Machine Learning and Generation AI: What is it suitable for 2025?

Less than five years later, machine learning was one of the main ways companies used artificial intelligence. In April 2021, we called machine learning “a wide and powerful form of AI… change every industry.”

However, after the release of CHATGPT-3.5 in 2022, many organizations shifted their focus to AI generation AI subfields that can be used to create new content. Generation AI teeth Machine learning (details below).

Traditional machine learning is a technology established in many organizations today, and today large companies focus on the use cases of generator AI. In a 2024 survey of senior data readers, 64% of respondents said generative AI could be the most transformative technology of their generation.

Generator AI is widely accessible and has many new applications, but you need to know that it's best to rely on other forms of AI, such as traditional machine learning.

We spoke with two MIT Sloan AI experts – Associate Professor and a professor of practice – Where generative AI is replacing predictive machine learning, where machine learning remains the most effective tool, and how companies use technology together.

What is machine learning?

Machine Learning is a type of artificial intelligence that allows computers to learn without being explicitly programmed. In traditional computing, machine learning programs can learn from examples if people need to write programs that provide detailed instructions to their machines about what steps to perform to complete a task.

Machine learning is used for many purposes, from predicting customer behavior to assess potential fraud in banking transactions, to creating coordinated search results on shopping sites.

The data used to fuel machine learning, including generative AI tools, can be spreadsheets, text, images, audio, or video numbers. The more data the machine learning model is trained, the more accurate the model is. For machine learning to work, patterns need to be within the data that applications can identify and analyze.

“The basic idea of machine learning is that it's much easier to collect data than to collect understanding,” Ramakrishnan said. For example, it's easier to provide a machine learning program with photographs of thousands of animals and tell them what depicts a dog, rather than trying to teach the program the complex ways that cats can distinguish them from dogs. By feeding data that labels data, you can learn how to communicate the differences between the two to yourself.

Machine learning “makes decisions that generalize patterns that we would otherwise not be able to find,” Gupta said. “It's as good as the data and models we have.”

Therefore, machine learning is perfect for situations with many data with thousands or millions of examples, such as recordings from conversations with customers, sensor logs from machines, ATM transactions, and more.

What is Generated AI?

Generation AI A new type of machine learning that allows you to create new content, including text, images, and videos, based on large datasets. Big language model – AI programs that can process and generate text – is a prominent type of generation AI. ChatGpt, a distinctive iteration of Generating AI, was released by Openai in 2022 and took off quickly as it was able to respond to user prompts written in plain languages, and then quickly generate new content. Other commonly used chatbots or LLMs include Claude of Mankind, Gemini from Google, Microsoft's Copilot, and Meta's Llama.

“Machine learning captures the complex correlations and patterns of data we have. Generation AI is moving further,” Gupta said. A specific, fine-tuned, generated AI model can identify relationships within traditional datasets that machine learning cannot. “That's where the edge is,” Gupta said.

Instead of creating predictions or identifying patterns, Generative AI creates new content. For example, you can answer questions, create emails, and brainstorm ideas. “There are so many use cases. gpt A recent model has shown that “we see many companies trying to find ways to use them within their own frameworks, such as transcribing calls in call centers, navigating policy documents, and helping new employees learn existing software code.”

However, Gupta warned that companies developing or using generative AI or machine learning should recognize potential issues such as inaccuracy and bias.

The best use cases for generator AI

In addition to the main capabilities generating new content, generative AI takes over the tasks traditional machine learning has performed historically. These situations are as follows:

When dealing with everyday language or general images. LLM is trained with a large amount of text or images and can be classified and detected as “off-shelf.” For example, companies may want to analyze online product reviews to identify user reports of product defects. This once meant building a trained machine learning model to identify such reviews. This is a process that requires effort, time and money. Today, companies can enter product reviews into LLM and ask if the dataset contains product improvement insights, said Ramakrishnan.

GPT-4 and similar models “can be more accurate than custom built machine learning models and allow applications to run faster,” he said.

Generated AI models are also becoming more affordable, Ramakrishnan noted, so over time, the price will be less due to using them.

If you want more accessible options. Using a generated AI model is something many software engineers can do without a lot of additional training, but building machine learning models requires technical expertise. Generic AI “is a democratizing force in that sense. It makes it more accessible,” Ramakrishnan said.

If the problem or opportunity is based on daily use of information, “try the generator AI first,” he advised. “Don't let you revert back to machine learning like you used to.”

If traditional machine learning is a better option

However, in some cases, machine learning is still the best option. These situations include:

If you have privacy concerns. Care should be taken when supplying your own, confidential or sensitive information to LLM due to the potential data leaks. It is also possible to build your own private model, but requires specialized technical skills that may not be readily available to your organization. In these situations, you might want to stick to the “old-fashioned way,” Ramakrishnan said.

If you are using knowledge of a very specific domain. LLM is trained with widely available data and is suitable for dealing with everyday information. However, it may not be as accurate as highly technical or niche tasks, such as medical diagnosis based on MRI images. “If you're working on a domain-specific problem that requires a lot of technical knowledge, there are a lot of jargon involved, and the specific problem you're working on is very specific to your company or organization… you'll probably want to go to tradition. [machine learning] Route,” Ramakrishnan said, but that the generated AI model is improving rapidly and can change over time.

If you already have a machine learning model. Organizations are putting a lot of effort into building machine learning programs for specific applications, including identifying potential fraud in credit card transactions. “Perhaps there's no great urgency to tear them apart and replace them with generative AI systems,” Ramakrishnan said. “The question is, what's new use cases, what's new? That's where the decision points really come into play.”

When to use machine learning and generator AI together

In some situations, machine learning and generation AI can be used together to get better results. These scenarios include:

If you want to enhance your machine learning model. “The algorithms don't have 220 visions of the world. They're as good as the models we offer. So if we can provide them [with] There is more context about the world using generative AI, and that only makes them better,” Gupta said.

She provided examples of datasets that include people's names, heart rates, and the speeds they performed. “Machine learning models can predict information such as cardiac fitness for each person, cluster them into groups, and perform performance benchmarks,” says Gupta. “The generated Ai-Augmented Machine Learning might be able to use the context outside of this data to narrow it down a little more from the names of those who infer age and other demographics.”

If you want to easily design machine learning models. When building a machine learning model, you can feed the generated AI tools to data and instructions about the desired functions and methods, build the model, evaluate it on other datasets, and report the accuracy of the model.

Generic AI “changes the lives and workflows of machine learning people,” Ramakrishnan said, noting that the output of the model needs to be continuously analyzed and criticized, and that hallucinations and errors should not be exacerbated.

When generating data for a machine learning model. If there is not enough data to properly train a traditional machine learning model, you can use generated AI to create synthetic data. This has the same statistical properties as the actual dataset.

When preparing structured data for a machine learning model. Tabular data in situations such as industrial settings often contain errors such as missing values that need to be addressed before using the data. Instead of having to clean up manually, you can upload your data to LLM and use anomalies and prompts to look for mistakes.

“Generated AI makes traditional machine learning workflows more efficient, from data sourcing to data cleaning to actual modeling,” says Ramakrishnan. “Every step in the process allows you to use the Generated AI as a kind of turbocharger. However, this is not a free lunch. The price you pay is a constant need for vigilance to ensure that the output of the LLM generation is accurate.”

Given the various AI tools out there, deciding which tools to use is another skill that AI practitioners need.

Ramakrishnan's main takeaway: “If you want to generate things, use Generation AI. If you want to predict things, try Generation AI first, using Daily things. [use] Traditional [machine learning]. It's just as easy. ”