Podcast
Questions and Answers
What is the primary focus of machine learning?
What is the primary focus of machine learning?
How does computer vision primarily gain understanding from images or videos?
How does computer vision primarily gain understanding from images or videos?
Which of the following best describes the role of natural language processing (NLP)?
Which of the following best describes the role of natural language processing (NLP)?
What aspect of AI systems is emphasized in continuous learning?
What aspect of AI systems is emphasized in continuous learning?
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In which scenario is AI most likely to impact business automation?
In which scenario is AI most likely to impact business automation?
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Which of the following best describes machine learning?
Which of the following best describes machine learning?
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Which application is primarily concerned with enabling computers to understand and generate human language?
Which application is primarily concerned with enabling computers to understand and generate human language?
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What is a key aspect of continuous learning in AI systems?
What is a key aspect of continuous learning in AI systems?
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How does AI contribute to business automation?
How does AI contribute to business automation?
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What does computer vision primarily focus on?
What does computer vision primarily focus on?
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Which of the following is NOT an element involved in intelligence as defined in AI?
Which of the following is NOT an element involved in intelligence as defined in AI?
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Which structure actively updates its internal models for continuous improvement?
Which structure actively updates its internal models for continuous improvement?
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What role does speech recognition play in AI?
What role does speech recognition play in AI?
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What is a primary application of natural language processing (NLP) in enhancing user interaction?
What is a primary application of natural language processing (NLP) in enhancing user interaction?
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Which of the following challenges does emotion and sentiment analysis face?
Which of the following challenges does emotion and sentiment analysis face?
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How do traditional chatbots differ from more advanced NLP systems?
How do traditional chatbots differ from more advanced NLP systems?
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What is a crucial factor enabling servers to effectively support backend logic for applications?
What is a crucial factor enabling servers to effectively support backend logic for applications?
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In the field of machine learning, what is an essential aspect of continuous learning?
In the field of machine learning, what is an essential aspect of continuous learning?
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Which statement best captures a trend in AI applications within businesses?
Which statement best captures a trend in AI applications within businesses?
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What does successful emotion and sentiment analysis require to accurately classify meanings?
What does successful emotion and sentiment analysis require to accurately classify meanings?
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Which of the following is a key challenge for AI systems in continuous learning?
Which of the following is a key challenge for AI systems in continuous learning?
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Study Notes
Introduction to AI
- AI is the science and engineering of making intelligent machines
- AI is about building and understanding intelligent entities, or agents
- Two main approaches to AI: Engineering and Cognitive modelling
What is involved in intelligence?
- Interacting with the real world
- Perceiving, understanding, and acting
- Speech recognition
- Image understanding
- Ability to take action
- Reasoning and planning
- Modelling the external world
- Solving new problems
- Planning and making decisions
- Dealing with unexpected problems and uncertainties
- Learning and adaptation
- Continuous learning and adaptation
- Internal models being continuously updated (e.g., a baby learning to categorize and recognize animals)
AI Sub-Fields
- Machine Learning (ML)
- Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
- Natural Language Processing (NLP)
- NLP is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
- Computer Vision
- Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos.
- From the perspective of engineering, it seeks to automate tasks that the human visual system can do.
Some of AI Application Areas
- AI utilizes techniques from several fundamental disciplines
- Mathematics
- Statistics
- Engineering
- Natural Science
- Computer Science
- Applications
- Computer Vision
- Natural Language Processing
- Information Retrieval
- Information Filtering
- Predictive Analytics
- Decision Analysis
- Robotics
- Facial recognition
- Chat bots
- Search engines
- Personalized shopping
- Credit scoring
- Utility demand management and response
- Robot-assisted surgery
Key Benefits of AI
- Create better products and services tailored to customers
- Reduce risk of failures or downtime
- Reduce costs thanks to predictive maintenance
- Increase operational efficiency
- Improve safety and compliance
- Instantly process data
- Get a better understanding of customers
AI in Big Tech Companies
- Google DeepMind
- OpenAI
- LLAMA by Meta
- Anthropic
- Bai Research
- Microsoft Research
- Apple
- WatsonX
AI in Google I/O 2017
- TensorFlow
- Google's Machine Learning System
- 2nd Generation TPU (Tensor Processing Unit)
- for TensorFlow Research Cloud
- Machine Learning API
- Google Cloud Machine Learning Engine
- Google Cloud Jobs API
- Google Cloud Vision API
- Google Cloud Speech API
- Google Cloud Natural Language API
- Google Cloud Translation API
- Google Cloud Video Intelligence API
- AI in Google's Products
- Google Assistant
- Google Lens
Goals of AI
- Thinking:
- Human: system that think like humans, Cognitive Science, Neuron Level, Neuroanatomical Level, Mind Level
- Rational: system that think rationally, Laws of thought (logic), A is X and X are Y then A is Y
- Acting:
- Human: system that act like humans, The Turing test, Understand language, Game Al, Control NPCs, Control the body
- Rational: system that act rationally, Doing the right thing, Maximize the goal achievement, given information, Doesn't necessary involve thinking, It involve solving
What's required for a machine to be Intelligent?
- Deep learning
- Machine learning
- Predictive analytics
- Translation
- Classification & Clustering
- Information extraction
- Speech to text
- Text to speech
- Image recognition
- Machine vision
- Natural language processing (NLP)
- Speech
- Expert systems
- Planning, scheduling & optimization
- Robotics
- Vision
AI Terms to Know
- Artificial Intelligence (AI)
- A field of computer science dedicated to the study of computer software making intelligent decisions, reasoning, and problem-solving.
- Machine Learning (ML)
- A field of AI focused on getting machines to act without being programmed to do so. Machines "learn" from patterns they recognize and adjust their behavior accordingly.
- Natural Language Processing (NLP)
- The ability of computers to understand or process natural human languages and derive meaning from them. NLP typically involves machine interpretation of text or speech recognition.
- Data Mining
- The process by which patterns are discovered within large sets of data with the goal of extracting useful information from it.
- Deep Learning (DL)
- A subset of machine learning that uses specialized algorithms to model and understand complex structures and relationships among data and datasets.
- Big Data
- The VOLUME, VARIETY and VELOCITY of the data creates challenges for data processing systems
- Algorithm
- Formula that represents a relationship between things. It's self-contained, step-by-step set of operations that automates a function, like a process, recommendation or analysis.
- Neural Network
- Computational approach that loosely models how the brain solves problems with layers of inputs and outputs. Rather than being programmed, the networks are trained with several thousand cycles of interaction.
Uses of AI
- Internet and Cloud
- Image classification, Language processing, E-commerce tagging, Digital personal assistants (e.g., Amazon’s Alexa and Apple’s Siri), Product recommendations
- Health Care
- Wearable health data recognition, Cancer cell detection, Diabetic grading, Drug discoveries
- Media
- Video search, Captioning, Programming recommendations (e.g., Netflix and Comcast), Virtual and augmented reality
- Security and Defense
- Face detection, Video surveillance, Geolocation, Real-time objects and threat detection (e.g., detect explosives and match faces to criminal databases in real time)
- Automation
- Store automation (e.g., Amazon’s new grab-and-go supermarket), Factory automation, Drones, Investment and insurance automation, Self-driving automobiles
What is Machine Learning?
- A Sub-Field of Artificial Intelligence (AI)
- Construction and study of systems that can learn from data
Types of Learning
- Supervised Learning
- Reliance on algorithm trained by human input
- Reduces expenditure on manual review for relevance and coding
- Unsupervised Learning
- High reliance on algorithm for raw data
- Large expenditure on manual review for review for relevance and coding
- Semi-Supervised Learning
- Reliance on analytics trained by human input
- Automated analysis using resulting model
- Reinforcement Learning
- Continually trained by human input
- Can be automated once maximally accurate
What Makes NLP so Hard?
- Ambiguity
- Non-standard language
- Complex entity names
- Phrasal verbs/idioms
- More Complex Language than English
- German
- Chinese
- Japanese
- Thai
Classical Machine Learning
- Classical machine learning began in the 1950s
- AI systems learned by ingesting data and getting better at recognizing patterns.
- The AI systems could predict things like the distance between points or the intensity of values.
- Algorithms:
- Decision tree
- Linear regression
- Logistic regression
Decision Tree
- A supervised learning algorithm
- Operates like a flowchart
Probabilistic Systems
- Linear Regression
- A relationship between things is graphed as a straight line to predict one variable from one or more others.
- Logistic Regression
- A relationship where the outcome is whether something is closer to 0 or 1 is graphed as an S curve
Neural Networks & Deep Learning
- Inspired by the human brain
- A building block, called perceptron, in a neural network is similar to a single neuron in the human brain.
- Input layer
- Hidden layers (one more or more)
- Output layer
Deep Learning Framework by Big Tech Companies
- Google: TensorFlow, Keras
- Facebook: Caffe2
- Amazon
- DSSTNE
- Microsoft: CNTK
Natural Language Processing (NLP)
- Study of interaction between computer and human languages
- Sub-Field of AI
- Aim: To build intelligent computer that can interact with human being like a human being.
- NLP = Computer Science + AI + Computational Linguistics
Speech-to-Text (TTS)
- A process of converting audio/speech to text.
- Steps:
- Text processing
- Feature extraction
- Life-like speech generation
- Speech
Computer Vision
- A field of computer science about how computers gain high-level understanding from digital images or videos.
Generative Adversarial Networks (GANs)
- A visual recognition system.
- Creates new drawings and photos by battling two convolutional neural networks (CNNs) against each other in a "contest" until one is better at producing art
Computer Vision Applications
- Drone or camera inspection
- Sensors (e.g., IBM Maximo visual inspection system)
Some key-points to remember
- Machine learning requires natural language processing (NLP) to understand human language. In the context of NLP, sentences are processed by identifying chunks of information.
- AI systems can make evidence-based, bias-free decisions.
- Debater Project uses 4 steps: Learn understand the topic, Build a position, Organize your proof, Respond to your opponent.
- Emotion detection identifies distinct human emotion types.
- Sentiment analysis is a measure of the strength of an emotion. A bot typically uses intent, entities, and dialog to complete interactions with users.
- CNNs and GANs are useful to analyze, classify, and generate images.
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Description
This quiz covers key concepts in artificial intelligence, including machine learning, computer vision, natural language processing, and continuous learning. Test your understanding of how these areas impact technology and business automation. Perfect for beginners looking to grasp foundational AI topics.
