Basics of AI

Artificial intelligence is a branch of computer science focusing on developing machines and algorithms capable of simulating human-like intelligence, learning, and problem-solving. Integrating AI into your financial advisory practice allows you to stay ahead of the curve in a rapidly evolving industry.

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Overview of AI

AI can be divided into two main categories :
Narrow AI and General AI.

Narrow AI

Narrow AI is designed to perform specific tasks with expertise. It is the most common form of AI in the financial industry, powering applications like robo-agents, chatbots, and predictive analytics tools.

General AI

General AI refers to machines that possess human-like cognitive abilities, enabling them to understand, learn, and adapt across a wide range of tasks. Although General AI remains a long-term goal in AI research, its potential applications are vast and could reshape the financial industry.

Basic AI Terms

Here are definitions and explanations for fundamental AI-related terms and concepts to help you better understand the technical aspects of artificial intelligence.

Algorithm

A set of rules or procedures computers use to solve problems or perform specific tasks. In AI, algorithms enable machines to learn and make decisions based on data inputs.

Machine Learning (ML)

A subset of AI that focuses on developing algorithms that enable computers to learn and adapt from data without explicit programming. ML systems improve their performance as they are exposed to more data over time.

Artificial Neural Networks (ANN)

Computational models inspired by the structure and function of biological neural networks. ANNs consist of interconnected nodes (neurons)that process and transmit information, enabling machines to learn and make decisions.

AI Ethics

The study and application of ethical principles to the development and use of AI technology, addressing issues like bias, fairness, transparency, and accountability.

Supervised Learning

A type of machine learning where algorithms learn from labeled data, using input-output pairs to predict future outcomes. The known output guides the learning process, allowing the model to refine its predictions over time.

Unsupervised Learning

A type of machine learning where algorithms learn from unlabeled data, identifying patterns and relationships within the data without prior knowledge of the desired output.

Reinforcement Learning

A type of machine learning where algorithms learn through trial and error, receiving feedback as rewards or penalties. This feedback helps the algorithm optimize its decisions to achieve a specific goal.

Natural Language Processing (NLP)

A subfield of AI that focuses on enabling computers to understand, interpret, and generate human language. NLP algorithms can analyze text and speech, allowing machines to engage in human-like communication.

Computer Vision

A subfield of AI that develops algorithms and techniques that enable computers to interpret and analyze visual information, like images and videos.

Data Mining

The process of extracting valuable insights and patterns from large datasets using machine learning, statistical analysis, and database management techniques.

Robotic Process Automation (RPA)

A subfield of AI that develops algorithms and techniques that enable computers to interpret and analyze visual information, like images and videos.

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