MIT researchers have found evidence suggesting that the brain may develop an intuitive understanding of the physical world through a process similar to self-supervised learning. Using models known as neural networks, they trained them using self-supervised learning techniques and found that the resulting models generated activity patterns similar to those seen in the brains of animals performing the same tasks. The researchers believe this indicates that the mammalian brain may use this strategy to learn representations of the physical world. These findings could also help improve AI models and understanding the brain.
The Brain’s Intuitive Understanding and AI: New Findings
Scientists believe that the brain develops an intuitive understanding of the physical world through a process called “self-supervised learning.” This type of machine learning allows computational models to learn about visual scenes based solely on their similarities and differences, without the need for labels or other information.
A pair of studies from researchers at MIT’s K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center provides new evidence supporting this hypothesis. The researchers trained neural network models using self-supervised learning and found that the activity patterns generated by the models closely resemble those seen in the brains of animals performing the same tasks.
Practical Implications for AI Solutions
These findings have practical implications for AI solutions that can assist in building better robots and understanding the brain. With AI, middle managers can:
- Identify Automation Opportunities: AI can help locate key customer interaction points that can benefit from automation.
- Define KPIs: It’s important to ensure that AI endeavors have measurable impacts on business outcomes.
- Select an AI Solution: Choose AI tools that align with specific needs and provide customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
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