AI Fundamentals and Machine Learning Overview

AI Fundamentals

• AI systems require algorithms, data, and hardware to function.
• Programming instructions are meticulous and time-consuming. Clarity and transparency are crucial.
• Machine learning allows computers to learn from data and adapt without explicit programming. This approach is often opaque in its decision-making process. Most present-day AI is based on ML principles.
• Artificial Intelligence is the overarching field devoted to making machines think like humans.

Machine Learning Subsets

• Machine learning enables computers to perform tasks without explicit programming.
• Deep learning is a subset of machine learning using artificial neural networks.

Biased Training Data

• Biased training data can lead to flawed machine learning outcomes. The "husky vs. wolf" example illustrates how AI models can misclassify due to skewed training data.

AI in Skin Cancer Detection

• Researchers developed AI models to detect cancerous skin lesions.
• The AI's predictions were sometimes erroneous, mistakenly associating common medical instruments with cancer.

Narrow & General AI

• Narrow AI is designed to assist with or take over specific tasks.
• General AI can transfer knowledge from one domain to another.
• Super AI is significantly more intelligent than humans.
• Human centered Al responds to human laziness creating more innovation

Human-Centered AI

• Humans are often lazy by design, which drives innovation. AI is a useful tool.
• View AI as a tool, not a religion.
• Al can be a third arm or a second brain.
• Transparency is needed.

Algorithms

• Algorithms are sets of instructions to solve problems or complete tasks.
• Self-learning algorithms use rules but often become complex or impossible to define fully with explicit rules.
• Artificial intelligence mimics the brain, making connections between input and output.

Self-Learning Algorithms

• Input and output examples are used to develop self-learning algorithms.
• A pattern is identified, and a rule is created to connect inputs and outputs.
• Attributes within the input data have values.
• A variable assigns weights to attributes, enabling efficient calculation of outputs from complex data.

Neural Networks

• Neural networks are a popular form of AI inspired by the structure of neurons in the human brain.
• They adapt and evolve their internal structure based on data.
• This adaptability makes neural networks valuable for complex problems.

Neural Networks (continued)

• Neural networks are sophisticated systems with multiple layers, processing input attributes, and outputting predictions.

AI Model Training

• Training Al models is like baking a cake. The recipe (algorithm) is used with different ingredients (data) for unique results.
• Overfitting occurs when a model is too complex for the existing training data.
• Underfitting occurs when the model is too simple. This should result in improvement with unknown data.

Supervised Learning

• Supervised learning uses labeled data to train models; the correct output is taught.
• Models are then tested to classify new data (e.g.spam).
• Regression involves predicting a continuous output value.

Unsupervised Learning

• Unsupervised learning uses unlabeled data to discover patterns or groupings within the data.
• No human input determines the outcome.

Reinforcement Learning

• Reinforcement learning involves learning by trying and evaluating actions and reactions.
• The aim is learning how to act in order to maximize rewards.
• This is demonstrated by AlphaGo which learned to play the game through practice and experience.

Natural Language Processing

• This involves AI understanding and using human language.
• Word embeddings map words to numerical vectors.
• Transformer architecture is a recent advancement in NLP.

Five Data Dimensions

• Five essential aspects of modern data: volume, variety, velocity, value, and veracity.
• These aspects are related to high quality structured data.

Veracity and Value of Data

• Data quality is crucial for effective algorithms.
• Garbage in, garbage out is a very important principle in Al.
• Poor data can lead to biased AI systems.

Gathering Consumer Data

• Gathering data and building a customer profile helps understand consumer behavior and optimize consumer experiences.

GDPR (General Data Protection Regulation)

• GDPR provides rules for handling and protecting personal information within the European Union.
• This is a concern for companies that collect and use consumer data.

AI Energy Consumption

• Training and running AI models consume significant energy.
• Large language models (e.g. GPT-4) can use an enormous amount of energy.
• Energy consumption of Al systems needs further study and potential solutions.

Energy Consumption of AI (Continued)

• AI systems like GPT-4 use a massive amount of energy during training and operation.

Optimizing AI Models

• Models can be optimized to operate on local devices like the Gemini Nano.

Microchips

• Microchip technology continues to decrease in physical size and increase in efficiency.

Slaughterbots

• Research on technology like autonomous flying and face recognition continues.
• New approaches to recharging devices are emerging.

AI Fundamental Principles

• Ethics, security, fairness and explainability are important considerations for developing AI systems.
• The idea of laws like Asimov's for robotics provides a framework for ethical development of AI.

This quiz explores the basics of artificial intelligence and machine learning, covering key concepts such as algorithms, data, and the implications of biased training data. It also discusses the application of AI in skin cancer detection and the significance of deep learning in modern AI systems.