What is Artificial Intelligence?
Artificial Intelligence (AI) refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem-solving, and decision-making. AI can process large amounts of data in ways that humans cannot.
What is Machine Learning?
Machine learning (ML) is a subset of Artificial Intelligence and is the science of getting computers to learn and act as humans do. It involves the construction of models that can analyze and interpret complex patterns in data, enabling computers to learn and improve from experience.
Artificial intelligence (AI) vs. machine learning (ML)
While AI and ML are not quite the same thing, they are closely connected. The simplest way to understand how AI and ML relate to each other is:
ARTIFICIAL INTELLIGENCE (AI)
- Definition: A broad term for machine-based applications that mimic human intelligence. Not all Al solutions are ML.
- Uses: Best for completing a complex human task with efficiency.
- Methods: It uses a wide range of methods, like rule-based, neural networks, computer vision, etc.
- Practice: Its implementation depends on the task. Often prebuilt and accessed via APIs.
MACHINE LEARNING (ML)
- Definition: ML is an artificial intelligence methodology. All ML solutions are Al solutions.
- Uses: Best for identifying patterns in large sets of data to solve specific problems.
- Methods: We manually select and extract features from raw data and assign weights to train the model.
- Practice: We train new or existing ML models for our specific use case. Prebuilt ML APIs are available.
How are AI and ML connected?
Artificial Intelligence (AI) started as a subfield of computer science with a focus on solving tasks that humans can but computers can’t do (for instance, image recognition). AI can be approached in many ways, for example, by writing a computer program that implements a set of rules developed by domain experts. Now, hand-crafting rules can be very laborious and time-consuming.
The field of machine learning—originally, we can consider it a subfield of AI—was concerned with developing algorithms so that computers can automatically learn models from data.
Let’s take an example where we wish to create a program that can identify handwritten numbers from pictures. One way would be to examine each of these pictures and create a set of (nested) if-this-than-that rules to determine which picture is shown in a given picture (by examining the relative positions of pixels, for example). Using a machine learning algorithm, which can fit a prediction model based on thousands of tagged image samples we may have gathered in a database, is an additional strategy. There is also deep learning, which is a branch of machine learning that describes a subset of models that perform certain tasks, like natural language processing and picture recognition.
In short, machine learning helps to develop “AI,” However, AI doesn’t necessarily have to be developed using machine learning, although machine learning makes “AI” much more convenient.
Benefits of using AI and ML together
AI and ML are beneficial to a vast array of companies in many industries, including retail, banking and finance, healthcare, sales and marketing, cybersecurity, customer service, transportation, and manufacturing. They use artificial intelligence and machine learning to increase profitability, work processes, and customer satisfaction.
Some of the key benefits of using artificial intelligence and machine learning are:
- Automation
- Improved Efficiency & Productivity
- Faster decision-making
- Adaptability
- Personalization
Conclusion
Artificial Intelligence and Machine Learning, are both being broadly used in several ways. There are lots of real-world examples of both technologies. Lastly, we can say that AI is responsible for solving tasks that require human intelligence but, ML is responsible for solving tasks after learning from data and providing predictions.
All the Machine Learning (ML) we do is part of Artificial Intelligence (AI), but not all AI is ML.