Here’s why using AI is helpful to our brains as vector databases

November 16, 2024 Editorial Staff 295 Views 0 Comments

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In the year 2014, Google made a breakthrough that changed how machines understood language: The Self-attention Model. This innovation enabled AI to understand context and meaning of human communication using mathematical vectors – precise numerical representations which capture relationships between ideas. This vector-based method has evolved to sophisticated vector databases that mimic how our brains retrieve and process information. How our brains think already in vectors

Think about vectors as GPS coordinates. Vector databases map meanings, concepts and relationships using mathematical coordinates, just as GPS does. You’re not only looking for exact matches when you search a database. Instead, you are searching patterns and relationships. This is similar to what your brain does while recalling memories. You may remember searching for lost car keys. This is exactly how vector databases work. The three core skills have evolved. These skills may seem familiar, but their use in AI communication demands a fundamental change in the way we use them. Understanding both the human and machine context is what reading becomes. Writing becomes precise, structured information that machines can understand. And querying — perhaps the most crucial new skill — involves learning to navigate vast networks of vector-based information in ways that combine human intuition with machine efficiency.

Mastering vector communication

Consider an accountant facing a complex financial discrepancy. In the past, accountants relied on their knowledge and manual searches of documentation. In the future, AI will augment their intuition and allow them to use vector-based software. The AI will not only search for keywords, but also understand the context of the issue. It uses a vast database of financial concepts, regulations, and previous cases. The key is learning to communicate with these systems in a way that leverages both human expertise and AI’s pattern-recognition capabilities.

But mastering these evolved skills isn’t about learning new software or memorizing prompt templates. Understanding how information is related and connected–thinking in vectors–is the key. You’re not simply sharing words with AI when you describe a notion. Instead, you are helping it navigate an enormous map of meaning. Ready to prepare for an AI-augmented world? Here are concrete steps you can take to develop each of the three core skills:

Strengthen your reading

Reading in the AI age requires more than just comprehension — it demands the ability to quickly process and synthesize complex information. Here are concrete steps you can take to develop each of the three core skills:

Strengthen your reading

Reading in the AI age requires more than just comprehension — it demands the ability to quickly process and synthesize complex information. To improve:

Study two new words daily from technical documentation or AI research papers. You can write them down and use them in different contexts. Read at least two or three pages of AI content every day. Focus on industry publications, technical blogs or research summaries. The goal isn’t just consumption but developing the ability to extract patterns and relationships from technical content.

Practice reading documentation from major AI platforms. Understanding how different AI systems are described and explained will help you better grasp their capabilities and limitations.

Evolve your writing

Writing for AI requires precision and structure. Your goal is to communicate in a way that machines can accurately interpret.

Study grammar and syntax intentionally. AI language models are built on patterns, so understanding how to structure your writing will help you craft more effective prompts.
Practice writing prompts daily. Each day, create three new prompts and then refine them. Pay attention to how slight changes in structure and word choice affect AI responses.
Learn to write with query elements in mind. Incorporate database-like thinking into your writing by being specific about what information you’re requesting and how you want it organized.

Master querying

Querying is perhaps the most crucial new skill for AI interaction. It’s about learning to ask questions in ways that leverage AI’s capabilities:

Practice writing search queries for traditional search engines. Begin with simple queries, and then make them more specific. This builds the foundation for AI prompting.
Study basic SQL concepts and database query structures. Understanding how databases organize and retrieve information will help you think more systematically about information retrieval.
Experiment with different query formats in AI tools. Try different phrasings to see how they affect your results. Document what works best for different types of requests.

The future of human-AI collaboration

The parallels between human memory and vector databases go deeper than simple retrieval. Both excel at compressing complex information and reducing it into manageable patterns. Both organise information in a hierarchical manner, starting with specific examples and moving on to more general concepts. Both excel at identifying patterns and similarities that may not be apparent at first. These evolved communication skills are essential to fully participate in the AI-augmented economic system. This is not the same as previous technological advances that replaced human abilities. It’s about enhancement. Vector databases and AI systems, no matter how advanced, lack the uniquely human qualities of creativity, intuition, and emotional intelligence.

The future belongs to those who understand how to think and communicate in vectors — not to replace human thinking, but to enhance it. Successful professionals will combine human creativity and AI’s analytical powers, just as vector databases combine mathematical precision with intuitive pattern matching. This isn’t about competing with AI or simply learning new tools — it’s about evolving our fundamental communication skills to work in harmony with these new cognitive technologies.
As we enter this new era of human-AI collaboration, our goal isn’t to out-compute AI but to complement it. It is not about learning new software but rather how to translate human insights into vectors and patterns AI systems can understand. By embracing this evolution in how we communicate and process information, we can create a future where technology enhances rather than replaces human capabilities, leading to unprecedented levels of creativity, problem-solving and innovation.
Khufere Qhamata is a research analyst, author of Humanless Work: How AI Will Transform, Destroy And Change Life Forever and the founder of Qatafa AI.

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