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MIT : FUTURE OF AI - Coggle Diagram
MIT : FUTURE OF AI
KEY CONCEPTS
Machine Learning: Machine learning is a subset of AI that enables systems to learn from data and improve over time without being explicitly programmed.
Natural Language Processing: Natural language processing is about developing computational models for interpreting and generating human language.
Embodied Agents: Embodied agents are AI systems that can interact with the physical world and communicate with humans in natural language.
Synthetic Data: Synthetic data is data produced by AI, which can be used to train AI models and improve their performance.
Generative AI: Generative AI is a type of AI that can generate new data, such as images, videos, and text, based on patterns learned from existing data.
Deep Learning: Deep learning is a type of machine learning that uses neural networks to analyze complex data and make predictions.
KEY CONCEPTS
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Cognitive Tasks: Activities requiring intellectual skills like thinking, problem-solving, and decision-making. AI is expected to augment human capabilities in these areas.
Physical Tasks: Activities requiring physical manipulation of the world. Robotics combined with AI will play a bigger role.
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Unsupervised Learning: Machine learning that can learn from small amounts of data or through interaction with the environment.
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Human-Machine Interaction: How humans and AI systems interact with each other. Needs to become more intuitive for widespread adoption.
Explainable AI: AI systems that can explain their reasoning and decision-making process. Crucial for building trust in AI.
Self-aware machines (mentioned cautiously): A future possibility where machines can understand themselves and their thought processes.
KEY CONCEPTS
Crafted Solutions, Not General Intelligence:
Current AI systems like AlphaGo are highly specialized for specific tasks, lacking the broad adaptability of human cognition.
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Humans exhibit more flexible, generalized intelligence that allows us to adapt across diverse domains.
While human-level artificial general intelligence (AGI) is likely decades away, not centuries, the excitement and attention around AI is attracting substantial talent, and breakthroughs may come from unexpected sources.
AI excels at specific tasks but struggles with things that come naturally to humans, like understanding physics or psychology.
strong AI (or artificial general intelligence) and weak AI (or narrow AI). Strong AI can learn any task a human can, while weak AI is designed for a single purpose.
HEALTHCARE
Diagnostics: AI improves diagnostics by facilitating early disease detection and more accurate diagnoses.
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EDUCATION
Personalized Learning: AI will transform education by providing tailored educational content and training based on individual needs.
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MANUFACTURING
Quality Control: AI is used for predictive maintenance, supply chain optimization, and robotics to improve efficiency and reduce costs.
Autonomous Vehicles: AI is used in manufacturing to train computers to think and evolve like humans, enhancing safety and efficiency.
CUSTOMER SERVICE
Virtual Assistants: AI-powered virtual assistants will streamline customer support and improve customer experiences.
Sentiment Analysis: AI will help businesses understand customer emotions and tailor responses accordingly.
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Retail / Inventory Management: AI is used for inventory management and targeted marketing to enhance customer experiences.
SECURITY : Facial Recognition: AI is used for facial recognition, surveillance, and threat detection to enhance public safety and combat cybercrime.
SUPERINTELLIGENCES AI
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The idea of machines treating humans as pets is not new and has been discussed since the early days of AI.
AI VS SUPERINTELLIGENCE
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Current AI achievements are seen as specialized problem-solving, not general intelligence.
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NEAR TERM OF AI
Machine Learning: Machine learning, driven by statistical methods, will continue to be a major force in AI applications over the next few years.
Value Creation: Rather than merely replacing humans, AI tools will focus on enabling new possibilities that weren't feasible before, often through a combination of computer programs and human expertise.
LONG TERM OF AI
Learning from Small Data: Future machine learning systems will learn from smaller datasets or instantaneous interactions, reducing the reliance on extensive manual labeling.
Beyond Pattern Recognition: Machines need to move beyond recognizing patterns and understand the semantics of entities (e.g., common-sense knowledge).
Intuitive Human-Machine Interactions: Enhancing the intuitiveness of interactions between humans and machines.
Self-Aware Machines: Developing AI systems that can explain their decision-making processes, fostering transparency and trust.
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