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New Technologies, TEAM 7 W11-1, Jorge Estrada 204169, Joel López 228926 -…

- - - - Post-quantum cryptography (PQC) involves developing cryptographic algorithms that are secure against the potential threats posed by quantum computers, which could break many of the encryption schemes currently in use.
    - - As quantum computing technology advances, it poses a significant risk to data security. PQC aims to protect sensitive information, ensuring confidentiality and integrity in the quantum era.
  - - - Financial institutions, healthcare providers, and government agencies are exploring PQC to safeguard sensitive data
        
        Secure Communication Channels: Ensuring confidential communications remain private even in the presence of quantum computers.
        
        Quantum-Resistant Encryption for Banking: Protecting financial transactions and customer data from future quantum threats.
  - - - Enhanced Security: Provides protection against both classical and quantum computational attacks.
      - Future-Proofing: Prepares organizations for the advent of quantum computing technologies.
    - - Performance Overhead: Some PQC algorithms may require more computational resources, potentially impacting system performance.
      - Standardization: Ongoing efforts are needed to establish universally accepted PQC standards.
- - - - Robots designed to perform multiple tasks in various environments, adapting to different functions without significant physical modifications.
    - - They enhance efficiency and flexibility in sectors like manufacturing and healthcare, reducing costs and increasing productivity.
  - - - Manufacturing, healthcare, and domestic services
        
        Robots on assembly lines capable of switching between different products and assistance robots in hospitals performing various support tasks.
  - - - One robot can handle multiple tasks, reducing the need for different specialized machines.
      - Decreases equipment and labor costs in the long term.
      - Robots can work continuously, maintaining consistent quality and performance.
    - - Requires advanced programming and integration of multiple sensors and actuators.
      - The cost of developing and deploying such robots is significant.
      - Requires specialized knowledge for operation and troubleshooting.
  - - - Future multifunctional robots will work more closely with humans, sharing tasks in collaborative environments (cobots).
    - - More advanced AI will enable these robots to learn and adapt faster to new tasks and environments.
    - - Expect to see smaller, more mobile multifunctional robots in homes and healthcare.
    - - Robots with interchangeable components to easily switch between functions.
- - - - AI governance platforms are tools and frameworks designed to oversee, regulate, and guide the ethical and legal use of artificial intelligence within organizations.
    - - These platforms help ensure that AI systems are fair, transparent, and aligned with organizational values and legal regulations. They are essential for building trust and preventing misuse or unintended harm caused by AI.
  - - - Finance (to ensure fair lending decisions)
      - Healthcare (to verify ethical use of AI in diagnosis)
      - Government and public sector (to monitor AI surveillance and citizen services)
    - - Monitoring AI decisions in autonomous vehicles for accountability
      - Detecting and mitigating algorithmic bias in hiring platforms
  - - - Promotes responsible AI development
      - Enhances transparency and accountability
      - Reduces legal and reputational risks for organizations
    - - Difficulty defining universal ethical standards
      - Balancing innovation and regulation
      - High cost and complexity of implementation
- - - - Agentic AI refers to artificial intelligence systems designed to operate autonomously, making decisions and executing tasks independently based on contextual understanding and predefined objectives. These systems can adapt to changing environments and solve complex, multi-step problems without continuous human intervention.
    - - Agentic AI has the potential to revolutionize various industries by automating complex workflows, enhancing decision-making processes, and improving efficiency. For society, it promises advancements in areas like personalized education, healthcare diagnostics, and smart infrastructure management.
  - - - Perception Modules: These gather and interpret data from the environment.
      - Planning Algorithms: They develop strategies to achieve specific goals based on the interpreted data.
      - Action Execution Systems: Responsible for carrying out the planned actions effectively. These components work in unison, enabling the AI to perceive its environment, plan accordingly, and execute tasks autonomously.
  - - - Efficiency: Automates complex tasks, reducing the need for human intervention.
      - Can adjust to new information and changing environments in real-time.
    - - Ethical Concerns: Potential for biased decision-making if not properly managed.
      - Security Risks: Autonomous systems may be vulnerable to hacking or unintended behaviors.
      - High Implementation Costs: Developing and integrating agentic AI can be resource-intensive.
- - - - Hybrid computing refers to systems that combine two or more types of computing architectures—typically classical (digital), analog, and increasingly quantum—to achieve optimal performance for different tasks.
    - - Hybrid systems can solve complex problems more efficiently by leveraging the strengths of each computing model. They are especially useful in scientific research, AI development, financial modeling, and real-time data processing.
  - - - Scientific research (e.g., simulations in physics and biology)
      - Financial sector (e.g., market predictions and risk modeling)
      - Weather forecasting and climate modeling
      - Artificial Intelligence (e.g., training deep learning models)
    - - Accelerated drug discovery using quantum-classical hybrid systems
      - Real-time traffic optimization using digital + analog computing
  - - - Better performance and speed for complex computations
      - Energy efficiency when using analog/quantum components
      - Flexible computing solutions tailored to problem types
    - - Integration complexity between different computing models
      - High development and maintenance costs
      - Limited workforce trained in hybrid systems
    - - Increased development of quantum-classical hybrid platforms
      - Growth of edge-AI hybrid systems for real-time smart devices
      - Use of cloud-based hybrid infrastructures for scalable computing solutions