AI Glossary for L&D, Part 3: Advanced Techniques and Applications

In Part 3 of our AI Glossary for L&D, we venture into the realm of the more specialized AI techniques and applications that are pushing the boundaries of what’s possible, including:

• Deep learning
• Supervised, unsupervised, and reinforcement learning
• Transfer learning
• Fuzzy logic
• Explainable AI

 

Advanced Techniques and Applications Pushing the Boundaries of AI

What is deep learning? 

Deep learning is a subset of machine learning in which artificial neural networks with multiple layers (hence “deep”) analyze data and solve complex problems. It’s like giving your AI a more intricate and powerful brain, so it can learn from vast amounts of information and discover patterns that would be impossible for humans to identify.

Imagine a child learning to identify different animals. Initially, they might just focus on basic features like size and color. But as they learn more, they start to recognize subtle differences in shapes, patterns, and behaviors. Deep learning works in a similar way—it allows AI to go beyond surface-level features and understand the nuances within data.

How does deep learning relate to other AI concepts?

Deep learning is the engine behind many advanced AI applications, including large language models (LLMs) and some types of generative AI (genAI).

 

What are supervised, unsupervised, and reinforcement learning?

Supervised, unsupervised, and reinforcement learning describe three fundamental types of machine learning. 

Recall from Part 1 of the AI Glossary for L&D where we explained that, in machine learning, “Systems are fed vast amounts of data and use it to recognize patterns, make predictions, and take actions using the data, all without human intervention.” Supervised, unsupervised, and reinforcement learning refer to the methods in which the data is fed and how the system is trained to recognize the data.

• In supervised learning, AI learns from labeled data and is given examples with known inputs and outputs. 
• In unsupervised learning, AI explores unlabeled data to discover hidden patterns and relationships. 
• In reinforcement learning, AI learns through trial and error, receiving rewards for correct actions and penalties for incorrect ones. 

Here’s a simple analogy to illustrate this concept. 

Imagine teaching a child to recognize fruits:

• Applying a supervised learning approach, you show them pictures of apples, bananas, and oranges, labeling each one.

• Applying an unsupervised learning approach, you give them a basket of different fruits and let them group them based on similarities.

• Applying a reinforcement learning approach, you give them a fruit and ask them to name it, rewarding them when they’re right.

How do supervised, unsupervised, and reinforcement learning relate to other AI concepts?

These learning paradigms are core concepts in machine learning. They are used to train various AI systems, including deep learning models.

 

What is transfer learning? 

Transfer learning is a technique where an AI model trained for one task is used as a starting point for a new, related task. This allows the AI to leverage its existing knowledge and learn the new task more efficiently. It’s like a chef who knows how to bake cakes using their skills to learn how to make pastries. 

How does transfer learning relate to other AI concepts?

Transfer learning is a technique often used in deep learning to accelerate the training process and improve performance on new tasks.

 

 

What is fuzzy logic? 

Fuzzy logic is a way of dealing with uncertainty and imprecise information. It allows AI to make decisions based on degrees of truth rather than strict true or false values. It’s like saying “somewhat likely” or “mostly true” instead of just “yes” or “no.”

Think of a thermostat. Instead of just turning on or off at a specific temperature, it can gradually adjust the heating or cooling based on a range of temperatures.

How does fuzzy logic relate to other AI concepts? 

Fuzzy logic is a different approach to AI that can be used in conjunction with other techniques, such as machine learning, to handle situations where data is imprecise or ambiguous.

How and where might fuzzy logic appear in L&D applications?

Fuzzy logic can be leveraged to:

• Design adaptive assessments: Tailor assessments to individual learner needs and performance levels, providing more nuanced feedback.
• Model complex decision-making scenarios: Simulate real-world situations where there are no clear-cut answers, helping learners develop critical thinking and problem-solving skills.
• Create personalized learning pathways: Adapt learning pathways based on learners’ preferences and interests, offering a more customized experience

What is explainable AI (XAI)?

Explainable AI aims to make AI’s decision-making processes more transparent and understandable to humans. It’s like having the AI show its work so we can understand how it arrived at a conclusion.

Imagine a doctor diagnosing a patient. They don’t just say “you’re sick”—they explain the symptoms, test results, and reasoning behind their diagnosis.

How does XAI relate to other AI concepts? 

XAI is crucial for building trust and accountability in AI systems, especially in sensitive areas like education, healthcare, finance, and legal domains. It can be applied to various AI techniques, including deep learning and machine learning.

How can L&D leverage or apply XAI? 

In L&D, XAI can help us understand why an AI model made a particular recommendation or assessment, fostering trust and transparency in AI-driven learning solutions.