☢ What are the 4 models of AI? ☢ In the field of AI, there are four main models that categorize different approaches and capabilities. These include: . ♞ Reactive Models: These models react solely to stimuli without any memory, unable to form past experiences. . ♞ Limited Memory Models: These models have a limited memory, allowing them to make predictions based on past experiences. . ♞ Theory of Mind Models: These models understand others' mental states, allowing them to predict and explain behavior. . ♞ Self-Aware Models: These models possess self-awareness, understanding their own mental states and emotions. . These models represent different levels of AI sophistication, with self-aware models being the most advanced and reactive models being the simplest. . 🔗 Read the complete guide here: https://www.linkedin.com/pulse/7-stages-artificial-intelligence-hassan-tariq-malik/ . . . #AIModels #ArtificialIntelligenceTypes #CategorizingAI #AIApproaches #ReactiveModels #LimitedMemoryModels #TheoryOfMindModels #SelfAwareModels #AIComplexity #AIProgression #UnderstandingAI #LevelsOfAI #AIAdvancements #AIInnovation #TechExplained #AIExperiences #CognitiveAI #AIUnderstanding #SelfAwarenessAI #AIDevelopment #FutureOfAI #AIExploration #TechInsights #AIInsights #AIInDepth #TechKnowledge #AIJourney

The progression of Artificial Intelligence (AI) through seven distinct stages. ☢ Symbolic AI: This early stage focused on representing knowledge using symbolic logic. It set the groundwork for AI by developing systems based on explicit rules and logical reasoning. ☢ Machine Learning: Computers began learning from data and making predictions. Machine learning revolutionized AI by enabling computers to learn patterns and make predictions without explicit programming. ☢ Data Mining: With the explosion of data due to the internet, data mining techniques emerged to extract insights from massive datasets, empowering data-driven decision-making. ☢ Knowledge Graphs: Knowledge graphs structured information, enhancing context and understanding within AI systems. They facilitated improved reasoning and inference by representing knowledge as interconnected nodes. ☢ Deep Learning: Neural networks with multiple layers, known as deep neural networks, enabled complex tasks like image recognition and natural language processing. This marked a significant advancement in AI capabilities. ☢ Generative AI: Large language models and generative models, like GPT-3, achieved human-like text generation. This stage introduced possibilities in automated content creation and raised ethical considerations. ☢ Explainable AI: As AI systems became complex and less transparent, the focus shifted to making AI models and systems more interpretable. Explainable AI aimed to address the "black box" issue, ensuring transparency, trust, and fairness. Each stage has contributed to AI's progress, creating a foundation for the development of the next generation of AI systems. https://www.linkedin.com/pulse/7-stages-artificial-intelligence-hassan-tariq-malik #AIStages #EvolutionOfAI #ArtificialIntelligenceJourney #UnderstandingAI #AIAdvancements #SymbolicAI #RuleBasedAI #MachineLearning #DataDrivenAI #DataMining #KnowledgeGraphs #DeepLearning #NeuralNetworks #GenerativeAI #HumanLikeTextGeneration #ExplainableAI #TransparencyInAI #AIProgression #TechInnovation #AIApplications #EthicalAI #ResponsibleAI #BuildingTrustInAI #NextGenAI #AIFoundation #TechInsights