Symbolic AI and Generative AI - What's the Difference?

While on a call with a colleague, he asked me if Symbolic AI was going to take over Generative AI. I asked him why he asked. He said he heard it on some podcast. I did not have a short answer, but I figured if he asked, others may, too. Here are some of my recent thoughts and readings on this.

Symbolic AI is based on the idea that intelligence can be built on symbols that represent knowledge using rules. It solves tasks by defining symbol-manipulating rule sets dedicated to particular jobs. This approach has its roots in the early days of AI research and was the dominant paradigm until the rise of machine learning and neural networks.

Generative AI, on the other hand, is a more recent development that uses machine learning algorithms and intense learning to learn patterns in data and generate new, plausible versions of that data. This can include text, images, or other types of data. Generative AI models, such as DALL-E, ChatGPT-4, and Stable Diffusion, have successfully created realistic and sometimes convincing outputs, but they can also produce outputs that infringe on copyright protections.

The critical difference between Symbolic AI and Generative AI is that Symbolic AI is based on explicit, rule-based representations of knowledge. In contrast, Generative AI is based on statistical patterns learned from data.

Symbolic AI is often seen as more transparent and explainable, as the rules and symbols used in reasoning are explicit and can be inspected, while Generative AI can be seen as a "black box" that generates outputs based on patterns it has learned but without an explicit understanding of the underlying knowledge.

Deeper Dive

Symbolic AI- Predefined Rules and Patterns

Symbolic AI, also known as "good old-fashioned AI," is focused on analyzing data and performing specific tasks based on predefined rules and patterns. [1] This approach relies on a structured, rule-based system miming human reasoning and problem-solving. Symbolic AI systems are designed to tackle well-defined problems by breaking them down into logical steps and applying predetermined algorithms.

One of the critical strengths of Symbolic AI is its ability to provide transparent and explainable outputs. The system's decision-making process can be traced back to the underlying rules and logic, making it easier for humans to understand and validate the results. [3] This transparency is precious in domains where accountability and interpretability are crucial, such as in legal, medical, or financial applications.

Generative AI - Creating Novel Content

In contrast, Generative AI is a fundamentally different approach that focuses on creating new and original content. [1] Rather than simply analyzing and classifying existing information, Generative AI uses machine learning algorithms to generate unique outputs, such as text, images, audio, or computer code. [4]

The critical difference between Generative AI and Symbolic AI is how they process and generate information. Generative AI systems learn patterns and relationships from large datasets, allowing them to create novel content that mimics the characteristics of the training data. [1] This approach enables Generative AI to tackle more open-ended and creative tasks, such as generating personalized marketing content, composing music, or even writing stories.

Symbolic AI and the Challenges of Structured Data

While Symbolic AI has strengths, it also faces certain limitations. One key challenge is its reliance on well-defined, structured data. [3] Symbolic AI systems require clear and unambiguous knowledge representation to function effectively, which can be labor-intensive and time-consuming to establish.

Os Keyes, a Ph.D. candidate at the University of Washington focusing on law and data ethics, notes that Symbolic AI models are "extremely brittle" and heavily dependent on context and specificity. [3] Symbolic AI systems may need help to adapt to complex, real-world scenarios where data is messy, incomplete, or ambiguous.

Hybrid Approaches - Combining Symbolic and Generative AI

To address the limitations of Symbolic AI and Generative AI, some researchers and organizations are exploring hybrid approaches that combine the strengths of both paradigms. [3]

One such example is DeepMind's recently published AlphaGeometry, which combines neural networks (a key component of Generative AI) with a Symbolic AI-inspired algorithm to solve challenging geometry problems. [3] This hybrid approach aims to leverage the pattern-recognition capabilities of Generative AI while maintaining the transparency and formal reasoning capabilities of Symbolic AI.

George Hotz, the founder of Symbolica, a startup focused on developing symbolic AI models, believes that "thinking symbolically is absolutely necessary to make progress in the field" and that "structured and explainable outputs with formal reasoning capabilities will be required to meet demands." [3] Hotz has assembled a team of industry experts to pursue this vision, recognizing the potential of combining Symbolic and Generative AI approaches.

The Future of AI - Navigating the Symbolic-Generative Spectrum

As the AI landscape evolves, the distinction between Symbolic AI and Generative AI may become increasingly blurred. Researchers and practitioners are exploring ways to leverage the strengths of both approaches, creating hybrid systems that can tackle a broader range of problems and deliver more robust and trustworthy solutions.

The choice between Symbolic AI and Generative AI, or a combination thereof, will ultimately depend on the specific requirements and constraints of the application domain. In some cases, the transparency and interpretability of Symbolic AI may be paramount, while in others, the creative and adaptive capabilities of Generative AI may be more valuable.

As the AI field progresses, organizations will need to carefully evaluate the trade-offs and potential synergies between these two approaches.

This will ensure that they can harness AI's full power to drive innovation, improve decision-making, and create meaningful impact across various industries.

References:

[1] "How Generative AI is Transforming Financial Industry," Analytics Insight, May 29, 2024,?(Link)

[2] Anurag Sahay, "Exploring the Generative AI Landscape with Nagarro's Anurag Sahay," DATAQUEST, May 15, 2024,?(Link)

[3] Devin Coldewey, "Symbolica hopes to head off the AI arms race by betting on symbolic models," TechCrunch, April 9, 2024,?(Link)

[4] "What Is Generative AI?," TOMORROW'S WORLD TODAY?, May 20, 2024,?(Link)

[5] Devin Coldewey, "Three reasons robots are about to become more way useful," MIT Technology Review, April 16, 2024,?(Link)

[6] Evan Ackerman, "When AI prompts result in copyright violations, who has to pay?," Freethink, April 10, 2024,?(Link)

[7] "Industrial-Level Generative AI is Put to the Test," RTInsights, June 5, 2024,?(Link)

[8] George Anadiotis, "The end-to-end AI chain emerges - it's like talking to your company's top engineer," ZDNet, April 16, 2024,?(Link)

[9] Rana el Kaliouby, "AI will make coding skills more, not less, valuable—and it's more important than ever for children to learn them," Fortune, May 29, 2024,?(Link)

[10] "Top Generative AI Tools," Analytics Insight, May 28, 2024,?(Link)

[11] Alec Holowka, "To understand the future of generative AI, we need better language to describe it," Rock Paper Shotgun, March 18, 2024,?(Link)

[12] Adam Engst, "How to Identify Good Uses for Generative AI Chatbots and Artbots," TidBITS, May 27, 2024,?(Link)

[13] Evan Selinger and Woodrow Hartzog, "Generative AI could leave users holding the bag for copyright violations," The Conversation, March 22, 2024,?(Link)

[14] Jon Reed, "Why semantic search is a better term than understanding for gen AI," diginomica, May 20, 2024,?(Link)

[15] Pratish Gopinath, "The Role of Generative AI in Today's World," CXOToday.com, May 29, 2024,?(Link)

[16] Sanjay Srivastava, "What is Generative AI in Education? Benefits and Challenges," DataDrivenInvestor, May 17, 2024,?(Link)

[17] Veda Raman, "Building Generative AI prompt chaining workflows with human in the loop | Amazon Web Services - AWS Blog," AWS, May 17, 2024,?(Link)

[18] Daphne Leprince-Ringuet, "What is generative AI and why is it so popular? Here's everything you need to know," ZDNet, April 23, 2024,?(Link)

[19] Leinar Ramos, "Generative AI adoption outpacing all other forms of AI," TechTarget, May 14, 2024,?(Link)

[20] Sanjay Srivastava, "Generative AI Model: GANs (Part 3)," Hackernoon, May 31, 2024,?(Link)