AI vs. GenAI vs. ML: Key Differences

The tech world loves its new terminology. If you’re a little geeky and socially awkward, using the latest buzzwords at cocktail parties is a way to make yourself either more interesting or totally insufferable. Either way, it’s a techie’s way to shine. For the rest of us, we should at least keep pace with high-level concepts—particularly with a technology such as AI, where the potential for disruption is high and the pace of evolution seems frantic. Here’s a quick look at some top-level terms in AI today.

What Is Artificial Intelligence (AI)?

Artificial intelligence refers to a broad and growing class of computer applications that seek to mimic functions of the human brain. The idea of AI has been around since the very early days of computers, when concepts like fuzzy logic and decision trees were used to create algorithms that seemed to produce human-like responses, albeit in a very limited way. At about the same time, computers were being used to simulate the neural networks of the brain—in other words, how the brain functions. These two notions have now come together to create computer models that mimic functions of the brain by imitating the way the brain operates, including how it learns.

AI Use Cases

Almost all AI and machine learning use cases rely heavily on having access to large amounts of data to learn patterns, make predictions, and perform tasks effectively. But it’s not all about quantity. The quality, relevance, and timeliness of data are also crucial for the success of any AI project because the focus is on analyzing data to make predictions, classifications, or decisions. It’s about understanding and interpreting information. Top uses cases now include the following:

• Chatbots capable of natural language conversations can be a big help for customer service teams, while sales and marketing professionals can use AI to help customize promotional offers based on customer history.
• Across job roles, AI can detect the language in spoken human speech and create concise summaries of long documents or meetings.
• Systems that can quickly spot attack indicators in security logs and do a deep analysis of digital images or videos to detect anomalies help companies make sense of log data. AI can also detect fraud indicators in financial transactions.
• A range of industries use AI to analyze inventory or order patterns to help fine-tune purchasing.

What Is Generative AI (GenAI)?

Generative AI is the technology behind large language models, or LLMs, including ChatGPT and many other AI tools. GenAI technology is a newer use of AI, and it goes beyond analysis of new data. It can generate natural language responses to user prompts, for example, and generate images or music based on what a user requests. A GenAI model is trained using massive sets of sample data that teach the model how a language such as English works and what information is stored in the data provided.

AI training is extremely compute-intensive, requiring many hours and many graphics processing units (GPUs) to fully train the model. Generative AI models typically don’t continue to learn after their initial training. Instead, they use their understanding of language and the information they’ve been trained on to either answer queries or analyze new data and provide answers about it in conversational language. Trained models can be fine-tuned to excel in a certain area—for example, a general-purpose LLM trained on GAAP accounting practices can help finance teams.

In addition, normally, a model wouldn’t know anything about a particular company. But, using an associated technique called retrieval-augmented generation (RAG), data can be provided to the LLM at the time of the query that will inform the answer. So, if you want to know what time of day your regional coffee shops sell the most espresso, you can provide sales data via RAG and the LLM can generate the answer.

Generative AI (GenAI) Use Cases

GenAI focuses on creating new content that resembles the data it trained with. It’s about synthesizing and generating information and builds on how AI and ML understand and interpret data.

Use cases include

• Drafting sales letters to clients for humans to review and providing personalized customer support via chat, drawing on client records to inform actions.
• Summarizing lengthy documents and transcripts and rewriting documents in a different language or style
• Creating product descriptions in your company’s style based on spec sheets
• Classifying the user’s intent in support tickets, saving time and frustration
• Drafting computer code for database access to enable authorized users to get the information they need faster
• Modifying digital artwork, including images, videos, sounds, and even voices, based on human prompts

What Is Machine Learning (ML)?

Machine learning is a particular AI technology that, as its name implies, learns by analyzing large data sets using simulated neural networks that mimic the function of the human brain. ML differs from generative AI in that the data sets are typically smaller and highly curated. The data is also classified so that the ML model can distinguish certain traits within the data. So, for instance, a model that’s intended to identify dog breeds in pictures will be shown many images with dogs of various breeds and in various positions. Once trained, the model will be able to differentiate between poodles and golden retrievers. Other models will be trained to identify possible fraud in financial transactions or cancerous areas on a chest X-ray.

Machine Learning (ML) Use Cases

ML tools can become extremely useful for a specific task, but they’re not as broadly useful as LLMs. ML models can often continue their learning process as they’re given new data to analyze and feedback on their accuracy. Use cases include

• Scanning network access logs to spot anomalies that might indicate a cyberattack
• Generating cross-sell and up-sell offers for customers based on their past purchases
• Predicting inventory problems based on real-time sales data
• Creating descriptive tags on social media images and videos
• Identifying hardware that’s at risk of failure in the near future based on sensor data
• Clustering customers based on purchasing patterns
• Dynamically pricing products based on supply and demand

What Are AI Agents?

Human agents, say a travel agent or real estate agent, perform tasks on behalf of their clients and are useful because of the specialized training they can bring to bear. For example, a customer may enlist the help of a travel agent to take a month-long trip to Africa. The customer tells the agent about what she wants to see and do and provides parameters, such as dates for travel and a budget. Then the agent works to set up the trip, reports back with options, and ultimately books the trip for the customer. AI agents conceptually work the same way. A human asks for a task to be completed. She provides details and parameters under which the agent will operate, and then the agent autonomously does the work and reports its results.

AI agents will typically use a powerful LLM to understand what the requester wants, and as it does, it will devise a plan to complete the task. Using RAG, the AI agent will get the information it needs, and it may request other information as needed to complete each part of its plan. The agent retains data on how it completed previous tasks, effectively giving it memory and the possibility to improve its process. An AI agent can also use tools such as ML models, automated robotic processes, and other LLMs—sometimes less powerful but more focused—to complete its task. It’s up to users to set parameters, such as when to pause for approvals before proceeding.

The benefit of AI agents is their ability to complete complex tasks autonomously and to leverage data, including that which describes company polices and procedures, as they go. As such, agents offer the potential of substantial improvements in productivity, speed, and accuracy.

AI agent use cases are myriad and include software development, customer support, order taking, inventory replenishment, HR benefits support, spontaneous cyberthreat response, and executive decision support.

Unlock New Opportunities with Oracle

Oracle offers a full suite of artificial intelligence technologies, including AI-augmented features within your applications, cloud infrastructure for deploying new AI-based software, and tools for software developers and data scientists.

Millions of users of Oracle Fusion Applications can use generative AI, machine learning, and AI agents today. For example, those using Oracle Fusion Cloud HCM can use AI to recommend training opportunities for workers. In Oracle Cloud Sales, AI can assist with managing leads, determining lead activation effectiveness, and making product recommendations. And in Oracle Fusion Cloud SCM, AI can help users with predicting product shipping times, dynamic discounting, and detecting product anomalies.

Building your own applications? Oracle offers powerful AI services in Oracle Cloud Infrastructure (OCI) with prebuilt ML models for your developers. Those services include APIs for generative AI, digital assistants, and language, speech, vision, and document understanding capabilities. Oracle also offers a suite of open source libraries and frameworks, in-database algorithms, and multiple data models for the complete lifecycle of machine model development.

The common thread across diverse AI use cases is the reliance on data to identify patterns, automate tasks, and make predictions. While the specific applications and technologies may vary, these core principles remain consistent. This data-driven, pattern-recognition, and automation focus is what makes AI a powerful tool for addressing a wide range of problems across different domains.

AI vs. GenAI vs. ML FAQs

Is AI the same as ML?

Machine learning is a subset of artificial intelligence. Most AI systems of interest today are built on machine learning concepts. Traditional ML models are built and trained to perform one function—say, spotting potential fraudulent bank transactions. Other AI systems go further and are more generally useful.

Is GenAI a subset of AI?

Yes, GenAI is a subset of AI. Comparatively new, GenAI models are much larger than previous AI models and are capable of generating new content based on prompts from users.