Artificial Intelligence (AI) has long been a captivating buzzword, evoking both fascination and fear. Unfortunately, much of the fear stems from a lack of understanding about this technology. Fear not! Like any complex concept, AI is built upon a collection of basic topics that are entirely manageable. The challenge lies in not complicating it with the overwhelming sea of hyper-niche jargon and complex debates that flood the news. But fear not again! We are here to ensure that no one feels left behind, as these concepts are entirely digestible. So, let's embark on a journey together, with a teacher-student analogy, to demystify AI, Machine Learning (ML), and Deep Learning, and discover how they shape the current AI landscape.
The Nest of Artificial Intelligence
Artificial Intelligence (AI)
Think of AI as the overarching concept of what we’re discussing. The goal of AI is to match or exceed human capabilities in various areas, including development, discovery, inference, and reasoning, among others. AI achieves this by emulating human-like attributes such as knowledge, motion, voice, text, and vision. In our analogy, imagine we have a blank slate of a student. AI represents a student's desire to mimic human intelligence and perform tasks that usually require human thinking. It's like teaching the student to be clever and autonomous, capable of making decisions, recognizing patterns, and even learning from experience. AI encompasses a broad spectrum of techniques, with Machine Learning and Deep Learning as essential subjects in the AI curriculum.
Machine Learning (ML)
This is a subset of Artificial Intelligence. ML allows AI systems to recognize patterns, make predictions, and take actions based on examples and experiences provided in the form of training data. For advanced systems to be able to have human-like (or superior) decision-making, they have to first be trained with massively large data sets. These are the 3 basic building blocks:
Supervised Learning - This uses labeled data to train ML models - there is human intervention in these models to structure, label and train this data. Imagine a teacher guiding a student by giving them examples (structured data) and telling them the correct answers. The AI system learns from these examples to make future predictions or decisions when it is given new, unseen data. For instance, in image recognition, let’s say the student is shown images with labels like "cat" or "dog," she then learns to recognize cats and dogs in new pictures.
Unsupervised Learning - Models take in unstructured (unlabeled) data to train machines with no fixed output variable. Think of this as learning without a teacher to guide you. In this method, the AI system explores the data on its own to find patterns or structures within it. It doesn't have labeled examples, so it tries to group similar data points together or discover hidden relationships. Unsupervised learning is often used for tasks like clustering, where the AI groups similar items together without knowing what they represent.
Reinforcement Learning - Reinforcement learning uses an agent and an environment to produce actions and rewards. This is like teaching the student through rewards and punishments. The AI agent interacts with an environment and receives rewards for making good decisions and punishments for bad ones. It learns to take actions that maximize the total reward it receives over time. A classic example of reinforcement learning would be training the student to play a game of chess – she learns by trying different moves and getting rewards for making good moves and penalties for bad ones until she becomes better at the game.
Great Resources:
https://www.youtube.com/watch?v=1FZ0A1QCMWc
https://www.youtube.com/watch?v=4RixMPF4xis
https://www.youtube.com/watch?v=q6kJ71tEYqM
Deep Learning
Deep Learning is an exciting subset of Machine Learning, taking inspiration from the human brain's structure. It's like having a bunch of interconnected neurons, just like the brain's decision-making cells, working together in multiple layers. Think of it as a group of clever students working together, each layer learning different aspects of a problem.
At the core of Deep Learning are Neural Networks, which act as complex nodes capable of analyzing information both forward and backward. Imagine a student tasked with sorting fruits like oranges, cherries, and apples from a basket. In traditional Machine Learning, a teacher would explain the specific features (let’s say just size, stem, and texture) that help the student identify each fruit.
Deep Learning, however, changes the game! There's no teacher involved. The student is like an adventurous explorer, free to investigate each fruit in its entirety. Instead of being told what makes an orange different from a cherry or an apple, the student's neural networks can take in a variety of data from the fruits (environmental setting, weight, color, comparison to other fruits around it) and figure out the distinctions all by themselves.
In this way, Deep Learning enables the student (AI) to process diverse information and make specific decisions based on the inputs they receive. It's like having clever students who can learn on their own and become experts in identifying different types of fruits just by exploring their unique characteristics. This autonomy makes Deep Learning a powerful tool in recognizing patterns, understanding complex data, and solving tasks without needing explicit instructions from a teacher.
Applications of Deep Learning
In upcoming blog posts, we will delve further into the massive influence of Deep Learning and explore the diverse array of AI products spanning different industries. Broadly speaking, Deep Learning's remarkable ability to identify intricate patterns and comprehend complex data has significantly transformed various sectors:
Computer Vision: Deep learning powers image and object recognition systems. It can identify objects, faces, and even detect anomalies in medical images.
Natural Language Processing (NLP): Deep learning models can understand and generate human language. It enables chatbots, voice assistants, and language translation.
Autonomous Systems: Deep learning is at the core of self-driving cars and drones, enabling them to navigate and make decisions in complex environments.
Healthcare: Deep learning aids in disease diagnosis, analyzing medical images, and predicting patient outcomes.
Recommendation Systems: Deep learning is used in personalized recommendations for products, movies, and content based on user preferences.
Great Resources:
https://www.youtube.com/watch?v=6M5VXKLf4D4
https://www.youtube.com/watch?v=jmmW0F0biz0
The foundation of AI rests upon these fundamental concepts, and its remarkable progress has given rise to a plethora of awe-inspiring products. We’re going to dig into some of these products and further foundational concepts in the world of Artificial Intelligence (LLMs, Transformers, Generative AI etc).