Applications of computer science( AI).pptx

This presentation provides a clear and concise overview of Artificial Intelligence (AI) and Machine Learning (ML). It explains how AI enables computers to perform human-like tasks and make intelligent decisions, while Machine Learning helps systems learn from data and improve automatically.
The slides cover:

Definition and scope of AI and ML

Types of Machine Learning (Supervised and Unsupervised)

Real-world applications such as self-driving cars, speech recognition, and healthcare

Disadvantages and ethical concerns of AI

Positive uses of AI for human benefit

Perfect for students and teachers exploring the foundations of AI and emerging technologies.

1. Application of computer science Prepared by: Samina Khan Fazaia Degree College Risalpur Cantt

2. Artificial intelligence & machine learning

3. Artificial intelligence Make computer-controlled machines that can intelligently perform human like tasks. Fixing an intelligent brain in machine that has decision making capabilities to solve complex problems. Computer can be trained to perform human like tasks. AI is the technology related with making intelligent machines & developing intelligent software. Solving complex problems that was not possible before.

4. Machine learning Branch of AI that teaches computer to learn from data inputs & experience like humans without direct programming. It focuses on using data and algorithms to help computers learn and make decisions like human . AI algorithms are based on ML to predict output. These algorithms facilitate computers to train on data inputs & analyze it to produce accurate output values.

5. Machine learning Machine learning is used for solving problems related to different areas like: Image recognition Speech recognition Fraud detection Identifying spams Video recognition Email phishing Translation Google search engine Virtual personal assistant Self driving cars Manufacturing Medical devices

6. Types of machine learning There are two types of machine learning Supervised machine learning Unsupervised machine learning

7. Supervised machine learning Supervised learning algorithms take a set of known input data & known output responses to build a model to make predictions. How to do the mapping to match the input to the output. Once the model is built, it is used to make new predictions on unseen data as well.

8. Supervised machine learning Example : predict price of car Predict a dataset having input like: Engine type & power Transmission type(manual or automatic) Number of seats Front wheel/rear- wheel drive Keyless entry Push button start Safety features Country of manufacturing

9. Unsupervised machine learning The training data is unseen & it is un-labelled(data is not assigned a category or group). The unseen data is fed into machine learning algorithm to train the model. The trained model tries to search for a pattern & gives the desired result. The unsupervised learning algorithm looks hidden pattern in the input data. It finds hidden patterns in the input data to make them suitable for clustering & data analysis.

10. example A bank wants to predict how capable an applicant is of repaying the loan

11. Stages of machine learning Collection of training data Creating algorithm Learning process Creating training model Predicting results

12. Disadvantages of AI AI can make errors & produce wrong results as they don’t apply formula like traditional algorithms. It is difficult to find out what went wrong in the AI algorithms AI can make wrong decisions that can harm people & destroy humanity, cause business to collapse or even cause death of a patient.

13. Disadvantages of AI AI algorithms are developed by humans who can intentionally or unintentionally introduce bias in them.(disability bias, cultural bias(inaccurate translations for non-English languages)). Self-driving cars can have accidents. Errors in AI algorithms in healthcare systems can output inaccurate information.(can recommend wrong medicine).

14. Disadvantages if ai Hackers can develop powerful AI algorithms to bypass cybersecurity and cause cyberattacks. AI based automation can lead to job losses in many areas. Dependence of AI can cause loss of creativity and critical thinking skills in human. Unexpected behavior of AI systems can result in having negative impact on individual & society.(addiction, stress, anxiety, health risk, dependency on technology, decreased critical thinking, loss of creativity, decreased human connection, cyberbullying).

15. Uses of ai for benefiting people AI designers must instill moral & ethical values in AI systems & focus on benefiting people. There are many benefits of AI systems: Helps in solving complex problems. Increase productivity Improved efficiency Provide better diagnosis & treatment of disease at early stage. Help in production of high quality product. Help in travel and transportation.

16. Uses of ai for benefiting people Voice assistant for disabilities Social connections all over the world. Education & learning Space exploration Enhanced cybersecurity Correct decision making

17. Uses of ai for benefiting people Help in developing better learning software & introduce new teaching techniques. Usage of AI-based assistants such as Siri & Alexa & google suggest products by monitoring our browsing habits. Provides security against cyberattacks. Enable new innovations in developing intelligent computer software

18. Siri(apple) Siri is a virtual assistants developed by Apple. It uses AI & Natural Language Processing to understand & respond to voice commands. It is integrated with apple devices(iPhone, iPad, Mac, Apple watch). Answering questions Sending messages Making calls Setting reminder Provide directions

19. Alexa Alexa is a virtual assistant developed by Amazon. Integrated with Amazon echo smart speakers & other devices. Perform tasks like: Playing music, provide news update Control smart home devices Setting alarms