As technology gains popularity, terms like AI, deep learning, and machine learning are often used interchangeably.owever, what sets each of them apart and makes them unique, and how do they relate to one another? Let’s explore their distinctions and significance.
The history of AI dates back to ancient times, when myths and folklore spoke of intelligent mechanical beings. However, the modern development of AI began in the 1940s. In 1950, Alan Turing, a brilliant mathematician and logician, proposed a groundbreaking concept known as the “Imitation Game” or the Turing Test. It aimed to measure a machine’s ability to exhibit human-like intelligence, sparking the quest for true AI.
During the 1950s and 1970s, the field of AI saw significant advancements in problem-solving and reasoning. Researchers developed early AI programs and languages like Lisp to facilitate AI research.
In the 1980s and 1990s, AI faced challenges due to unrealistic expectations and limited computing power, leading to what was known as the “AI winter.” However, AI research rebounded with the emergence of new algorithms and techniques.
From the late 1990s to the present, AI has experienced remarkable growth due to advances in machine learning, neural networks, and big data. AI applications have become pervasive in various industries, including natural language processing, computer vision, robotics, and more.
Today, AI continues to evolve rapidly, driving innovations and reshaping numerous aspects of our daily lives and industries worldwide. The legacy of the “Imitation Game” lives on, propelling AI research and inspiring new generations of AI pioneers.
So, what really is Artificial intelligence?
AI aims to create systems that can perform tasks that typically require human intelligence. It focuses on imitating human-like behavior in specific domains or even as a whole, such as exploring, reasoning, and decision-making.
As AI advanced, It needed a way to understand its surroundings and make decisions similar to humans. This, in turn, gave rise to Machine learning.
Machine Learning is a subset of AI where computers can learn from data without being explicitly programmed for every task. It involves giving the computer data and allowing it to make predictions and decisions based on that data.
There are two main types of ML:
1. Supervised Learning: The computer learns from labeled data, where each data point has input features and a corresponding output label. It aims to learn the mapping between inputs and outputs to make accurate predictions based on new, unseen data.
2. Unsupervised Learning: The computer explores unlabeled data without specific output labels. Its goal is to find inherent patterns, structures, or relationships within the data without predefining categories.
In simple terms, supervised learning is when you give a computer data and tell it what to do with it; the computer then learns the data and uses it in other scenarios, whereas in unsupervised learning, you’re just giving the computer data with no context, and the job of the computer is to find relations amongst the data and any hidden patterns. Essentially, in unsupervised learning, the learner is not provided with context or guidance on what to do with the data given; it has to find its own path and identify hidden patterns.
This then leads into the realm of Deep Learning, which makes use of Neural Networks, similar to the billions of interconnected neurons in our human brain, to achieve remarkable feats in AI.
Deep Learning is a subfield of Machine Learning that focuses on using artificial neural networks to process and understand complex data. The word deep implies it has multiple layers of Neural Networks, the backbone of deep learning, and they are inspired by the structure of the human brain.
The power of Deep Learning lies in its ability to learn hierarchical representations from the data. Deep Neural Networks consist of multiple layers of interconnected nodes, where each layer processes data and extracts more abstract and meaningful features as it goes deeper. This capability allows DL to handle intricate tasks, such as image and speech recognition, with impressive performance.
Makes sense? No? Let’s illustrate this using a candy sorting task.
Real-Life Example: Candy Sorting:
To illustrate the difference between ML and DL, let’s consider a candy sorting task.
Imagine you have a big box of colorful candies, and your task is to sort them into different groups based on their colors. However, there are no labels or instructions on how to sort them. You need to figure out a way to group the candies on your own. And here’s how ML and DL would differ in the sorting:
Machine Learning Approach: In traditional ML, we manually define rules to sort candies based on simple features like color, such as mostly red in one category and mostly blue in another, and so on. While this approach works for simple tasks, it may struggle with complex scenarios and subtle variations.
Deep Learning Approach: In DL, we use a deep neural network to automatically learn patterns from images of candies. The neural network processes the data layer by layer, recognizing complex patterns such as shades, texture, shape, etc. and sorting candies more accurately.
In this example, deep learning allows the computer to develop its own understanding of the candy colors by learning from examples, much like how our brain learns to recognize colors over time. This ability to automatically learn from and adapt to data is what makes deep learning so powerful. It’s no surprise that deep learning is ubiquitous in our daily lives. From browsing the internet to using speech recognition tools, generated captions on YouTube videos, and even biometric recognition on smartphones and self-driving cars, the applications of deep learning are endless!
Their Relationship: AI, ML, and DL
AI is the parent set of DL, encompassing all attributes and capabilities that humans possess. ML and DL are subsets of AI, and DL uses a specialized subfield within ML. DL’s power lies in its ability to automatically learn intricate representations from data, making it effective in handling complex tasks.