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AI Glossary

What you need to about

AI app development

Artificial Intelligence (AI) has become a driving force in modern app development, powering everything from chatbots to predictive analytics. But with AI comes a wave of technical jargon that can leave even the most tech-savvy scratching their heads. If terms like "neural networks," "reinforcement learning," or "LLMs" sound like another language, don’t worry - you’re not alone.

At The Distance, we believe AI should be accessible, not intimidating. That’s why we’ve put together this essential glossary, breaking down key AI terms in simple, digestible language. Whether you're a business leader looking to integrate AI into your app or a developer navigating AI-powered solutions, this guide will give you the clarity you need.

  • AI refers to the simulation of human intelligence in machines, enabling them to learn, reason, and solve problems. It’s the foundation of smart assistants, recommendation engines, and even autonomous systems. In app development, AI helps automate tasks, enhance user experiences, and drive business insights.

  • As a subset of AI, machine learning allows systems to learn and improve from experience without being explicitly programmed. Instead of following a rigid set of rules, ML models analyse data patterns to make predictions - like how Netflix suggests movies based on your viewing history.

  • AI that creates new content, whether it’s text, images, music, or even code. Generative AI powers tools like ChatGPT and Midjourney, enabling apps to generate human-like responses, design visuals, and assist in content creation.

    Fun fact: All the images used in this blog have been created with generative AI!

  • These are AI models trained on vast amounts of text data to understand and generate human-like language. Examples include OpenAI’s GPT models and Google’s Gemini. LLMs are used for chatbots, content generation, and language translation in app development.

  • It's a field of AI that allows computers to interpret and make decisions based on visual data, like images and videos. It’s used in facial recognition, augmented reality (AR), and object detection, enhancing app features such as photo editing and real-time translations.

  • It's a type of machine learning where an AI model learns by trial and error, receiving rewards or penalties based on its actions. It’s used in gaming AI, robotics, and self-improving recommendation algorithms.

  • Deep learning a more advanced type of machine learning that mimics the way the human brain processes information. Deep learning models use neural networks with multiple layers to recognise patterns in vast amounts of data. It’s the technology behind facial recognition, speech translation, and self-driving cars.

  • Inspired by the structure of the human brain, neural networks consist of interconnected layers of nodes (or "neurons") that process information. These networks power deep learning models, enabling AI to perform complex tasks like image recognition and natural language processing.

  • NLP enables machines to understand, interpret, and generate human language. It’s what powers voice assistants like Siri and Alexa, as well as chatbots and AI-driven customer support systems. NLP helps apps communicate more naturally with users.

  • AI models learn from data, which means they can inherit biases present in that data. Ethical AI development focuses on ensuring fairness, transparency, and accountability to prevent biased decision-making in apps.

  • AI that processes data locally on a device rather than relying on cloud-based systems improves speed and privacy, making it ideal for mobile apps, smart cameras, and IoT devices.

  • APIs allow developers to integrate AI functionalities into apps without building models from scratch. AI-powered APIs, such as Google’s Vision API or OpenAI’s ChatGPT API, help add features like image recognition or language processing.

  • An MCP is a standardised protocol/service that provides access to data and services from a 3rd party data source or service. Designed specifically for integration with AI workflows to provide further context to an LLM.

  • It's an AI-driven data analysis that forecasts future outcomes based on historical data. Apps use predictive analytics for personalised recommendations, fraud detection, and demand forecasting.

  • It's an AI technique used to determine the emotional tone behind text data. It helps apps analyse customer feedback, social media posts, and reviews to gauge user sentiment.

  • AI-powered programs that simulate human conversation. They’re widely used in customer service apps, allowing businesses to automate responses, answer FAQs, and assist users in real-time.