AI Flashcards
Define what is Artificial Intelligence
The term Artificial Intelligence (AI) was coined by John McCarthy, an emeritus Stanford Professor, in 1955.
He defined AI as “the science and engineering of making intelligent machines.”
Early AI research focused on programming machines to act intelligently (e.g., playing chess).
Today, AI emphasizes machines that can learn, somewhat like humans.
weak ai
Weak AI (Narrow AI): Designed for specific tasks, lacks general intelligence
(also called narrow AI) AI type that analyzes large amounts
of data and can be trained to solve specific problems. They are
powerful in their specifically trained area but not as flexible as
human intelligence in other areas.
strong ai
Strong AI (AGI): Hypothetical AI with human-like intelligence, self-awareness, and problem-solving abilities.
also known as artificial general intelligence (AGI) or
general AI, is a theoretical form of AI used to describe a certain
mindset of AI development. If researchers are able to develop
Strong AI, the machine would require an intelligence equal to
humans; it would have a self-aware consciousness that has the
ability to solve problems, learn, and plan for the future
list of Artificial Intelligence Applications (Systems)
Computer Vision
* Natural Language Processing
* Robotics
* Speech Recognition
* Intelligent Agents
* Monitoring and Surveillance Agents
* User Agents
Q: How are Artificial Intelligence (AI), Machine Learning (ML), Neural Networks (NN), and Deep Learning (DL) related?
A:
- Artificial Intelligence (AI): The broadest field, involving any machine that mimics human intelligence.
- Machine Learning (ML): A subset of AI that enables machines to learn from data without explicit programming.
- Neural Networks (NN): A subset of ML, inspired by the human brain, consisting of layers of interconnected “neurons.”
- Deep Learning (DL): A specialized type of neural network with multiple layers that can analyze complex patterns in large datasets.
🔹 Each is a subset of the previous:
AI ⊃ ML ⊃ Neural Networks ⊃ Deep Learning.
Example:
- AI: Chess-playing program.
- ML: Spam detection.
- Neural Networks: Handwritten digit recognition.
- Deep Learning: Facial recognition in self-driving cars.
What is an expert system, and give an example?
An expert system mimics human expertise in a domain.
Example: MYCIN (1970s) assisted physicians by recommending treatments for infectious diseases.
How does Machine Learning work?
ML develops algorithms that learn from data to make predictions without explicit programming.
Example: Spam detection in emails.
What is a neural network?
A set of virtual neurons that process information similarly to the human brain by assigning numerical weights to connections.
What are the four key components of a neural network?
Inputs
Weights
Bias/Threshold
Output
What is deep learning, and how is it different from ML?
Deep learning is a subset of ML that learns patterns from unstructured, unlabeled data.
Examples of Deep Learning Applications:
Speech recognition
Image recognition
NLP
Drug discovery
Customer relationship management
How does a neural network process an image?
Raw image input
Identifies pixel brightness & color
Detects edges & shadows
Recognizes features (eyes, lips, etc.)
Forms a complete representation (e.g., a face)
What are potential opportunities and threats of AI in court procedures?
Opportunities:
Faster case analysis
Enhanced defense & prosecution strategies
Predictive legal outcomes
Threats:
Bias in AI models
Ethical concerns
Lack of human oversight
carracteristic of Current Neural Networks
Because of improvements in algorithms and increasingly powerful
computer chips and storage, deep learning researchers are able
to model many more layers of virtual neurons in neural networks
than previously.
* Current neural networks can simulate billions of neurons.
what is an information system (IS) and give example
defined technically as a set of
interrelated components that collect, process, store, and distribute information
to support decision making and control in an organization.
examples: Transaction Processing Systems: e.g., ATMs and airplane seat reservation
systems.
* Decision support Systems: e.g., resource replenishment software.
* Enterprise resource planning: e.g., SAP R3
* E-commerce webpages
Traditional Organizational Roles Relatied to IS
1)Direct user: the users that interact directly with the existing IS.
* Skillset needed: know-how, how the IS operated, e.g., operator of a ticket reservation system.
2)Indirect user: users who depend on the results of direct users’ interactions with
existing IS.
* Skillset needed: know-what, what can be expected of the system, e.g., a general manager in a travel agency.
3)Designer: individuals who design, implement, test, and maintain IS.
* Skillset needed: problem-solving, how IS are built, e.g., computer scientist, system analyst, software developer,
UI/UX designer
what is Low Code
the collection of tools that provides visual, drag-and-drop interfaces
so one can build apps by connecting pre-built components.
* Within a single platform, one has access to data, logic, and UX design tools to create everything from simple
database apps to complex process automation solutions.
What is Citizen Developer
individuals with little to no coding experience - to build and
deploy custom business applications.
