Generative AI vs. Machine Learning: How are they different?

Machine Learning

Machine learning and AI are transforming the way 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, and AI products are predicted to be worth more than $1 trillion by 2030.

As machine learning and AI advance, the emergence of generative AI brings new ways to process and use complex data, but also creates new challenges for enterprises. Before embarking on an AI initiative, IT and business leaders need to understand the fundamentals of machine learning and recent advancements such as generative AI.

What is Machine Learning?

Machine learning is a field of software engineering that analyzes data to find patterns and then uses those patterns to help humans make decisions based on vast amounts of similar new and existing data. Essentially, machine learning algorithms look at patterns in past decisions and causal relationships, then predictively replicate those same decisions to help users and businesses.

Let's say a teacher visits a retail website and regularly purchases pencils. A machine learning platform implemented as an AI-powered personal shopper can recognize that repeat customer, including their pencil purchasing history data.

Taking into account the website’s existing stock and availability, the personal shopper tool can present that customer with a short list of pencils available in regular quantities, saving the shopper time, improving their experience, and increasing the chances of a sale.

Machine learning utilizes a variety of algorithms to build specialized software models. The models are systematically trained to access existing data and provide different outputs. Given a set of parameters, the most desirable or attractive output is selected, enhancing the model's learning.

As such, models often go through initial training and validation and then improve over time as more inputs, data, responses, and choices become available, resulting in self-learning capabilities with little or no human intervention.

Machine learning models require access to significant data resources to learn and perform properly, and often require regular updates and retraining as data evolves and changes. This continuous training is a key part of machine learning model management that all AI-enabled enterprises should adopt.

Machine Learning Use Cases

Machine learning algorithms can handle a wide range of tasks in key business areas.


As in the previous example, machine learning algorithms can use past and current sales data to personalize offers, recommend products, predict sales and order volumes, and cater to visitors based on their browsing and purchasing behavior.


Machine learning algorithms can access vast amounts of business data to assist with a variety of tasks, including identifying trends, predicting business outcomes, and finding bottlenecks in processes and supply chains.

health care

Machine learning algorithms can leverage patient data to aid in diagnosis and track infection patterns, such as monitoring exposure during the COVID-19 pandemic.


Machine learning algorithms can leverage data from IoT devices to track the performance of manufacturing machines, 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 prevention, anti-money laundering, personalized financial planning, and overall process optimization.

Customer Service and Support

Machine learning algorithms are the basis of interactive chat tools that help customers with their questions and issues, increasing user engagement and helping to find the right solutions to common customer problems.


Machine learning algorithms can model key 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 allow 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 leverage algorithms and insights from a variety of machine learning disciplines, including natural language processing and computer vision. Advanced models frequently used in generative AI applications include:

  • Generative Adversarial Networks (GANs). GANs are an important type of deep learning algorithm. They rely on multiple neural networks that compete with each other to generate new, more desirable data based on existing data. One network (the generator) creates the new output, while the other network (the discriminator) determines whether the new data is real or AI-generated. Over time, the generator becomes better at creating original data, and the discriminator can no longer distinguish between the new data and the original data.
  • Transformers. These models use a mathematical technique called self-attention along with neural networks to identify context and establish relationships between data points. Transformers are the basis of many AI applications, such as text-to-speech conversion and drug research based on understanding genetic sequences in DNA.
  • Large-scale language models (LLMs). Popular generative AI platforms such as ChatGPT use LLMs to interpret user queries, called prompts, and generate advanced text, images, and even software code in response.
  • Multimodal AI. A multimodal AI model can interpret multiple types of data, including images, text, audio, video, etc. For example, a multimodal model can generate a video with background music based on a text prompt.

In practice, generative AI works like any other machine learning system: the generative AI system is first trained extensively on relevant data. Once trained, the generative AI system accepts user prompts outlining a request, which may contain highly structured and complex elements.

The generative AI system translates the prompts into specific elements and provides output to the user, which is often scored or evaluated by a human user whose feedback helps further train and improve the generative AI system.

Use Cases for Generative AI

Enterprises are adopting generative AI in all major areas where older forms of machine learning are used. The difference between generative AI use cases and other types of machine learning use cases such as predictive AI is primarily in the complexity of the use case and the type of data processing involved.

Simpler machine learning algorithms typically operate on simpler causal relationships. In contrast, generative AI tools can provide deeper and more creative responses, thus giving rise to new use cases.


Generative AI can update product displays, called planograms, based on dynamic conditions such as sales trends, inventory levels, and competitive data. Pricing can also be adjusted dynamically. Generative visualization tools create images of people wearing or using different products, allowing for virtual try-ons. AI can also generate detailed product descriptions and generate customized promotional and product recommendations.


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

Advanced chatbots can automatically translate complex questions into their underlying meaning, then contextually analyze that meaning to generate highly accurate, conversational responses for the next generation of automated assistance.

health care

Generative AI can automatically transcribe and summarize clinical notes, interpret images and test results to aid in diagnosis, and even tailor treatments for patients based on complex factors such as genetics, lifestyle, and symptoms.


Generative AI generates and evaluates design options, helping 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 aid in design and manufacturing. Going beyond traditional forms of machine learning, generative AI models can also use diagnostics to diagnose equipment failures and recommend actions or guide technicians through repairs and maintenance.

Financial Services

Generative AI can also support carefully curated investment strategies and portfolios to achieve specific financial goals and drive new financial advisory and asset management services for brokerage clients and advisors. LLM can also power advanced tools like stock screening using natural language interaction. Additionally, it can help process and generate vast amounts of financial documents, such as business filings, loan documents, insurance policies, and regulatory documents.

Customer Support

Generative AI builds on existing chatbots that can use language interfaces to parse and interpret context and semantics (including the stress level and emotional state of the user), enabling more responsive and accurate virtual assistants across many types of markets.

Overview: Generative AI and Machine Learning

Simply put, machine learning teaches computers how to understand specific data and perform specific tasks. Generative AI builds on that foundation and adds new capabilities that seek to mimic human intelligence, creativity, and autonomy.

Generative AI Machine Learning
It enables machines to solve problems by simulating human intelligence and supporting complex human interactions. It allows machines to train on historical data and learn from new data with some degree of autonomy.
The goal is to create systems that can perform complex tasks and interactions with a degree of autonomy. The goal is to learn from the data and continually enhance and improve the accuracy of the model.
It has a wide range of potential applications and diverse functions within that range. The potential applications are wide-ranging, but the range of functions within them is relatively narrow.
It mimics human decision making. Algorithms are used to learn and manipulate predictive models to aid human decision making.
Works with all types of data: structured, semi-structured, and unstructured. Typically, you would only use structured and semi-structured data, since machine learning algorithms can have a hard time processing unstructured data due to the lack of context.
They use logic and decision-making to learn, reason, adjust and self-correct over time. It learns using statistical models, but can only adjust or self-correct based on user feedback or new data.

Although simple ML models may be able to answer questions, their scope is limited and they typically do not perform tasks with a high degree of autonomy. For example, an ML model or system may analyze business data to find business opportunities, but the model or system can only act on the data it has and in response to user queries.

In comparison, generative AI tools could be implemented as virtual assistants that can provide more comprehensive support. For example, a generative AI assistant could answer phone calls and interact with users using natural language, dynamically gather information from users, diagnose problems, manage schedules, 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 industries.

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