Key Concepts in Artificial Intelligence (AI)

While artificial intelligence (AI) may be new to some, many businesses have already begun using the technology with great success. However, for business leaders, it can still be challenging to understand how AI can be applied in their company. This article provides a simple introduction to key AI concepts that can be useful for small and medium-sized businesses.

Artificial intelligence (AI) can be traced back to the mid-20th century, but the concept and research around AI began even earlier. When Chat GPT became available to the public in 2021, interest in artificial intelligence for everyday use exploded.

Illustration image.

Artificial General Intelligence (AGI)

AGI stands for Artificial General Intelligence. The goal of AGI is to create a form of artificial intelligence that is as versatile and intelligent as human intelligence. AGI can handle a variety of tasks in the same way that humans can.

Artificial Narrow Intelligence (ANI)

ANI, or Artificial Narrow Intelligence, is AI that is designed to perform specific tasks within a limited domain, without the ability to generalize to other tasks. Examples include chatbots, recommendation systems, and image classification.

Machine Learning (ML)

Machine Learning is a subset of AI that focuses on developing algorithms that allow computers to learn from data and identify patterns without being explicitly programmed.

Machine Learning, also known as ML, is a branch of artificial intelligence that emphasizes creating algorithms and techniques enabling computers to learn from data. Furthermore, it allows extracting patterns without explicit programming.

Here are the subcategories:

• Supervised Learning: Models are trained with labeled data.

• Unsupervised Learning: Models try to find patterns in unlabeled data.

• Reinforcement Learning: Models learn through rewards and punishments.

• Semi-supervised Learning: A combination of labeled and unlabeled data.

Deep Learning

This is a branch of machine learning that uses artificial neural networks with multiple layers to learn from large amounts of data and perform complex tasks such as image recognition, natural language understanding, and speech recognition.

Large Language Models (LLM)

LLM stands for Large Language Models, which are advanced models trained on vast amounts of text data to understand, generate, and manipulate natural language. These models, such as GPT-3 and GPT-4, can be used for text generation, translation, and summarization.

Natural Language Processing (NLP)

NLP focuses on the interaction between computers and human language. The goal is to enable computers to understand, interpret, and generate human language in a meaningful way.

Generative Artificial Intelligence (Generative AI)

Generative AI refers to AI systems that can create or generate new content, such as text, images, music, and more, based on the training data they have received. Examples include:

• Text Generation: Models like GPT-3 and GPT-4 can write articles, stories, and customer service responses.

• Image Generation: Models like DALL-E can create images based on text descriptions.

• Music Generation: AI systems that can compose musical pieces.

Retrieval-Augmented Generation (RAG)

RAG combines information retrieval and text generation to fetch relevant information from large datasets and generate meaningful and precise answers. This provides more accurate, relevant, and up-to-date responses compared to traditional LLMs. RAG represents a significant improvement over traditional LLMs, which may have limited training data, inaccurate answers, and hallucinations (the tendency to generate plausible but false or inaccurate information).

Computer Vision (CV)

This branch of artificial intelligence deals with the analysis, understanding, and interpretation of visual data from digital images or videos. Examples include facial recognition, autonomous vehicles, and medical image analysis.

Robotics

Robotics focuses on the design, construction, and programming of robots to perform specific tasks autonomously. This field combines mechanical engineering, electronics, and computer science.

Robotic Process Automation (RPA)

In connection with robotics, it is natural to mention Robotic Process Automation. RPA refers to the use of software to automate repetitive and rule-based tasks that are usually performed by humans on computers, such as data entry or processing tasks.

What Does This Mean for Your Business?

Understanding and implementing AI technologies like AGI, ANI, ML, LLM, NLP, RAG, CV, Robotics, and RPA can give small and medium-sized businesses significant competitive advantages. By leveraging these technologies, businesses can improve efficiency, reduce costs, and make better-informed decisions. There is a wealth of good information available about artificial intelligence, and below you will find a selection we have used in this article.

Feel free to have an informal conversation with us about artificial intelligence. While this is an exciting technology, it may not necessarily be suitable for everyone.

Feel free to have an informal conversation with us about artificial intelligence. While this is an exciting technology, it may not necessarily be suitable for everyone.

Sources

ChatGPT (also used to streamline the text)

IBM Research AI

Stanford AI Lab

Google AI

IBM Research - RAG 

Promptingguide

Databricks - RAG