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What is artificial intelligence? 

Get an overview of artificial intelligence (AI) and its many use cases. Plus, learn how to start building, deploying, and managing AI-powered apps using Azure services.
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AI definition

Artificial intelligence (AI) refers to computer systems or models that are designed to perform tasks that normally require human thinking, such as understanding language, recognizing patterns, solving problems, and making decisions. Traditional AI systems follow only fixed rules, but newer AI systems, like cognitive AI, can learn from data and improve over time, which allows them to become more accurate in handling new situations. 

AI is used in many of our everyday tools, including virtual assistants, recommendation systems, and chatbots. 

  • AI models identify patterns and relationships in data that can be used to generate new predictions, recommendations, and responses.
  • Organizations across major industries are already using AI to boost productivity and drive innovation in real-world use cases.
  • Looking to the future, AI is expected to expand opportunities, contribute to economic growth, and foster long-term innovation.

How AI works

AI models built on the generative pre-trained transformer (GPT) use machine learning to analyze large amounts of data, such as text, images, or numbers, identifying patterns and relationships that can be used to produce outputs, such as predictions, recommendations, or responses.

The process typically follows a simple cycle:

  1. Data is collected and prepared 
  2. The model is trained to recognize patterns 
  3. Results are tested and improved through feedback 

With each cycle, the AI model adjusts itself, becoming more accurate and capable of handling new situations more effectively. Essentially, AI works by continuously learning from data, refining its understanding, and applying that learning to deliver better results over time. 

Use case examples of AI

As organizations continue to innovate, AI is becoming a foundational tool for boosting productivity and driving innovation across teams and industries. The following examples highlight a few common, real-world use cases:

  • Workflow automation across software development: Developers are already using AI-powered solutions, like GitHub Copilot, to help write code, test software, and automate workflows. As a result, development teams have more time and energy to focus on more valuable work.
  • Document summarization: Organizations across all industries use GPT to generate short summaries from reports, meeting transcriptions, and emails. This helps teammates understand key insights without having to dive into lengthy material, in turn boosting productivity.
  • Healthcare research and analysis: In the medical field, doctors use multimodal LLMs to quickly analyze medical images and complex patient data. This allows them to diagnose diseases more efficiently and provide personalized care plans based on the individual’s profile.
  • Retail: Retail operators use AI models to forecast demand, manage inventory, and personalize recommendations for customers shopping online or in stores.
  • Enhanced security: By verifying identity through unique facial features, AI-enhanced facial recognition technology prevents unauthorized access to devices, secure facilities, and sensitive data. This added layer of protection reduces the risk of breaches and strengthens overall security posture.

Future trends

The future of AI is expected to be transformative. As AI services and tools continue to evolve, they will become more integrated into everyday life. Artificial intelligence as a service (AIaaS), for instance, now gives teams and organizations across industries access to AI capabilities through APIs and cloud services, allowing them to work smarter, solve complex problems faster, and unlock new levels of creativity and innovation at a cost-effective price.

By providing humans with more efficient, responsive, and accessible systems, AI is poised to significantly expand opportunities for individuals and society as a whole, acting as a powerful partner and contributor to economic growth and long-term innovation. 

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Frequently asked questions

  • AI is used to make work faster, smarter, and more efficient across many industries by automating tasks, analyzing large amounts of data, and helping people make better decisions. It can also be used to improve everyday experiences by powering things like virtual assistants, chatbots, and recommendation engines.  
  • An example of AI may be a virtual assistant like Siri, which can be prompted to answer questions and perform tasks via voice or text. Another example is a recommendation service, like those used by streaming or shopping platforms, which can suggest products or content based on your behavior and preferences. 
  • AI is a broad field that focuses on systems that can analyze data, recognize patterns, and make decisions. Generative AI is a specific type within AI that is designed to produce new outputs, such as text, images, or code, based on what it has learned.  
  • AI learns by studying large amounts of data and finding patterns. It makes predictions or decisions, compares them to correct answers or feedback, and adjusts itself to improve over time. AI learns through a repeated cycle: observe data, make a guess, get feedback, and improve with practice.