Generative AI vs Machine Learning

Artificial Intelligence(AI) has recently become a hot topic across industries transforming sectors like finance, healthcare, education and research. The two of its subfields are Generative AI and Machine Learning(ML), but both of these terms are often confused for one another. we will explore the difference in purpose, techniques and capabilities in this blog.

What is Generative AI ?

Generative AI refers to subset of AI models that are capable of generating new data. The models generates this new data from the data which it had been trained on. Instead of just analyzing patterns, they create new content such as text, images, audio, video, or code.

What are the types of Generative AI ?

We have now understood what is Generative AI, Let’s see what are the types of Generative AI.

1. Generative Adversarial Networks (GANs)

   GANs consist of two neural networks, a generator (which creates fake data) and a discriminator (which tries to spot the fakes). Over multiple iterations, the generator gets so good that its outputs look realistic.

2. Variational Auto Encoders (VAEs)

   VAEs compress data into a latent space (encoding) and then reconstruct it (decoding). They ensure the compressed data follows a predictable pattern, making them great for generating structured outputs.

3. Transformer-based models

   Transformers use attention mechanism to capture the relationship between tokens and even long range dependencies making them powerful for tasks like text generation or translation.

   Note: Not all transformer-based models are generative(eg., BERT).

4. Diffusion Models

   Diffusion models work by gradually adding noise to data (like blurring an image) and then learning how to invert the process. Beginning with random noise, they can generate highly realistic images step by step.

5. Recurrent Neural Networks (RNNs)

   RNNs are intended to work with sequential data where the order of input is important. They process data step by step while maintaining a hidden state (a memory) that captures information from previous steps to influence the current output.

6. Autoregressive Models

   These models generate one token at a time using their own previous outputs to predict the next step.

   Note: All transformer-based LLMs like GPT are autoregressive.

7. Flow-Based Models

   Instead of adding or removing noise, these models use reversible function to transform simple data distributions into complex ones. They’re precise but computationally heavy.

8. Hybrid Models

   Hybrid model combine methods like using a VAE’s structure with a GAN’s creativity which allows to get the best of both worlds.

What is Machine Learning ?

Machine learning is a field of AI where machines learn to recognize patterns, make prediction, make decision without being explicitly programmed for those specific tasks.

Types of Machine Learning

We have now understood what is Machine Learning, Let’s see what are the types of Machine Learning.

1. Supervised Learning

   Supervised learning involves training the model on labelled data where each input data has a corresponding output data. Classification and Regression are two main categories of supervised learning.

2. Unsupervised Learning

   Unsupervised learning involves training the model on unlabelled data which helps them learn the underlying relationship and patterns without pre-defined output. It is divided into two categories which are clustering, association rule learning.

3. Semi-Supervised Learning

   It is an approach that combines small amount of labelled and large amount of unlabelled data to train the model. The goal is to train the model for accurate prediction of output variables based on input variables like in supervised learning but the difference is in the approach which will be useful when handling large amount of unlabelled data and it becomes too difficult to label all of it.

4. Reinforcement Learning

   Reinforcement learning involves an agent which can interact with the environment and receive rewards or penalties based on its action. The agent adjust its decision making strategy to maximize rewards over time. It learns iteratively to make better decision by improving its behavior based on feedback.

When Traditional Machine Learning is the Better Choice ?

• Traditional ML algorithms are exceptionally well-suited for structured data problems where relationships between input features and output labels are well-defined but not excessively complex.

• When we have data that are labelled, traditional machine learning models are better suited and can perform better.

• When our use case does not involve generation of new data then traditional machine learning is the better choice. Compared to Generative AI models, Machine learning models require less computational power, memory and training time.

• Generative AI models are very difficult to interpret because of its complex neural networks, But traditional machine learning models are easy to interpret and explain how the model arrived at that specific output value.

When Generative AI is the Better Choice ?

• Machine learning models are not capable of generating new data, In such use cases Generative AI becomes the better choice.

• Generative AI models which are trained on programming languages can perform better at code generation, code completion, bug fixes and suggestions. As machine learning models are not better at data generation, Generative AI models becomes an obvious choice.

• When you test software systems, it’s difficult to anticipate all possible user inputs or edge cases. Generative AI models can generate diverse test inputs, covering rare or extreme edge cases and simulate realistic user behavior.

• Generative AI excels at customization tasks such as emails, thumbnails and product descriptions. Generative AI models outperform machine learning models which require personalization and creativity.

When to use Machine Learning and Generative AI together ?

• ML and GenAI both together can complement each other in many ways such as Software testing, Chatbots, Personalization, Marketing etc.

• For example we will explore some of the real-time use cases to better understand how ML and Generative AI can work together. In Software testing ML models can predict areas of low test coverage or detect anomalies, while Generative AI can auto-generate test cases, stubs, mocks, even edge cases.

• In Chatbots, ML can be used to classify the user intents or analyze the sentiments to make the Generative AI responses even more natural and human-like.

• Using ML we can analyze the user behaviors and with Generative AI we can generate contents more personalized for the users.

• In marketing comes the great potential of ML and Generative AI, with ML we can forecast churn rate and Generative AI generates content such as email or offers for better customer retention.

Best Use Cases for Machine Learning

• Machine learning models can analyze patterns and identify suspicious activity in real time, such as credit card fraud, insurance claim fraud and fake account detection. ML models can learn patterns in user behavior and can detect anomalies more effectively

• ML models are best suited for personalizing recommendations by analyzing user behavior and product features, including product suggestions on e-commerce applications and movie or show recommendations on streaming platforms. Machine learning models track user preferences and build user profiles to suggest relevant items, increasing user engagement and sales.

• Machine learning excels at predicting future outcomes based on historical data, such as predicting stock prices and sales forecasting. ML models learn complex relationships in data that traditional statistical models might struggle with.

• ML can classify sentiment from text like positive, negative or neutral in applications such as analyzing product reviews and in monitoring brand sentiment on social media. This helps businesses quickly understand public perception about the brand and take actions.

• Machine learning models can identify patterns in images which have a significant impact in healthcare industry. Pattern recognition tasks are well-suited to ML algorithms that learn discriminative features from large labeled datasets.

Best Use Cases for Generative AI

• Generative AI models can generate entirely new content from text prompts. These models are trained on large amounts of data and capable of generating texts, audios, videos and images.

• Generative AI excels at natural language processing so it is used in conversation systems that can understand and respond dynamically to human input. Unlike rule-based Chatbots, these systems can handle open-ended, context-rich conversations.

• Generative AI can upscale low-resolution images to high-resolution images. These models are capable of generating high quality and realistic images. A good example is Ghibli image generated by ChatGPT

• Generative AI models trained on massive codebases (e.g., Code Llama) can understand programming logic and generate functional code snippets, documentation, and test cases from prompts.

• Generative AI can read thousands of customer reviews or feedback entries and summarize the key insights automatically. This is useful for businesses trying to quickly understand customer sentiment and improve their products or services.

How does Keploy’s GenAI Powered Testing Platform Help you Automate Testing ?

Writing test cases can be time consuming and error-prone therefore Keploy leverages Generative AI models to auto-generate test cases from the application source code.

Want to automate testing using GenAI?

With Keploy, you can automate test case generation for both unit and API testing—like having an AI-powered QA engineer on your team.

How to use Keploy AI powered Unit Testing (OpenSource)

If you’re looking for a vertical unit testing agent that:

• Runs on your infra OR

• Uses your API key to test within workflows

…then meet Keploy-gen! It leverages LLMs to:

1. Understand code semantics

2. Generate meaningful unit tests

Prerequisites

AI model Setup – Set the environment variable API_KEY.

export API_KEY=xxxx

API_KEY can be from either of one these:

• OpenAI’s GPT-4o directly [preferred].

• Alternative LLMs via liteLLM

• Azure OpenAI

  Then you need to install the Keploy locally use the below command :

 curl --silent -O -L https://keploy.io/install.sh && source install.sh

Please ensure you’ve set the API key, as mentioned in pre-requisites above:

export API_KEY=xxxx

Generating Unit Tests

• Run the following command in the root of your application.

  • For Single Test File: If you prefer to test a smaller section of your application or to control costs, consider generating tests for a single source and its corresponding test file:

 keploy gen --sourceFilePath="<path to source file>" --testFilePath="<path to test file for above source file>" --testCommand="npm test" --coverageReportPath="<path to coverage.xml>"

To know more about Keploy-gen, Click here

Keploy-gen is an open-source product! If you’re tired of setting up locally, we have an enterprise option where you don’t need to configure anything.

We offer two ways to use it:
1️⃣ As a PR Agent
2️⃣ Directly in your VS Code

You can try all enterprise features completely free!

1. VSCode Extension

2. PR Agent

To know more Keploy offering: Check out here

How to Automate the API Testing using Keploy AI

If you are someone spending most of your time writing test cases, and are looking for solutions that automate the process and also validate the test cases without human intervention, try Keploy API Testing. It is a platform where you can generate API test cases for your application without writing the test cases yourself. Worried about security? You can run API test cases locally on your machine. To see the reports and test cases, Keploy provides you with an intuitive dashboard.

To Try Keploy API Testing Agent, Visit : https://app.keploy.io/home

Key differences between Generative AI vs Machine Learning

Challenges in Generative AI and Machine Learning

Challenges in Generative AI

• Generative models are pre-trained on large amount of data from the internet which may contain cultural and political biases which leads to the models generating biased responses.

• Pre-training or fine-tuning the generative models are more demanding as it requires massive amount of GPUs, memory and power.

• Since the generative models are trained from existing materials, it may leads the model to unintentionally generate phrases from those original materials, raising concern about plagiarism and copyrighted materials.

• It’s difficult to guarantee or restrict exactly what generative models will produce. Outputs can be unpredictable, hallucinatory, or even nonsensical.

Challenges in Machine Learning

• Machine learning models depend heavily on the quality and quantity of data. Poor, noisy, incomplete or insufficient data leads to poor model performance.

• Over the training iterations the model starts memorizing the training data which leads to poor performance on unseen data, this is called overfitting. When the model is not given even iterations on the training data, the model fails to understand the patterns or relationship between data leading to poor performance, this is called under fitting.

• Many ML models, especially ensemble methods and deep learning, behave like black boxes, it’s difficult to understand how or why they make those specific decisions.

• A model that works well in training but may not perform well when deployed due to issues like latency, cost, or integration with real-time systems.

Conclusion

Machine learning models excels at analyzing patters, make predictions from data and in decision making while GenAI models excels at creating new contents ,data and in simulation. They serve different yet complementary roles in the AI ecosystem.

But choosing the right approach depends on our specific needs whether it’s analyzing data, generating new content, or blending both for smarter solutions.

With innovations like Keploy’s GenAI platform, the boundaries between these technologies are becoming increasingly synergistic, especially in areas like automated software testing.

FAQs:

1. Can Generative AI replace Machine Learning?

   No, Generative AI and Machine Learning serve different purposes—ML focuses on predictions and pattern analysis, while Generative AI is used for content creation. They don’t replace each other but can complement one another in certain use cases.

2. Is Generative AI a type of Machine Learning?

   Yes, Generative AI is a subfield of ML that uses techniques like neural networks to create new data such as text, images, audio, or code based on patterns learned from large datasets."

3. Which is better: Generative AI or ML?

   There's no definitive answer to which is better it depends on your use case. Traditional ML is ideal for analyzing structured data, making predictions, detecting anomalies, or performing classification and regression. Generative AI is better suited for content creation, personalization, and recommendations.

4. What tools are commonly used in Generative AI?

   Popular tools include OpenAI’s GPT for text generation (chatbots, writing, summarization, and code), Stable Diffusion for text-to-image generation, and Keploy AI for testing. MidJourney specializes in artistic image creation, while HuggingFace offers a wide range of open-source models

5. How is Keploy AI different from other AI testing platforms?

   Keploy AI not only generates tests but also verifies the tests generated by AI against your application. Unlike other testing platforms, Keploy ensures that the generated tests are actually validated within the context of your application. This unique verification step sets Keploy apart from other AI-powered testing platforms.