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Artificial Intelligence VS Machine Learning: Full Guide
The two major terms of computer science known as Artificial Intelligence and Machine learning, are correlated with each other. They have proven to be the most efficient thus, the most trending technologies to be used in the creation of intelligent systems. Artificial Intelligence VS Machine learning is one of the hot topics that need to be focused on when looking to develop a smart system. With the immense importance that these two technologies withhold, it’s unfortunate that many tech-driven organizations falsely claim to use these, leading to deceiving their customers. A report recently stated that 40% of European startups that claim to use AI (Artificial Intelligence) and ML (Machine Learning) don’t use the stated technologies.
In order to fully understand the core, we need to dive into what they actually mean and what features differentiate one from the other.
What is AI & ML?
These two technologies are commonly confused with one another leading to false claims of usage. The best and the shortest answer to artificial intelligence VS Machine learning is that:
- Artificial intelligence is the broader concept of systems/machines having the ability to carry out tasks that would be considered “smart” by the human brain.
- Machine learning is an application of AI-based around the idea that machines shall be given open access to data for them to analyze and learn for themselves. It is the study of computer algorithms that in return allow computer programs to automatically improve through experience.
Difference between Artificial Intelligence and Machine Learning
Artificial intelligence is a field of computer science that makes the computer mimic actual human behavior, meaning that they act and like real humans. The two words that it comprises of, “Artificial” and “Intelligence” means human-made thinking power.
Machine learning allows a system to make predictions and decisions by analyzing historical data without being explicitly programmed to perform any particular task. It can be divided into three subcategories:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
This table will help you get a better understanding of Artificial Intelligence VS Machine Learning
| Machine Learning | Artificial Intelligence |
| ML is a subset of AI learning automatically from the given data. | AI is a technology that allows the machine to simulate human-like behavior. |
| Allows machines to learn from the given data and figure out an appropriate output. | Make smart computers that assist human in solving complex problems |
| Deep learning is the main subset. | Machine learning and Deep Learning are two main subsets. |
| Limited Scope. | Wide range of scope. |
| Concerned about accuracy and pattern. | Concerned about maximizing the chance of success. |
| Three divisions: Supervised learning, Unsupervised learning, Reinforcement Learning | Three divisions: Weak AI, General AI, Strong AI. |
| Deals only with Structured and semi-structured data. | Deals with Structured, semi-structured, and unstructured data. |
What is Artificial Intelligence but not Machine Learning
Imagining building artificially intelligent systems without Machine learning, this analogy of Artificial intelligence VS Machine learning is made to be broken. Machine learning is considered the heart of AI and may sound like a major contradiction even thinking of parting them. However, the strong bond that they seemingly possess is not true. Historically AI preceded ML and researchers have found a way of putting Artificial intelligence into use without the integration of Machine learning by any means.
If you instill a small amount of knowledge into a machine, you may call it an engineering product. However, if you insert a vast amount of knowledge into it enabling the system to make decisions better than a human mind then that’s AI, without Machine Learning.
There are two ways in which computers gain the ability to perform tasks like humans, which are:
- A set of rules are defined to the computers
- Computers learn all by themselves
In the first approach a definite set of rules have been integrated within the systems and the machines act according to that, such behavior is called intelligent. A car accelerating when there is no car ahead to a limit and decreasing the speed to a certain speed when it encounters a speed breaker means it is following a set of rules without making predictions and decisions on its own. This is called the Knowledge-Based Approach in AI. In this way, no concept of machine learning is integrated within the system as the machine is not learning anything on its own and just following the rules it has been provided with.
Looking at the recent time, this approach is still used in many areas however, the results have not been found to the as efficient and effective in many cases compared to a situation where Machine learning has been thoroughly used.
Does Artificial intelligence include Machine Learning?
Looking at the second approach mentioned above that is “Computers learn all by themselves” instigates a computer behavior in which it makes spontaneous decisions based on the set of previous data it has been provided with. It analyzes the historical data that is provided by the human and automatically devises the correct set of rules that have to be implemented regarding the situation at hand. This approach where the computer infers rules and learns by itself by making use of the provided data is called Machine Learning. This war of Artificial Intelligence VS Machine learning can be put to bed as here both of them are being used simultaneously.
When training an ML model to be used with AI, it requires giving the ML algorithms big chunks of data and one of the many learning models to extract processed, meaningful information out of it. It works for specific domains where we are aiming to create models to detect separate items.
Final Conclusion: Artificial Intelligence VS Machine Learning
Today we see thousands of areas where Artificial intelligence has been used to make our daily life tasks easy. The simple interaction that is seen between the human and machines in Google Home, Siri, or Alexa have been empowered by AI, but what’s behind them is the training models and predictions systems of Machine Learning. By understanding the Artificial Intelligence VS machine learning dilemma, these tools can be applied in the operations to bring a more fruitful output.
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