Por Everton Gago, Chief Data Officer atact digital
The power to accurately and efficiently search for and retrieve information is an imperative need. This need is even more prominent in the emerging field of Generative Artificial Intelligence (Generative AI), where sophisticated search engines play a vital role. Let us explore together the relevance and complexity of search engines in the current era.
Information search and retrieval is essential to formulate appropriate contexts
We live in a world where context is a king. Generative AI is revolutionizing several industries, from entertainment to healthcare, and heavily relies on contextually relevant information to operate. Search engines therefore act as the “nervous system” of this emerging technology. AI algorithms that provide tailored recommendations for consumers or assist in medical decision-making need to access large volumes of data. Efficient search and retrieval of information ensures that resulting insights are accurate and applicable. Without a proper interaction between Generative AI and search engines, the relevance and applicability of these algorithms would be seriously jeopardized.
These are complex engines that require sophisticated approaches
Search is a key step to create relevant contexts and enrich the Generative AI capacity. These artificial intelligence models are designed to generate content, such as texts, images and even music, based on patterns learned from existing data. However, to produce accurate and coherent results, these models need to understand the essence of the information available on the Internet.
In this context, semantic search stands out as an advanced approach that goes beyond traditional keyword searches. It focuses on understanding the meaning behind queries, capturing the underlying intent and context. This is essential for creating a solid knowledge base, allowing Generative AI models to use relevant information for their creations. Semantic search also benefits from two concepts:
- vector search > involves representing words and documents as mathematical vectors, where the proximity between vectors reflects semantic similarity.
- embeddings > are dense representations of words or phrases in a vector space, capturing meanings and semantic relationships.
“Neural search” is one of the most promising techniques in this area. It is an approach that employs deep learning models to understand the semantics and intent behind a query. These models are trained to read natural language more accurately and contextually, providing more relevant and enriching results.
Importantly, searching for and using these advanced techniques require deep expertise in data science and software engineering, as well as adequate computational resources.
Close collaboration between data scientists, engineers and business leaders
The complexity inherent to modern search engines requires a collaborative, multidisciplinary approach. Data scientists, engineers and business leaders need to work together to understand business needs, develop robust technical solutions and implement them effectively. Business leaders should be aware of the possibilities and limitations of the technology, while data scientists and engineers should understand the business objectives and requirements. This close collaboration is key to aligning technological and business objectives, as well as ensuring that search engines meet the emerging needs of Generative AI and the business as a whole.
We can conclude that efficient search engines are more than a technological tool: they are an essential component of a successful business strategy in the era of Generative AI. The interdependence between search and Generative AI, while not immediately clear, is a reality that technology and business leaders need to recognize and embrace. Through sophisticated approaches like “neural search” and collaboration across diverse disciplines, companies can leverage this interdependence to drive innovation and growth - i.e., evolve!
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