Breaking Down AI Jargon: Understanding Key Terms and Concepts
- 2 min read
- AI and ML
The field of Artificial Intelligence (AI) and Machine Learning (ML) is rife with technical jargon that can be overwhelming for newcomers. This glossary-style post aims to demystify some of the key terms and concepts in AI and ML, making the field more accessible to everyone.
Key Terms Explained
Artificial Intelligence (AI)
- The broader concept of machines being able to carry out tasks in a way that we would consider “smart”.
Machine Learning (ML)
- A subset of AI, focused on the idea that machines can learn from data, identify patterns, and make decisions with minimal human intervention.
Neural Networks
- Algorithms designed to recognise patterns, interpreting sensory data through a kind of machine perception, labeling, and clustering raw input.
Natural Language Processing (NLP)
- The ability of a computer program to understand human language as it is spoken and written is referred to as natural language.
Deep Learning
- A subset of ML based on artificial neural networks, with representation learning. Learning can be supervised, semi-supervised, or unsupervised.
Supervised Learning
- A type of ML where the algorithm is trained on labeled data or data that has an answer key.
Unsupervised Learning
- ML that uses data that is neither classified nor labelled and allows the algorithm to act on that data without guidance.
Reinforcement Learning
- A type of ML is where an agent learns to behave in an environment by performing actions and seeing the results.
Predictive Analytics
- The use of data, statistical algorithms, and ML techniques to identify the likelihood of future outcomes based on historical data.
Conclusion
Understanding these key terms is crucial in navigating the world of AI and ML. As these technologies continue to evolve, becoming familiar with their language will not only enhance comprehension but also enable more informed discussions about their impact and potential.