Artificial Intelligence vs Machine Learning, what’s the difference?

Artificial Intelligence (AI) and Machine Learning (ML) seem to be used interchangeably these days although they are not quite the same thing. We hear the two terms mentioned often in discussions about Big Data, Analytics, and the broader waves of technological advancements which are changing our world. So artificial intelligence vs machine learning, what exactly is the difference?

Artificial Intelligence is a broad concept and older than Machine Learning. In fact, many people consider machine learning to be a subset of artificial intelligence.

Where does AI end and Machine Learning Begin?

But what is AI? Speaking broadly, self-driven cars, smart homes and automated chatbots are all examples of Artificial Intelligence. AI is closely related to the field of robotics and we are seeing intelligent robots being deployed across multiple sectors, such as bioengineering and healthcare, manufacturing, military, agriculture, and the service industry.

Computer scientists offer many different definitions of artificial intelligence (AI), but at its core, AI involves machines that think and act as humans. On a practical level, this consists of programming computer systems or algorithms that perform certain tasks the same way a human would, optimizing many aspects of manual labour. In essence, this is the ‘intelligence’ part of AI.

There are many applications of Artificial Intelligence (AI) today. From virtual assistants like Amazon’s Alexa, Google’s Siri and Microsoft’s Cortana to modern gaming applications and Natural Language Processing. Giving to a computer or machine the ability to understanding the natural language spoken by humans and perform tasks accordingly produces immense benefits in a many sectors. The manufacturing sector, for one, has managed to make considerable improvements thanks to the deployment of Industrial Robots. These are more sophisticated robots with efficient processors and a huge amount of memory that can adjust to new environment and collect data using light, temperature, sound, etc.

We are only going to see more applications of AI in the coming years in areas, such as facial recognition, autonomous vehicles, military simulations, and many more.

So Artificial Intelligence vs Machine Learning, how exactly is it different?


Plain and simple, Machine Learning is a type of Artificial Intelligence. However, it goes one step further than AI to give the ability to a computer to learn without being explicitly programmed to do so. Machine Learning relies on algorithms to parse data, learn from it and make decisions accordingly. It is a development of self-learning algorithms, and Artificial Intelligence is the basis of developing a system or software that ‘thinks’ like a human.

We know that data mining uses algorithms to look for patterns in a given set of information. Machine learning does the same thing, but changes its program’s behavior based on what it learns. So, in a way, it learns from the data it parses, adapts and makes decisions accordingly.

One application of machine learning that has become very popular recently is image recognition. These applications initially require human input, here people look at many pictures and tell the system what the image in the picture is. After thousands of repetitions, the software builds a database of similar patterns and learns how to associate these patterns of pixels with horses, dogs, cats, flowers, trees, houses, etc., and can then make a pretty good guess about the content of images.

Many web-based companies today use machine learning to power their recommendation algorithms. Facebook’s newsfeed, Amazon’s highlights and Netflix’s movie suggestions are all based predictions that arise from patterns in their existing data.

The results that machine learning produces are immensely beneficial to enterprises that adopt it and speak for themselves. More and more companies are beginning to use machine learning for predictive analytics. As big data analysis becomes more popular, machine learning technology has become more commonplace, and is now a standard feature in many analytics tools.

These days, machine learning is mainly associated with statistics, data mining and predictive analytics. While any system can exhibit some AI features, machine learning systems don’t necessarily need to have any other features of artificial intelligence and they are a step above basic AI. Machine learning achieves AI but AI doesn’t necessarily implement machine learning.

In fact, computer scientists and developers view Machine Learning as a huge leap for AI. Because it requires massive amounts of data to work, and this data is being collected by the billions of sensors that are continuing to come online in the Internet of Things, machine learning is essentially ushering in the next stage of AI while also driving the adoption of the Internet of Things. It is because of AI that IoT is useful but Machine Learning is what makes it possible.

On the industrial side, both AI and Machine Learning can be applied to predict when machines will need maintenance or analyze manufacturing processes to make efficiency gains, thus saving millions of dollars.

The bottom line is that AI and Machine Learning are deeply intertwined. There is no Artificial Intelligence without Machine Learning and vice versa.

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