While GenAI has gained significant attention in recent times, businesses have long used AI for vital tasks like fraud detection and personalization. Learn the distinctions between GenAI and Analytical AI and how you can unleash the potential of AI in your business.
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GenAI became popular due to advancements in AI’s conversational capabilities, including its ability to retrain knowledge,conversation history and answer questions from this knowledge, history, and any other relevant context the user introduces. Consequently, GenAI acquired somewhat of a reputation as being exclusively an intelligent conversational solution that can do it all.
GenAI models are the large language models that are creating new content by taking a prompt as input and generating new outputs. For example, it can generate images, poems, or in the case of automation and automated assistance, help to generate code. The GenAI models tend to be, by default, fairly generic and trained on a very wide variety of information available on the internet.
When thinking about GenAI, this changes because the large language models that have been trained to date have effectively been trained on the public internet. The result is that they're very generalized across all the corpus of human information that's on the internet and they're less specific to a particular business' use case. Using large language models out of the box may not allow a company to differentiate itself from their competitors, but could raise the bar for productivity.
Analytical AI is used to classify, group, or predict things by leveraging tuned models that are very specific to a business' data and use cases. Analytical AI is typically applied for very pointed solutions. Many of the cases are common enough and happen in a standard enough way that they have been packaged up into an AWS service. Fraud detection, text to speech, speech to text - these are some of the many forms of artificial intelligence or machine learning that are available to consume without the effort of training and maintaining the underlying models.
Custom models built from company data with proprietary information can address company specific problems for use cases like fraud prevention. Companies like Mastercard, Visa, and American Express are very interested in fraud detection, not only to prevent it, but also to avoid any non-fraudulent transactions from being declined unnecessarily.
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 traditional AI models is necessary to understand when to apply one over the other.
The distinction from a technical perspective is somewhat simple for AI practitioners:
But then, some of these architectures can be used in tasks primarily associated with traditional AI, such as forecasting and recommender engines. Would this make them non-generative?
The distinguishing characteristics of these two are not entirely black and white, especially when they do not involve technical details.
The primary function of Analytical AI and GenAI is the closest characteristic to a black-and-white distinction.
Analytical AI refers to a collection of machine learning algorithms that serve the primary function of automatically identifying patterns in structured and unstructured data to solve analytical tasks faster and more efficiently than humans, such as being able to
These algorithms result in highly specialized models for the specific analytical task. They can handle a single data type (e.g., unimodal) and perform a single task using that data type.
GenAI, on the other hand, 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.
Unlike Analytical AI, 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.
Moreover, these models possess and are becoming increasingly capable of a wide range of tasks, such as conversing/chatting, writing code, generating images, generating structured and unstructured data, translating, transcribing audio, describing images and videos, recreating text, images, and videos based on stylistic characteristics of other content, understanding acting upon causality and reasoning, and even tackling brain-computer interactions.
Analytical AI and GenAI fall within the machine learning (ML) domain. Both aim to learn from data to perform some tasks. Similar to humans, machines learn in various ways, the most popular of which are:
Analytical AI models are trained using supervised, unsupervised, semi-supervised, and reinforcement learning techniques. 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 Analytical AI models that are costly to train, like recommender systems or computer vision models, which constitute a small segment of Analytical AI models.
GenAI models are trained using techniques different from those of Analytical AI. For example, large Language Models (LLMs) are trained using the 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.
Analytical AI and GenAI can be trained on structured and unstructured data to perform their respective functions but with some nuance.
Analytical AI algorithms learn from structured numerical 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 Analytical AI algorithm can learn the necessary patterns within this data to replicate the decisions our fraud experts have made, effectively automating this task.
Analytical AI can also learn from unstructured data. However, this data must be transformed into structured numerical data before training commences. For example, to train an Analytical AI model to distinguish between spam emails and those that are not, the email text must first be transformed into numerical structured data using approaches such as bag-of-words and n-grams.
Once training is complete, these models can be queried 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), where two models are pitted against each other, where one (the discriminator) learns to differentiate between fake and real transactions and the other (the generator) learns to generate synthetic transactions to fool the discriminator. By doing so, these two models learn what typical transactions look like and become capable of identifying unusual transactions (i.e., fraudulent transactions). These two models use the same structured numerical data described above.
Similar to Analytical AI models, these models can be queried 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, as with analytical AI. 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. Analytical AI 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 Analytical AI, such as classifying transactions into fraud/non-fraud buckets or forecasting. The same can be said of Analytical AI, such as email understanding for classification into spam/non-spam buckets, sentiment analysis, or image understanding for text extraction.
The figure below shows some common use cases and their association with Analytical and GenAI.
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.
An Analytical AI approach to automating this requires curating a dataset of emails and their previously determined support category. Emails must be transformed into numerical values and the categories into category IDs, resulting in a structured numerical dataset on which an Analytical AI model gets trained to learn the relationship between email text and categories. This model can then be queried whenever a new email arrives to determine its support category automatically.
GenAI can also be applied in this case in many ways. One such way would be to provide an LLM with the support categories’ descriptions along each new email, prompting it to infer the best category to which the email belongs. This approach requires no labeled data.
In this particular example, both approaches accomplish a classification task, an Analytical AI task, emphasizing that Analytical AI and GenAI solve different problems but can also solve the same problem differently.
This overlap between Analytical AI’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 Analytical AI. If you lack data, look into using pre-trained Analytical AI models or GenAI, as described in the support email classification example. 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 the expertise to deploy the right flavor of AI for successful outcomes. Caylent's generative AI offerings reduce barriers to entry, helping organizations of any size amplify the power of their data, expedite the experimentation cycle, and accelerate the adoption of new AI technologies. Embrace the future of business with AI, and let us help you achieve the technological transformation that will fuel tomorrow’s innovation. For some examples, take a look at our Generative AI offerings.
As Caylent's VP of Customer Solutions, Mark leads a team that's entrusted with envisioning and proposing solutions to an infinite variety of client needs. He's passionate about helping clients transform and leverage AWS services to accelerate their objectives. He applies curiosity and a systems thinking mindset to find the optimal balance among technical and business requirements and constraints. His 20+ years of experience spans team leadership, technical sales, consulting, product development, cloud adoption, cloud native development, and enterprise-wide as well as line of business solution architecture and software development from Fortune 500s to startups. He recharges outdoors - you might find him and his wife climbing a rock, backpacking, hiking, or riding a bike up a road or down a mountain.
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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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Mark Olson
Connie Chen
Khobaib Zaamout