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Neuro plasticity

Answer

Neuroplasticity, also known as neural plasticity or brain plasticity, refers to the brain's ability to reorganize itself by forming new neural connections throughout life. This adaptability allows the neurons (nerve cells) in the brain to compensate for injury and disease and to adjust their activities in response to new situations or changes in their environment
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Neuroplasticity can be observed at multiple scales, from microscopic changes in individual neurons to larger-scale changes such as cortical remapping in response to injury. The brain engages in plasticity in two key ways:
  1. Structural Plasticity: This involves changes in the physical structure of the brain, such as the growth of new neurons (neurogenesis) or the formation of new connections between neurons (synaptic plasticity).
  2. Functional Plasticity: This refers to the brain's ability to move functions from a damaged area of the brain to undamaged areas.
The concept of neuroplasticity has replaced the formerly held position that the brain is a physiologically static organ, and it is now known that the brain remains plastic and malleable even into adulthood
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Neuroplasticity carries significant implications for medical practices, such as rehabilitation from brain injuries. Therapies that harness neuroplasticity, such as physical therapy, cognitive therapy, and brain training programs, aim to rewire the brain and improve function after injury or neurological diseases
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The term "plasticity" was first used in relation to the nervous system by William James in 1890, but the concept was not widely accepted until the second half of the 20th century. The modern understanding of neuroplasticity has been greatly influenced by research in the latter half of the 20th century, which has shown that many aspects of the brain can be altered or are "plastic" even in the adult stage
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Neuroplasticity is a complex process that is still being elucidated, but it is known to occur in at least three phases following an injury like a stroke or traumatic brain injury (TBI). These phases include the initial damage and cell death, followed by a period of reorganization and finally, a phase of stabilization of new connections and pathways
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Neuroplasticity: How Experience Changes the Brain
What Is Neuroplasticity? How It Works
The Principles of Neuroplasticity - Hohman Rehab and Sports Therapy
Neuroplasticity: How Your Brain Can Change and Adapt
How Neuroplasticity Changes The Brain - Driven
Neuroplasticity | UC Davis Biotechnology Program
Neuroplasticity Explained: How New Experiences Change Your Brain ...
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GOFAI

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GOFAI, which stands for "Good Old-Fashioned Artificial Intelligence," refers to the classical symbolic approach to artificial intelligence. It is characterized by a focus on symbolic reasoning and logic, using tools such as logic programming, production rules, semantic nets, and frames. The term was coined by philosopher John Haugeland in 1985. However, after around 60 years, the GOFAI paradigm has been considered to have failed to live up to its promises, leading to a shift towards other approaches such as connectionism, evolutionary programming, and situated robotics
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The GOFAI approach was based on the "symbol system hypothesis," which posited that it is possible to construct a universal symbol system that is intelligent. This approach was criticized for its inability to model human intelligence effectively and for its challenges in handling perceptual problems. The failure of GOFAI led to the emergence of other paradigms in AI research, such as connectionist AI and behavior-based AI
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In summary, GOFAI, or symbolic AI, was a classical approach to artificial intelligence that focused on symbolic reasoning and logic. It was criticized for its limitations in modeling human intelligence and handling perceptual problems, leading to its decline in favor of other AI paradigms.
alpha beta pruning - Historical weakness of GOFAI in relation to ...
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GOFAI for Game Dev – scidata
GOFAI
GOFAI (Good Old Fashioned Artificial Intelligence AI) Shirt
PDF] GOFAI SUM : A Symbolic Summarizer for DUC | Semantic Scholar
Microsoft's new AI for Beginners course
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MAS ai

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MAS AI can refer to different concepts. It could stand for "Music Analytics Service," as seen on the MAS website
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Additionally, it is used in the context of "Model for ASsessing the value of Artificial Intelligence in medical imaging (MAS-AI)," which is an HTA-based framework to support the introduction of novel AI technologies into healthcare
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Furthermore, MAS can also stand for "Multi-Agent System," which is a computerized system composed of multiple interacting intelligent agents, commonly used in various applications such as robotics, gaming, and industrial systems
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Multi agent systems

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A multi-agent system (MAS) is a computerized system composed of multiple interacting intelligent agents. These agents can be software entities, robots, humans, or human teams. MAS allows agents to interact with each other to achieve common goals, and it is used in various fields, including artificial intelligence, economics, and sociology. MAS research addresses technical problems such as designing algorithms for agents to achieve specified goals, communicating and propagating information among agents, and the emergence of norms, conventions, and roles within the system. MAS offers benefits such as increased efficiency, improved accuracy, increased flexibility, reduced costs, and increased scalability. However, it also presents challenges, including the complexity of designing and managing the system, the need for agents to cooperate and coordinate, and the ability of agents to learn and adapt to changing conditions. Despite these challenges, MAS is considered a powerful approach for solving complex problems
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Multi-agent system - Wikipedia
Characteristics of Multi-agent System Environment. | Download ...
Processes | Free Full-Text | Multi-Agent Systems and Complex ...
2: General structure of a multi-agent system [42]. | Download ...
Multi Agent Systems - an overview | ScienceDirect Topics
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what are some real-world applications of multi-agent systems

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Some real-world applications of multi-agent systems (MAS) include:
  1. Online Trading: MAS is used in online trading for tasks such as automated negotiation and decision-making.
  2. Disaster Response: MAS is applied in disaster response scenarios for tasks like search and rescue operations after large-scale disasters.
  3. Target Surveillance: MAS is utilized for target surveillance, involving the coordination of multiple agents to monitor and track targets.
  4. Social Structure Modeling: MAS is employed in modeling social structures, which can be beneficial in fields such as sociology and anthropology.
  5. Transportation and Logistics: MAS finds applications in optimizing transportation and logistics operations, including route planning and resource allocation.
  6. Power Systems and Smart Grids: MAS is used in power systems and smart grids for tasks such as load balancing, high scalability, and self-healing networks.
  7. Vehicular Traffic Management: MAS can be used to model vehicular traffic with controlled autonomous vehicles, involving crowd dynamics and coordinated traffic flow.
  8. Manufacturing: MAS is applied in manufacturing for tasks such as production scheduling, resource allocation, and coordination of autonomous systems.
  9. Healthcare: MAS is used in healthcare for applications like medical imaging, disease modeling, and healthcare resource management.
  10. Robotics: MAS finds extensive use in robotics for tasks such as multi-robot coordination, distributed perception, and collaborative decision-making
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