In artificial intelligence , one of the most pressing challenges is ensuring that the decisions made by machine learning models are understandable by humans. This is where Explainable AI (XAI) comes in. In this article, we will look into what XAI is, why it's important, and provide real-world examples for investors. What is Explainable AI (XAI)? Explainable AI refers to methods and techniques used in the domain of artificial intelligence that make the outcomes of machine and

In recent years, the field of deep learning has expanded beyond traditional data types like images and texts, venturing into more complex structures like graphs . This expansion has given birth to Graph Neural Networks (GNNs) . For investors, understanding GNNs and the relationship with Causal AI can provide insights into their potential applications and implications in various industries. What are Graph Neural Networks? A GNN is a type of neural network specifically desi

For many investors, the rise of artificial intelligence in decision-making processes, especially in fields like finance, healthcare, and...

In the world of investing, data analysis is crucial for making informed decisions. But what if the data you're analyzing leads you down a...

In the rapidly evolving landscape of artificial intelligence, the focus has traditionally been on developing advanced algorithms and...

Directed Acyclic Graphs (DAGs) also known as Causal Graphs are a type of data structure that have applications in various fields, from computer science to finance. With the rise of Large Language Models (LLMs) there's an opportunity to leverage these models in the creation and analysis of DAGs . This article will explore how investors can utilize LLMs to create and interpret DAGs , providing a competitive edge in the financial world powered by Causal AI . What is a Direct

The Securities and Exchange Commission (SEC) filings are a treasure trove of information for investors, analysts, and financial...

The world of artificial intelligence and machine learning is vast and filled with intricate terminologies that can sometimes be confusing. Two such terms that are often used interchangeably but have distinct meanings are "inference" and "prediction." For investors venturing into AI, understanding the difference between these two can be crucial. This article looks into the concepts of inference and prediction, highlighting their differences and providing real-world examples.