1. Machine Learning (ML)
Definition:
Machine learning is a subset of artificial intelligence where computers are trained to learn from data and make decisions or predictions without being explicitly programmed.
Example:
An email spam filter uses machine learning to identify spam messages. By analyzing thousands of examples of spam and non-spam emails, it learns to classify new emails accurately.
2. Neural Networks
Definition:
Neural networks are computing systems inspired by the structure and function of the human brain. They consist of layers of interconnected nodes (or “neurons”) that process and analyze data to identify patterns and relationships.
Example:
Neural networks are used in facial recognition systems. By analyzing features like eyes, nose, and mouth in images, they can identify individuals with high accuracy.
3. Accountability in AI
Definition:
Accountability in AI means that developers, organizations, and users are responsible for the outcomes and impacts of AI systems. This includes addressing biases, errors, and harm caused by AI.
Example:
If an AI system used for hiring results in discriminatory decisions, the organization deploying the system must take responsibility, investigate the issue, and make corrections to ensure fairness.
4. Algorithmic Bias
Definition:
Algorithmic bias occurs when an AI system produces unfair outcomes due to biased data or flawed design.
Example:
A loan approval AI system trained on data that underrepresents women may inadvertently deny loans to women more often than men, reflecting bias in the training data.
5. Explainability
Definition:
Explainability refers to the ability of an AI system to provide clear, understandable reasons for its decisions.
Example:
If a credit card application is denied, an explainable AI system can detail specific factors, such as low credit score or high existing debt, that led to the decision.
6. Artificial General Intelligence (AGI)
Definition:
AGI is a theoretical form of AI capable of understanding, learning, and performing tasks at the same level as a human across a wide range of domains.
Example:
Unlike current AI systems that are specialized (like a chatbot or image classifier), AGI would be able to write a novel, solve complex math problems, and play chess—all with human-like competence.
7. Natural Language Processing (NLP)
Definition:
NLP is a branch of AI that focuses on enabling computers to understand, interpret, and generate human language.
Example:
NLP powers virtual assistants like Siri and Alexa, allowing them to understand spoken commands and respond appropriately.
8. Ethical AI
Definition:
Ethical AI refers to the development and use of AI systems that prioritize fairness, transparency, and accountability, while minimizing harm and promoting societal benefit.
Example:
An ethical AI healthcare system ensures that diagnostic algorithms work equally well for patients of all ethnicities by using diverse training data.
9. Deep Learning
Definition:
Deep learning is a subset of machine learning that uses neural networks with many layers (hence “deep”) to process large amounts of data and solve complex problems.
Example:
Deep learning is used in self-driving cars to process images from cameras, recognize objects like pedestrians or traffic signs, and make driving decisions.
10. Artificial Intelligence (AI)
Definition:
AI refers to machines or software that simulate human intelligence to perform tasks like problem-solving, learning, and decision-making.
Example:
A chatbot answering customer service questions is an example of AI at work.
11. Data Anonymization
Definition:
Data anonymization is the process of removing or encrypting personally identifiable information from datasets to protect individual privacy.
Example:
In healthcare research, patient names and addresses are replaced with anonymized IDs to ensure their privacy while allowing researchers to analyze the data.
12. Generative AI
Definition:
Generative AI refers to AI systems that can create new content, such as text, images, music, or code, based on the data they’ve been trained on.
Example:
ChatGPT is an example of generative AI, capable of creating human-like text responses based on user input.
