Chapter 10 - Emerging Technologies in Computer Science
Comprehensive Questions & Answers for Class 9 Computer Science (Punjab Board)
Artificial Intelligence (AI)
Artificial intelligence denotes the simulation of human thinking ability in computer systems to think and learn in a manner like humans.
Historical Context and Evolution of AI
The term AI was first invented by John McCarthy in 1956 during the Dartmouth conference, regarded as the origin of Artificial intelligence as a discipline of research. The journey of AI has been several key milestones.
1950s-1960s: Early AI research focused on problem solving and symbolic methods.
1970s-1980s: The development of expert systems that mimicked human decision-making.
1990s: The rise of machine-learning, where computers began to learn from data.
2000s: Advances in deep learning, natural language processing and robotics have significantly expanded AI's capabilities.
2011s: Voice assistant was used for voice command and voice recognition.
2023s-Present: Chat GPT was introduced that is an AI social model which is designed to understand human-like-text-based input.
Use of AI in Healthcare
Health Care:
AI is used for diagnosing diseases personalizing treatment plans and predicting patient outcomes.
Role of AI Techniques in Machine Learning Models
Machine learning is a type of Artificial intelligence where computers learn from experience and improve our time without being explicitly programmed. It's like teaching a computer by showing it lots of examples and it figures out how to do things on its own.
IOT (Definition)
IOT is a network of physical objects, or "thing" that are equipped with sensor's software and other technologies to facilitate the exchange of data with other devices and system over the internet.
Significance of IOT in Connecting Devices
IOT is significant because it allows for the seamless integration of physical and digital worlds. This connection enables devices to collect and share data, which can be analysed to improve efficiency, provide better services, and create new opportunities in various fields such as health care, agriculture and smart homes.
Potential Risks Associated with AI & IOT
Data Privacy:
As AI and IoT devices collect vast amounts of data, concerns about data privacy become paramount. personal and sensitive information can be at risk of being misused or accessed by unauthorized parties. Ensuring data privacy involves implementing robust security measures to protect data from breaches and authorized access.
Algorithm Bias:
Training of AI systems is conducted on large data sets and if these data sets comprise biases, the AI model can inadvertently perpetuate or even amplify these biases.
Social Impact of AI & IOT
Daily life:
AI and IoT technologies are enhancing over daily lives by providing smart solutions for homes health care and transportation. Smart home devices like thermostats and security systems offer convinience and energy efficiency.
Work Environment:
AI is revolutionizing workplace by automating repeated repetitive tasks, improving efficiency and enabling data driven decision-making. IoT devices in industrial settings optimized production processes and monitor equipment health, reducing downtime and maintenance costs.
Society at large:
AI and IoT have the potential to address large scale societal challenges such as climate changes, health care accessibilities and urbanization.
Concept of Algorithm Bias
Algorithmic Bias :-
Training of AI system is conducted on large data sets and if these data sets comprise biases, the AI model can inadvertently perpetuate or even amplify these biases. This can lead to unfair outcomes in various applications such as hiring processes, law enforcement and lending practices. and Addressing algorithmic bias makes analyses of training data and implementing techniques to relieve biases.
Ethical Consideration in AI & IOT
Data Privacy :-
As AI and IoT devices collect vast amount of data, concerns about data privacy becomes paramount.
Algorithm Bias :-
Training of AI system is conducted on large datasets, and if these datasets comprise biases, the AI models can inadvertently perpetuate or even amplify these biases.
Various Applications of AI in the field of Education
AI has numerous applications in education, transforming the way students learn, teachers teach and institutions operate.AI can enhance the educational experience in various ways:
Personalization:
AI can tailor learning experience to individual student's needs, abilities, and learning styles.
Efficiency:
AI can automate administrative tasks, freeing up instructions time to focus on teaching and mentoring.
Accessibility:-
AI can enhance accessibility and inclusion in education, particularly for students with disabilities.
Engagment:
AI can create immersive and interactive learning environment that simulate real-world experience.
Feedback:-
AI can provide immediate feedback and assessment, helping students track their progress and identify areas for improvement.
Explainable (white box) vs unexplainable AI Model
| Explainable Algorithms | Unexplainable Algorithms |
| Explainable algorithms are those where the decision making process is transparent and understandable. | Unexplainable algorithms are those where the decisions making process is not easily interpretable. |
| It is also called white box algorithm. | It also called black box algorithm. |
| These algorithm allow user to see and understand how decisions are made. | These algorithm involve complex computations that make it difficult to understand how particular decisions was reached. |
| By understanding the model's logic, developers can identify errors in data. | It is difficult to identify the errors. |
Components of IOT system
Components of IOT system:-
An IOT system consists of the following components.
Sensor:
These are devices that detect and measure physical properties like temperature, humidity, light and motion - Sensor collects data from the environment.
Actuator :
These are devices that convert energy in motion - In IOT an actuator can act on data to generate output.
Devices:-
These include everydays objects like smart watches, refrigerators, and cars that are connected to the internet - Devices use the data collected by sensors to perform specific tasks.
Network :
These are the communication pathways that connects sensors and devices to the internets, allowing them to share data. Network can be wired or wireless.
Data Analysis :
This involves processing and analysing the data collected by sensors to gain insight and make decisions. Data analysis can be done on the device itself, in the cloud or on the central server.
Example :
One of the practical uses of IOT is a smart home system - There are several interconnected appliances in a smart home including the temperature control system lighting and surveillance cameras.
IOT Applications in Transportation Domain
IOT Applications :
The internet of things (IOT) is transforming many aspects of our lives by connecting devices and systems in various domains.
Transportation :-
IOT is enhancing transportation system, making them more efficient and safer. Connected devices, smart traffic lights and real-time tracking systems are some examples of how IoT is used in transportation.
Potential Privacy Concerns Associated with IOT Deployment
While IOT offers many benefits, it also raises security and privacy concerns. As more devices are connected to the internet, the risk of cyber-attacks increases. Its important to ensure that IoT system are secure to protect personal data and privacy.
Examples of security Measures:-
Strong Passwords:
Use strong unique passwords for all IOT devices to prevent unauthorized access.
Regular Updates :
Keep the software and firmware of your IoT devices up to date to protect against known vulnerabilities.
Encryption :
Ensure the data transmitted between devices is encrypted to protect it from being intercepted by hackers.
Impact of AI & IOT on Job Market
Impact of AI and IoT on job market :
AI and IOT will create new job opportunities in fields like AI development deployment and maintainance. - AI and IOT can enhance productivity, leading to increased efficiency and competitivenes in various Industries.
Work Environment :
AI is revolutionizing work places by automizing repetitive tasks, improving efficiency and enabling data-driven decision-making. - IOT devices in industrial setting optimize production processes and monitor equipments health reducing downtime and maintenance costs.
Role of Policy and Regulatory Frameworks in AI and IOT
Data Protection laws :
Implementing strict data protection regulations to ensure that personal data is collected stored and processed securely. Examples Include the general Data protection Regulation (GDPR) in Europe.
Ethical Guide lines :
Establishing ethical guidelines for the development and deployment of AI systems to ensure fairness, transparency and accountability. organization like the IEEE have developed guidelines for ethical AI.
Bias Mitigation Standards :
Developing standards and best practices for identifying and mitigating biases in AI models. This include guidelines for diverse and representative data collections and techniques for bias detection and correction.
Security Standards :
Enforcing security standard for IoT devices to protect them from cyber attacks. This includes regular software updates encryption and secure authentication mechanisms.
Algorithmic Bias and its Mitigation
Algorithmic Bias :-
Training of AI system is conducted on large data sets and if these data set comprise biases the all models can inadvertantly perpetuate or even amplifies these biases. This can lead to unfair outcomes in various application such as hiring process law of enforcement and lending practice. Addressing algorithmic bias makes analysis of training data and implementing techniques to seve biases.
Ethical Principles for AI and IOT
Establishing ethical guidelines for the development and deployment of AI systems to ensure fairness and transparency and accountability organizations like the IEEE have developed guidlines for the ethical AI.