AI Nuggets

Understanding the key differences between RPA (Robotic Process Automation), AI (Artificial Intelligence), and Gen AI (Generative AI):

Robotic Process Automation (RPA):

• Capabilities: RPA excels at mimicking human actions in a structured environment, performing tasks such as data entry, form filling, and routine processes.

• Decision-making: RPA systems do not possess cognitive capabilities; they follow predefined rules and instructions without the ability to adapt or learn.

• UiPath: UiPath is a widely-used RPA platform that enables businesses to automate repetitive tasks. It provides a visual, drag-and-drop interface for creating automation workflows that mimic human actions.

• Blue Prism: Blue Prism is another RPA tool that automates rule-based processes. It offers a digital workforce that can execute tasks across various applications in the same way a human employee would.

Artificial Intelligence (AI):

• Capabilities: AI systems can include various technologies such as machine learning, natural language processing, and computer vision. They can learn from data, make predictions, understand language, and recognize patterns.

• Decision-making: AI systems, particularly those powered by machine learning, can make decisions and improve their performance over time through learning from experience.

• IBM Watson: IBM Watson is a comprehensive AI platform that offers a range of services, including natural language processing, machine learning, and computer vision. It is used for building AI-powered applications and solutions.

• Google Cloud AI Platform: Google Cloud AI Platform provides a set of tools and services for developing and deploying machine learning models. It supports tasks such as training and prediction, making it easier for developers to integrate AI into their applications.

Generative AI (Gen AI):

• Capabilities: Gen AI goes beyond traditional AI by not only recognizing patterns or making decisions but also creating entirely new outputs, such as generating text, images, audio, video, simulations or even code.

• Adaptability: Generative AI systems often exhibit a high degree of adaptability and creativity, enabling them to produce novel and contextually relevant outputs.

• OpenAI GPT (Generative Pre-trained Transformer): OpenAI's GPT series, such as GPT-3, is an example of Generative AI. GPT models are trained on vast amounts of text data and can generate human-like text based on prompts. They are known for their ability to generate coherent and contextually relevant content.

• DeepArt: DeepArt is an example of Generative AI applied to art. It uses deep neural networks to analyse and recreate images in the style of famous artists. Users can upload their photos and have them transformed into artwork inspired by the chosen artistic style.

In summary, while RPA focuses on automating repetitive tasks through rule-based processes, AI encompasses a broader range of technologies that enable machines to perform intelligent tasks and make decisions. Generative AI, as a subset of AI, specifically emphasizes the ability to autonomously create new and meaningful outputs, showcasing a higher level of creativity and adaptability compared to traditional AI systems.