### How Human Brains are Teaching AI New Skills:
**ASU Researcher Ying-Cheng Lai Draws Inspiration from Human Thought Processes to Enhance Machine Learning**
Artificial intelligence (AI) is advancing at an impressive rate, yet it hasn't surpassed human intelligence. Our brains' remarkable adaptability and creativity have allowed us to tackle challenges and complete complex tasks for millennia, whereas AI is still in its infancy.
Ying-Cheng Lai, a Regents Professor at Arizona State University (ASU), specializes in working with intricate data and understanding chaos to push human goals forward. His research aims to enhance computing systems' ability to handle dynamic data—information that evolves over time.
"Memorizing complex patterns is second nature for humans. We recognize faces and countless other things with ease. However, asking a computer to do the same is incredibly challenging," explains Lai, an electrical engineering faculty member in the School of Electrical, Computer, and Energy Engineering at ASU’s Ira A. Fulton Schools of Engineering. "Significant progress has been made over the past 20 to 30 years, but it remains tricky, especially with dynamic patterns."
Lai draws inspiration from human memory to create a dynamic system of machine learning memory using reservoir computing. This system can intake data, recognize and store patterns, and project those patterns over time.
This innovative approach can help AI tackle problems previously deemed unsolvable with traditional static methods. Dynamic machine learning memory could enable us to better utilize past information to predict future events, such as electric grid failures or critical climate change tipping points.
Collaborating with his former doctoral student Ling-Wei Kong and Gene Brewer, a psychology professor at ASU, Lai tested new, sophisticated machine learning strategies using biological memory techniques effective for humans.
Their findings were published in the research journal *Nature Communications*.
Lai believes that endowing AI with human-like capabilities—the "special talents we have"—will enable computing systems to harness the best of both human and artificial intelligence.
Most machine learning-based associative memories are designed for static patterns, like pictures of cats. However, this method falls short when dealing with dynamic patterns that evolve over time, much like a photo cannot capture the entirety of a cat’s life.
Lai aims to overcome the limitations of static data in dynamic scenarios, such as predicting the survival or extinction of species. A fundamental requirement for this is that the machine learning architecture must be capable of "self-evolution," or automatically improving based on what it learns.
Inspired by how the human brain handles these tasks, Lai and Brewer examined memory storage and recall strategies.
Brewer adds that psychological principles based on human thought processes and memory distribution in the brain can greatly inform and enhance machine learning algorithms.
By leveraging these interdisciplinary insights, Lai and his team are paving the way for AI systems that are more adaptive, intuitive, and capable of tackling dynamic challenges.
## Drawing Inspiration from the Human Brain:
**Unlocking Dynamic Memory: Insights from Human Cognition**:
Human cognition research has traditionally focused on static information, like remembering a specific word. However, this approach overlooks the dynamic nature of our world and experiences, which are much richer and more complex. As Gene Brewer explains, "These experiences are encoded into memory and can be recalled similarly to how one remembers a scene from a classic movie."
Recent research efforts aim to better understand how people remember dynamic situations. Scientists have identified key stages in memory processing: encoding, maintenance, and retrieval. In the encoding phase, the brain establishes a memory, which is then maintained even when it's not actively thought about. When retrieval is needed, specific cues often help recreate the original experience from memory.
Cues are typically associative—based on connections between different pieces of information. For example, recognizing a face might help recall a name.
**Mimicking Biological Memory with Artificial Neural Networks**:
Artificial neural networks, such as reservoir computing, are designed to emulate biological memory and associative cues to handle dynamic data. Lai and his team have tested their reservoir computing system with hundreds of complex, dynamic patterns. During training, the system organizes these patterns into different "basins" within a larger "reservoir" of memory.
This process is akin to organizing ingredients in a kitchen: after a grocery trip, you sort and store ingredients (patterns) in various sections (basins) of your kitchen (reservoir). The system then continuously produces time-varying information, similar to preparing various recipes from the ingredients.
Despite advancements, the inner workings of machine learning systems can remain mysterious.
**Testing and Advancing Dynamic Machine Memory**:
To evaluate how well the system stores and recalls patterns, the team provided it with various hints to retrieve information. They tested different strategies, such as using index cues or associative memory techniques, to determine which methods best recalled specific patterns. The research focused on optimizing trade-offs between speed and accuracy for effective dynamic pattern recall.
Lai’s current research shows that the reservoir computing system can manage limited dynamic data, such as the variables influencing the chaotic movement of a double pendulum. Moving forward, Lai aims to delve deeper into the basin structure within the reservoir computing system to better understand how memories are stored.
Further exploration could lead to more advanced reservoir computing systems, enhancing AI’s capability to tackle dynamic societal challenges and fostering more imaginative solutions for scientists and engineers.
**Unlocking Dynamic Memory: Insights from Human Cognition**:
Human cognition research has traditionally focused on static information, like remembering a specific word. However, this approach overlooks the dynamic nature of our world and experiences, which are much richer and more complex. As Gene Brewer explains, "These experiences are encoded into memory and can be recalled similarly to how one remembers a scene from a classic movie."
Recent research efforts aim to better understand how people remember dynamic situations. Scientists have identified key stages in memory processing: encoding, maintenance, and retrieval. In the encoding phase, the brain establishes a memory, which is then maintained even when it's not actively thought about. When retrieval is needed, specific cues often help recreate the original experience from memory.
Cues are typically associative—based on connections between different pieces of information. For example, recognizing a face might help recall a name.
**Mimicking Biological Memory with Artificial Neural Networks**:
Artificial neural networks, such as reservoir computing, are designed to emulate biological memory and associative cues to handle dynamic data. Lai and his team have tested their reservoir computing system with hundreds of complex, dynamic patterns. During training, the system organizes these patterns into different "basins" within a larger "reservoir" of memory.
This process is akin to organizing ingredients in a kitchen: after a grocery trip, you sort and store ingredients (patterns) in various sections (basins) of your kitchen (reservoir). The system then continuously produces time-varying information, similar to preparing various recipes from the ingredients.
Despite advancements, the inner workings of machine learning systems can remain mysterious.
**Testing and Advancing Dynamic Machine Memory**:
To evaluate how well the system stores and recalls patterns, the team provided it with various hints to retrieve information. They tested different strategies, such as using index cues or associative memory techniques, to determine which methods best recalled specific patterns. The research focused on optimizing trade-offs between speed and accuracy for effective dynamic pattern recall.
Lai’s current research shows that the reservoir computing system can manage limited dynamic data, such as the variables influencing the chaotic movement of a double pendulum. Moving forward, Lai aims to delve deeper into the basin structure within the reservoir computing system to better understand how memories are stored.
Further exploration could lead to more advanced reservoir computing systems, enhancing AI’s capability to tackle dynamic societal challenges and fostering more imaginative solutions for scientists and engineers.
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