Artificial Intelligence: From Theory to Practice
LANSEN - Learning languages with sense
ERASMUS+ Project Nr: 2024-1-ES01-KA220-SCH-000253288
16 January 2025
by Rui Baltazar baltazar.rui@gmail.com
Rui Baltazar
Artificial Intelligence:
Theory
What is AI? Defining the Landscape
Machine Learning
Natural Language Processing
The ability of systems to learn and improve from data without being explicitly programmed.
The understanding and generation of human language by computers, enabling seamless communication.
Computer Vision
Robotics
The ability of machines to interpret and understand digital images and videos.
The integration of AI with physical systems, creating autonomous and intelligent machines.
The History and Evolution of AI
1950s-1970s
Early AI research and the exploration of symbolic reasoning, expert systems, and neural networks.
1980s-1990s
Advancements in machine learning and the emergence of practical AI applications.
2000s-Present
Exponential growth in data and computing power, fueling breakthroughs in deep learning and AI's widespread adoption.
Practical Applications of AI
Healthcare
Transportation
AI-powered tools for early disease detection, personalized treatment plans, and streamlined administrative tasks.
Autonomous vehicles, intelligent traffic management, and optimized logistics and supply chain operations.
Finance
Customer Experience
AI-driven fraud detection, investment analysis, credit risk assessment, and personalized financial advice.
Chatbots, personalized recommendations, and predictive analytics to enhance customer interactions and engagement.
Challenges and Limitations of AI
Prejudice
Privacy
AI systems can inherit and amplify human prejudice present in training data, leading to unfair and discriminatory outcomes.
The collection and use of personal data by AI systems raises concerns about individual privacy and data rights.
Safety
Transparency
Ensuring the safety and reliability of AI-powered systems, especially in high-stakes applications, is a critical challenge.
The complex, "black box" nature of many AI models makes it difficult to understand and explain their decision-making processes.
Ethical Considerations in AI
Accountability
Establishing clear lines of responsibility and liability for AI-driven decisions and their consequences.
Algorithmic Fairness
Ensuring AI systems do not perpetuate or amplify societal biases and discrimination.
Human-Centric Design
Prioritizing the well-being and interests of humans in the development and deployment of AI technologies.
The Future of AI: Trends and Predictions
Increasingly complex and powerful neural networks driving breakthroughs in areas like computer vision and natural language processing.
Advancements in Deep Learning
General AI
The pursuit of artificial general intelligence (AGI), which would possess human-level abilities across a wide range of tasks.
Ethical AI Frameworks
The development of guidelines and standards to ensure the responsible and ethical development of AI systems.
Human-AI Collaboration
The integration of AI as an empowering tool that augments and enhances human capabilities, rather than replaces them.
Artificial Intelligence:
Practice
https://this-person-does-not-exist.com/en
https://chat.openai.com/
https://bard.google.com/chat
https://www.canva.com/ai-image-generator/
https://leonardo.ai/
https://app.questionwell.org/quiz-game/create
https://www.lightxeditor.com
https://gamma.app/signup?r=z92mi1p38rh4nsz
https://app.magicschool.ai
https://vidnoz.com/
www.synthesia.io
www.aixploria.com
Thank you very much!!
Esto es un párrafo listo para contener creatividad, experiencias e historias geniales.
LANSEN Artificial-Intelligence-From-Theory-to-Practice.pptx
Rui Baltazar
Created on November 12, 2024
Start designing with a free template
Discover more than 1500 professional designs like these:
View
Essential Business Proposal
View
Project Roadmap Timeline
View
Step-by-Step Timeline: How to Develop an Idea
View
Artificial Intelligence History Timeline
View
Mobile Phone Call
View
Momentum: Tools Tutorial
View
Momentum: Onboarding Video
Explore all templates
Transcript
Artificial Intelligence: From Theory to Practice
LANSEN - Learning languages with sense
ERASMUS+ Project Nr: 2024-1-ES01-KA220-SCH-000253288
16 January 2025
by Rui Baltazar baltazar.rui@gmail.com
Rui Baltazar
Artificial Intelligence:
Theory
What is AI? Defining the Landscape
Machine Learning
Natural Language Processing
The ability of systems to learn and improve from data without being explicitly programmed.
The understanding and generation of human language by computers, enabling seamless communication.
Computer Vision
Robotics
The ability of machines to interpret and understand digital images and videos.
The integration of AI with physical systems, creating autonomous and intelligent machines.
The History and Evolution of AI
1950s-1970s
Early AI research and the exploration of symbolic reasoning, expert systems, and neural networks.
1980s-1990s
Advancements in machine learning and the emergence of practical AI applications.
2000s-Present
Exponential growth in data and computing power, fueling breakthroughs in deep learning and AI's widespread adoption.
Practical Applications of AI
Healthcare
Transportation
AI-powered tools for early disease detection, personalized treatment plans, and streamlined administrative tasks.
Autonomous vehicles, intelligent traffic management, and optimized logistics and supply chain operations.
Finance
Customer Experience
AI-driven fraud detection, investment analysis, credit risk assessment, and personalized financial advice.
Chatbots, personalized recommendations, and predictive analytics to enhance customer interactions and engagement.
Challenges and Limitations of AI
Prejudice
Privacy
AI systems can inherit and amplify human prejudice present in training data, leading to unfair and discriminatory outcomes.
The collection and use of personal data by AI systems raises concerns about individual privacy and data rights.
Safety
Transparency
Ensuring the safety and reliability of AI-powered systems, especially in high-stakes applications, is a critical challenge.
The complex, "black box" nature of many AI models makes it difficult to understand and explain their decision-making processes.
Ethical Considerations in AI
Accountability
Establishing clear lines of responsibility and liability for AI-driven decisions and their consequences.
Algorithmic Fairness
Ensuring AI systems do not perpetuate or amplify societal biases and discrimination.
Human-Centric Design
Prioritizing the well-being and interests of humans in the development and deployment of AI technologies.
The Future of AI: Trends and Predictions
Increasingly complex and powerful neural networks driving breakthroughs in areas like computer vision and natural language processing.
Advancements in Deep Learning
General AI
The pursuit of artificial general intelligence (AGI), which would possess human-level abilities across a wide range of tasks.
Ethical AI Frameworks
The development of guidelines and standards to ensure the responsible and ethical development of AI systems.
Human-AI Collaboration
The integration of AI as an empowering tool that augments and enhances human capabilities, rather than replaces them.
Artificial Intelligence:
Practice
https://this-person-does-not-exist.com/en
https://chat.openai.com/
https://bard.google.com/chat
https://www.canva.com/ai-image-generator/
https://leonardo.ai/
https://app.questionwell.org/quiz-game/create
https://www.lightxeditor.com
https://gamma.app/signup?r=z92mi1p38rh4nsz
https://app.magicschool.ai
https://vidnoz.com/
www.synthesia.io
www.aixploria.com
Thank you very much!!
Esto es un párrafo listo para contener creatividad, experiencias e historias geniales.