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AI Vs Human Learning
Context:
The rise of artificial intelligence (AI) has sparked incredible advancements in technology, but it also sheds light on how our own brains work, providing a fascinating intersection of machine learning and neuroscience.
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While AI has made great strides in tasks like analysing large datasets, playing complex games like chess, and predicting patterns, it still has a long way to go in mimicking the way humans learn and understand the world.
Key Differences Between Human and Machine Learning
Origins of Learning
- AI models are inspired by animal brains, but human brains have evolved over millennia, adapting to both immediate biological needs and dynamic environments. This has given humans the ability to interact with and adapt to the world in highly complex ways.
- Humans and animals do not just acquire static knowledge but engage in ongoing, dynamic learning through feedback loops. This allows them to adapt to environmental changes and respond to unpredictability.
Human Learning: More Efficient, Less Data-Dependent
- Humans need less data to achieve the same level of proficiency in tasks. For instance, a human can master chess after playing only tens of thousands of games, while the AI AlphaZero requires 40 million games to reach grandmaster-level performance.
- Humans excel in transfer learning, which means they can apply knowledge from one context to another with ease, a challenge for AI systems that often struggle to generalise across different domains.
Curiosity and Sensory Learning
- Humans are naturally curious, driven by the need to understand what they don’t know. Children, in particular, explore the world through multi-sensory experiences, which are richer and more context-dependent than the data fed to AI systems.
- This leads to richer learning and deeper generalisation that allows humans to understand not just objects but also motives—like a child running towards danger or knowing when they are hungry.
Motor-Skill Learning and Energy Efficiency
- Humans are incredibly efficient at learning motor skills and making quick, coordinated movements. In contrast, neural networks used by machines struggle with tasks involving motion and movement due to the complex parameters that need to be optimised continuously.
- The human brain is energy-efficient, making decisions rapidly with lower power consumption compared to machines, which can process large datasets very quickly but consume more energy.
Where AI Excels?
- Reliability: Unlike humans, AI systems don’t suffer from fatigue, emotional decision-making, or distractions. They can perform tasks repeatedly with high precision and without deviation.
- Optimisation: AI models can explore countless solutions to complex problems and often discover strategies that human experts would not think of.
- For example, in games like chess or Go, AI systems have been known to develop moves that surprise even the best human players.
- Processing Large Datasets: AI is exceptionally good at analysing huge amounts of data, something that would be impractical for humans to do manually. This is why AI is already embedded in systems like smartphones, helping with everything from speech recognition to personalised recommendations.
The Need for Different Models: Symbolic vs. Connectionist
- The article touches upon two key approaches in AI:
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- Symbolic AI (used in early models) represented information through logical rules and symbolic reasoning (if-then statements).
- Connectionist AI (modern neural networks) uses pattern recognition and connections between nodes, drawing inspiration from how the brain processes information.
- There is ongoing debate about which model (symbolic or connectionist) better represents human learning. Despite AI’s success with connectionist models, it still operates quite differently from how the human brain works, which leads to questions about the true nature of human cognition.
Impact on Neuroscience
- The success of artificial neural networks has deepened our understanding of how the brain can store and access memories. Instead of static storage, memories may be dynamic, activated and adjusted over time, leading to better memory and learning processes.
- This connection between AI and neuroscience may help bridge the gap in understanding the fundamental principles of cognitive behaviour, such as how humans learn and adapt.
