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Generative AI vs. machine learning: How are they different?

Generative AI differs from simpler forms of machine learning in several ways, but both can enhance efficiency, personalize customer experiences and drive revenue growth.

Machine learning and AI are transforming how businesses operate: improving efficiency, streamlining workflows, maintaining security and compliance, and creating new opportunities for revenue and growth.

The numbers are impressive. According to industry research, the machine learning market is expected to reach a valuation of over $200 billion by 2029, while AI offerings are projected to be worth over $1 trillion by 2030.

As machine learning and AI advance, the emergence of generative AI offers new ways of processing and using complex data, but it also poses new challenges for businesses. Before embarking on any AI initiative, IT and business leaders should understand the fundamentals of machine learning and recent advances such as generative AI.

What is machine learning?

Machine learning is a field of software engineering that analyzes data to find patterns, then uses those patterns to assist humans in decision-making based on enormous volumes of similar new and existing data. In essence, machine learning algorithms look at past decisions or cause-and-effect patterns and then seek to predictively replicate those same decisions to assist users or businesses.

Suppose a teacher visits a retail web site and routinely buys pencils. A machine learning platform implemented as an AI-powered personal shopper could recognize that returning customer, including their data history of buying pencils.

Taking into account the website's existing stock and availability, the personal shopper tool could then present that customer with a short list of available pencils in their usual quantity. This saves the shopper time, improves their experience and increases the potential for a sale.

Machine learning relies on an array of algorithms to construct purpose-built software models. The model is systematically trained to deliver a variety of outputs by accessing existing data. The most desirable or attractive outputs are selected to reinforce the model's learning given a set of parameters.

Thus, models often undergo initial training and validation and are then refined over time as more inputs, data, responses and selections become available. This creates the capability for self-learning with little to no human intervention.

Machine learning models require access to considerable data resources to learn and function properly, and they often require periodic updating and retraining as data evolves and changes. This continuous training is an essential part of machine learning model management that every AI-enabled business should embrace.

Use cases for machine learning

Machine learning algorithms can handle a wide variety of tasks in major business areas.


As in the example described previously, machine learning algorithms can use past and current sales data to personalize offers, make product recommendations, predict sales and ordering volumes, and cater to visitors based on their browsing and purchase behaviors.


Machine learning algorithms can access vast amounts of business data to assist with a range of tasks, such as identifying trends, predicting business outcomes, and finding process or supply chain bottlenecks.


Machine learning algorithms can use patient data to help with diagnoses or track patterns of infection, such as monitoring exposure during the Covid-19 pandemic.


Machine learning algorithms can use data from IoT devices to track manufacturing machine performance, monitor material and process workflows, and recommend process optimizations.

Financial services

Machine learning can assist the banking and financial services industry with tasks such as fraud protection, money laundering prevention, personalized financial planning and overall process optimization.

Customer service and support

Machine learning algorithms are the backbone of interactive chat tools that assist customers with questions and problems, thereby increasing user engagement and helping find appropriate solutions to common customer issues.


Machine learning algorithms can model important marketing considerations such as customer churn, targeting and segmentation, leading to more efficient and effective sales efforts.

What is generative AI?

Generative AI builds on machine learning by adding new capabilities to models that enable them to create or synthesize new data, such as text or images, based on the existing data used to train the model.

Generative AI tools can use algorithms and insights from a range of machine learning disciplines, including natural language processing and computer vision. Some of the sophisticated models frequently used in generative AI applications include the following:

  • Generative adversarial networks (GANs). GANs are an important type of deep learning algorithm. They rely on multiple neural networks, which compete with each other to produce more desirable new data based on existing data. One network (the generator) creates new output, while the second (the discriminator) tries to determine whether the new data is real or AI-generated. Over time, the generator's ability to create original data improves until the discriminator can no longer distinguish the new data from the original data.
  • Transformers. These models employ a mathematical technique called self-attention, along with a neural network, to identify context and establish relationships between data points. Transformers are foundational to a number of AI applications, including text-to-speech conversion and drug research based on understanding gene sequences in DNA.
  • Large language models (LLMs). Popular generative AI platforms like ChatGPT use LLMs to interpret user queries, known as prompts, and then generate sophisticated text, images and even software code in response.
  • Multimodal AI. Multimodal AI models can interpret multiple types of data, such as images, text, audio and video. For example, a multimodal model could generate a video with background music based on a textual prompt.

In practice, generative AI operates similarly to other machine learning systems. The generative AI system is first extensively trained on relevant data. Once trained, the generative AI system accepts user prompts, which outline the request and can include highly structured and complex elements.

The generative AI system translates the prompt into specific elements and delivers the output to the user. In many cases, the results can then be scored or rated by human users, who provide feedback that helps further train and refine the generative AI system.

Limitations of generative AI

Generative AI has quickly gained a reputation as a powerful and creative tool for human users. However, it has notable limitations.

First, generative AI cannot imagine; it can only synthesize using its training data. This makes generative AI outputs unique, but not necessarily original. For example, if a user requests an image in the style of Picasso from a generative AI tool that has never been trained on Picasso's paintings, the model cannot understand the request or mimic Picasso's style in its output. This limitation also raises issues around originality, copyright and legal ownership of intellectual property.

Second, generative AI is not perfect. It does not possess perfect understanding and does not produce ideal output in all circumstances or in response to all prompts. Like any other AI, generative AI can produce strange or unintended results that might not track with the user's prompt, a phenomenon known as hallucination.

Use cases for generative AI

Businesses are adopting generative AI in all the principal areas where older forms of machine learning appear. The difference in use cases for generative AI versus other types of machine learning, such as predictive AI, lie primarily in the complexity of the use case and the type of data processing it involves.

Simpler machine learning algorithms typically operate on a more straightforward cause-and-effect basis. Generative AI tools, in contrast, can offer deeper and more creative responses, resulting in new use cases.


Generative AI can update product displays, known as planograms, based on dynamic conditions such as sales trends, inventory levels or competitive data; even pricing can be adjusted dynamically. Generative visualization tools can build images of people wearing or using different products, enabling virtual try-ons. AI can also produce detailed product descriptions and generate customized promotions and product recommendations.


Generative AI can find business value in unstructured content such as maps, catalogs, ordering and supply chain relationships, emails, and expansive document collections or filings.

Advanced chatbots can automatically translate complex questions into underlying semantic meanings, analyze those meanings for context, and then generate highly accurate and conversational responses for next-generation automated assistance.


Generative AI can automatically transcribe and summarize clinical notes, interpret images and test results to assist with diagnoses, and even create personalized treatments for patients based on complex factors such as genetics, lifestyle and symptoms.


Generative AI can generate and evaluate design options to help manufacturers select the most optimized, efficient and cost-effective designs and processes while improving supply chain visibility.

Similarly, generative AI can find insights and validate models to assist in design and manufacturing. Generative AI models can move beyond older forms of machine learning to use diagnostics to diagnose equipment failures and recommend actions, even guiding technicians through repairs and maintenance.

Financial services

Generative AI can support carefully curated investment strategies and portfolios to meet specific financial goals and even drive new financial advisory or wealth management services for brokerage clients and advisors. LLMs can also power sophisticated tools such as stock screening using natural-language interaction. In addition, they can help process and generate vast amounts of financial documentation, such as business filings, loan documents, insurance policies and regulatory documents.

Customer support

Generative AI builds on existing chatbots that can parse and interpret context and semantics -- even the user's stress level or emotional state -- using a verbal interface. This enables more responsive and accurate virtual assistants for many types of markets.

Overview: Generative AI vs. machine learning

In simple terms, machine learning teaches a computer to understand certain data and perform certain tasks. Generative AI builds on that foundation and adds new capabilities that attempt to mimic human intelligence, creativity and autonomy.

Generative AI Machine learning
Enables a machine to solve problems by simulating human intelligence and supporting complex human interactions. Enables a machine to train on past data and learn from new data with some level of autonomy.
Aims to create a system that can perform complex tasks and interactions with a level of autonomy. Aims to learn from data to continuously enhance and improve model accuracy.
Has a broad range of potential applications and a wide assortment of capabilities within that range. Has a broad range of potential applications but a relatively narrow assortment of capabilities within that range.
Mimics human decision-making. Uses algorithms to learn and operate predictive models, assisting in human decision-making.
Works with all types of data including structured, semi-structured and unstructured. Typically only uses structured and semi-structured data, as machine learning algorithms can struggle with unstructured data because of a lack of context.
Uses logic and decision-making to learn, reason, adjust and self-correct over time. Uses statistical models to learn, but can only adjust or self-correct with user feedback or new data.

Simple ML models can sometimes answer questions, but they have a limited scope and typically do not perform tasks with any significant level of autonomy. For example, an ML model or system might analyze business data to find a business opportunity, but it can only act on the data it has and in response to a user's query.

In comparison, a generative AI tool might be implemented as a virtual assistant capable of providing more comprehensive support. For example, a generative AI assistant could answer calls and interact with users using natural language, dynamically gather information from users, diagnose problems, handle scheduling, and guide callers through diagnostics and solutions.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 20 years of technical writing experience in the PC and technology industry.

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