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Blog | Mar 6, 2024

Generative AI vs Machine Learning

Generative AI vs Machine Learning
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You’ve likely heard the term intelligent automation (IA) bandied around for a while. It’s a form of cognitive automation that uses business process management (BPM), artificial intelligence (AI) and robotic process automation (RPA) to automate end-to-end workflows.

Now, with the emergence of generative AI, organizations are looking at how they can use new tech with their existing automation solutions to achieve better value while maintaining AI compliance and AI governance.

With traditional AI automation taking on a shiny new engine, we’re going to look at the components driving the car. But first, let’s flip open the manual:

  • Generative AI can generate content such as images, video and text based on its training data.
  • RPA can mimic human actions to perform rules-based, repetitive tasks.
  • AI systems simulate human intelligence to make decisions.
  • BPM orchestrates systems, people and digital workers.
  • Machine learning algorithms can learn from data.
  • Natural language processing (NLP) is a subset of AI that processes human language, enabling virtual assistants or AI chatbots to communicate with people.

We’ve explored generative AI vs predictive AI. We’ve also looked at RPA and machine learning (ML), and how you can prepare for generative AI. Now, let’s pop the hood and look at how gen AI and machine learning run separately and in a system. In this guide, we’ll teach you:

  • How gen AI and ML can collaborate.
  • The differences between gen AI and ML.
  • How you can apply ML to improve business processes.

Is Generative AI the Same as Machine Learning?

ML is a component within generative AI.

If intelligent automation (IA) is the car, then machine learning (ML) is the new driver using GPS to help steer them in the right direction. Think of the GPS as your large language model (LLM) where you train your generative AI models on large data sets. Good quality training data ensures your car doesn’t take a wrong turn or end up on the edge of a cliff.

The cool thing about ML is that it learns from past experiences. Like the new driver, the more time your machine spends on the road and driving in the same areas, the more it can come up with new, faster routes on its own.

Throw generative AI in the mix, and you’ve got a self-driving car. Gen AI takes over the car’s computer, learning the best routes like the driver without requiring human intervention. Then, guess what the driver gets to do: read a book, answer a work call or take a nap (eventually, we hope – the tech’s not quite there yet for self-driving cars).

What Is the Difference Between Machine Learning and Generative AI?

Machine learning is a subset of AI that uses algorithms to analyze data and learn from it, then make predictions and informed decisions based on that data. With ML, you don’t need to explicitly program every rule to tell your computer how to behave. ML algorithms automatically learn and improve from their experience by using statistical techniques to identify patterns and find relationships between inputs and outputs.

Meanwhile, gen AI is a branch of AI that uses machine learning techniques to create new content. Gen AI models can learn patterns and relationships in a dataset well enough to create new data that resembles the training data.

In other words, gen AI is focused on creating; ML is focused on learning. And ML helps gen AI do its thing.

What are the use cases for generative AI?

Let’s fuel up and explore some industry use cases on the road.

  • Marketing: This one is at the top of most peoples’ minds. Gen AI can create images, videos or music, and even give designers ideas by helping them create logos and designs.
  • Healthcare: Help clinicians reach an early patient diagnosis by helping radiologists detect cancer spots in medical images. Gen AI is well-equipped to read large, unstructured data to identify anomalies faster and better than a human.
  • Banking and finance: Help financial professionals detect fraud early on or manage portfolios.
  • IT: Help developers code faster with gen AI generating code using a vast database of programming techniques.

Want to know more? Read our blog on generative AI use cases.

What are the benefits of generative AI?

We’re going to see this definition expand a lot in the coming years, but here are a few of gen AI’s benefits:

  • Boosts creativity: Gen AI augments human creativity by providing new ideas based on prompts. It can inspire creativity via its diverse outputs.
  • Automating the boring stuff: With IA as a secure process wrapper around gen AI, you can automate repetitive tasks to save time and resources for your organization.
  • Save money: Gen AI can streamline workflows to reduce overhead and help allocate resources more effectively.
  • Synthesize data: Gen AI can generate synthetic data to augment existing datasets or simulate scenarios for training AI models.
  • Enhance decision-making: Gen AI can provide insights, recommendations and even alternative options to support decision-making. It can even generate simulations, forecasts and scenarios.

What are the use cases for machine learning?

ML has been around for a while, working across industries to make business processes better and augment work. So, what are some applications of machine learning?

  • Image recognition: ML can identify objects, people or scenes for applications such as facial recognition in surveillance.
  • Making predictions: Before gen AI, organizations were using ML for forecasting demand, predicting sales and customer churn, and helping manage inventory more effectively.
  • Recommendations: ML can provide personalized product recommendations for online shoppers or music recommendations so people can hear the content they like.
  • Detecting anomalies: With its ability to analyze and understand patterns, ML is designed to detect unusual behavior or outliers in data. This can be useful for detecting fraud in finance or equipment failures in manufacturing.

What are the benefits of machine learning?

ML has a lot of potential across industries and departments. Used with IA’s umbrella, you can do even more.

  • Easily identify trends and patterns: ML can review large volumes of data to discover specific trends that might not be apparent to humans. This helps people do their work more effectively and ensures quality data assessments.
  • Personalization: ML personalizes experiences for users by analyzing their behavior, preferences and interactions and giving them a tailored experience based on that.
  • Efficiency and optimization: ML algorithms optimize processes and workflows by helping identify bottlenecks and inefficiencies. This can make it easier for organizations wanting to automate their processes.
  • Reduced costs: Like gen AI, ML can also help businesses save costs by automating tasks and reducing operational expenses.
  • Scalability: ML systems scale to handle large volumes of data and user bases, making them suitable for any size of enterprise.
  • Risk management: ML can help mitigate risks by identifying potentially fraudulent activities, predicting failures and even assessing creditworthiness.

Machine Learning and Gen AI working together

Gen AI naturally contains ML to function. It’s the next evolution of a long-standing digital technology. ML algorithms power the personal by understanding patterns, and gen AI uses this to synthesize new material.

Knock Knock. Who’s There? The Future

In a lot of ways, ML is a stepping stone for technologies like generative AI (as much as it has also been absorbed into gen AI’s larger umbrella). In a few years, we’ll likely be looking at another cognitive technology and wondering how we got there. In an ideal world, gen AI will bring together the human aspects with the machine to automate the work people shouldn’t have to do, and help us stay creative, innovative and interesting.

In essence, the idea behind intelligent automation and gen AI is to continue fueling a forward-thinking outlook. And that’s what we’re all about at SS&C Blue Prism. We believe technology is boundless – only limited by human (and machine) imagination. Where we are today will look a lot different in ten years. That’s why we’re encouraging businesses like yours to think bigger with generative AI and automation.

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