5 Key Differences Between Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning are two spearheading technologies of today taking over the world rapidly. Both of these words, being evolved from computer science, are very closely related to each other.

This is why most people confuse one with the other. However, there are many key differences between artificial intelligence and machine learning, settling each other aside. But before we understand these differences, let’s learn about AI and ML individually and then jump to artificial intelligence vs. machine learning.

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What is Artificial Intelligence?

Artificial intelligence allows computers and machines to perform and mimic human intelligence tasks. Some prominent features of artificial intelligence are impersonating human minds, eliminating tedious tasks, data ingestion, and cloud computing.

For example, earlier, people across the world used to carry printed maps while traveling, but now we navigate using Google Maps in a single click, all thanks to AI.

AI systems are classified into three categories: ANI (Artificial Narrow Intelligence), which is a goal-oriented program to perform a single task; AGI (Artificial General Intelligence), which allows the machine to learn and understand things as humans do; ASI (Artificial Super Intelligence) is a hypothetical AI where machines can surpass humans.

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What is Machine Learning?

Machine Learning studies algorithms and statistical models that machines use to perform a specific task without explicit instructions. Most industries across the globe have recognized the importance of machine learning when applied to products ranging from financial services to healthcare.

Some key features of machine learning include: automating repetitive tasks, excellent compatibility with IoT, great accuracy, and boosting business intelligence. The ML algorithms are divided into supervised, unsupervised, and reinforcement learning.

Since we now understand AI and ML individually, let’s explore the differences between artificial intelligence and machine learning.

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5 Key Differences Between Artificial Intelligence and Machine Learning

Artificial Intelligence	Machine Learning
artificial intelligence means embedding human intelligence into machines.	Machine learning is a sub-division within AI that efficiently uses statistical methods to do data-related work.
AI consists of machines that can perform all sorts of tasks independently.	Conversely, ML analyzes a series of algorithms to create a program that can automate data-related tasks.
AI works toward embedding intelligence into machines to help humans solve complex problems.	ML aims to train machines with data to perform tasks with great accuracy.
The scope of AI is vast and diversified.	The scope of ML is limited when compared to AI.
The skills required to work with AI are Programming, data science, algorithms, data mining, and robotics.	The skills required to work with ML are mathematics, data modeling, statistics, and programming languages.
Artificial Intelligence can deal with all kinds of data.	Machine learning can deal with only structured and semi-structured data.
It uses logic and decision models	It uses statistical models
AI is a part of data science	ML is a part of AI and data science
Examples of AI are Siri, Tesla’s self-driving cars, and Alexa.	Examples of ML are Google search engines, news classification, and stock prediction.

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What can AI and ML do together?

As most companies rely on vast amounts of data generated, it can be difficult for humans to handle using manual ways. This is where AI and ML come into the picture by benefiting companies across various industries by creating and driving endless possibilities.

Here are some of the prominent ones:

Discover Insights: AI and ML can enable companies to discover and leverage valuable insights from a more excellent range of structured and unstructured data sources.

Faster Decision Making: Since these technologies cut down manual tasks, it automatically reduces human errors, increasing accuracy and fastening the decision-making process.

Boosting Efficiency: Automating the process that humans initially did can cut down huge costs and frees up time for other priorities.

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Companies using AI and ML

Alibaba: The company uses AI in its daily operations to predict customer behavior and to create intelligent cities.

Amazon: Amazon uses AI to ship things before even the user plans to buy the product.

Apple: Apple uses AI and ML in products such as the iPhone to enable the FaceID feature and for its voice assistants.

Yelp: The company uses machine learning algorithms to help its human staff to categorize, compile and label images more effectively.

Pinterest: It uses ML to discover content and recommend its users using ML algorithms.

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Conclusion

Even though there are a lot of differences between artificial intelligence and machine learning, both technologies are being used to solve various real-time problems across industries.

While AI is responsible for solving tasks without the need for human intelligence, machine learning is working to put data together and make predictions. But remember, ML is part of AI, while AI is part of the greater spectrum of data science, and both cannot be the same. They are different in their ways but can create wonders when they work together.