Top 10 Must-Know AI Terms You Can’t Miss in Today’s Era

Top 10 Must-Know AI Terms You Can’t Miss in Today’s Era

The term "AI" has been around since the 1950s, but it gained mainstream attention around 2022 given the recent rapid advances in machine learning. AI breakthroughs are significantly impacting every area of our lives. AI today has truly gone mass. Here’s a guide from Microsoft that will help you understand key AI terms and join the global conversation.  

  • Artificial intelligence 

    Artificial intelligence (AI) is a super-smart computer system that can imitate human tasks like understanding language, making decisions, translating languages, analyzing sentiment, and learning from experience. AI processes vast amounts of data through algorithms to create models that automate tasks requiring human intelligence. While sometimes directly interacted with, like asking Bing Chat for help, AI often works in the background, suggesting words, recommending songs, and providing relevant information. 

  • Machine learning 

Machine learning (ML) is a subset of AI and the method to achieve it. In ML, computer systems learn to identify patterns and make predictions by running data through algorithms repeatedly with varying inputs and feedback, much like practicing piano scales millions of times to sight-read music. ML is ideal for solving complex problems like image recognition and language translation, which are difficult for traditional programming. 

  • Large Language model 

Large language models (LLMs) use machine learning to mimic human communication. Based on neural networks (NNs), which are inspired by the human brain, they are trained on massive amounts of text to learn language patterns and relationships. LLMs can translate languages, answer questions, summarize text, and even write stories, poems, and code. 

  • Generative AI 

Generative AI leverages the power of large language models to make new things, not just regurgitate or provide information about existing things. It learns patterns and structures and then generates something that’s similar but new. It can make things like pictures, music, text, videos and code. 

  • Hallucinations 

Generative AI can create stories, poems, and songs but can also produce inaccurate responses, called "hallucinations" or "fabrications," because it can't distinguish between real and fake information. Developers address this by "grounding" the AI with additional information from trusted sources to improve accuracy.  

  • Responsible AI 

Responsible AI ensures systems are safe and fair at every level, including the machine learning model, software, user interface, and application access rules. It's crucial for systems that make important decisions in areas like education and healthcare, as they can reflect human biases from their training data. A key aspect is understanding the training data and mitigating biases to better represent all of society. 

  • Multimodal models 

A multimodal model can simultaneously work with different data types, such as images, sounds, and text. It combines this information to perform tasks like answering questions about images, making it the ultimate multitask. 

  • Prompts 

A prompt is an instruction in language, images, or code that tells the AI what task to perform. Engineers and users must carefully design prompts to achieve the desired outcome from large language models, much like specifying details when ordering a sandwich to get exactly what you want. 

  • Copilots 

A Copilot is a digital assistant that helps with tasks like writing, coding, summarizing, and searching across various applications. Enabled by large language models, copilots understand natural language and assist in decision-making and data analysis. They are built with Responsible AI guardrails for safety and security. Like an aeroplane copilot, they assist but you remain in control. 

  • Plugins 

Plugins for AI function similarly to apps on a smartphone, enabling AI applications to fulfill specific needs without altering the core model. They facilitate interaction with other software, access to new information, performing complex calculations, and interfacing with other programs, thereby enhancing the AI's capabilities by integrating it more deeply into the digital ecosystem. 

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