Data mesh and data fabric are two architectural paradigms that are shaping the future of data management and analytics. At their core, both aim to address the complexities of handling vast and diverse data in modern organizations, but they approach the challenge from different angles. In this...
Synthetic data is artificially generated data that imitates real-world data without using actual information. It is generated through algorithms and statistical techniques to capture the same patterns and characteristics found in real data. Instead of relying on sensitive or private data,...
We encounter content chunks every day. Think of a recipe. A recipe contains content components like a title, a list of ingredients, cooking times, pictures of food, and instructions that contain individual steps. These are “chunks” that together compose a recipe. The process of chunking this...
We live in an on-demand world. Customers expect every product delivered to the doorstep, every service ordered online, and any information they seek immediately available at their fingertips. When customers have questions, they expect answers–on demand. As customer expectations have risen, so have...
The field of Natural Language Processing (NLP) has witnessed significant advancements, yet it continues to face notable challenges and considerations. These obstacles not only highlight the complexity of human language but also underscore the need for careful and responsible development of NLP...
In machine learning, embeddings are a technique used to represent complex, high-dimensional data like words, sentences, or even entire documents in a more manageable, lower-dimensional space. An analogy would be nice. Right. Think about Lego bricks. A lot of them. High-dimensional data is like the...
Neural networks involve a series of algorithms designed to recognize patterns, interpret data, and make decisions or predictions. They are modeled loosely after the human brain’s architecture. Neural networks have become a cornerstone of AI technologies alongside others, such as rule-based...
We need more than just artificial intelligence. We need virtual experts that are accurate, authoritative, and effective. It’s not enough to deploy AI technologies to answer customer service questions, assist a doctor’s medical diagnosis, identify a negotiator’s key clauses in a contract, provide...
Semantic search goes far beyond the words that people use in their searches, to interpret the intent behind the words, and the greater context in which people are asking. Traditional lexical or keyword-based technologies cannot accomplish this. The relevance and actionability of information that...
Listing use cases for artificial intelligence (AI) is like describing how to use the internet — the AI implementation examples are nearly infinite, constrained only by the inventive minds behind the wheel. AI is being implemented in business practically everywhere, from personalizing sales...
Generative artificial intelligence (GenAI) has emerged as a powerful tool for content creation, but often requires a human in the loop to ensure the outputs are valuable. The value of GenAI includes productivity gains, opportunity for revenue growth, and better accuracy for your organization’s...
Artificial intelligence (AI) is transforming the way businesses operate across various industries, but the question remains: What jobs will AI replace? Where will AI provide new opportunities and challenges? Which industries will experience the most disruption? In this comprehensive discussion,...