI platforms using voice inputs (such as Siri or Alexa). This technology helps to clear the background noise and disturbances and helps convert speech to text which is then processed further and provides appropriate response.
Top applications
Here are some notable conversational AI applications that you might know about:
- Retail: Conversational AI chatbots taking your order and answering your order-related queries
- Finance: Chatbots providing assistance related to the right financial product or transactions
- Telecom: Offering you insights about your mobile plans, and usage, and answering queries
Generative AI
“Designed to generate natural and realistic content, including texts, images, and videos”
Definition: Generative AI is a form of artificial intelligence that can generate realistic, human-like content such as emails, poems, paintings, and even audio from prompts (text inputs) given to them.
The following components provide Generative AI the power it needs:
- Generative Adversarial Networks (GANs)
It is a type of generative AI model that can generate realistic and high-quality content. It consists of two important elements; a generator that can create images and a discriminator that evaluates it and distinguishes it from real. They both work in conjunction to create human-like output.
- Variational Autoencoders (VAEs)
Variational Autoencoders (VAEs) are a type of generative model that compresses data into a lower-dimensional space while learning a latent representation that allows for generating new, similar data.
Applications of Generative AI
Generative AI is now widely used across several industries for the following applications:
- Discover new drugs and design new molecules with desired properties
- Personalize content for marketing campaigns
- Create realistic pieces of text, audio, videos, and images for a wide range of applications.
Features | Conversational AI | Generative AI |
Focus | Human-like interaction | Content creation |
Applications | Chatbots, virtual assistants, customer service, education | Art, design, code generation, drug discovery, personalized content |
NLU | More emphasis is given to NLU to understand user sentiment and context | Limited focus on NLU as output is given on text prompts or analysis. |
Content Management | Efficiently manages existing content and provides responses based on retrieved information. | Can help create entirely new content based on learned patterns. |
Response Generation | Response to a user query is generated based on pre-defined options or retrieved information | Generate creative text formats, images, code, audio, etc., based on training data |
User Interaction | Helps with continuous and interactive dialogue | There is no direct user interaction |
Creativity | Has limited creativity, and focuses on mimicking existing patterns | Greater focus on creativity and generating unique content |
Diversity | Responses are also limited as per pre-defined options and training data | Can offer a wide range of content with unique outputs |
So, now that you understand what conversational AI and generative AI are, it’s time for you to learn these AI skills and take your career to the next level. Enroll in top artificial intelligence certification programs that will help you learn the working and intricate concepts of both generative AI and conversational AI technology. Not just your AI career, these technologies can be used across all job roles and in all industries. So, find the best AI certification program for you that covers these components of AI, and excel in your career.
Conclusion
Generative AI and conversational AI both are evolving rapidly and have completely transformed various aspects of our daily lives. With these fields progressing exponentially, we might see even more sophisticated and impactful applications in the future. Are you using any of these technologies to make your life easier? If not, then you cannot remain long without using them. So, master these tools, and improve your productivity.
