AI Jargon Explained: Key Terms Adult Learners Should Know

Image credit: Google DeepMind on Pexels

 

By Lilian H. Hill

 

While it may seem like jargon to non-experts, Artificial Intelligence (AI) terminology is a specialized vocabulary that describes the concepts, technologies, and processes enabling machines to replicate aspects of human intelligence. As AI transforms industries such as healthcare, finance, and manufacturing, familiarity with this vocabulary is essential for staying current with ongoing AI developments and innovations. The terminology presented here contains commonly used vocabulary in AI. To aid in comprehension, the terms are categorized into foundational terms, key concepts, practical applications, and concerns associated with AI.

 

Foundational AI Terms

Algorithm: A set of rules or procedures used by an AI system to perform tasks, such as sorting data or identifying patterns. Algorithms are the step-by-step instructions that guide every AI model (Cormen et al., 2009).

 

Artificial Intelligence (AI): Refers to the development of computer systems capable of performing tasks typically requiring human intelligence, such as perception, reasoning, learning, and decision-making (Russell & Norvig, 2020).

Large Language Model (LLM): Advanced AI systems trained with vast amounts of text data to understand and generate human-like language.

Machine Learning (ML): A subset of AI that involves the use of algorithms and statistical models that enable computers to learn from data and improve their performance without being explicitly programmed (Murphy, 2012). ML is foundational to most current AI applications. Deep learning, supervised learning, and unsupervised learning are different types of machine learning:

·       Deep Learning: A branch of machine learning involving neural networks with multiple hidden layers, enabling the modeling of complex, high-level abstractions in data such as image or speech recognition (Brown et al., 2020; Goodfellow et al., 2016).

·       Supervised Learning: A machine learning method where a model is trained on labeled data to learn the mapping from inputs to outputs (Hastie et al., 2009).

·       Unsupervised Learning: A form of machine learning that identifies patterns or groupings in unlabeled data (Murphy, 2012).

 

Neural Network: A computational model inspired by the human brain’s network of neurons, designed to recognize patterns and make predictions. These models are the backbone of many AI systems today (LeCun et al., 2015).

Key Concepts in AI

Natural Language Processing (NLP): The study and application of techniques that allow machines to understand, interpret, and generate human language (Jurafsky & Martin, 2020). It underpins applications such as chatbots and language translation tools.

Generative AI: A type of AI that can produce original content such as text, images, or music by learning patterns from training data. Examples include text generation, image generation, and music generation (Bommasani et al., 2021):

·       Text generation. a type of large language model that uses deep learning and transformer architecture to understand and generate human-like text (Brown, 2000). Chat Generative Pre-trained Transformer, more commonly known as ChatGPT, is a popular example.

·       Image Generation: an AI model that generates images from textual descriptions using deep learning. It can create original, coherent, and contextually relevant images from complex natural language prompts. One example is DALL·E.

·       Music Generation: Music created, composed, or produced with the aid of artificial intelligence involves the use of AI algorithms and models trained on extensive datasets of existing music. These systems can generate new musical content—including melodies, harmonies, rhythms, and lyrics. Suno AI is an example.

Training Data: The labeled or unlabeled dataset used to teach a machine learning model how to identify patterns and make predictions. The quality and diversity of training data heavily influence the accuracy of the model (Zhou et al., 2019).

Practical AI Applications

Automation: The use of technology, including AI, to perform tasks with minimal human intervention. Automation can increase efficiency but also raises concerns about labor displacement (Brynjolfsson & McAfee, 2014).

 

Chatbot: An AI application designed to simulate conversation with human users, often using NLP to interpret queries and generate responses. They are widely used in customer service and education to respond to routine inquiries (Shum et al., 2018).

Computer Vision: A field of AI that enables computers to interpret and make decisions based on visual data, such as images or video. It is used in facial recognition, medical imaging, and autonomous vehicles (Szeliski, 2010).

Intelligent Learning Management Systems (ILMS): Integrate AI-powered interactive features into traditional LMS that automate content management, personalize learning, boost engagement, improve accessibility, enable real-time communication and assessment, and deliver curated content (Hill & Conceição, 2024).

 

Concerns With AI

Bias: Systematic errors in AI outputs resulting from biased data, flawed algorithms, or inequitable system design. Bias is a significant ethical concern in the development of responsible AI (Barocas et al., 2019).

 

Black Box: A term used to describe AI systems whose decision-making processes are not transparent or interpretable, making it difficult to understand how conclusions are drawn (Burrell, 2016).

Explainability (Interpretability): The degree to which a human can understand the internal logic or decision-making process of an AI model. High explainability is critical in domains like healthcare and criminal justice (Doshi-Velez & Kim, 2017).

 

Hallucination: A phenomenon where AI models produce outputs that are plausible, but factually incorrect or nonsensical. For example, a chatbot that provides a reference to a nonexistent source.

Singularity
A hypothetical future point when AI surpasses human intelligence, potentially leading to rapid, uncontrollable changes in society. Though speculative, it raises questions about AI safety and governance (Kurzweil, 2005).

These terms provide a foundational understanding for adult learners venturing into the realm of AI. 

 

Join Our Conversation

At the Adult Learning Exchange Virtual Community, we invite you to share your experiences, tools, and questions in the AI Literacy Forum, moderated by Drs. Lilian Hill and Simone Conceição. Together, we can explore how to harness AI for more inclusive, effective, and empowering adult learning.

  

References

Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine Learning. fairmlbook.org.

Bommasani, R., et al. (2021). On the opportunities and risks of foundation models. Stanford Institute for Human-Centered AI.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.

Brynjolfsson, E., & McAfee, A. (2014). The second machine age. Norton.

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1).

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to Algorithms. MIT Press.

Doshi-Velez, F., & Kim, B. (2017). Towards A rigorous science of interpretable machine learning. arXiv:1702.08608.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer.

Hill, L. H., & Conceição, S. C. O. (2024). AI-Powered learning management system (LMS) platforms: Implications for teaching and learning. ELearn Magazine. https://doi.org/10.1145/3702011

Jurafsky, D., & Martin, J. H. (2020). Speech and language processing (3rd ed.). draft.

Kurzweil, R. (2005). The singularity is near. Penguin.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.

Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.

Shum, H.-Y., He, X.-D., & Li, D. (2018). From Eliza to XiaoIce: Challenges and opportunities with social chatbots. Frontiers of Information Technology & Electronic Engineering, 19(1), 10–26.

Szeliski, R. (2010). Computer vision: Algorithms and applications. Springer.

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.

Zhou, Z.-H., et al. (2019). Deep learning and its applications. National Science Review, 6(1), 45–57.