In essence, ML is training a computer to recognize patterns in historical data to make predictions on new data.
Generative artificial intelligence is a subset of deep learning because it can adapt models built using deep learning, but without retraining or fine tuning.
Generative AI is a type of AI that can create new content, including conversations, stories, images, videos, music, and code.
Like all artificial intelligence, generative AI is powered by ML models. However, generative AI is powered by very large models that are pretrained on vast collections of data.
Use cases for businesses.
Generative artificial intelligence contributes to healthcare in the following ways:
AWS HealthScribe: empowers healthcare software vendors to build clinical applications that automatically generate clinical notes by analyzing patient-clinician conversations.
Personalized medicine: By generating treatment plans based on a patient's specific genetic makeup, lifestyle, and disease progression, AI can contribute to more effective, personalized care.
Medical imaging: AI can enhance, reconstruct, or even generate medical images, like X-rays, MRIs, or CT scans, which can aid in better diagnosis.
Life sciences
Generative artificial intelligence contributes to life sciences in the following ways:
Drug discovery: AI can generate new potential molecular structures for drugs, accelerating the process of drug discovery and reducing costs.
Protein folding prediction: AI can predict the 3D structures of proteins based on their amino acid sequence, which is crucial for understanding diseases and developing new therapies.
Synthetic biology: AI can generate designs for synthetic biological systems, such as engineered organisms or biological circuits.
Financial services
Generative AI contributes to financial services in the following ways:
Fraud detection mechanisms: Generative AI can help create synthetic datasets to improve AI/ML systems by simulating various money-laundering patterns.
Portfolio management: Generative AI can simulate various market scenarios and help in the creation and management of robust investment portfolios.
Debt collection: AI can generate the most effective communication and negotiation strategies for debt collection, increasing the rate of successful collections.
Across the banking industry, for example, AI technology can deliver value equal to an additional $200-$340 billion annually if the use cases above are fully implemented (McKinsey Report 2023).
Manufacturing
Generative AI contributes to manufacturing in the following ways:
Product design: Generative AI can be used to create new product designs based on set parameters and constraints. It can generate multiple design options and optimize for factors like cost, materials, performance, and so forth.
Process optimization: AI can generate the most efficient production processes by modeling different scenarios and optimizing for variables such as cost, time, resource usage, and so forth.
Preventative maintenance: By analyzing historical production data, AI can predict maintenance schedules that will provide the most efficient machine outputs and reduce downtimes.
Material science: AI can help generate new material compositions with desired properties.
Retail
Generative AI contributes to retail in the following ways:
Pricing optimization: Generative AI can model different pricing scenarios to determine optimal pricing strategies that maximize profits.
Virtual try-ons: AI can generate virtual models of customers for virtual try-ons, improving the online shopping experience.
Store layout optimization: AI can generate the most efficient store layouts to improve the customer shopping experience and boost sales.
Product review summaries: AI can generate review summaries for products so consumers can quickly find pertinent information.
In retail and consumer packaged goods, the potential generative AI impact is significant at $400 billion to $660 billion a year. (McKinsey Report 2023).
Media and entertainment
Generative AI contributes to media and entertainment in the following ways:
Content generation: Generative AI can be used to create scripts, dialogues, or even complete stories for films, TV shows, and games.
Virtual reality: Generative AI can create immersive and interactive virtual environments for games or simulations.
News generation: AI can generate news articles or summaries based on raw data or events.