Artificial intelligence and machine learning are two terms that are being used interchangeably by many companies, consumers, and other people. However, the two terms have key differences that’ll help provide as proof that the two are completely different. But, that doesn’t mean that the two hot buzzwords aren’t connected.
Since machine learning and artificial intelligence have become a part of our everyday lives that we incorporate in almost all of the things we do. We have to know each of their features that make them unique and to use them more efficiently and effectively to benefit not just us, but the people around us who don’t know the full potential of technology.
Although machine learning and artificial intelligence are related, you should keep in mind that they aren’t the same. Since the two are not the same, they also don’t share the same strengths when incorporated into the same tasks. For you to take your business or career to the next level, here is a comparison of machine learning and artificial intelligence to let you know when to use them.
- 1 The Basics of Artificial Intelligence
- 2 The Basics of Machine Learning
- 3 What are the Capabilities of Machine Learning?
- 4 Takeaway
The Basics of Artificial Intelligence
It’s essential that you know the simple definitions of the two different concepts. Artificial intelligence or AI is any machine capable of performing tasks that are intelligent and not repetitive, tedious tasks that can be programmed.
The features of artificially intelligent machines allow them to adapt to different situations and aren’t programmed to do an action in a repetitive motion. Whenever a system or machine is able to complete a task based on a set of designated rules while using a specific algorithm to solve problems, the intelligent behavior of the system or machine is what is termed as artificial intelligence.
Artificial intelligence is also classified into two essential groups namely applied and generalized.
Applied Artificial intelligence
Applied artificial intelligence is a more common fundamental group than general artificial intelligence because they are basically an application of artificial intelligence. Applied artificial intelligence’s aim is to replicate human intelligence for a devoted purpose. It also includes everything from automated driving to intelligent stock-trading systems.
Generalized Artificial Intelligence
Generalized artificial intelligence is the less common fundamental group because of its complexity that makes it more difficult and challenging to create. Generalized artificial intelligence is capable of handle various kinds of different tasks similar to the capabilities of human beings.
Although generalized artificial intelligence systems and machines aren’t common. The continuous research of many researchers and developers have made advancements in the field of generalized artificial intelligence. The never-ending research on generalized artificial intelligence is the reason for the development of machine learning.
The Basics of Machine Learning
Machine learning is the application and branch of artificial intelligence in which the systems and machines comprise of different algorithms and statistical models. The different algorithms and statistical models of machine learning are what makes the systems and machines to perform actions and utilize their own abilities.
Technologies with machine learning also provide freedom for the systems and machines to operate and perform tasks without the need for complete human assistance. In addition to the lack of complete human assistance, programmers don’t have to perform explicit programming instructions for systems and machines to operate.
The unique characteristics of machine learning found in technologies are that they have the ability to identify patterns as well as interferences. The identified data of patterns and interferences can then be analyzed and learned to apply to their future actions and execute judgment-based decisions.
What makes machine learning a technological advancement is that they have algorithms that don’t call for a need for the traditional form of programming. The traditional form of programming involves feeding the machine with instructions. However, machine learning comprehends and executes given data based on the coding of the algorithm, not programming.
Machine learning also comprises of three categories of algorithms that allow the system to develop its own logic depending on the given data you put into a specific machine learning algorithm.
Supervised Learning Algorithm
The supervised learning algorithm is responsible for bringing the designated data to develop a system that has the ability to predict the possibilities of a new array of data. The supervised learning algorithm also has two subsets and these are classification and regression.
The results of the prediction in classification supervised learning are discrete and categorical. However, the results of the prediction in regression are continuous and numerical.
Unsupervised Learning Algorithm
The unsupervised learning algorithm is responsible for the uncategorized and unlabeled data. An unsupervised learning algorithm will then perform is abilities to make meaning out of the data by creating structure and identifying patterns from the given data.
Similar to the supervised learning algorithm, the unsupervised learning algorithm also has two subsets, namely clustering and dimensionality reduction.
Reinforcement Learning Algorithm
The reinforcement learning algorithm is responsible for taking the appropriate action to get the most reward out of a specific situation. Further, the reinforcement learning algorithm will also find the best behavior it should capitalize on depending on the situation.
What are the Capabilities of Machine Learning?
Due to the neural networks found in machine learning that gives them the ability to think like a human being. The neural networks allow machine learning to mimic a human being’s brain without being biased while still being more accurate and faster. These features provide machine learning systems with endless possibilities.
The machine learning systems we have today have enabled computers to observe texts from different points of view while categorizing them into positive or negative. For instance, they’ll be able to know where a song is most likely going to make a person happy than sad.
There are also machines that resulted in technological innovation and advancement that allow them to compose with dedicated themes. The themes incorporated with their composition will depend on the piece that they’ve already heard and processed.
Another essential application of machine learning is that it can change the way people communicate with each other especially with different languages. The field of AI namely natural language processing or NLP heavily relies on machine learning to automate customer services in businesses to cater to people with different languages.
The features of artificial intelligence and machine learning are useful depending on the needs and what you’re using it for. Systems that contain artificial intelligence or machine learning improve workplaces and workforces because they are a source of solutions to bring in effectivity and efficiency in the operations and processes of businesses.