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IBM AI Coursera Course > Intro to AI (Week 1 - Section 1): What is AI? > Flashcards

Intro to AI (Week 1 - Section 1): What is AI? Flashcards

What is AI? Applications and Examples of AI

1
Q

What is the definition of AI?

A

AI stands for Artificial Intelligence, which refers to the development of computer systems that can perform tasks that typically require human intelligence

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2
Q

How does IBM define AI?

A

IBM defines AI as augmented intelligence, where machines are designed to extend human capabilities rather than replace them

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3
Q

What is the concept of augmented intelligence?

A

Augmented intelligence is the idea that AI should assist and enhance human decision-making and problem-solving abilities, rather than replacing human experts.

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4
Q

What is the difference between weak/narrow AI and strong/generalized AI?

A

Weak/narrow AI is designed for specific tasks and domains, while strong/generalized AI can handle a wide range of unrelated tasks and learn new ones.

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5
Q

What are some examples of weak/narrow AI applications?

A

Examples of weak/narrow AI applications include language translators, virtual assistants, self-driving cars, AI-powered web searches, recommendation engines, and intelligent spam filters.

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6
Q

What is supervised learning in AI?

A

Supervised learning is a machine learning technique where the AI model is trained using labeled data, with known inputs and desired outputs.

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7
Q

What is unsupervised learning in AI?

A

Unsupervised learning is a machine learning technique where the AI model learns patterns and relationships in unlabeled data without any predefined outputs.

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8
Q

What is reinforcement learning in AI?

A

Reinforcement learning is a machine learning technique where the AI model learns through trial and error, receiving feedback in the form of rewards or penalties to improve its performance.

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9
Q

What are the fields of study that contribute to AI?

A

Computer science, electrical engineering, mathematics, statistics, psychology, linguistics, and philosophy are some of the fields that contribute to AI.

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10
Q

How does computer science and electrical engineering contribute to AI?

A

Computer science and electrical engineering determine how AI is implemented in software and hardware, respectively.

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11
Q

How do mathematics and statistics contribute to AI?

A

Mathematics and statistics help in developing viable models, measuring performance, and analyzing data in AI.

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12
Q

What role do psychology and linguistics play in AI?

A

Psychology and linguistics contribute to understanding how AI might work by providing insights into human cognition, language processing, and behavior.

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13
Q

How does philosophy contribute to AI?

A

Philosophy provides guidance on intelligence, ethics, and moral considerations in the development and use of AI systems.

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14
Q

What are the potential ethical considerations of AI?

A

Ethical considerations in AI include issues related to privacy, bias, transparency, accountability, job displacement, and the impact on society

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15
Q

How has AI already impacted various domains in society?

A

AI has already made an impact in domains such as healthcare, finance, transportation, customer service, education, and many others by improving efficiency, accuracy, and decision-making processes.

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16
Q

What is Artificial Intelligence?

A

Artificial Intelligence is the application of computing to solve problems in an intelligent way using algorithms.

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17
Q

How can AI mimic human intelligence?

A

AI can mimic human intelligence by analyzing images and videos, understanding speech, and recognizing patterns.

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18
Q

What is the main goal of AI?

A

The main goal of AI is to impart the ability to think and learn on machines.

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19
Q

Define Machine Learning

A

Machine Learning is a subset of AI that uses mathematical algorithms to find patterns in data and make predictions.

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20
Q

How does AI extract knowledge from data?

A

AI extracts knowledge from data by learning and understanding patterns within the data and reproducing them on new information.

21
Q

What is the difference between AI and machine learning?

A

AI is a broader concept that encompasses various technologies, while machine learning is a specific approach within AI that focuses on using mathematical algorithms to find patterns in data.

22
Q

How does AI automate tasks?

A

AI automates tasks by using computers to complete them automatically with minimal human intervention.

23
Q

What are some ethical considerations in AI?

A

Ethical considerations in AI include issues related to privacy, bias, transparency, and the impact on jobs and society.

24
Q

How can AI be used in real-life applications?

A

AI can be used in various real-life applications such as healthcare, finance, transportation, customer service, and cybersecurity.

25
Q

What are the potential benefits of AI?

A

The potential benefits of AI include increased efficiency, improved decision-making, enhanced productivity, and the ability to solve complex problems.

26
Q

What are the concerns associated with AI?

A

Concerns associated with AI include job displacement, ethical dilemmas, privacy concerns, and the potential for misuse of AI technologies.

27
Q

How does AI impact different industries?

A

AI has the potential to transform industries by automating tasks, improving processes, and enabling new capabilities such as personalized recommendations and predictive analytics.

28
Q

What role does data play in AI?

A

Data is crucial for AI as it is used to train machine learning models and enable AI systems to learn and make predictions.

29
Q

What are some popular AI applications?

A

Some popular AI applications include virtual assistants (e.g., Siri, Alexa), recommendation systems, autonomous vehicles, and fraud detection systems.

30
Q

How can AI be used to augment human capabilities?

A

AI can augment human capabilities by automating repetitive tasks, providing insights from large amounts of data, and assisting in decision-making processes.

31
Q

What is Generative AI?

A

Generative AI is an AI technique capable of creating new and unique data using deep learning techniques and vast datasets.

32
Q

What is Augmented Intelligence?

A

AI that enables experts to scale their capabilities.

33
Q

What is Deep learning?

A

Technique used by Generative AI models to generate new data.

34
Q

What is Large Language Model (LLM)

A

AI based on deep learning for natural language processing and generation.

35
Q

What are the Benefits of Generative AI

A

Creativity, cost and time savings, personalization, scalability, robustness, and exploration of new possibilities.

36
Q

Use case of Generative AI: Healthcare and precision medicine

A

Use case of Generative AI in identifying genetic mutations and providing tailored treatments.

37
Q

Use case of Generative AI: Agriculture

A

Use case of Generative AI in optimizing crop yields and creating robust plant varieties.

38
Q

Use case of Generative AI: Biotechnology

A

Use case of Generative AI in developing new therapies and drugs.

39
Q

Use case of Generative AI: Forensics

A

Use case of Generative AI in analyzing DNA evidence and identifying suspects.

40
Q

Use case of Generative AI: Environmental conservation

A

Use case of Generative AI in protecting endangered species.

41
Q

Use case of Generative AI: Creative fields

A

Use case of Generative AI in producing unique digital art, music, and video content.

42
Q

Use case of Generative AI: Gaming

A

Use case of Generative AI in creating interactive game worlds.

43
Q

Use case of Generative AI: Fashion

A

Use case of Generative AI in designing virtual try-on experiences and recommending personalized fashion choices.

44
Q

Use case of Generative AI: Robotics

A

Use case of Generative AI in designing new robot movements and adapting to changing environments.

45
Q

Use case of Generative AI: Education

A

Use case of Generative AI in creating customized learning materials and interactive learning environments.

46
Q

What are conventional AI models?

A

Conventional AI models rely on pre-defined rules and patterns to perform tasks and make decisions.

47
Q

How does Generative AI differ from conventional AI models?

A

Generative AI can generate new data, fosters creativity and innovation, is more adaptable and versatile, and enables personalized experiences and exploration of new possibilities.

48
Q

What are the limitations of conventional AI models?

A

Conventional AI models are limited by the pre-defined rules and patterns they are trained on, which can restrict their ability to handle new or unique situations.

49
Q

What are the benefits of Generative AI over conventional AI models?

A

Generative AI can create new and unique data, allowing for more creative and innovative solutions. It is not limited by pre-defined rules and patterns, making it more adaptable and versatile in handling new situations.

IBM AI Coursera Course (3 decks)

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