Machine Learning is a subfield of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without being explicitly programmed to do so. The primary goal of machine learning is to allow computers to learn from experience, much like humans do.
According to International Organization for Standardization (ISO), Machine Learning (ML) refers to the ability of a computer system to learn from data without being explicitly programmed. (iso.org).
Machine learning algorithms are designed to improve over time as they are exposed to more data. They accomplish this by identifying patterns within the data and adjusting their outputs or actions based on these patterns. For instance, a machine learning algorithm used for email filtering learns to distinguish between spam and non-spam messages by analyzing the features of emails marked by users as spam in the past. The more data it processes, the more accurately it can perform its task.
ML is behind many of the technological advancements we see today. It powers the recommendation systems of services like Netflix and Amazon, drives the predictive text and voice recognition capabilities of our smartphones, and is key to the development of autonomous vehicles. In short, ML is a crucial component of the AI revolution, enabling intelligent systems to adapt and improve over time.
How does ML differ from AI?
While ML and AI are often used interchangeably, they do not refer to the same thing. To better understand this, one might picture AI as a large circle that encompasses various technologies and approaches designed to make machines intelligent. Within that circle, there is a smaller one representing ML.
AI is the broader concept of machines being able to carry out tasks in a way that we would consider ‘smart’ or ‘intelligent.’ It is about designing and implementing intelligent behavior, including the ability to learn and improve. AI includes a range of techniques and approaches, not all of which involve learning from data. For instance, rule-based systems, which operate based on pre-set if-then rules, are a form of AI that doesn’t involve learning.
On the other hand, ML is a subset of AI that uses statistical methods to enable machines to improve with experience. It revolves around the idea that machines can be given access to data and learn for themselves. It is, in a way, the leading edge of AI, as it is through learning from data that many modern AI achievements have been made possible.
In other words, all ML is AI, but not all AI involves machine learning. ML is just one of many tools and approaches used in AI research. AI that doesn’t involve learning from data may be based on predefined rules or other forms of human input.
Another term used in this context is Deep learning. Deep learning is a subfield of machine learning that focuses on training artificial neural networks to learn and make predictions or decisions without explicitly being programmed. Neural networks are inspired by the structure and function of the human brain, specifically the interconnected network of neurons. Deep learning algorithms use multiple layers of artificial neurons, known as neural networks, to extract high-level features from raw data and make sense of complex patterns. Deep learning models can handle large-scale and unstructured data, such as images, audio, text, and video, with exceptional accuracy.
Figure 1 – AI, ML, and Deep learning, Reference: Google
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