The Neural Network Revolution: Why AI Will Make Traditional Databases Obsolete

The evolution of data storage and management has seen a significant shift in recent years. Traditional databases like relational and NoSQL have long been the backbone of data-driven industries, but as we enter the era of artificial intelligence, the limitations of these systems are becoming clear. Drawing upon my own experiences at companies like The New York Times, The Wall Street Journal, and now Hearst, I have witnessed first-hand the transformative power of AI in media companies where much of the data is complex unstructured content that doesn’t map to traditional data stores. In this blog post, I argue that AI neural networks will soon replace traditional databases, rendering them obsolete. I discuss the advantages of large language models and explore how neural networks offer a more suitable solution for storing and analyzing the vast amounts of unstructured real-world data that our complex universe generates.

My Personal Journey with Traditional Databases

Growing up in India, I began my foray into computer programming when I was in 2nd grade (or “class 2” as it’s known in India). I started out writing code in BASIC to solve math problems and make text and graphics-based video games (remember GW-BASIC, anyone?) to learn and play. My friend, Rishab Ghosh and I shared a passion for computers and programming. It was on Rishab’s Sinclair ZX Spectrum computer that he’d sometimes bring to my home and we’d write code together that I still have memories of seeing error messages like “R tape loading error.”

My early programming experience was on Commodore 64 and BBC Micro computers. In 5th grade, I got my own IBM-PC compatible computer and soon felt proficient in Logo, Pascal, and C, as well as databases including dBase III and Borland Paradox.

My Evolving Book Collection Database

As a young bibliophile, I amassed a collection of books that I cherished deeply. I would often read the same book multiple times, discovering new insights and lessons with each subsequent reading. The ever-evolving nature of my understanding was akin to the constant updating of a neural network. To manage my growing collection, I built databases to store each book’s metadata (I don’t remember if back then I knew it as metadata or simply data), alongside my learnings, reflections, and personal notes (such as who or what a certain character or place in a story reminded me of). My databases that evolved over time also kept track of the books I lent to friends, ensuring that I could remind them to return my precious volumes when the time was right. (Any of my childhood friends reading this who used to forget to return my books, you know who you are 😃.)

As I evolved my books database containing ever-changing information, I encountered limitations with traditional relational databases like dBase III and Borland Paradox. To overcome these obstacles, I had to design my own data structures using Pascal, enabling me to capture both structured and unstructured information related to my book collection.

The Interconnected Contacts Database

In my teenage years, I developed an electronic contacts database to keep track of friends’ and acquaintances’ details, such as birthdays, family members, and phone numbers. Back then, not every household in India had a phone, so I often needed to store alternative contact methods, like a neighbor’s number. To avoid duplicating information, I devised a way to link relationships between my contacts, reflecting the interconnected nature of our human relationships, especially in 1980s India where everyone seemed like an “uncle”, “aunty”, neighbor, or otherwise related.

Traditional relational databases I used as a kid, dBase III and Borland Paradox, were ill-suited to handle such complex interrelationships. As a result, I turned to creating more custom data structures that more accurately represented real-world information and its inherent intricacies.

The Mind Mapping Exploration

I’ve been a fan of mind mapping for a long time, as I find that mind maps resemble neural networks and how our brains actually store information. As a teenager, I learned about mind maps and first began drawing them using Harvard Graphics and Paintbrush/MS Paint, graphics software of the 1980s running on MS-DOS and MS Windows. This enabled me to visualize information and relationships for a variety of use cases, such as planning school projects, brainstorming ideas, and organizing my thoughts. However, it wasn’t convenient for querying. So, I again turned to programming custom data structures and visualizing them using Borland’s BGI graphics library. Anyone remember learning to make data visualizations from the impressive BGIDEMO.PAS and BGIDEMO.C samples that used to ship with Turbo Pascal and Turbo C?

This exploration of mind mapping further solidified my understanding of the limitations of traditional databases and the potential of neural networks in managing complex information. The lessons I learned from my early experiences with mind maps would continue to inform my professional life. In fact, much later as an adult I created a CTO Mind Map, which I still use to this day, illustrating the ongoing relevance of these early explorations.

Such anecdotes from my childhood reflect my early encounters with the limitations of traditional databases and the need for complex interconnected data structures to store both clear and fuzzy information and relationships. My long-standing passion for augmenting the human brain with computers via programming and data created the my need to seek human brain like structures to store and query information. As I continued write software to handle data, I became more convinced of the power of neural networks as a means of managing and storing complex information in a more intuitive and effective manner.

With the popularity of Large Language Models like GPT-4, when I started work on Ragbot.AI, my new augmented brain and AI assistant open source project it became clear to me: Traditional databases will become history like some of the computing products from my childhood days that I mention above.

I am convinced and more committed than ever to embracing and championing the potential of neural networks for managing and understanding the vast, interconnected world of information.

Advantages of Large Language Models

Large language models like GPT-4 have demonstrated their ability to understand, generate, and manipulate human language with remarkable precision. By leveraging deep learning techniques, these models can process and analyze vast amounts of data, making them ideal candidates for managing complex unstructured information. Having served as an advisor to AI startup, I have seen the potential of these models to revolutionize the way we interact with data. Some key advantages of large language models include:

  1. Scalability: Neural networks can efficiently scale to accommodate increasing volumes of data, which is essential in the age of big data.
  2. Flexibility: These models can easily adapt to various data types, structures, and formats, allowing for seamless integration of disparate data sources.
  3. Predictive capabilities: AI-driven models can identify patterns and trends in data, enabling them to generate predictions and insights that traditional databases cannot.
  4. Continuous learning: Neural networks are capable of learning and evolving over time, ensuring that they remain up-to-date and relevant.

Real-World Data and the Limitations of Traditional Databases

Traditional databases, like relational and NoSQL, were designed to manage structured data. This means that they excel at organizing information in the form of tables, columns, rows, and key-value pairs. However, a significant portion of real-world data is unstructured, existing in formats like text, images, audio, and video. The rigidity of traditional databases makes it challenging for them to efficiently store and process these complex data types.

In my work at media companies, I have witnessed the challenges of managing diverse and complex data types. In contrast, neural networks are inherently suited to managing unstructured data. They can learn to understand and interpret complex patterns in raw data, making them ideal for processing and storing information in its natural, unstructured form. This ability allows neural networks to more accurately represent the intricacies of our universe and human life.

Examples of Neural Networks in Data Management

  1. OpenAI’s Codex: This AI system is a powerful example of how large language models can be used for complex tasks, such as programming, data analysis, and even natural language processing. By understanding and generating human-readable code, Codex demonstrates the potential of neural networks to revolutionize data management and analysis.
  2. DeepMind’s AlphaFold: This groundbreaking AI system predicts protein structures, a task that requires processing and analyzing vast amounts of unstructured data. The success of AlphaFold in this domain showcases the potential of neural networks to manage and analyze complex data that traditional databases struggle with.

Having seen the transformative power of technology in various sectors, I believe that the adoption of AI-driven neural networks will render traditional databases obsolete, as they offer a more accurate, efficient, and insightful way of storing and analyzing data. By moving beyond the limitations of relational and NoSQL databases and embracing the potential of AI-driven models, we can create a data-driven future that more accurately represents our complex universe and the human experience.

Vector Databases: Paving the Way for AI-Driven Data Management

To fully grasp the trajectory of databases transitioning towards an AI-driven paradigm, it’s essential to explore the domain of vector databases. Emerging as an influential player in the data management scene, vector databases bridge the gap between traditional databases and the powerful capabilities of large language models, playing a pivotal role in this transformational journey.

A vector database, fundamentally, is designed to handle vector data – data that consists of multi-dimensional arrays, usually representing large, complex structures, such as those found in the realm of machine learning and AI. This form of database allows efficient indexing and searching of large quantities of high-dimensional vectors, something that traditional databases struggle to do effectively.

An introduction to vector databases by Gal Lahat

Notable players in the vector database market include:

  1. Milvus: An open-source vector database that aims to power AI applications with fast vector similarity search and analytics. It offers flexibility, robust scalability, and can handle hybrid transactional-analytical processing tasks.
  2. Pinecone: Another leading vector database that enables developers to build applications that can perform efficient vector search at scale. Pinecone supports machine learning models natively and simplifies the complexity of managing vector data.
  3. Weaviate: Weaviate is an open-source, GraphQL and RESTful API-based, real-time vector search engine. It allows you to perform a vector search, run classification algorithms, and much more.
  4. Faiss: Created by Facebook AI Research (FAIR), Faiss is a library for efficient similarity search and clustering of dense vectors. Though not a database in the strict sense, Faiss enables efficient handling of vector data.

These databases and libraries cater to the requirements of modern data analytics by providing a scalable and efficient way to handle vector data. They are designed to cater to the increasing use of artificial neural networks and large language models, offering a more flexible and powerful alternative to traditional databases.

However, it’s crucial to note that while vector databases are indeed pushing the boundaries of data management, the leap towards artificial neural networks functioning as databases is still an emerging frontier. The way forward will likely be a combination of these tools, each serving specific use-cases and applications, together enabling a more comprehensive, efficient, and AI-centric approach to managing and leveraging data.

Other People’s Viewpoints: The Multifaceted Reality of Data and the Role of AI

After I published the above blog post, some my colleagues who I respect for their knowledge and expertise in data, Mike Nuzzo, Zack Packer, and Dennis O’Harlem shared their perspectives on this topic with me. So I am adding this section as a counterpoint to my prediction above and to further dissect the idea of artificial intelligence and neural networks in data management.

Zack pointed out that data, in essence, falls into two categories: one representing our sensory perception and cognition (virtualizing both the physical and metaphysical), and the other embodying the structural underpinnings of engineering, mathematics, and broader scientific disciplines. The transformative impact of AI and neural networks is undeniable for the first group, making data more organic and seemingly self-generating.

However, when it comes to the structural or foundational data that translates into physical structures (like buildings and bridges) or metaphysical structures (like software applications), AI’s role appears more complementary than revolutionary. The structural integrity of a pixel, for example, is a prerequisite for an AI to generate or predict the next pixel in an image. AI can enhance these structures, but their existence is paramount.

Dennis, emphasized the significance of databases and their defined guarantees for application requirements and compliance needs. Future AI systems may indeed behave like multi-paradigm databases, but for them to be considered a true replacement for conventional databases, it’s crucial to reason about their expected behavior and guarantees. In this scenario, the AI or ML component becomes an access approach, potentially paired with an optimized datastore. This could be likened to some graph databases, which use traditional RDBMS and key-value stores as backends and tailor their API for graph-type operations.

Mike provided valuable feedback on the implications of AI and machine learning, especially in the context of predictive computation. An AI’s predictive nature, rooted in its learning model, inevitably inherits the biases embedded in its learning data. This is an essential insight that isn’t always obvious to the everyday reader. In some cases, AI models may inadvertently perpetuate or even amplify biases based on subtle inputs, leading to outcomes more skewed than human interpolation might suggest. This has far-reaching implications across sectors, but in the media industry, it could potentially guide readers down non-neutral paths.

My colleagues’ point is that while AI and neural networks hold tremendous potential in transforming data management, they won’t necessarily replace the need for structured data storage across all industries. As always, thoughtful application and understanding of these tools, their limitations, and their implications will be critical to making the most of the AI-driven data revolution.

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