What is Generative AI vs AI? Key Distinctions and Business Impact
The differences between AI and generative AI, their applications, and why businesses must understand both to harness transformative opportunities
Introduction: Artificial Intelligence and Generative AI
The importance of differentiating between AI and GenAI
Understanding the differences between artificial intelligence (AI) and generative AI (GenAI) is crucial for businesses aiming to leverage these technologies. Identifying the one most suitable for their needs helps companies cut through the hype, enabling them to invest in solutions that deliver value rather than getting caught up in trends that might not align with their strategic goals.
Furthermore, navigating the ethical challenges associated with AI and GenAI is integral to responsible technology adoption. AI systems can inherit biases from their training data, which can lead to unfair outcomes in areas such as hiring or lending. GenAI raises unique ethical concerns related to content creation, such as intellectual property issues. By understanding the specific challenges each technology presents, businesses can implement guidelines that promote responsible use. This proactive approach fosters trust among stakeholders, enhancing long-term value and success.
What Is Artificial Intelligence?
AI’s history, applications, and techniques
AI is an area of computer science dedicated to building systems that can mimic human intelligence. This involves creating machines that can learn from experience, reason through complex problems, develop solutions, and make decisions—much as we do.
A Quick History of AI
AI has been around for decades, beginning in the mid-20th century when it was a formal discipline focused on rule-based systems and symbolic reasoning. In the following years, machine learning gained momentum, leveraging statistical methods and neural networks, though hardware constraints limited progress. By the 2000s, advancements in computing power and access to big data fueled breakthroughs in AI, making applications such as image and speech recognition practical. The 2010s saw deep learning revolutionize the field, enabling sophisticated pattern recognition and decision-making. Most recently, the 2020s have brought Generative AI to the forefront, captivating attention with its ability to create human-like text, images, and other content.
AI’s Applications
AI can be used in many aspects of our daily lives. For instance, predictive analytics uses AI to forecast outcomes, such as weather predictions or stock market trends, to help businesses and individuals make informed choices. Recommendation systems, such as those seen on e-commerce platforms, use AI to suggest products based on consumer behavior. Additionally, AI powers image and speech recognition technologies, which are found in virtual assistants such as Siri and Alexa, as well as in security systems that recognize faces. AI also thrives in areas including robotics, optimization, and even medical diagnostics, showcasing its vast potential to improve various fields.
Techniques Behind AI
Various techniques are used to develop AI, including rule-based systems, which essentially depend on predefined if-them conditions; machine learning (ML), where systems learn from data over time; and neural networks, which mimic the human brain’s interconnected neurons. Neural networks are the foundational technology of deep learning, a more advanced subset of machine learning that processes vast amounts of data through multiple layers, aiming to emulate human-like complex pattern recognition and high-level decision-making.
What Is Generative AI?
A closer look at an influential subfield of artificial intelligence
GenAI is a subfield of artificial intelligence focused on creating new forms of content, including text, images, audio, and even code. Unlike traditional AI systems that analyze or classify existing data, GenAI generates entirely new data, producing creative outputs that can resemble those made by humans.
Generative AI’s Applications
The applications of GenAI are broad and increasingly impactful—and we’ve only scratched the surface of its potential. For instance, ChatGPT can produce conversational text, draft automated emails, and even compose whole articles. Applications such as DALL-E and Midjourney can create visuals from text prompts. GitHub Copilot and similar tools assist developers by suggesting code snippets based on their inputs. GenAI can be used to create music, videos, and even synthetic data, showcasing versatility and the ability to boost productivity.
Techniques Behind Generative AI
At the heart of GenAI are advanced deep-learning techniques. One of the most significant is transformer architecture, which has revolutionized natural language processing (NLP) and content creation. Large language models (LLMs), built on these transformers, can understand and generate human-like text by processing vast datasets. Similarly, diffusion models generate images and other creative outputs by starting with random noise and gradually refining it into coherent, high-quality data.
Why Is There So Much Hype Around Generative AI?
The paradigm shift in AI capabilities, accessibility, and application
GenAI sparks excitement because it represents a significant shift in what artificial intelligence can do, how accessible it is, and how it can be applied. Unlike earlier AI technologies focused on analyzing data, GenAI opens new avenues for making original content, reshaping our concepts of technology and creativity.
This current GenAI hype is due to decades of research and recent advancements in hardware, such as powerful graphics processing units (GPUs) and Tensor Processing Units (TPUs). Access to massive datasets also plays a key role, providing the raw material these models need to learn and generate content. ChatGPT and DALL-E are great examples of tools that benefit from this progress and make AI feel more accessible to a broader audience.
Unlike foundational AI systems that require a technical background to understand, GenAI application interfaces are often intuitive, allowing people to experiment with their ideas without being highly tech savvy. This shift toward a more user-friendly technology landscape makes GenAI a valuable tool for many professionals and enthusiasts.
Key Qualities Behind the Generative AI Hype
- Human-like creativity: GenAI can produce content that feels human, crafting original creations from scratch. Tools such as ChatGPT can generate essays that closely mimic human writing, while DALL-E can translate text descriptions into artwork, capturing personal ideas and concepts visually. Users can thus explore creativity through GenAI in ways that still feel personal and authentic.
- Accessibility and useability: GenAI tools allow people to engage with AI models through simple natural language prompts and graphic user interfaces (GUIs), making the interaction intuitive. This means anyone can harness AI’s power. For example, users can create professional-grade images without artistic training—or generate complex code snippets without extensive programming skills. This empowers more people to innovate in ways that were previously reserved for experts.
- Broadly applicable across industries: GenAI can be applied to many industries, from content marketing and entertainment to healthcare and programming. This flexibility makes it appealing to both businesses and consumers. For example, in marketing, companies can use GenAI to generate catchy ad copy. In the healthcare sector, it can create synthetic patient data, allowing researchers to train models without running into privacy concerns or HIPAA compliance issues over real patient data.
- Breakthroughs in AI model capabilities: Recent breakthroughs, particularly with LLMs such as GPT-4o and transformer-based architectures, are a leap forward in the ways AI creates content. These advanced models exhibit impressive contextual understanding, grasping nuances in language previously elusive to AI systems. This means they can create more coherent, relevant responses that feel more natural. As a result, users get a richer experience when chatting with a virtual assistant, generating creative writing, or tackling complex queries.
- Social and economic implications: GenAI is reshaping how we approach work and daily life, leading to discussions about its broader social and economic implications. While it automates routine processes and enhances creative capabilities, it also raises important questions about the future of employment, as many traditional roles may become obsolete. There are also ethical considerations, such as concerns with plagiarism, source obfuscation, data privacy, bias, and accountability in AI-generated outputs.
Common Misconceptions About AI vs. GenAI
How these technologies work and the unique roles they play
Several common misconceptions blur the lines between AI and GenAI, leading to confusion about what each term truly means. Many people think AI is just about generating content, but that’s actually just the GenAI part of the story. The overarching field of AI encompasses a wide range of functions including analysis, prediction, decision-making, and content creation.
So what are some of the most common misconceptions around AI and GenAI?
Myth 1: “GenAI and AI are completely different.”
Reality: This isn’t quite true. GenAI is a specialized subset of AI. All generative AI is AI, but not all AI focuses on content creation. AI includes tasks such as prediction, classification, optimization, and decision-making, while GenAI specifically creates new outputs such as text, images, audio, or synthetic data.
Myth 2: “AI cannot be creative without GenAI.”
Reality: Creativity in AI isn’t exclusive to GenAI. Non-generative AI can also exhibit creativity by solving complex problems or identifying novel patterns, albeit in structured ways. For example, chess-playing algorithms such as AlphaZero “create” new strategies through decision-making.
Myth 3: “GenAI can do everything AI can do.”
Reality: GenAI excels at producing new content but isn’t designed for data classification, anomaly detection, or optimization tasks. These tasks often rely on traditional AI or ML approaches. For example, a GenAI language model could come up with a detailed explanation about why a machine might fail, but it’s not performing the same kind of automated sensor data analysis that an AI predictive maintenance system uses to forecast machine failures.
Myth 4: “AI is always data driven, while GenAI is not.”
Reality: Both AI and GenAI rely on data for training. However, GenAI models require vast datasets to learn how to generate realistic outputs, while other AI models may work effectively with smaller datasets tailored to specific tasks.
Myth 5: “GenAI is smarter than traditional AI.”
Reality: GenAI isn’t inherently smarter. In fact, traditional AI often outperforms GenAI in tasks requiring precision, structured decision-making, or domain-specific expertise. However, the lines are starting to blur here as GenAI models become more sophisticated—and specialized small language models (SMLs) are ushering in the next era of more efficient, domain-tailored GenAI.
Data: Foundational to AI and GenAI
The raw material for training, optimization, and customization
The Foundation
Data is the backbone of AI and GenAI, acting as the essential raw material that fuels these advanced technologies. Just as a chef needs fresh ingredients to whip up a delicious meal, AI models require vast and diverse datasets to learn from and identify patterns, relationships, and contextual information. This process enables them to perform impressive tasks such as understanding natural language, recognizing images, and generating relevant content. The richer and more varied the data, the better these systems can perform, making data quality paramount.
One of the remarkable features of AI and GenAI tools is their ability to continuously learn and adapt over time. These systems can refine their output through feedback loops as they are exposed to new data. This ongoing learning process helps them stay current, whether in fields such as healthcare, where precision is crucial, or finance, where quick adaptability can lead to better decision-making.
However, not just any data will do. High-quality, domain-specific datasets are needed to produce accurate results. For instance, data in healthcare needs to be meticulously organized and verified to avoid life-threatening mistakes. In retail, data about customer behaviors must be well maintained for personalized shopping experiences.
Postgres® for AI
Postgres stands out as an ideal database for AI applications due to its scalability, performance, and versatility. Organizations require databases that can grow alongside their needs, and Postgres excels in handling massive datasets efficiently. This capability is especially important for AI tasks, where processing and analyzing large volumes of information is critical for success. Furthermore, Postgres supports various data models, including JSON and vectors, which are crucial for advanced AI features such as embedding storage and similarity search.
Another significant advantage of Postgres is its seamless integration with popular AI frameworks such as TensorFlow, PyTorch, and Griptape. This compatibility streamlines the development process and ensures that teams can easily leverage existing tools and libraries to build sophisticated AI applications. Furthermore, Postgres maintains ACID compliance, guaranteeing data integrity and reliability—essential qualities when working with sensitive information and trusting machines to come to accurate conclusions or create relevant content.
Postgres is also versatile enough to handle hybrid transactional and analytical workloads, making it well suited for AI-powered use cases such as fraud detection and recommendation systems. In these scenarios, organizations must analyze data in real time while managing ongoing transactions, and Postgres can juggle these demands. Being an open source solution with a vibrant ecosystem, Postgres offers cost-effective scalability and extensibility, enabling organizations to customize their systems.
Future Trends for AI and GenAI
What businesses can look forward to
GenAI still feels brand-new, but the landscape is changing rapidly. Here are some emerging capabilities set to drive big impact:
Multimodal AI Systems
Increasingly sophisticated GenAI models can power cognitive AI applications that create and process content across multiple modalities (e.g., text, images, audio, and video) in a unified framework.
Example: OpenAI’s GPT-4o integrates text and image processing. Future models are expected to handle audio and video seamlessly.
Specialized Generative Models
Companies are developing domain-specific GenAI models tailored to industries including healthcare, finance, and law. These models prioritize accuracy, regulatory compliance, and specialized vocabulary.
Example: GenAI for drug discovery, financial forecasting, or legal document drafting
Synthetic Data Generation
GenAI is being adopted to create high-quality synthetic datasets for training machine learning models, addressing challenges such as data scarcity, bias, and privacy.
Example: Generating synthetic medical images to improve diagnostic AI without compromising patient privacy
Generative AI in Programming
AI-powered coding assistants are becoming more sophisticated, enabling developers to generate, debug, and optimize code. This trend is accelerating software development and lowering the barrier to entry for non-developers.
Example: GitHub Copilot and Amazon CodeWhisperer
Ethical AI and Governance Tools
As GenAI adoption grows, so does the need for tools to ensure responsible use. There’s a rising focus on building safeguards against misuse, bias, and copyright infringement.
Example: AI models that watermark outputs or include content provenance tracking
Real-Time Generative Applications
GenAI is being integrated into real-time applications such as video conferencing (e.g., AI-generated backgrounds), live translation, and conversational agents capable of fluid, context-aware dialogue.
Example: AI that generates and translates subtitles in real time during video calls
EDB: A Valuable Partner for AI Workloads
Expert guidance and robust solutions in a fast-changing tech landscape
GenAI’s evolution has made AI technologies more accessible than ever. Now businesses of all sizes can leverage GenAI to create innovative solutions—from chatbots that enhance customer service to tools that generate synthetic data for training models. As GenAI continues to evolve, it propels innovation within AI itself and paves the way for advancements in sectors such as healthcare, finance, and entertainment.
Postgres is vital in this landscape as a foundational open source database well suited to support AI and GenAI applications. Its scalability, performance, and ability to handle diverse data types make it an ideal choice for businesses looking to manage and analyze large datasets efficiently. However, navigating the rapidly changing world of AI can feel overwhelming, and organizations often struggle to keep pace with the latest developments. Here, EnterpriseDB (EDB) shines as a valuable partner, offering expert guidance and robust solutions that help businesses harness the power of Postgres while managing their data challenges.
EDB Postgres AI
EDB Postgres AI is a sovereign data and AI platform for seamlessly deploying, managing, scaling, and observing transactional, analytical, and AI workloads. With the increasing complexity of data requirements, it combines various aspects of data management and automation to enhanceefficiency. One of its standout features is Hybrid Control Plane, which provides a single pane of glass across the entire ecosystem. This integration brings together centralized management, database automation, and advanced observability, allowing businesses to meet the growing demand for traceable AI practices, ultimately fostering trust in their AI systems.
Additionally, EDB Postgres AI includes an innovative AI Accelerator, which automates AI data pipelines so that organizations always use the highest-quality and most current operational data for their GenAI inferencing applications. This helps reduce hallucinations—instances of AI generating incorrect or misleading outputs.
Whether you want to enhance your existing systems or explore new opportunities, EDB is here to assist.
AI Workloads: Content and Resources
Dive deeper into how EDB enables efficient, high-quality AI operations
Postgres by itself is not well suited to running analytical queries. Learn how EDB Postgres AI can help execute these workloads quickly and easily.
How can AI Accelerator solve common enterprise challenges with GenAI inference applications?
EDB Chief Architect for Analytics and AI Torsten Steinbach outlines the data landscape in this fascinating video.
AI is the simulation of human intelligence by machines, particularly computer systems. It encompasses a variety of technologies designed to perform tasks such as reasoning, learning, problem-solving, language understanding, and content creation.
GenAI is a specific type of AI that focuses on creating new content, such as images, text, music, or code. It can generate outputs that resemble human-created content by using models trained on vast quantities of existing data.
The primary difference is that while AI can include analysis, decision-making processes, and content creation, GenAI is the content creation part specifically—aiming to produce original outputs based on learned patterns from training data.
Common AI applications include recommendation systems (such as those used by Netflix), virtual personal assistants (Siri or Alexa are examples), and image and speech recognition technologies.
Examples include chatbots such as ChatGPT and Claude, image generation tools including Midjourney and Adobe Firefly, and music composition applications such as Suno that can create original songs or soundtracks.
Yes, they can complement each other. For instance, a traditional AI system might analyze user data to inform a GenAI model that creates personalized content based on that analysis.
While both types of AI require data, traditional AI often works with structured datasets for analysis and prediction. In contrast, GenAI thrives on large, diverse datasets that may include unstructured data, such as images, text, and audio—which is part of why Postgres, with its natural ability to handle diverse data models, is such an excellent database for AI and GenAI workloads.
AI models generally utilize supervised or unsupervised learning techniques to optimize decision-making. GenAI, however, often employs techniques of generative adversarial networks (GANs) or variational autoencoders, which focus on learning data distributions to create new examples.
Challenges include addressing potential biases in created content, protecting the data used for model training and inference, ensuring the fidelity of generated outputs, and managing the significant computational resources required for model training and inference.
The future of both AI and GenAI includes advancements in automation, enhanced creative capabilities, and broader applications across industries. There is also a growing focus on the ethical implications of GenAI.
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