What is artificial intelligence?
Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.
Applications and devices equipped with AI can see and identify objects. They can understand and respond to human language. They can learn from new information and experience. They can make detailed recommendations to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car).
But in 2024, most AI researchers and practitioners—and most AI-related headlines—are focused on breakthroughs in generative AI (gen AI), a technology that can create original text, images, video and other content. To fully understand generative AI, it’s important to first understand the technologies on which generative AI tools are built: machine learning (ML) and deep learning.
Introduction to Machine Learning
Machine learning (ML) is a branch of artificial intelligence (AI) that enables computer systems to learn from data and improve their performance without explicit programming. It involves the development of algorithms capable of analyzing data, identifying patterns, and making predictions or decisions based on the extracted insights.
Types of Machine Learning
Machine learning is generally categorized into three main types:
1. Supervised Learning:
The model is trained using labeled data, meaning the input comes with known outputs.
Examples: Email spam detection, image recognition, and medical diagnosis predictions.
2. Unsupervised Learning:
Used when there are no predefined labels, aiming to uncover hidden patterns and structures in the data.
Examples: Customer segmentation, anomaly detection, and recommendation systems.
3. Reinforcement Learning:
The model learns through trial and error, receiving rewards for correct actions and penalties for mistakes.
Examples: Game-playing AI (e.g., AlphaGo), robotics, and self-driving cars.
Applications of Machine Learning
Machine learning is widely used across various industries, including:
Healthcare: Disease diagnosis, personalized medicine, and drug discovery.
Finance: Fraud detection, risk assessment, and algorithmic trading.
Retail: Customer behavior analysis, inventory management, and recommendation engines.
Autonomous Systems: Self-driving cars, robotics, and smart assistants.
Challenges in Machine Learning
Despite its potential, ML faces several challenges, such as:
Data Quality: ML models depend on large and high-quality datasets.
Bias and Fairness: Models can inherit biases from training data, leading to ethical concerns.
Interpretability: Many ML models, especially deep learning, function as “black boxes,” making their decision-making processes difficult to understand.
Introduction to Deep Learning?
Deep learning is a specialized subset of machine learning that uses artificial neural networks to process large amounts of data. Unlike traditional ML, deep learning algorithms can automatically extract features from raw data, making them highly effective for complex tasks such as image recognition, natural language processing, and speech recognition.
The key component of deep learning is the neural network, which consists of multiple layers of interconnected nodes. A deep neural network typically has more than three layers, allowing it to learn complex patterns and representations.
Some common deep learning applications include:
Computer Vision (e.g., facial recognition and autonomous driving)
Natural Language Processing (e.g., chatbots and language translation)
Healthcare (e.g., disease diagnosis and drug discovery)
Deep learning requires vast amounts of data and computational power, often relying on GPUs and cloud computing. Due to its ability to handle unstructured data (like images and text), it is becoming increasingly important in AI research and real-world applications.