Understand the difference between Generative AI and Machine Learning to help you choose the technology most effective for your business.
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It would seem that GenAI is the epitome of AI, nullifying the need for all other types of AI. Though we may eventually get there, GenAI, as it is today, is not the golden hammer for all your AI problems. A clearer understanding of these models and distinguishing their capabilities from those of ML models is necessary to understand when to apply one over the other.
The distinguishing characteristics of these two are not entirely black and white, especially when they do not involve technical details.
Generative AI refers to algorithms that understand and generate structured and unstructured data on par with or even better than humans (e.g., understanding and generating code, images, audio, videos, and 3D models). These models are becoming increasingly multimodal, with a single model capable of simultaneously handling different data types, such as Anthropic Claude Sonnet’s and Amazon’s Titan Multimodal Embeddings’ text and image capabilities.
Key characteristics of generative AI include:
These models possess and are becoming increasingly capable of a wide range of tasks, such as:
Machine Learning is a subset of Artificial Intelligence that focuses on developing algorithms capable of learning from and making predictions or decisions based on data. Unlike traditional programming, where explicit instructions are provided, Machine Learning models improve their performance through experience. While Machine Learning primarily focuses on pattern recognition and prediction tasks, it differs from Generative AI in that it typically does not create new content or data from scratch. Instead, Machine Learning models analyze existing data to make informed decisions or predictions about future outcomes.
Key characteristics of Machine Learning include:
Machine Learning has numerous applications across various industries. Some common use cases include:
I will outline the differences between Generative AI and Machine Learning by examining GenAI and ML from several perspectives:
The purpose of Generative AI and Machine Learning differs in their primary objectives and the types of tasks they are designed to perform.
Machine Learning's primary purpose is to analyze existing data to make predictions, classifications, or decisions. ML models are trained on historical data to identify patterns and relationships, which they then use to process new, unseen data. The focus is on extracting insights and automating decision-making processes based on past information.
Generative AI's primary purpose is to create new, original content that mimics human-like output. These models are designed to understand and generate various forms of data, such as text, images, or audio. The focus is on producing novel, contextually appropriate content that can be used for creative tasks, content generation, or complex problem-solving.
Both Machine Learning and Generative AI aim to learn from data to perform some tasks. Similar to humans, machines learn in various ways, the most popular of which are:
We can use supervised, unsupervised, semi-supervised, and reinforcement learning techniques on Machine Learning models for training. They can be trained from scratch or using transfer and online learning techniques. However, most are trained from scratch due to their relatively small size and data requirements and the proprietary nature of the data used to train them. So, the utility of transfer and online learning techniques is limited to large models that are costly to train, like recommender systems or computer vision models.
GenAI models use different training techniques from those of ML. For example, with Large Language Models (LLMs), we would use a self-supervised learning technique, attempting to predict the missing word in a given sentence and the following sentence in a given paragraph. By doing so, these models learn the semantics and syntax of the target language, giving them the ability to understand it. This language understanding can then be used for downstream tasks, such as summarization, translation, or question-answering.
The deep learning nature of GenAI demands a larger dataset than is required for most Analytical AI models. So, they are typically trained on public sources of information, such as Wikipedia pages or large image repositories, resulting in larger models than most Analytical AI models - hence the popularity of transfer learning and online learning techniques with this type of AI.
This is why GenAI is typically pre-trained and useful out of the box but can also be fine-tuned to understand nuanced domain-specific terminology or to perform specific tasks, such as identifying named entities (e.g., people, organizations, locations, dates, etc.) within a body of text.
Many pre-trained Analytical AI models, especially large ones, are useful off-the-shelf. However, this usefulness is more prominent with GenAI models due to the broad applicability of their tasks (e.g., understanding language), multiple task capabilities, and increasing multimodality.
ML and GenAI can be trained on structured and unstructured data to perform their respective functions but with some nuance.
ML algorithms learn from structured data to perform a task. For example, given a table containing historical credit card transactions, such as customer ID, purchase date and time, transaction location code, amount spent, currency code, etc., and a label for each transaction indicating whether it is fraudulent or not (obtained from our esteemed fraud analysts), an ML algorithm can learn the necessary patterns within this data to replicate the decisions our fraud experts have made, effectively automating this task.
ML can also learn from unstructured data. However, we need to transform this data into structured numerical data before training commences. For example, to train an ML model to distinguish between spam emails and those that are not, we have to convert the email text into numerical structured data using approaches such as bag-of-words and n-grams.
Once training is complete, we can query these models by passing a new instance of data in the same structure to what they have been trained on to receive numeric predictions, forecasts, recommendations (e.g., product IDs), or patterns in the data, such as clusters/segment IDs and their members.
GenAI algorithms can also learn from structured and unstructured data, although they are more popularly associated with unstructured data. As mentioned above, LLMs, for example, are trained on raw text using the self-supervised learning technique.
These algorithms are also capable of learning from structured numerical data as well. One of the most popular examples of this learning is the fraud detection use case using Generative Adversarial Networks (GANs). Another example comes from an advancement made by Amazon’s research team, where they introduced Chronos, an approach for using LLM architecture to perform forecasting.
We can query these models by passing them a new instance of the data (a question + context, a text excerpt to be translated, a credit card transaction, etc.) in the same structure as what they have been trained. However, in most cases, these models are not restricted to outputting numerical data. They can also generate unstructured data, such as textual responses, translations, or audio transcripts.
Though both these types of AI can have models of varying sizes, GenAI has become increasingly massive. ML models are typically small compared to GenAI, with some being large, such as recommender systems or computer vision models (e.g., think Amazon/Netflix recommender systems or computer vision models for autonomous vehicles). GenAI models, on the other hand, are typically large, with some being small, especially unimodal, single-task GenAI models.
GenAI can understand and generate content, which is the only capability exclusive to GenAI. This capability includes chatting, document generation, document understanding, summarization and translation, named entity recognition, music creation, data generation, protein folding, semantic search (i.e., searching through Knowledge Bases for relevant information), code writing, and content personalization.
However, as mentioned above, GenAI can be leveraged in tasks traditionally associated with ML, such as classifying transactions into fraud/non-fraud buckets or forecasting. The same can be said of ML, such as email understanding for classification into spam/non-spam buckets, sentiment analysis, or image understanding for text extraction.
Another example demonstrating this overlap is support email classification. This task aims to automate determining the support category a customer requests in their email, which brings substantial cost savings for organizations receiving many such emails.
This overlap between ML’s and GenAI’s capabilities feeds the misconception that GenAI has nullified the need for any other type of AI. Despite how it seems, GenAI is not (yet) a General Intelligence (GI) solution or a golden hammer to solve all AI-related problems. They each have their places and times to be used.
In many cases, both these types of AI can be used together to improve the outcomes of a use case. However, it would still help to identify which to start with. In situations where you are looking to classify, predict, forecast, cluster, or recommend and have adequate data, use Machine Learning. If you lack data, look into using pre-trained models or GenAI. In situations where you are looking to produce content, use GenAI.
From predictive and analytical use cases to content development and customer experience, we have tactful expertise in deploying the right flavor of AI to achieve successful outcomes. Embrace the future of business with AI, and let us help you achieve the technological transformation that will fuel tomorrow’s innovation.
Dr. Khobaib Zaamout is the Principal Architect for AI Strategy at Caylent, where his main focus lies in AIML and Generative AI. He brings a solid background with over ten years of experience in software, Data, and AIML. Khobaib has earned a master's in Machine Learning and holds a doctorate in Data Science. His professional journey also involves extensive consulting, solutioning, and leadership roles. Based in Chestermere, Alberta, Canada, Khobaib enjoys a laid-back life. Outside of work, he likes cooking for his family and friends and finds relaxation in camping trips to the Rocky Mountains.
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Khobaib Zaamout