How Artificial intelligence, Machine Learning, and Deep Learning differ from each other?
Accountability refers to the process as well as norms that make decision makers answerable to ones for whom decisions are taken i.e., the decision maker and the beneficiary. The recent emphasis on revolutionised democracy seeking increased accountability from the government has brought into focus itRead more
- Accountability refers to the process as well as norms that make decision makers answerable to ones for whom decisions are taken i.e., the decision maker and the beneficiary.
- The recent emphasis on revolutionised democracy seeking increased accountability from the government has brought into focus its need and importance in Governance and government functioning.
- Ethical Governance
- In a world that feels increasingly complex and interconnected, ethical governance emerges as a critical compass.Ethical governance guides organisations and governments through the challenges of decision-making while ensuring fairness, transparency, and accountability. but first we need to understand What is Governance?
Governance refers to the frameworks, processes, and systems by which orsanisations, institutions, and governments are directed, controlled, and held accountable.
- and as for the Ethical Governance:-
- Ethical governance refers to the system of rules, practices, and processes by which businesses, organisations, and governments conduct themselves in a manner that is honest, responsible, and respectful of all stakeholders involved.
- It’s about making decisions that not only aim for success or profitability but also consider the welfare of employees, communities, the environment, and society at large.
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are interconnected fields that differ in their scope, complexity, and application: *Artificial Intelligence (AI)* 1. Scope: Developing intelligent systems that mimic human behavior. 2. Goal: Automate tasks, reason, and solveRead more
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are interconnected fields that differ in their scope, complexity, and application:
*Artificial Intelligence (AI)*
1. Scope: Developing intelligent systems that mimic human behavior.
2. Goal: Automate tasks, reason, and solve problems.
3. Techniques: Rule-based systems, decision trees, optimization algorithms.
4. Applications: Expert systems, natural language processing, robotics.
*Machine Learning (ML)*
1. Scope: Subset of AI, focusing on learning from data.
2. Goal: Enable systems to improve performance on tasks without explicit programming.
3. Techniques: Supervised, unsupervised, and reinforcement learning.
4. Applications: Image classification, speech recognition, recommendation systems.
*Deep Learning (DL)*
1. Scope: Subset of ML, focusing on neural networks with multiple layers.
2. Goal: Automatically learn complex patterns in data.
3. Techniques: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs).
4. Applications: Image recognition, natural language processing, autonomous vehicles.
*Key differences:*
1. Complexity: AI > ML > DL (in terms of scope and complexity).
2. Data dependency: ML and DL rely heavily on data, whereas AI can operate with or without data.
3. Learning style: ML learns from data, while DL learns hierarchical representations.
4. Accuracy: DL typically outperforms ML and AI in tasks requiring complex pattern recognition.
*Relationships:*
1. AI encompasses ML and DL.
2. ML builds upon AI foundations.
3. DL is a specialized form of ML.
*Real-world examples:*
1. AI: Chatbots, expert systems.
See less2. ML: Image classification, sentiment analysis.
3. DL: Self-driving cars, language translation.