Artificial intelligence (AI) has been a part of our lives for years, quietly powering everything from search engines and product recommendations to curated music playlists and predictive text on our phones. Recently, however, the world has been active with discussions about AI, specifically generative AI, which has captured public interest and ignited enthusiasm for all sorts of purposes. The rapid user adoption of models like OpenAI’s ChatGPT has significantly boosted its popularity, exposing many to this technology. This surge has led many businesses to view generative AI as a key driver for the next wave of digital transformation and automation. But what exactly is generative AI, and how does it differ from the AI we’ve become accustomed to?
To understand the transformative nature of generative AI, it’s helpful to first look at traditional AI, which is also sometimes referred to as narrow AI. Traditional AI has been in development for years and most are familiar with it, but they are unaware the tools are AI. It operates using classical data science and a systematic approach. This typically involves a structured process: data collection, data preparation, data analysis, feature engineering, training, and data validation.
Traditional AI is fundamentally focused on prediction or classification tasks, identifying what something is or forecasting what will happen next based on existing data. A key characteristic is that it operates within pre-established and predefined boundaries on which it has been trained. These boundaries are essentially the rules and instructions that are coded into the model. Consequently, traditional AI can only act based on predefined conditions, constraints, and potential outcomes. This makes the outcome deterministic and relatively predictable. For instance, a traditional AI chatbot might generate responses based on predefined scripts, effectively automating customer service within its domain that was largely determined by humans. While highly effective for the specific tasks they are designed for, these systems generally stay within their defined lanes and cannot easily learn and adapt to situations outside their programmed knowledge. Compared to generative AI models, traditional AI models are also generally less complex and require fewer computing resources, allowing them to run on a variety of hardware, from small edge devices to large cloud clusters. They also typically have no inherent adaptive techniques other than needing more labeled data and going through a full machine learning retraining loop. Traditional AI models are typically trained on smaller datasets of labeled data.
In contrast, generative AI is a unique and fascinating advancement in AI technology that is fundamentally different. Its core capability is to create new content or data from existing ones. Unlike traditional AI which focuses on prediction or classification, generative AI is about creating something novel and original that is not simply modified or copied from its training data. It involves replicating existing patterns, imagining new ones, crafting new scenarios, and creating new knowledge. Generative AI is described as an evolution of deep learning.
While traditional AI follows a deterministic, rule-based approach, generative AI leans toward a probabilistic approach. The outcome in generative AI is calculated based on probabilities influenced by the input data and the patterns learned during its extensive training. This probabilistic nature enables these AI systems to create outputs that were neither hard-coded nor explicitly taught to the system. The “magic” of generative AI lies in this potential to generate something novel and original, a task previously believed to be solely the domain of human ingenuity.
Generative AI doesn’t replace classical data science; instead, it enhances and complements it in many ways. It can help deal with new types of data and content, evaluate the quality and validity of generated outputs, and ensure the ethical and responsible use of generative AI.
Let’s look at some specific areas where generative AI differs significantly from traditional AI:
- Creation versus Prediction: As mentioned, this is the most fundamental difference. Traditional AI excels at predicting or classifying based on existing data. For example, identifying spam emails or predicting stock prices. Generative AI, on the other hand, is designed to generate entirely new content. This can include generating realistic text like articles or emails, creating images from descriptions, writing code, composing music, generating videos and even designing new objects or structures.
- Approach and Outcome: Traditional AI uses a systematic, rule-based approach, resulting in a deterministic and predictable outcome within its defined parameters. Generative AI, however, uses a probabilistic approach, resulting in outcomes that can be varied and were not explicitly programmed. This contributes to the non-deterministic nature of generative AI models, meaning identical inputs might yield slightly different outputs, although settings like temperature can influence the output.
- Training Data and Method: Traditional AI models are typically trained on smaller datasets of labeled data. A model for image classification, for instance, might be trained on a few thousand labeled images. Generative AI models are usually trained on massive datasets of existing content. A generative AI model for image generation might be trained on millions of images, while large language models (LLMs) are trained on vast amounts of text data, such as books, articles, and websites. The training methods also differ; generative models require methods like self-supervision and multi-task learning on a massive scale, which are significantly longer and more expensive due to the vast data and computational resources needed.
- Model Complexity and Resources: Traditional AI models are generally less complex and require fewer computational resources, offering flexibility to run on various hardware. Generative AI models, particularly LLMs, are large and complex and, for the most part, require large cloud compute nodes, often accessed via an API. The complexity and scale contribute to higher costs compared to traditional AI.
- Adaptation: Traditional AI lacks inherent adaptive techniques beyond standard retraining methods. Generative AI, possessing vast world knowledge, offers various adaptive techniques to tailor its responses or incorporate specific, often proprietary, data. Key techniques include prompt engineering, retrieval-augmented generation (RAG), model adaptation, and fine-tuning. These allow users to guide the model’s behavior and incorporate external or private knowledge without necessarily retraining the entire model.
- Modality of Interaction: A significant difference, especially with LLMs, is the primary way users interact with the model: using a prompt. A prompt is a set of instructions telling the generative AI system what content to create. The effectiveness of the output is directly tied to the quality of the prompt. This is a more expressive modality than the structured input data typical of traditional AI, allowing users to convey requirements, intent, empathy, and emotion through language. Prompt engineering, the art and science of crafting effective prompts, is a new and rising area.
Large Language Models (LLMs) are central to the recent excitement around generative AI. They are a type of generative AI model trained on immense text data to understand and generate human-like text. LLMs are general-purpose models capable of handling diverse language tasks like answering questions, summarizing text, translating languages, and generating code, often without task-specific training data, provided the right prompt is given. Models like OpenAI’s GPT family, Anthropic’s Claude, Google’s PaLM and Gemini, and others are examples of these foundational LLMs.
While generative AI’s potential is vast and exciting, it’s important to note that it’s not a silver bullet and should not always be used. Enterprises must consider financial, legal, technical, and moral aspects. Risks include generating incorrect or biased content, creating fabricated responses (hallucinations), and potential misuse. Organizations need a solid plan for using generative AI, ensuring it aligns with business goals and incorporates safeguards, especially in critical scenarios where a human should remain in the loop.
For enterprises, adopting generative AI requires a thoughtful and strategic approach. Key considerations include starting small with pilot projects (“crawl, walk, and run”), defining clear objectives and use cases, establishing robust governance and responsible AI policies, experimenting and iterating given the non-deterministic nature, designing for potential failures (as many models are accessed via cloud APIs with inherent latency and complexity), expanding existing architecture rather than starting anew, determining how to leverage proprietary data (often via techniques like RAG), and managing the potentially high costs.
Importantly, generative AI and traditional AI are not mutually exclusive. In most cases, generative AI can help assist and complement existing investments in traditional AI that enterprises already have. For example, a chatbot might use both generative models for free-form conversation and predictive models for specific tasks like sentiment analysis. Classical data science techniques are also crucial for evaluating the quality and validity of generated outputs and ensuring responsible use .
In conclusion, while traditional AI has provided a foundation of intelligent systems focused on prediction, classification, and automation within defined boundaries, generative AI represents a significant evolution focused on the creation of new, novel content. It leverages vast datasets and probabilistic models to achieve this creative capability. While more complex, resource-intensive, and non-deterministic than its traditional counterparts, generative AI opens up a world of possibilities for innovation and efficiency, particularly when guided by effective prompting and integrated strategically within enterprise architectures. The future of AI in the enterprise likely involves a blend of both traditional and generative techniques, each leveraged for its unique strengths to solve complex problems and create value.