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Artificial intelligence (AI) has quickly transformed from a vague, theoretical technology into an almost ubiquitous term seemingly overnight. Tools like ChatGPT, Voice.AI, DALL-E and Gemini have made AI widely accessible and found their way into many business processes and personal uses. But these tools only scratch the surface of AI and don’t account for many varieties and use cases of the technology.
If you’re looking to put AI to work for you, you should start by understanding the basics to determine when and how to use which type of AI. Here’s a look at some key AI-related terms that will help you understand the basic functions and breadth of this technology. Master these terms and you’ll be on your way to productive conversations about leveraging AI in your business.
- Artificial Intelligence (AI): AI is a blanket term describing a range of computer science capabilities designed to perform tasks typically associated with human beings.
- AI Models: Software programs trained to perform specific tasks when analyzing large data sets. Models are trained using specific data to recognize certain patterns and relationships, and then use this information to make predictions.
- Big Data: Big data describes large and diverse datasets that are huge in volume and also rapidly grow in size over time and that cannot be easily managed or analyzed with traditional data processing tools.
- Conversational AI: AI technology that enables computers and software systems to engage in human-like conversations. It focuses on creating interactive and dynamic interactions between humans and machines, typically through chatbots, virtual assistants or voice-based interfaces.
- Data Science: A cross-discipline combination of computer science, statistics, modeling and AI that focuses on utilizing as much as it can from data-rich environments. Data science requires massive amounts of data from various sources in order to build the models to make intelligent business decisions.
- Data Mining: Data mining describes the process whereby you dig through data to discover hidden connections and patterns, and then use this data to predict future trends. Most often it uses a combination of machine learning and artificial intelligence and is very much related to Big Data.
- Deep Learning (DL): Deep learning is a subset of machine learning that uses artificial neural networks to teach computers to perform tasks in a way that mimics the human brain. Deep learning models can analyze complex data patterns, such as text, images, and sounds, to make predictions and provide insights.
- Generative AI: AI that creates new content like images, text, sound, etc. ChatGPT and Gemini are examples of tools using generative AI.
- Hyper-personalization: Just like it sounds, hyper-personalization involves leveraging data, analytics, AI and automation to deliver highly customized and individualized customer experiences.
- Intelligent Automation (IA): Sometimes called cognitive automation, IA uses automation technologies — such as artificial intelligence (AI) and robotic process automation (RPA) — to streamline and scale decision-making.
- Large Language Models (LLMs): A type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
- Machine Learning (ML): Machine learning is a subset of AI that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, machine learning uses algorithms to analyze large amounts of data, learn from the insights, make informed decisions, and continuously, automatically learn and improve at a massive scale as more data is observed.
- Natural Language Processing (NLP): NLP is a branch of artificial intelligence focusing on the interaction between computers and human language. It involves understanding, interpreting, and generating human language meaningfully and contextually relevantly.
- Neural Network: A type of machine learning model designed to mimic the working of the human brain. Training data are fed into an input layer, where each represents a specific feature of the data. The neurons in subsequent hidden layers process this information by assigning weights to the connections allowing the network to capture patterns and relationships within the data.
- Predictive Analytics: The process of using data to forecast future outcomes. This process uses data analysis, machine learning, artificial intelligence and statistical models to find patterns that might predict future behavior.
- Robotic Process Automation (RPA): RPA refers to using software robots (bots) to automate repetitive and rule-based tasks within business processes. This technology mimics human interactions, performing tasks such as data entry, manipulation, file transfers, etc.
- Sentiment Analysis: A natural language processing (NLP) technique used to determine the emotional tone or sentiment from a portion of text. The intent is to try to determine the attitude, emotions or feelings of the author. Commonly, the analytics will classify responses as positive, negative, or neutral, although more granular sentiment classifications are also possible.
Knowledge is power, as they say, and the first step to adopting advanced technologies into your organizations is to gain a better understanding of the basics. The team at TrueML Products is here to break down the buzzwords into manageable, actionable initiatives for your business. With a foundation in machine learning and AI, TrueML Products’ experts are ready to be your guide to leveraging a SaaS solution with these kinds of advanced technologies already baked in.