Decoding AI Jargon: A Handy Guide to Essential Terms in the Workplace

As a result of the rapid integration of AI across almost all sectors of the economy, today’s workforce is operating in the most technically transformative era since the Industrial Revolution. Consequently, AI terminology is no longer just a series of buzzwords used by computer engineers. It’s a vital language that everyone seeking to advance their career needs to speak.

Unsurprisingly, the tech industry, including software and information services, is at the forefront of AI adoption, driving advancements in cloud computing, data analytics and the development of AI tools and applications. But healthcare, finance, manufacturing, and transport are not far behind. Retail, energy, telecommunications, agriculture, education, entertainment, marketing, and of course recruitment and human resource management, are all integrating AI into their core operations.

In short, there’s not a single sector of the economy untouched by AI and becoming familiar with these common AI terms will help you to keep at the forefront of industry trends.

Your indispensable guide to AI language

1. Agent: A system that can make independent decisions by observing its simulated or physical environment, and use sensors or actuators to reach a goal. E.g., driverless cars, robotic vacuum cleaners.
2. Algorithm: A step-by-step procedure for calculations or problem-solving. Machine learning systems use algorithms to digest data, and use data to make predictions.
3. Artificial General Intelligence (AGI): AI with cognitive abilities close to that of humans, for understanding, learning, and applying knowledge.
4. Artificial Intelligence (AI): The mimicking of human intelligence by machines, including learning, reasoning and problem-solving.
5. Augmented Reality (AR): The use of technology to superimpose virtual objects – computer-generated images – onto the real world, to create a view that encompasses both.
6. Bias: Inherent prejudice in data or algorithms affecting AI decisions based on machine learning. E.g., unintentional gender bias resulting from historical recruitment data.
7. Big data: Extremely large data sets able to be collected and analysed by a computer, to reveal patterns and trends for decision-making purposes. E.g., analysing consumer behaviour from millions of online shopping transactions to predict future buying trends.
8. ChatGPT: A Generative Pre-Trained Transformer chatbot developed by OpenAI for generating human-like text responses to input expressed in natural language.
9. Chatbot: A software application that can conduct a conversation with a human, usually in text, and retrieve information in a similar way to a search engine. E.g., customer service chatbots programmed with answers to FAQs.
10. Computer vision: AI that interprets and understands visual information, such as images and videos, to detect objects or classify images. E.g., a security system using facial recognition.
11. Corpus: A large collection of text or voice data used for training machine learning models. ChatGPT, for example, was trained on text from websites, books, news articles and blog posts, social media, Wikipedia, and conversational data.
12. Data mining: The process of discovering connections and patterns in large data sets, to predict results, solve problems or create strategies.
13. Deep fake: A combination of ‘deep learning’ and ‘fake’, to denote synthetic media in which a person in an existing image or video is replaced with someone else’s image, often with intent to deceive.
14. Deep learning: A subset of machine learning, which, instead of relying on algorithms, trains neural networks to simulate the way the human brain processes information and makes decisions.
15. Explainable AI: AI systems whose reasoning can be easily understood, and are more likely to be trusted, because they provide explanations for their actions. E.g., an AI credit scoring system giving reasons for its decision to accept or reject a loan application.
16. Generative AI: AI that can generate new, original content – including text, audio, images, video, and code as well as resumes and cover letters – because of studying patterns in the data corpus used to train it. ChatGPT is one of the best-known examples.
17. Generative Pre-trained Transformer (GPT): GPT refers to a specific series of models developed by OpenAI which use the Transformer architecture for generating text. The term ‘generative’ emphasises the model’s ability to generate new text based on the input it receives.
18. Hyperparameter: A configuration, set in advance, to optimise the machine learning process and guide how an algorithm learns. Hyperparameters may govern, for example, the learning rate, batch size, and the number of layers and neurons in a neural network.
19. Large Language Model (LLM): LLM is a broad term which refers to any large-scale machine learning model designed to understand, interpret, generate, and work with human language. Unlike GPTs, LLMs are not based on any specific architecture. An example of an LLM that is not a GPT is BERT (Bidirectional Encoder Representations from Transformers), developed by Google to understand the nuances and context of search engine queries.
20. Machine learning: AI that learns from data without explicit programming or other human assistance. E.g., facial and voice recognition.
21. Natural Language Processing (NLP): AI’s ability to understand and reproduce human written and spoken language, allowing it to interact with humans.
22. Neural network: A deep learning technique modelled on the human brain, using interconnected neurons or nodes to process and transmit information.
23. Overfitting: The result when a machine learning model is too closely fitted to its training data, reducing its ability to process new data and find generalised patterns. E.g., a stock market prediction model that performs exceptionally well on historical data but fails to predict future stock price movements accurately.
24. Parameter: An internal variable of a machine learning model, learned from the training data and adjusted during the training process. These parameters differ from hyperparameters, which are constant external settings fixed before the training process begins.
25. Predictive analytics: The use of historical data and patterns to forecast future events or results.
26. Reinforcement learning: A machine learning training method based on trial and error, and feedback and rewards, when interacting with an environment. E.g., AI learning by playing a video game, improving its strategy over time through trial and error to maximise its score.
27. Supervised learning: AI trained on ‘labelled’ data, such as emails pre-labelled as ‘spam’ or ‘not spam’ when training a machine learning model to detect spam emails by finding common patterns in them.
28. Transfer learning: AI taking the knowledge it acquired from previously learned data and applying it to a new task.
29. Unsupervised learning: Machine learning training where models find patterns in unlabelled or unstructured data, without human intervention or feedback.
30. Virtual Reality (VR): Completely immersive three-dimensional computer-generated environments. E.g., a VR simulation for medical students to practise surgery in a risk-free, realistic virtual environment.

Develop your familiarity with the AI glossary

Understanding the contents of this AI glossary will showcase your comprehension of concepts previously confined to computer experts. AI has become increasingly essential for professionals across various fields and so being able to discuss AI confidently could benefit you in upcoming interview or workplace meetings.

Now that you are feel informed on AI concepts, why not send your resume to Adecco? We have a multitude of opportunities, in a variety of different industries.