Generative AI uses deep learning—a sophisticated form of machine learning (ML) that handles complex tasks and large datasets—to create new content in response to simple natural language prompts. Like a chef who cooks unique dishes, a musician who composes songs, or an author who writes stories, generative AI is creative and innovative.
Generative AI is a subset of AI, which refers to any system or machine that can perform human-like tasks by using ML models to identify and mimic patterns in the data it collects. Through continuous feedback loops, the system or machine gradually improves its performance.
From automating routine operations to personalizing customer experiences, organizations increasingly rely on AI for business to boost efficiency, drive innovation, and build a competitive edge. However, the field of AI encompasses a diversity of technologies that operate in different ways.
Generative AI’s capability to generate novel output, whether text, images, music, or code, represents a significant advancement in AI technology. In just a short time, it has opened endless possibilities for organizations across industries.
Every type of AI has a specific purpose that serves different business needs. By understanding what each type can and can’t do for your organization, you can maximize its potential. Here are some common AI types:
Traditional AI automates and optimizes specific tasks. Because it relies on ML models with predefined rules and algorithms, it’s most widely used in industries for repetitive tasks where efficiency and precision are crucial, such as in manufacturing or data processing. Traditional AI includes predictive AI and conversational AI.
Predictive AI forecasts outcomes based on analysis of historical data. It can analyze past behaviors, detect patterns, and predict future outcomes with high accuracy. Predictive AI is foundational in finance, healthcare, manufacturing, and marketing.
Conversational AI powers chatbots and virtual assistants that facilitate natural language interactions between humans and machines throughtext or voice interfaces. Conversational AI relies on ML models and natural language processing (NLP) to comprehend natural language and generate human-like responses.
Generative AI, predictive AI, and other AI types have a wide range of practical applications across various industries and business functions. Here are some examples of how different types of AI are used:
Applications of generative AI include:
Given the rapid growth of AI for business, leaders must proactively address associated risks. These risks include potential bias in AI training data, a lack of transparency into how algorithms make decisions when generating output, and intentional misuse of AI for malicious purposes, such as spreading disinformation and creating deepfakes.
As part of its commitment to advancing responsible AI practices, Microsoft created six Responsible AI principles to help guide development and use of generative AI and other AI systems.
As a key player in the next wave of AI-powered business transformation and innovation, generative AI promises to continue to reshape how organizations function and interact with customers.
Look for the following trends:
Ongoing improvements in ML models will include smarter training algorithms, self-supervised learning, and other advancements in model architecture and training. This will result in higher-quality outputs and more intuitive user experiences.
Complementary use of generative AI with other AI types will enhance system capabilities and increase efficiency. For example, in product development, organizations can use predictive AI to identify future market demands, generative AI to suggest new products that satisfy those demands, and conversational AI to gather customer feedback to continually refine product designs.
Deeper integration of generative AI with other AI types will strengthen complex decision-making and problem-solving processes. For example, in customer service, chatbots or virtual assistants that combine NLP with generative AI can dynamically create intelligent, personalized responses based on real-time analysis of user needs, sentiments, and context.
The emphasis on responsible AI will increase. Businesses, governments, academia, and other organizations will continue to stress fairness, transparency, accountability and other practices in AI development and deployment. Learn more about Microsoft’s commitment to using AI responsibly. Also, access tools and processes to help your organization effectively manage AI risks.
Understanding what makes generative AI and other AI types unique is key to gaining the greatest advantages from each one, whether it’s working alone or in unity with other AI.
Unlike predictive AI, generative AI doesn’t forecast outcomes based on historical data. Unlike conversational AI, it doesn’t generate human-like dialogue. It creates new work with minimal human input while constantly iterating and improving on its output—something essential to driving innovation and maintaining competitiveness in today’s digital world.
Continue to learn about generative AI versus other AI types, and how you can best put generative AI to use in your organization
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