The two most prominent technologies that are making waves in the AI industry are Conversational AI and Generative AI. They have revolutionized the way humans interact and work with machines to create content. Both of these technologies have the power and potential to automate many tasks that would otherwise take hours, days and months for humans.
Conversational AI is characterized by the ability to think, understand, process, and respond to human language in a natural way, just like human conversation. On the other end, Generative AI is defined as the ability to create content autonomously such as creating original content for art, music, and texts.
The amalgamation of Conversational AI and Generative AI leads to:
• deep conversation
• Human like experience
• Enterprise Support
• decision support
Exploring Conversational AI
Conversational AI employs NLP and finds applications where the human touch is important. The best applications of conversational AI are chatbots and virtual assistants. The most popular example would be Amazon’s Alexa. They are just as adept at gathering, understanding, and sharing information as human virtual assistants. Rule-based and ML-based approaches are majorly used to build conversational AI systems.
Understanding Generative AI
Generative AI is inspired by creativity and content-creation. This technology produces fresh content in record time, which can range from simple text to complex digital artworks. The development of GTP-3 and other pre-trained transformer (GTP) models has been a trendsetter in content creation.
Technologies included
With the use of NLP, conversational AI performs tasks such as speech recognition and intent recognition, enabling systems to understand content, tone, and intent and have meaningful conversations. Generative AI relies on deep learning techniques like GTP models and variational autoencoders to generate fresh human-like content.
training data requirements
Training conversational AI requires massive datasets of human interactions. It is through these training data that the AI learns to interpret and respond to a multitude of inputs. Generative AI models need datasets to understand styles, tones, patterns, and data types.
User experience and human interaction
Conversational AI believes in meaningful conversations. Thus, providing quick, direct, clear and relevant answers. Generative AI doesn’t engage directly, but contributes to the user experience by coming up with useful content like blogs, music, and visual art.
Restrictions and ethical concerns
Conversational AI may face a bit of a struggle with context and nuanced interpretations which often lead to misunderstandings. Generative AI raises ethical concerns related to widespread misinformation and biases due to inaccurate training data. Therefore, it becomes necessary to strike a balance between autonomy and moral responsibility. If the training data is accurate and error-free, the final AI model will be flawless.
what does the future hold?
The trend we see for conversational AI is more natural and context-aware interactions with emotional connections. The future of generative AI depends on creating different types of content, such as scripts, to further contextualize the context digitally.
use cases:
• Conversational AI is used in industries like healthcare, finance and e-commerce where personalized assistance is provided to customers.
• Generative AI is mostly applied to creative domains like content creation, entertainment, design, etc.
wrapping it up
It would be correct to claim that Conversational AI and Generative AI are two sides of the same coin. Each has its own set of positives and advantages for creating content and data for different uses. Depending on the final output required, AI model developers can select and deploy them coherently.
Also Read: Unlimited Possibilities through RLHF
Source: www.datasciencecentral.com
