Machine Learning vs. AI: Differences, Uses, and Benefits
Machine Learning vs. AI: Differences, Uses, and Benefits
Artificial intelligence and machine learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.
Although these are two related technologies and sometimes people use them as a synonym for each other, but still both are the two different terms in various cases.
Below are some main differences between AI and machine learning along with the overview of Artificial intelligence and machine learning.
What is Artificial Intelligence ?
Artificial intelligence is a field of computer science which makes a computer system that can mimic human intelligence. It is comprised of two words "Artificial" and "intelligence", which means "a human-made thinking power." Hence we can define it as,
The Artificial intelligence system does not require to be pre-programmed, instead of that, they use such algorithms which can work with their own intelligence. It involves machine learning algorithms such as Reinforcement learning algorithm and deep learning neural networks. AI is being used in multiple places such as Siri, Google?s AlphaGo, AI in Chess playing, etc.
Based on capabilities, AI can be classified into three types:
- Weak AI
- General AI
- Strong AI
Currently, we are working with weak AI and general AI. The future of AI is Strong AI for which it is said that it will be intelligent than humans.
Machine learning is a subset of AI, which uses algorithms that learn from data to make predictions. These predictions can be generated through supervised learning, where algorithms learn patterns from existing data, or unsupervised learning, where they discover general patterns in data. ML models can predict numerical values based on historical data, categorize events as true or false, and cluster data points based on commonalities.
Differences: AI vs. machine learning
Artificial Intelligence | Machine Learning | |
What is it? | AI is broad term for machine-based applications that mimic human intelligence. Not all AI solutions are ML. | ML is an artificial intelligence methodology. All ML solutions are AI solutions. |
Best suited for | AI is best for completing a complex human task with efficiency. | ML is best for identifying patterns in large sets of data to solve specific problems. |
Methods | AI may use a wide range of methods, like rule-based, neural networks, computer vision, and so on. | For ML, people manually select and extract features from raw data and assign weights to train the model. |
Implementation | AI implementation depends on the task. AI is often prebuilt and accessed via APIs. | You train new or existing ML models for your specific use case. Prebuilt ML APIs are available. |
Benefits and the future of AI
AI and machine learning provide various benefits to both businesses and consumers. While consumers can expect more personalized services, businesses can expect reduced costs and higher operational efficiency.
It’s little surprise that the global market for AI is expected to increase exponentially in the coming years. According to Grand View Research (GVR), the global market size for artificial intelligence is projected to expand from $136.6 billion in 2022 to a whopping $1.8 trillion in 2030 . Some common benefits for businesses using AI and machine learning in the real world include:
The ability to quickly analyze large amounts of data to produce actionable insights
Increased return on investment (ROI) for associated services due to decreased labor costs
Improved customer satisfaction and experiences that can be tailored to meet individual customer needs
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