Glossary of AI Related Terms

1. Artificial Intelligence (AI):

   • Definition: AI refers to systems that exhibit intelligent behavior by analyzing their environment and performing tasks autonomously to achieve specific goals1.

   • Summary: AI enables computers to make decisions like humans.

2. Machine Learning (ML):

   • Definition: ML is a subset of AI that focuses on creating algorithms that allow computers to learn from data and improve their performance over time.

   • Summary: ML enables systems to learn from experience without being explicitly programmed.

3. Deep Learning (DL):

   • Definition: DL is a specialized form of ML that uses neural networks with multiple layers to model complex patterns in data.

   • Summary: DL powers applications like image recognition and natural language processing.

4. Natural Language Processing (NLP):

   • Definition: NLP involves enabling computers to understand, interpret, and generate human language.

   • Summary: NLP makes it possible for machines to communicate with us using natural language.

5. Neural Networks:

   • Definition: Neural networks are computational models inspired by the human brain, consisting of interconnected nodes (neurons).

   • Summary: Neural networks learn patterns and relationships from data.

6. Large Language Model (LLM) is a deep learning algorithm capable of performing various natural language processing (NLP) tasks. LLMs use transformer models and are trained on massive datasets, which is why they’re called “large.” These models can recognize, translate, predict, or generate text and other content1.

   • Summary: LLM's have revolutionized applications in chatbots, virtual assistants, content generation, research assistance, and language translation.

7. Supervised Learning:

   • Definition: In supervised learning, models learn from labeled data, where input-output pairs are provided during training.

   • Summary: Supervised learning predicts outcomes based on known examples.

8. Unsupervised Learning:

   • Definition: Unsupervised learning involves finding patterns in unlabeled data without explicit guidance.

   • Summary: Unsupervised learning discovers hidden structures in data.

9. Reinforcement Learning (RL):

   • Definition: RL trains agents to make sequential decisions by interacting with an environment and receiving rewards or penalties.

   • Summary: RL optimizes actions to maximize cumulative rewards.

10. AI Ethics:

    • Definition: AI ethics addresses the responsible and fair use of AI technologies, considering societal impact and bias.

    • Summary: AI ethics ensures ethical deployment of AI systems.

11. Bias in AI:

    • Definition: Bias refers to unfair or discriminatory outcomes in AI models due to biased training data or algorithms.

    • Summary: Addressing bias is crucial for equitable AI.

Common Questions:

1. Is Machine Learning the same as AI?

   • No, ML is a subset of AI. AI encompasses a broader range of techniques beyond ML.

2. Is there another type of AI besides LLM (Large LAnguage Models)?

   • Yes, besides LLM, there are various other AI paradigms, including neural networks, rule-based systems, and evolutionary algorithms.

3. Is an AI agent the same as AI automation?

   • No, an AI agent refers to an autonomous decision-making entity, while AI automation involves automating tasks using AI.

4. What is an AI swarm?

   • An AI swarm refers to a collective of simple AI agents working together to solve complex problems, inspired by natural swarming behavior.

Remember that AI has diverse applications beyond business, including healthcare, transportation, and scientific research. Understanding these terms will empower us to navigate the exciting world of AI

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