What is the difference between AI and ML?

You might have heard a lot about Artificial Intelligence (AI) and Machine Learning (ML). Both are heavily technological terms and the difference is not quite clear. In this blog post, we will help you differentiate the two and have a deeper understanding of what AI and ML are. Keep on reading not to feel embarrassed when your colleagues talk about AI and ML.

What is artificial intelligence (AI)?

Let’s first understand what AI means. To put it in simple words, AI means that machines think like humans. If you compare with regular computers where all the functions are prescribed, AI is different in that the machines can “think”. They can analyze the data for correlations and patterns, and use these patterns to make predictions about future states. The machines review millions of examples and make “predictions” about their state. There are dozens of Artificial Intelligence apps and here is how to build an AI app if this subject also interests you.

What is machine learning (ML)?

Machine learning is a branch of artificial intelligence. Let’s suppose that AI is the brain then ML is the neuron. ML allows machines to learn from data and past experiences. By doing so, machines are able to make predictions with minimum human intervention. For example, ML allows computers to recognize pictures of different people. They do that by learning to program themselves through experience. You may also be interested in how to build a Machine Learning app. If so, the linked article might interest you.

What is the difference between AI and ML?

To put it in simple words, AI is the bigger space where ML functions as its application. This seems not very clear, I guess. Let’s have a look at this picture.

It is clear that AI is more than Machine Learning. It is composed of NLP, Robotics, etc.

Let’s look at some examples. Self-driving cars, smart assistants, and virtual travel booking agents are all examples of AI. On the other hand, image recognition is the function of ML. As is clear, when we talk about AI we mean a lot more capabilities than only ML since ML is only a component of AI.

How do AI and ML work together?

Now let’s contemplate on what is the correlation between AI and ML. The idea behind ML is that machines should be able to learn and adapt their experience. When it comes to AI, it is more about the execution of “Smart” functions. In other words, AI puts ML into execution. It applies machine learning, deep learning, and other techniques. And all of these are done to solve very practical problems like operating drones or voice assistants.

Capabilities of AI and Machines Learning

In order to understand the capabilities of Machine Learning, let’s look at their algorithms.

Neural networks: these networks simulate the human brain. They do that with a huge number of linked processing nodes.

Linear regression and logistic regression: With this algorithm’s help, it is possible to predict numerical values based on linear relationships between different values. With the help of logistic regression, it is possible to make predictions based on yes/no questions.

Clustering: with clustering, patterns can be identified so that they can be grouped.

Decision trees: with the algorithm, it is possible to predict numerical values and classify data into categories.

Random forest: under this algorithm, decisions of decision trees are combined.

Now let’s look at the capabilities of AI:

Machine learning: Machine Learning lets AI applications learn from data using mathematics and statistics.

Neural Network: It is a network of interconnected units that resemble human neurons. These units process information based on external inputs.

Deep Learning: This is similar to the neural networks but it makes use of huge amounts of networks with multiple layers of processing units.

Computer vision: It is based on Deep Learning and pattern recognition of picture and video data.

Natural Language Processing (NLP): This is the most advanced use of Artificial Intelligence that lets machines analyze, understand, and ultimately converse in human language.

Benefits of AI and Machine Learning

Artificial Intelligence and Machine Learning have penetrated many fields, including Science, Economics, Law, and many more to greater heights. Here is what AI and ML have benefitted in different areas.

Space exploration

AI and ML gather lots of data and make manipulations with this data when it comes to space exploration. The human brain would not be able to process so much data. We have a lot to learn when it comes to discovering the secrets of the Cosmos and AI and ML are great assistants in that.

Healthcare

Today AI and ML allow doctors to identify patterns that are not visible to human eyes. They are used in treatment and diagnosis.

Digital Marketing

Customer service has become a lot easier today. AI-driven content is powered by Machine learning which learns the patterns of user behavior. This is how Google can advertise depending on the review that we give on the product.

Transportation

Self-driving cars are an excellent example of AI-driven technology. GPS and other rout directing technologies are also powered by AI and ML.

Education

An example may be to identify patterns of learning among students and suggest courses based on them. This practice is used at Coursera. If a student answers the same question wrongly several times, the tutor is alerted to focus on that mistake.

Ecommerce

Virtual assistants are a very good example of AI and ML-driven technology. Chatbots and other assistants are excellent in customer care.

Environment protection

An example is Wildtrack which identifies, monitors, and tracks animals. The company protects animals from threats.

Final Thoughts

In this blog post, we provided some insights into the difference between AI and ML. Since the difference is blurred because ML is part of AI, we did our best to make the distinction clear but you may need to do further research. Since technology is advancing in the direction of Artificial Intelligence, you may need this information not only for your general interest but in your professional career.

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