Generative AI vs. Machine Learning

Given how fast AI is evolving, lately terms like generative AI and machine learning are often thrown around interchangeably. But are they really the same thing? Not quite.

While they share some common ground, these two concepts are distinct in their own right.

This article will get into more details about each one and their differences, but just know that genAI is, as its name implies, perfect for generating new concepts (whether it’s in the form of text, audio or images) and machine learning (ML) is perfect for making predictions.

So, what exactly sets them apart, and why does it matter? That’s why you’re here, so let’s get started.

Core concepts

Before you can actually understand the difference between these two branches of AI, you first need to understand what they are and their guiding principles.

GenAI: Definition and core concepts

Generative AI is a subset of artificial intelligence focused on creating new content. This technology leverages generative models, which are designed to produce data that mirrors the distribution of a given dataset. Essentially, these models can generate new, original outputs based on the patterns and structures they’ve learned from existing data.

Generative models work through a process of training on large datasets, where they learn to understand the underlying structure and features of the data. Once trained, these models can generate new data points that are statistically similar to the training data. For instance, GPT-3, a well-known generative AI model, can produce humanlike text based on a few input prompts. Similarly, there are models capable of generating images, music, and even code.

Applications of generative AI are practically endless, and we’re seeing new and innovative ways of using it every day. Some examples include natural language processing tools like GPT-4o (the latest public model from OpenAI), which can write articles, create poetry, or even answer questions.

In the realm of image generation, tools like DALL-E and Midjourney create new images from textual descriptions, opening up new possibilities for creative industries. There are even tools and models that allow you to create music and full songs based on a simple prompt.

ML: Definition and core concepts

Machine learning is another crucial subset of AI, primarily focused on building systems that learn from data to make predictions or decisions without being explicitly programmed. ML models and algorithms analyze patterns in data to improve their performance over time.

ML is broadly categorized into two types: supervised and unsupervised learning. Supervised learning involves training a model on a labeled dataset, where the correct output is provided for each example in the training data. This approach is commonly used for tasks like classification (e.g., spam detection in emails) and regression (e.g., predicting housing prices).

Unsupervised learning, on the other hand, deals with unlabeled data. The model tries to find hidden patterns or intrinsic structures in the input data. Examples include clustering (e.g., customer segmentation) and anomaly detection (e.g., identifying fraudulent transactions).

ML applications are everywhere in today’s world. From recommendation systems (like those used by Netflix and Amazon) to speech recognition (such as Siri and Alexa), ML models drive many of the technologies you interact with daily and have been doing so for years.

Key differences

While the core difference is already very clear, let’s deep dive in this section into the nuts and bolts of what makes genAI different from ML.

GenAI: Objective and output

In the case of genAI, its primary objective is to create new data. Whether it’s crafting a new piece of art, generating realistic images, or composing music, generative AI focuses on producing original content that didn’t exist before. Mind you, the end result might not be ideal—we’re all very familiar with the twisted images you can create or the horrible-sounding music you can create this way as well.

While these models have improved significantly since they first came about, the quality of the output is never assured. Not only that, but given their “creative” nature, these models are also eager to “hallucinate” new concepts that weren’t asked for and may not even be factual (one of the main problems with tools such as ChatGPT is that they can potentially give you incorrect information).

ML: Objective and output

In contrast, the goal of machine learning is to predict or classify existing data. ML models analyze past data to make informed predictions about future events or categorize data into predefined classes. Think of it as a tool for understanding and making sense of the data we already have.

While ML might not sound as “sexy,” it’s been around for decades, and it’s part of your everyday life, even if you don’t see it. Every time you click on a recommendation or like a piece of content, you’re feeding the ML monster, giving it more information about what you like to consume, so it knows what to offer you next. It makes finding what you want an easy task, considering the amount of content that’s out there.

GenAI: Model training

Training generative AI models involves teaching them to generate new data that aligns with the patterns found in the training dataset. This process emphasizes creativity and data generation capabilities. For instance, a generative AI model trained on thousands of paintings might create entirely new artworks that mimic the style of the originals.

ML: Model training

When training machine learning models, the focus is on achieving high accuracy in prediction or classification tasks. This involves optimizing the model’s ability to make correct predictions or accurately classify data based on the input it receives. For example, a machine learning model might be trained to identify spam emails by analyzing features common to spam messages (such as casing in the title, grammar errors, or even finding urgency in the message inside it).

GenAI: Applications and use cases

This technology shines in areas like art and content creation, where innovation and originality are paramount. Artists and designers use generative AI to produce unique works, while businesses leverage it for content creation, such as writing articles or generating marketing materials.

Additionally, generative AI plays a role in data augmentation, creating synthetic data to enhance the training of other AI models.

It’s important to remind everyone that the output of these models needs to always be reviewed and validated. In many situations, it can be used as a fantastic starting point, but human involvement is usually required to finish the specific piece of content.

ML: Applications and use cases

ML is widely applied in fields where prediction and classification are crucial. Fraud detection systems rely on machine learning to identify suspicious activities by analyzing transaction patterns. Recommendation systems, like those used by streaming services and online retailers, employ ML to suggest content or products based on user preferences.

Process automation, from customer service chatbots to supply chain optimization, also benefits greatly from machine learning technologies.

How generative AI and ML complement each other

While genAI and ML are quite different and have very dissimilar objectives, they can complement each other and work together. Let’s take a look at how that can play out in real-world scenarios where both solutions can help companies move forward.

Integrating generative AI with ML

One of the powerful ways generative AI can complement machine learning is by generating synthetic data. In scenarios where obtaining real-world data is challenging or expensive, generative AI can create realistic datasets that help train more robust ML models. This synthetic data can fill gaps in the original dataset, providing a richer and more varied training ground for machine learning algorithms.

Generative AI can also enhance the quality and diversity of training datasets. By generating new examples that reflect the characteristics of the original data, it ensures that machine learning models are exposed to a wider range of scenarios during training. This leads to improved generalization and performance of ML models when they are deployed in real-world applications.

Real-world use cases for ML and generative AI

• Healthcare: In healthcare, generative AI and machine learning work together to enhance diagnostic accuracy and treatment plans. Generative AI can create realistic medical images, such as MRIs or X-rays, which are used to train ML models for better disease detection and diagnosis. These models can be integrated into automated diagnostic systems, streamlining the process and ensuring more accurate and timely results.
• Finance: In the finance sector, fraud detection systems benefit significantly from the synergy of generative AI and ML. Generative AI simulates fraudulent transactions, providing a rich dataset for training ML models to detect fraud. These models can be embedded into automated monitoring systems that continuously analyze transaction data, flagging suspicious activities in real-time and reducing manual oversight.
• Process automation: In process automation, generative AI and ML can optimize workflows and improve efficiency. Generative AI can simulate various operational scenarios, providing data that helps ML models predict bottlenecks and inefficiencies. These insights can be integrated into business process management systems (BPMS) like Camunda, enabling automated adjustments and optimizations to keep processes running smoothly and efficiently.
• Customer service: Combining generative AI and ML can significantly enhance customer service automation. Generative AI can create realistic dialogue scenarios, training ML models to handle a wide range of customer inquiries. These models can be deployed in chatbots and virtual assistants, automating customer support and ensuring quick, accurate responses, thereby improving customer satisfaction and reducing the workload on human agents.
• Retail: In retail, generative AI and ML can optimize inventory management and personalized marketing. Generative AI simulates customer behaviors and market trends, providing valuable insights for ML models that predict demand and recommend products to customers. These insights can be used to automate inventory restocking and personalized marketing campaigns, ensuring that customers receive timely and relevant product recommendations.

Camunda’s role in GenAI and ML integration

Process automation can also benefit from adding AI and leveraging its many facets.

Camunda, for example, is no stranger to pushing the boundaries of what AI can do in this space. Over the years, Camunda has been adopting both machine learning and generative AI to improve the user experience and significantly enhance the capabilities of their tools.

The following are some examples of how AI has been helping improve the offering at Camunda.

Human workflow orchestration with generative AI

Camunda uses generative AI to enhance human-centric workflows through pre-existing integrations with providers such as OpenAI and Hugging Face. For example, in a vendor evaluation process, ChatGPT extracts key information from applications, performs sentiment analysis, and formats the data for easy processing within Camunda. This automation helps in screening applications, generating consistent emails, and writing website descriptions, reducing manual workload and improving efficiency​.

AI-infused end-to-end orchestration

Camunda’s Copilot, an AI-driven tool, simplifies complex process modeling tasks. It offers Modeler suggestions for transforming descriptions into efficient BPMN tasks and provides an AI-assisted Form Builder that generates user task forms from simple prompts. These features help organizations adapt quickly to changing conditions, ensuring faster and more efficient process development​.

AI-powered process optimization

By leveraging machine learning-ready datasets, Camunda enables businesses to uncover valuable insights from their process execution data. This integration allows for continuous improvement and optimization of business processes, reducing costs, and enhancing customer experiences. AI helps in intelligent task routing and decision-making, making processes more agile and responsive to market demands.

Enhanced business analytics

Camunda integrates AI to enhance business analytics by automating data matching and prediction tasks. For instance, AI can automate the correlation of invoices to receipts and predict customer behavior trends. This integration helps businesses streamline operations, improve accuracy, and proactively address issues like fraud detection and customer satisfaction.

You can read more examples on how Camunda uses AI to enhance analytics in this detailed article.

Accessing machine learning models

Camunda’s integration with platforms like Hugging Face (through ready-made connectors) allows businesses to incorporate advanced machine learning models into their workflows. This capability enhances business processes by providing intelligent routing, data analysis, and automated decision-making, leading to more informed and efficient operations​​

Conclusion

In summary, generative AI focuses on creating new content, such as text, images, or music, by learning patterns from existing data. In contrast, ML is primarily used to predict outcomes and classify data based on learned patterns. While both technologies have unique strengths, they complement each other in powerful ways, especially when integrated into business processes.

Because of that, Camunda leverages both generative AI and ML to enhance business process management. By embedding AI into its platform, Camunda empowers organizations to streamline operations, adapt quickly to market changes, and maintain a competitive edge.

In summary, the synergy between generative AI and ML, when used effectively through platforms like Camunda, gives businesses the power to transform and stay ahead of the competition.