Pangeon

Does AI Really Understand—or Just Imitate? Imagine a small shed in rural Japan where people submit written questions and receive written answers. They believe they’re interacting with an intelligent system. But inside the shed? It’s just an American who doesn’t speak Japanese, following a massive book of instructions—matching questions on the left to pre-written answers on the right. The people outside feel understood. But were they? This is how large language models operate. They don’t comprehend meaning—they predict responses based on statistical patterns. If a fluent Japanese speaker replaced the man in the shed, the answers might remain the same, but the internal process would be entirely different. The difference? True understanding requires connection, context, and meaning. AI connects words probabilistically, while humans connect ideas conceptually. The real challenge isn’t just improving AI’s answers—it’s figuring out if AI can ever think the way we do. Want to watch the rest of the TED Talk? Go to the link in my website page in my bio! #Pangeon #ThePangeon #AI #AGI #ChatGPT #GPT #OpenAI #Sora #Tech #FutureTech #Startup #ArtificialIntelligence #MachineLearning #DeepLearning #AIResearch #NeuralNetworks #UnderstandingAI #TechPhilosophy #CognitiveScience #FutureOfAI #AIExplained

Marvin Minsky, one of AI’s early pioneers, believed that all intelligence could be built on symbolic reasoning—defining everything as discrete symbols and relationships. He even predicted that within 20 years, we’d have AI as powerful as the human mind. That prediction, of course, never came true. Here’s why: human perception isn’t just symbolic. Take a simple moment—looking outside at a field of grass. You didn’t count or register every single blade of grass as an individual symbol. Your brain estimated, processed patterns, and created an understanding without needing precise details. This ability to generalize, approximate, and adapt is something purely symbolic AI struggled to replicate. This is why modern AI has evolved beyond symbolic reasoning alone. The real challenge lies in blending structured logic with pattern recognition—a problem we’re still working to solve today. #Pangeon #ThePangeon #AI #AGI #ChatGPT #GPT #OpenAI #Sora #Tech #FutureTech #Startup #ArtificialIntelligence #MachineLearning #DeepLearning #AIResearch #NeuralNetworks #CognitiveScience #SymbolicAI #FutureOfAI #TechInnovation #AIHistory

We optimize AI models by minimizing a value called loss—reducing errors to guide the system toward better performance. But does minimizing "loss" mean an AI feels relief? Does increasing it mean it suffers? No. Just because we name a variable "pain" doesn’t mean the system experiences it. An AI adjusting its loss function is no different than writing a number on a piece of paper—it’s a process, not a feeling. True suffering requires consciousness, awareness, and an internal experience—things AI, as we understand it today, simply doesn’t have. This distinction is critical as we advance AI. We can make it better at tasks, but we shouldn’t mistake optimization for experience. #Pangeon #ThePangeon #AI #AGI #ChatGPT #GPT #OpenAI #Sora #Tech #FutureTech #Startup #AIConsciousness #AIEthics #MachineLearning #DeepLearning #NeuralNetworks #FutureOfAI #AIPhilosophy #AIResearch #ArtificialIntelligence #TechInnovation

Should You Be Afraid of AI? A lot of people are worried about AI—how powerful it’s becoming, how fast it’s evolving, and what it could mean for the future. But fear often comes from misunderstanding. AI isn’t a mind, a person, or a force beyond control—it’s a system trained on probabilities, patterns, and data. The real conversation isn’t about whether AI is dangerous but about how we use it. Are we implementing safeguards? Are we ensuring transparency? Are we using AI to enhance human potential rather than replace it? These are the real questions we should be asking. Want to watch the rest of my TED Talk? Go to the link in my bio for more #Pangeon #ThePangeon #AI #AGI #ChatGPT #GPT #OpenAI #Sora #Tech #FutureTech #Startup #AIEthics #FutureOfAI #MachineLearning #AIExplained #DeepLearning #ResponsibleAI #TechPolicy #AIInnovation #AIResearch #Automation

How AI "Pays Attention"—And Why It’s Just a Fuzzy Guess Attention in AI is often simplified as a number between zero and one—zero means something is ignored, one means it's fully focused on. But instead of explicitly teaching AI the structured relationships we see in language, we let it guess. What emerges is a matrix—a web of connections between words, where some are weighted heavily (dark lines, closer to one) and others are weak (light lines, closer to zero). The result? A fuzzy version of a tree—not a structured hierarchy, but a probabilistic map of relationships. This is how language models interpret meaning—not through true understanding, but by weighing connections statistically. It’s a powerful technique, but one that highlights how AI sees the world very differently than we do. Want to watch the rest of the TED Talk? Head to the think in my profile to see more #Pangeon #ThePangeon #AI #AGI #ChatGPT #GPT #OpenAI #Sora #Tech #FutureTech #Startup #MachineLearning #AIResearch #NeuralNetworks #DeepLearning #NaturalLanguageProcessing #AIExplained #TechInnovation #UnderstandingAI #AttentionMechanisms #FutureOfAI

When AI Fills in the Gaps: The Problem of Confident Misinformation I asked GPT about the early life and great works of 17th-century philosopher McGruff the Crime Dog. Without hesitation, it produced a detailed, confident response—his early influences, his philosophy, and even how his parents supported him. The problem? McGruff the Crime Dog isn’t real, let alone a 17th-century philosopher. This highlights a fundamental issue with AI: it doesn’t “know” truth. It predicts plausible answers based on patterns, even when no factual basis exists. The confidence of AI-generated misinformation can be misleading, especially when applied to real-world decision-making. How do we ensure AI connects truth rather than just patterns? The future of AI isn’t just about making it smarter—it’s about making it accountable. Want to hear more of my TED Talk? Head to the link in my bio to listen to the rest! #Pangeon #ThePangeon #AI #AGI #ChatGPT #GPT #OpenAI #Sora #Tech #FutureTech #Startup #AIEthics #MachineLearning #Misinformation #DeepLearning #TechAccountability #TruthInAI #DataScience #AIResearch #ResponsibleAI #AIExplained

Imagine walking through rural Japan and stumbling upon a small shed where people submit written questions and receive written answers. To the outside world, it seems like magic—until you step inside and find an American who doesn’t speak a word of Japanese. He’s simply following a massive book, matching questions in one column to pre-written answers in the other. The people outside feel understood. But are they? This analogy mirrors how AI models work today. They don’t understand language the way humans do—they predict responses based on vast amounts of data. A fluent Japanese speaker in that shed would produce the same answers, but with true comprehension. The AI, like the man with the book, connects patterns but lacks deeper understanding. The real question is: How do we distinguish between pattern recognition and true comprehension? If we mapped AI-generated answers like a conspiracy board, would it reveal meaningful connections—or just statistical guesses? Want to listen to the rest of this TEDTalk? Head on over to Youtube to listen to the rest! Linked in my bio. #Pangeon #ThePangeon #AI #AGI #ChatGPT #GPT #OpenAI #Sora #Tech #FutureTech #Startup #ArtificialIntelligence #MachineLearning #DeepLearning #PhilosophyOfAI #UnderstandingVsPrediction #Cognition #AIResearch #NeuralNetworks #TechEthics #FutureOfAI

Reverse engineering isn’t just about breaking things down—it’s about understanding the process of learning itself, whether in machines or in nature. When you teach a computer to "talk, listen, and walk," you start from scratch, venturing into a dark forest of unknowns much like the early stages of human development. This journey reveals how many untold lessons Mother Nature has perfected over billions of years, culminating in the intricate design of the human brain. Instead of fearing the rise of an artificial brain, we should stand in awe of our own natural intelligence—a marvel of evolution that guides us every day. By bridging these principles, we gain insights into both technological innovation and the deep-rooted wisdom of natural evolution. #Pangeon #ThePangeon #AI #AGI #ChatGPT #GPT #OpenAI #Sora #Tech #FutureTech #Startup #ReverseEngineering #Innovation #DigitalTransformation #NaturalEvolution #ComputationalThinking #TechPhilosophy #LearningJourney #ScienceAndTech #Inspiration #FutureOfTech

Even though a large portion of people speak English, no two people interpret words exactly the same way. A word might have multiple definitions in the dictionary—what you see as definition 17 could be perceived as definition 14 by someone else. We rarely ask, "What did you mean by that?" because we assume shared understanding. Yet, this subtle ambiguity reveals the true complexity of language—a reminder that every word carries its own unique, personal nuance. #Pangeon #ThePangeon #AI #AGI #ChatGPT #GPT #OpenAI #Sora #Tech #FutureTech #Startup #Language #Communication #Linguistics #SemanticAnalysis #WordChoice #LanguageNuance #CulturalDiversity #MeaningfulWords #CriticalThinking #EnglishLanguage

Government agencies are sitting on mountains of unstructured data—some of it not even digitized. Extracting useful insights requires more than just AI; it requires precision, traceability, and accountability. At scale, a system that structures data while linking back to original sources could revolutionize research, environmental impact studies, and policymaking. The key? Making sure every insight is auditable, verifiable, and adaptable over time. #AI #DataScience #MachineLearning #DigitalTransformation #GovTech #BigData #UnstructuredData #Pangeon #ThePangeon