Let us put two scenarios: A & B. In scenario A, a student who knows French as an additional language has a far greater chance of learning faster in an Ivy college in comparison to their counterparts, who do not. Doesn’t that create an unequal bias when it comes to accessing information? Undoubtedly, it does! And that’s what the majority of the AI models have been subjected to when it comes to changing the world.
Why?
Because Generative AI models are trained on a structured data model. However, when you compare structured data vs unstructured data, more than 80% to 90% of the available data-sets belong to the latter category. With that said, it would categorically take ages for the Generative AI models to get trained with efficiency to spearhead a new tech revolution where machines function as efficiently as humans. But, that narrative is slowly and steadily fading under the influence of Vector Database.
What is a Vector Database?
To put simply, a vector database ideally stores information, which isn’t available in a structured manner like texts, images and audios to acquire signals from the noise. For example, imagine someone is keeping books in a library based on the colors instead of publication and authors. What if someone’s color blind. Would he/she be able to find the book he/she wants easily despite the library being inundated with it. Afraid, he/she couldn’t easily. However, if he/she is so much invested, he/she would have to go through each and every book present in the library to identify the one that he/she seeks. The data problem with Generative AI revolves around the above given example and Vector Database, with its pre-existing models has been solving this problem in the following ways as demonstrated below.
Mapping the Data-Structure
When handling the data-base, instead of specifying specific field labels for data, for example, “employees under 5 years experience, keep in TAB A” “Below 5 Years of Experience, Keep in Tab B”, the vector database shall been using machine learning and deep learning to keep the databases on specific graphs. Upon storing the information on such graphs, the training models shall be learning from the sentiment score assigned for the datasets. These scores are mined based on the properties which are very similar or nearby to the existing models. In this way,it becomes even easier to identify signals from the noise of many data-sets which haven’t been structured or predefined before tapping out meaning from them.
Processing Wider Data-sets
The erstwhile problems where machines have to be trained at par with the existing data-sets to derive meaning from them could be oversimplified with Vector databases. How? Since these databases do not rely on tags, labels and metadata, it will be possible to track property based on specific wider metrics rather than a narrowed down approach. Hence, there could be significant improvements in the search results boosting the productivity and efficacy of using datasets for pretrained models.
Up-to- Date Training
The complexity and volume of data is growing on a daily basis. As a result, the existing Generative AI models are facing an imminent challenge where they are unable to get processed data at the right time. For example, the ChatGPT model is already 2 years behind with respect to providing accurate results. It is still using the 2021 data which could deter its performance when it comes to getting accurate results with respect to the time. AI Models like BERT, Word2Vec, GloVe, or GPT can benefit at large when they use Vector Database models as it can narrow down their learning curve. In the process, they can generate even better results.
What Will Be the Future of Vector Databases?
In the span of just 4 to 5 years, even when the buzz around AI didn’t reach its true potential, spending on database management catapulted from $38.6 billion to $80 billion. As per the report from Gartner, IT spending has already increased by 5% in 2023 and it shall be scaling further. With that being said, and under the influence of Generative AI model’s unprecedented demand, the demand for training these models will rise exponentially, which shall lead to the demand for easy to adopt technology that can fill-up the void. In this requirement, Vector Database models fit in appropriately justifying the narrative that they would be revolutionizing how AI models will mature for the future ahead.
Conclusion
Vector databases hold the power to fundamentally transform how we engage with and utilize generative AI. They deliver unparalleled efficiency and performance and reveal the expansive capabilities of AI-driven solutions. At VE3, we have in-depth comprehension of the significant implications and unique benefits that vector databases offer. Our highly proficient team excels in integrating and enhancing vector database solutions, establishing us as your top choice for tapping into the transformative potential of generative AI. With VE3 standing as your technology ally, you can unlock new opportunities and propel innovation throughout your business by smoothly chartering your course towards a smarter and more efficient future. Reach out to us today to explore how VE3 can assist in revolutionizing your organization’s alliance with generative AI.
