تحول رقمي

Digital Transformation

• Digital transformation (تحول رقمي) is a process of changing the way work is done using new digital technologies, increasing productivity and improving efficiency.
• It has greatly impacted communication and information flow between people and devices.
• All industries are progressing rapidly due to this.
• The more advanced technologies become, the more data they produce, creating a continuous cycle of change.
• Innovation provides new possibilities for businesses and communities.
• Examples of digital transformation in business and society include:
  • Shift from traditional to digital communication channels (e.g., chat messages, social media instead of letters).
  • Preference for streaming services and tablets over live events and cinema.
  • Rise of online shopping and e-commerce for various goods (electronics, books, groceries, clothes) instead of visiting stores.
  • Increase in online banking for personal and business transactions.
• Companies like Google and Amazon are utilizing data for decision-making.
• In 2010, energy and construction companies held the highest stock market value, but in 2020, companies using data-driven business models took their place.

Artificial Intelligence (AI)

• AI (الذكاء الاصطناعي) is the science and engineering of creating intelligent machines, especially intelligent computer programs.
• AI systems mimic human intelligence to perform tasks and make decisions, improving themselves through gathered data.
• AI exists in different models:
  • Recommendation engines: Provide automated recommendations for shopping and TV shows based on user habits. Platforms like YouTube, Amazon, LinkedIn use these engines.
  • Customer support chatbots: Use AI to understand customer problems and provide better answers. Platforms like Amtrak, the Saudi Post, and Saudi Ministry of Health's interactive chat service (937) use chatbots.
  • Intelligent assistants: Perform tasks and schedule meetings for users by analyzing personal information in emails and text messages. Examples include Apple Siri, Amazon Alexa, and Microsoft Cortana.
• National Center for AI (NCAI) is a key part of Saudi Arabia's AI leadership and global innovation in this field.

AI and Data in Digital Transformation

• AI and data are driving digital transformation.
• Modern interactions are becoming more complex, generating large amounts of data.
• AI can analyze data and provide insights, empowering individuals to use critical thinking and experience to make developmental decisions in their work and personal lives.

Machine Learning (ML)

• ML (تعلم الآلة) is a subfield of AI that develops algorithms enabling computers to understand learning patterns from available data to make predictions, classifications, or decisions based on new data.
• It analyzes data and detects patterns, then handles new data to provide new insights.

Neural Networks

• Neural networks (الشبكة العصبية) are computational models in AI inspired by the biological neural networks of the brain.

Natural Language Processing (NLP)

• NLP (معالجة اللغات الطبيعية) is a branch of AI dealing with understanding or generating human language in text or speech.
• Used in language translation, mobile phone conversations, text prediction, and intelligent assistant understanding.

Types of Machine Learning

• Supervised learning: The user feeds the algorithm historical data to predict new data values.
  • Regression analysis: Predictions, like future stock prices.
  • Classification analysis: Categorization, like classifying a picture as a boat or ship.
• Unsupervised learning: Dealing with large, unlabeled datasets to find patterns in unstructured data through observation and grouping. Used by retail giants to categorize customers based on preferences and improve marketing and sales campaigns.
• Reinforcement learning: The agent interacts with the environment to identify suitable input data. The agent aims to reach a final or winning state by receiving small rewards or punishments. (e.g., chess game).

Data Ethics in AI

• Data ethics studies ethical principles regarding the use of data by companies and governments. It's crucial for protecting people from exploitation given the data being utilized by AI-powered algorithms.
• Examples of bad data ethics in AI include:
  • Bias and discrimination
  • Decision responsibility
  • Unjustified results
  • Privacy violation
  • Social isolation
  • Unreliable results

AI Applications in Life

• Manufacturing: Improves production efficiency and capabilities of automated and intelligent machines.
• Retail: Predicts customer needs through various shopping scenarios.
• Cities: Reduces energy and transport costs in smart cities suited to citizens' needs.
• Medicine: Improves patient diagnosis and develops personalized medications.
• Transportation: Introduces self-driving vehicles or trains.
• Banking: Analyzes income and spending to provide recommendations for savings or investments.

Future AI Developments

• AI technology has evolved since the 1950s, affecting nearly every field.
• Technical equipment and components: Instead of CPUs processing AI data, new Neural Processing Units (NPUs) are created for neural network calculations, up to 25 times faster than CPUs in AI applications.
• Autonomous systems: Self-driving vehicles, collaborative production aids, and local robots use NPUs for real-time object detection and deep neural network predictions, faster than traditional CPUs.
• Health and Biology: Deep learning networks in drug discovery, virus prediction, and other biological concerns, such as brain function, gene interactions, and protein folding analysis.
• AI will drastically change daily life and society.

Practical Applications (Exercises)

• Various practical exercises are provided, including website interactions, research, and analysis, to apply learned concepts. This helps students practically apply the learned AI concepts.