Conversational AI vs. Generative AI: A Comparative Analysis

When discussing artificial intelligence, names like ChatGPT, Claude, or Gemini frequently come up. Yet, the AI field is vast, supported by decades of research.

AI encompasses various technologies, from AlphaZero, Google’s chess and Go-playing AI, to DALL-E, OpenAI’s image-generating AI. Recently, two particular branches of AI have gained prominence: Conversational AI and Generative AI.

Let’s explore which of these technologies is more effective: Conversational AI or Generative AI.

Generative AI: Creating New Data

ChatGPT is the most famous instance of generative AI, which is AI models that have been trained to create new creative outputs.

So, these models are used to generate essays, images, or even code. Some examples of generative AI applications are ChatGPT, Claude, Dall-E, and others.

How Does Generative AI Work?

Generative AI is trained on large datasets, including:

1. Web articles
2. Social media posts
3. Stock Images and videos
4. E-books
5. Newspapers, journals and magazines.

The AI models take these datasets and recognize patterns within them. This helps them generate new texts, images, and even videos based on the instructions they’re given.

These models are powered by Generative Adversarial Networks (GANs), Variational AutoEncoders, and Transformers.

Let’s take a look at the tech that powers Generative AI chatbots.

Under the Hood

One could argue that the recent advances in Generative AI are possible because of the seminal Google paper, “Attention is All You Need.” The paper introduced a new neural net architecture that could generate new creative data outputs, which has led to the development of the numerous generative AI models available now.

Generative AI models today use several types of architecture, like Transformers; the most prominent ones are:

1. Generative Adversarial Networks (GANs) — These AI models have two parts — “The Generator” and “The Discriminator.” The generator creates something new, and then the discriminator criticizes the output. The discriminator model can classify images as “fake” when recognizing them as AI-generated, so as the generator learns from the discriminator, the outputs improve. Finally, the GAN model learns how to fool the discriminator model and create realistic text and pictures.
2. Variational Auto-Encoders (VAEs) — These models are built with an encoder and a decoder. The encoder takes general data and turns it into a vector (a probabilistic numeric representation of the data). Then, the decoder samples the vectors to try and reconstruct the original data again. This process generates new and varied outputs that create new images and texts.
3. Transformers — Transformers are currently the most dominant AI architecture because they can contextualize large amounts of data. Essentially, the model takes in a large amount of data and turns it into an embedding (a vector space with many numbers representing the data). Now, using the numbers in the embedding, the transformers can use their training data to answer.
4. Diffusion — Made famous by recent image generator models, diffusion models work by adding more data points (pixels) to their training data (images). By adding and removing these data points, the models can generate entirely new images from their training data using the inputs or prompts provided.

Now that we’ve learned about the technology that makes generative AI work, let’s discuss its use cases in the current world.

Use-Cases of Generative AI

1. Text Generation

Claude and ChatGPT 4 can generate entire essays based on your prompts.

2. Image Generation

Dall-E, Stable Diffusion, and Midjourney can produce new images using their training data.

3. Data manipulation

Newer models like ChatGPT 4 and Claude Sonnet can understand large amounts of data and give you specific answers.

4. Writing Code

Most new models can code basic applications and run them natively to give you answers.

While generative AI has multiple use cases, it also suffers from some critical limitations at the current stage. Let’s examine them.

Limitations of Generative AI

The current generative AI models are probabilistic and have the following limitations:

1. Hallucinations

Generative AI models can often make up answers if they lack training data to guide them to the correct conclusions.

2. Speed

Since these are large models, the speed of response can be slower.

3. Mathematical Reasoning

These models are trained to generate new texts and images and can struggle with logical tasks and fundamental mathematical problems unless fine-tuned.

Now that we understand how generative AI works and its use cases, let’s look at conversational AI.

Conversational AI: Talking Like a Human

Conversational AI is a technology that allows humans to talk with machines in natural language. This can include everything from rule and button-based chatbots to natural language powered by generative AI.

Most recent advances in conversational AI are powered by Gen AI models that allow it to generate responses to human interactions.

Some examples of conversational AI are — Kommunicate Chatbot, Amazon Alexa, Siri, etc.

How Does Conversational AI Work?

Conversational AI models are trained on:

1. Human text conversations
2. Phone call recordings
3. Digital meeting recording

By training on massive amounts of human conversation data, these models can understand the semantics of human conversations. They can have introductory small talk and solve minor problems for humans.

Most recent conversational AI models use generative AI on the back end. However, some specific technologies are essential.

Under the Hood

The basic technologies underlying a conversational AI model are:

1. Natural Language Processing (NLP) — NLP machine learning technology understands the input given by a human being. This takes in the natural language input and transforms it into data that an AI model can understand.
2. Dialogue Management — The AI model uses a dialogue manager to formulate an answer. This can be as simple as a look-up table (in the case of rule-based models) and as complex as generative AI (in the case of human language chat and voice bots).
3. Reinforcement Learning from Human Feedback — Conversations need to develop continuously, so most conversational AI applications accept ratings from human users to improve and learn better tactics over time. This process is called RHLF.

Conversational AI lets machines talk like humans and thus has many widespread business applications.

Use-Cases of Conversational AI

Conversational AI has been around for decades, and it is currently used for

1. Customer Support

Resolving repetitive queries at scale.

2. Sales

Understanding and initiating sales calls.

3. Customer Experience

Providing personalized customer service at scale.

Most enterprise use cases for generative AI have been through conversational AI modules. However, conversational AI as a whole has some limitations.

Limitations of Conversational AI

In practice, conversational AI models have the following limitations:

1. No Answers for Out-of-Context Queries

Unlike generative AI, conversational AI is limited to a smaller training dataset, making answers to out-of-context queries impossible.

2. Not Good at Complex Tasks

While conversational AI can solve repetitive queries and automate many functions, it’s not good at solving complex queries.

3. Needs User Feedback

A conversational AI model only improves with use and feedback, so feedback mechanisms are crucial.

As you can see, conversational AI and generative AI have differences and similarities. To further our understanding, let’s compare how the two types of models work.

Differences Conversational AI vs. Generative AI

Conversational AI and generative AI aren’t exactly mutually exclusive models. Most modern conversational AI applications use generative AI to function (there are non-generative AI-based conversational AI applications, too).

So, some conversational AI applications are generative, and some aren’t, while some generative AI applications are conversational and some aren’t.

Businesses across the spectrum have experienced significant benefits by adding generative AI to their conversational AI systems:

1. Organizations using AI for over 3 years have seen a 37% increase in ROI.
2. Organizations that have been using AI for 3 years or less have seen a 117% increase in ROI.

Conversational AI vs Generative AI: What to Choose?

As we pointed out, conversational AI isn’t distinct from generative AI. Conversational AI applications often use generative AI to generate responses to human questions. So, regarding real-life use cases, your choices depend entirely on how you use AI in your business.

Conversational AI is good at converting large databases into digestible conversations. At the same time, generative AI is great at building new synthetic data that you can use for marketing, designing, coding, etc.

For example, if you need an AI application to handle customer support, using a conversational AI chatbot powered by generative AI (like Kommunicate) might be your best bet.

Fundamentally, it’s essential to understand the capabilities of conversational AI and generative AI models and use them together. Both types can work together to unlock new growth opportunities and create better customer experiences.