A Simple Guide to the Differences Between AI and Machine Learning
Artificial Intelligence (AI) and Machine Learning (ML) are often used correspondently, but they are not the same thing. While both have revolutionized the way we use technology, they have a few differences. If you tend to mix them up quite often, read this blog till the very end, as I’ll be exploring the key differences between AI and ML so that you don’t make a mistake between the two again.
Before we get any further into the differences, here’s a quick look at what AI and ML are.
What Is Artificial Intelligence (AI)?
Artificial Intelligence refers to machines capable of performing tasks that would normally require human intelligence, for example, understanding natural language, recognizing speech and images, and making decisions. AI systems can be programmed to make decisions based on data and to learn from experience, allowing them to improve their performance over time.
What Is Machine Learning (ML)?
A subset of Artificial Intelligence, Machine Learning allows machines to learn from data without being explicitly programmed. Instead, ML algorithms use statistical models to analyze data and learn patterns, which they use to make predictions or decisions. The more data an ML algorithm is exposed to, the better it predicts outcomes.
The Key Differences Between AI vs. ML
Now that you know the basics of these two concepts, below are their differences.
AI is a broader concept, while Machine Learning is a subset of AI.
AI encompasses a wide range of techniques, including Machine Learning, Natural Language Processing (NLP), Robotics, and Computer Vision. In contrast, Machine Learning is just one of the techniques used in AI that involves training machines on large datasets to make predictions or decisions.
AI is capable of decision-making, while Machine Learning is not.
AI systems can make decisions based on data and rules. In contrast, Machine Learning algorithms can only make predictions based on data, such as which product a customer is more likely to buy based on their purchasing history.
AI is more complex than Machine Learning.
AI systems are more complex than Machine Learning algorithms because they are capable of decision-making, which requires a higher level of intelligence. Machine Learning algorithms, alternatively, are designed to perform specific tasks, such as image recognition or fraud detection.
AI is more versatile than Machine Learning.
AI systems can be used on a broader array of applications, from self-driving cars to chatbots to virtual assistants, while Machine Learning algorithms are typically designed for specific tasks.
Machine Learning requires large amounts of data, while AI does not.
Machine Learning algorithms entail large amounts of data to train, while AI systems may or may not require as much data depending on the complexity of the task. For instance, an AI system designed to recognize faces in images may require less data than an ML algorithm designed to predict stock prices based on market trends.
To put it more clearly, here are a few examples.
One example of an AI system is chatbots like ChatGPT. Chatbots are programs designed to interact with customers in a natural language conversation. These use natural language processing (NLP) to interpret our message and provide us with a suitable response. The AI system behind the chatbot is trained on a large dataset of human language to understand and respond to customer queries. Chatbots are an example of an AI system because they require a wide range of skills, including NLP, speech recognition, and decision-making, to function correctly.
On the other hand, an example of ML in action is how we receive personalized recommendations on Netflix. Netflix uses an ML algorithm to recommend movies and TV shows to us based on our viewing history. The algorithm analyzes a user’s viewing history, along with the viewing history of other users who have watched similar content, to predict which movies and TV shows they are most likely to enjoy. Put simply; ML learns from data to make predictions.
To Sum Up
In summary, both AI and ML are powerful tools transforming how we live and work. By understanding the differences between the two, we can better appreciate the unique strengths and limitations of each technology and use them to achieve our goals.
Ultimately, the choice between AI and Machine Learning depends on the application and the problem that needs to be solved. So, businesses can also make better decisions about which technology to use by understanding their differences.
Got any more queries regarding the two in mind? Feel free to comment below.