Artificial Intelligence (AI)

AI is a branch of computer science focused on creating computers that mimic human behavior.
The concept of intelligent machines existed in ancient times but became a reality in recent years.
AI systems can beat human chess players and perform other complex tasks.

Understand and appreciate the role of AI.
Learn AI terminology.
Compare AI with human intelligence and traditional information processing, discussing its strengths and weaknesses.

Turing's Test: A test to evaluate a computer's ability to exhibit human-like intelligence.
The test involves a human evaluator engaging in conversations with both a human and a computer.
If the evaluator cannot reliably determine which is which, the computer is considered to have passed the test.
Early versions of The Turing Test did not involve a computer; it was an experiment to determine if a machine could think.

Game Playing: Programming computers to play games like chess or checkers. Deep Blue, an IBM computer, beat Garry Kasparov in 1997.
Speech Recognition: Technology that allows computers to recognize and understand human speech. While initially promising, it has practical limitations and reverted to keyboard input for convenience.
Natural Language Processing (NLP): Enables computers to understand and respond to human language, allowing human-to-computer interaction without specialized technical knowledge.
Computer Vision: Enables computers to interpret and process visual information, analyze images, and make useful decisions regarding them. Has applications in various fields.

Computer systems are still being developed to understand spoken language and their surroundings.
NLP aims for computers to interact with humans without specialized language proficiency.
Developing NLP capabilities is more difficult than expected.
Computer vision has applications in image recognition, video analysis, augmented reality, and medical imaging.

Expert systems are computer programs designed to mimic the decision-making processes of human experts.
Early hopes for expert systems to help in various fields proved difficult in practice.
Heuristic classification is one effective expert system type.

Perplexity AI: Free basic and pro searches for quick answers.
Consensus: Free plan for searching over 200 million research papers with AI-generated summaries.
Quizgecko: A tool for creating and sharing quizzes.
Gamma AI: Used for creating and sharing presentations, documents, and web pages (image search and document reformatting included).
Edpuzzle: Platform for creating interactive video lessons that embed questions, notes, and audio.
Podcastle AI: Converts text into high-quality audio for podcasts.

Mymap Al: Creates mindmaps from notes and ideas.
Socratic Al: Provides explanations, videos, and step-by-step solutions in response to student questions.
Fotor: Free tool producing images from texts.
FutureMe: Enables users to write messages to their future selves to plan goals.

Rapid advancements in machine learning, NLP, and computer vision technologies have accelerated AI capabilities.
AI is now deeply integrated into various technologies, from virtual assistants to autonomous vehicles.
AI holds the transformative potential to revolutionize global industries and resolve challenges.

Data acquisition, collection, and processing of diverse data are essential for training AI models.
Model training applies algorithms to make machines capable of learning and improving from experience.
Deployment involves integrating trained models into real-world applications to solve complex issues.

Language understanding enables AI systems to comprehend and interpret languages, including context and nuances from human speech.
Conversational abilities allow chatbots and virtual assistants to engage in natural human-like dialogues.
Multilingual capabilities enhance NLP support across diverse language and cultural settings.
Personalized interactions adapt to individual user preferences for a more personalized user experience.

Image recognition: Enables AI to distinguish and classify objects, people, and scenes in digital images.
Video analysis: Utilizes computer vision to understand and interpret the content and context of videos, interpreting visual information.
Augmented reality: Combines computer vision and AR techniques to merge digital information into the physical world.
Medical imaging leverages computer vision for analyzing medical scans and diagnostics.

Perception: Enables autonomous systems to interpret their surroundings via sensors and computer vision.
Decision making: Includes development of robust algorithms for near real-time decision making in complex environments.
Control: Guides autonomous systems’ operations, such as precision control over robotic movements or vehicle navigation.

Bias and Fairness: Address potential biases within AI systems to achieve equitable and honest decision-making processes.
Transparency and Accountability: Develop AI systems with clear decision processes and mechanisms to track responsibilities and oversee operations effectively.
Privacy and Security: Safeguard personal data and implement secure procedures to prevent data breaches or misuse.
Human-AI Interaction: Ensure that AI technologies enhance human capabilities rather than replace them.

Automation and Augmentation: AI automates tasks to make human workers more productive and free them to manage more strategic tasks.
Workforce Transformation: Skills upgrading and reskilling workers is required for the integration of AI systems into the workplace.
New Job Creation: The growth of AI is creating new job roles and industries that depend heavily on AI expertise.

Data collection: Maintain transparency and obtain informed consent when collecting personal data.
Data Storage: Implement robust security measures to protect personal information from unauthorized access or breach.
Data Usage: Establish clear policies for the ethical and responsible use of AI-powered data analysis.

Continuous Innovation: Drive breakthroughs in AI, utilizing and improving technologies in new and innovative ways.
Persisting Challenges: Address issues like biases, privacy, and ethics to maintain responsible and appropriate AI practices, requiring ongoing consideration.
Collaborative Approach: Promote interdisciplinary collaborations between AI technologists, policymakers, and ethicists to manage potential risks.