We live in the information age. But you already knew that – after all, we’re literally surrounded by information.
From the arrival of the transistor in 1947 and the optical amplifier in 1957, the modern world has raced away from the Industrial Revolution and wholeheartedly embraced a world defined by information technology.
If you really want to understand how we got here, Aeon’s essay ‘Indexing the information Age’ is an excellent overview of how the modern world truly arrived.
Today, though, what matters isn’t how much information there is. It’s all about how you get to it, how you organize it, and how you use it.
It’s in this context that vector databases will be so vitally important.
History of vectors
Vectors are a concept found in both math and science, as well as data science. For an excellent overview of how these worlds collide, look no further than The New Stack’s ‘Vector Databases: Where Geometry Meets Machine Learning’.
In math, a vector is defined as a quantity that has both magnitude and direction but no position. Think of things like velocity or acceleration.
In the late 19th century, they were used by Josiah Willard Gibbs and Oliver Heaviside to express the new laws of electromagnetism discovered by James Clerk Maxwell.
Importantly, vectors help analyze the relationships between different things and make predictions.
That’s why they’re so handy in data science and machine learning. Here, they allow us to transform unstructured data from the world into structured mathematical objects.
Once we realized we could do this, it basically meant we could translate the world into the language of computers – math.
How Do Vector Databases Work?
In machine learning vectors are typically an ordered list or a sequence of numbers. This represents a piece of data.
Any piece of information can be turned into a vector, or vectorized, and by doing so we establish a consistent format for the vast array of data types we find out in the world.
Vector databases work by searching for similarities between vectors. Because vectors can deal with high-dimensional data – data points that span many hundreds or even thousands of dimensions – it’s possible to look for all sorts of similarities.
This also allows for a much faster search. Instead of scanning every result to see if it matches the query, as is the case with relational search, algorithms optimized for things like approximate nearest neighbor can find the most similar vectors in a vast collection very quickly.
If you’d like to explore the way these fascinating mechanisms work in more detail, check out ‘What Are Vector Databases?’ published on MongoDB.
Why Do Vector Databases Matter?
Data is unavoidably at the core of an increasingly complex and competitive world obsessed with information. In economics, retail, or politics, the ability to process that data is what provides a cutting edge.
Vector databases turn that data into invaluable, refined information. For CEOs this could revolutionize business strategy. For developers, it could revolutionize efficiency.
To get an in-depth idea of how they’re defining the next wave of technological innovation, dive into Stack Overflow’s ‘From Prototype to Production: Vector Databases in Generative AI Applications’ which you can read on their website.
How Are Vector Databases Being Used?
Vector databases are excellent when it comes to things like video or image recognition because they can quickly find pieces of data that are similar. They don’t do this by matching pixels but by grasping what the underlying pattern is and looking for others that are close.
Another example is natural language processing and text search, which have come on leaps and bounds in recent years thanks to vector databases. That’s because they can unravel the semantic meaning of the text fed into them and look for others that are similar.
Where chatbots were once cringingly awful because they clearly had no idea what the user was asking of them, today’s generative AI responders are eerily accurate.
Generative AI is bound to be the technology that defines humanity’s next great leap forward, but vector databases are the framework that hold it all up.
They’re what allow all that information rich data to become something we can use – by translating it into a language that computers understand and then leveraging their vast processing power to sift and sort it.
There is no doubt they will define the next generation’s interaction with data. To get ahead of the curve, check out our post, ‘5 Reasons to Learn Data Analytics Skills here on Pro-Reed’ and thank us later.
