AI is a hot topic of discussion these days, with the potential to inspire some and frighten others.

To begin, it's important to understand the distinction between Generative AI and Applied AI, as they represent two distinct categories of artificial intelligence. In short, Generative AI creates new content, while Applied AI solves specific problems or performs specific tasks.

Generative AI

Generative AI refers to systems that are capable of creating new, original content, such as images, videos, and text, that did not exist before. These systems use algorithms to generate content based on patterns and data, and they can learn from their own output to improve over time. Generative AI is often used in creative fields such as art, music, and design, as well as in research and development.

Examples

• Healthcare - transform X-rays and CT scans into lifelike images that aid in diagnosis.

• Logistics & Transportation - convert satellite images into maps, allowing exploration of uncharted areas.

• Travel - enhance airport face identification and verification systems by creating a complete facial image from photographs captured at various angles, simplifying the process of identifying and verifying the identity of passengers.

• Marketing - produce outbound marketing messages, improving the effectiveness of upselling and cross-selling tactics.

Why You Should Care

With Generative AI, the potential for research and testing is limitless, as it has the ability to produce original data that has not yet been created.

• Generative AI can enhance current algorithms by generating training data for new neural networks or developing advanced deep learning architectures.

• Essentially, generative AI functions as a machine that creates improved machines.

Applied AI

Applied AI, on the other hand, refers to systems that are designed to solve specific problems or perform specific tasks. These systems use machine learning algorithms to analyze data and make predictions or decisions based on that data.

Examples of Applied AI

• Speech Recognition - speech recognition is an applied AI technology that enables machines to transcribe human speech into written text. It is used in virtual assistants, transcription software, and voice-activated devices like smart speakers.

• Computer Vision - computer vision is an applied AI technology that allows machines to analyze and understand images and video. It is used in facial recognition, object detection, and autonomous vehicles.

• Fraud Detection - fraud detection is an applied AI technology that uses machine learning algorithms to identify fraudulent transactions or behavior. It is used in financial services, e-commerce, and other industries where fraud is a risk.

• Predictive Maintenance - predictive maintenance is an applied AI technology that uses machine learning algorithms to analyze data from sensors and other sources to predict when equipment is likely to fail. It is used in manufacturing, transportation, and other industries to reduce downtime and maintenance costs.

• Recommendation Systems - recommendation systems are applied AI technologies that use machine learning algorithms to suggest products or content to users based on their previous behavior or preferences. They are used in e-commerce, entertainment, and other industries to improve customer experience and increase sales.