The Differences: Understanding How Machine Learning is Different from Artificial Intelligence

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“Artificial Intelligence” is no new term. It’s been loaded down with box office features of advanced futures whose civilizations have thrived and fell due to the concept of artificial intelligence, machines effectively assuming human-like roles. Beyond cinema entertainment, artificial intelligence (or AI from here on) encompasses more than a humanoid machine that can take over the world. Many of our current systems use some type of AI or Business Intelligence (BI) to better understand patrons within different industries. How?

Although the theory of AI has been around for more than a hundred years, the idea of implementing machines to complete or process certain tasks has been in the midst since the 1950s. Can machines learn? Are they capable of replacing mankind? What is big data and how is it used with machine learning, AI, and deep learning systems?

Who makes the decisions: Machines or Programmers?

By expert definition, AI has the ability for computer systems to recognize, handle, and process ambiguity by correlating adjusted responses from previous error assertions and collected data, learning from it in order to accurately postulate or formulate a response or prediction to base newer future predictions on the correct way to behavior in similar future experiences.

This sounds extremely complicated to the average person, but for those in the industry, it speaks almost to exactly how AI units work in today’s time. As humans, we make predictions based on multiple levels of ambiguity and reasoning, and through machine learning and deep learning, computer systems are also able to process certain levels of reasoning to make several accurate conclusions based on previously gathered information.

Back up! Did someone just say deep learning? We still haven’t discussed the difference between artificial intelligence and machine learning. Taking a moment to clear the air, let’s start with knowing more about Deep Learning.

What is Deep Learning?

AI is essentially a blanket that encompasses the concepts of machine learning, which features the subset deep learning. Deep Learning is the computation of digital neural networks formed by algorithms based on the human brain, learning from huge sets of data. Much like our learning experiences, Deep Learning algorithms work similarly. Most of us learn through repetition or repeatedly performing a task, adjusting our technique each time to increase the odds of positive outcomes or improved results. The term deep learning refers mainly to the multiple levels of layers that are responsible for evoking the ability to learn. I know it may still sound a little confusing.

Let’s simplify what deep learning is. If there is a problem that requires any level of thought or reasoning to form a conclusion; deep learning can be used to get trained on existing data sets and learn to solve the problem. Deep learning takes an insane amount of data in order to improve its capabilities. Unknown to most, we, as a race, produce roughly 2.5 quintillion bytes of data, which is the contributing resource to what makes deep learning attainable.

How does Machine Learning differ from Artificial Intelligence?

We will start by blowing away all the smoke and clouds that surround the terms Machine Learning and Artificial Intelligence. If for a moment you believe them to be the same or able to be interchangeably used, please erase, delete or scrap that idea. They are not the same!

Machine Learning is considered a subset or component of AI and it is just a single way is executed. Machine learning is definition dependent, meaning it relies on having programmed definitions of behavioral rules to cross-examine compare big sets of data to localize a pattern. This is commonly used to solve problems requiring certain classification computing. Machine Learning houses the subset Deep Learning, which we discussed earlier as having the ability to cognitively learn based on human-brain focused algorithms. The biggest takeaway on Machine Learning is that it relies heavily on having defining parameters to base decisions on when processing data and results. With that being said, “How does ML stack up against DL?”

Is Machine Learning equal to Deep Learning?

Here is another misconception in the AI world, and the answer is simply, No! Since Deep Learning is a subset or more concentrated area of focus of Machine Learning, they are not the same. As defined above, ML requires categorical denouncements in order to classify, learn, and make predictions on future encounters based on processed information. On the other hand, Deep Learning encompasses the ideas of complex biological neural networks that are capable of learning and developing thought much like humans and animals. However, the ability to replicate how animals and humans process data is still far away.

In plain language, Deep Learning stacks several layers of ML algorithms to structure a reinforced neural network to make it possible for computers to think independently. Deep Learning is a driving force that enhances Machine Learning and brings it to the next level of being able to distinguish categorical differences in everyday tasks to create functional rationality in solving future tasks.

Which Machine Learning algorithm should I use?

While AI, ML, and DL are powerful tools that are changing the way we live our day to day lives, knowing how to implement them into your projects is a whole new ballgame. For some single-layered projects, basic ML algorithms will do just fine, but what about when you need to be stronger than the simple equations that are currently available. For starters, you’ll need to determine what type of algorithm is best-fitted for your project. Currently, there are three basic type of algorithm styles to use.

  • Supervised, which means you feed data into the machine to assist it in learning or looking patterns and behaviors to make future predictions. Depending on its use, Supervised learning may also go by other terms like Classification for when it’s being used to predict or assign labels or categories into classifications. Classification algorithms also have variations like multi-class classification, which is used when there are more than two categories in the prediction process. Supervised learning can also be referred to as Regression when using it to predict a value. Another term is Anomaly Detection, which is used when looking for outliers and other non-common occurrences. Supervised Learning is one of the most popular types of ML that is used.
  • Unsupervised learning exists when there are no labels attached to the collected data points. Instead, the purpose is to have the data orchestrated in a way that provides structure and organization. This type of learning is highly useful when grouping data clusters or attempting to find alternate ways of simplifying complex and dense information.
  • Reinforcement learning allows the algorithm to decide on an appropriate action in correlation with each data point. This algorithm also shoots feedback back to the machine to indicate the correctness of the selected response. Based on this information, the algorithm can make adjustments to improve accuracy. This type of ML algorithm is most used in the robotics field. As an example, sensor readings from movements of a robot at a certain point in time are used as data points. From here on, the algorithm will decide what move to make next, based on the relayed information of the previous function. This type of application is a natural fit for the IoT (Internet of Things).

Why does Machine Learning need a GPU?

Is it smart to use a CPU when dealing with Machine Learning? When comparing flexibility, a CPU will outshine GPUs. If that’s true, then why the heck are GPUs the center focus on most devices and especially a huge rave in the AI, ML, and DL world? When dealing with AI and Machine Learning there is a lot of intense computational resource requirements.

A CPU typically consists of 2-8 highly performing cores and is a workhorse at pounding out complicated linear tasks, but still falls short when dealing with Machine Learning. GPUs really brag on their ability to handle parallel operations more efficiently. This is mainly due to their dedicated focus on parallelism. When it comes to repetition of the same tasks, a GPU is able to process a huge amount of similar information at one time, which means GPUs significantly speed up the process. A final example pitting a CPU against a GPU could be best explained by using the word search function of a word processor. Instead of scanning the document one word at a time as with a CPU, a GPU takes the information line by line, breaking it down into scannable rows of parallel data that is quickly scanned for the set criterion.

Advancing AI by Innovatively Advancing ML & DL

The differences between the three should now be clear.

Artificial Intelligence is the totality of cognitive machine improvements that are able to make logical predictions based on processed information. This information is assembled and categorized using certain Machine Learning algorithms that are made up of a complex neural network of information processing that aims to mimic biological responses to given information, which can be explained by understanding Deep Learning. While there are numerous differences, the three work in conjunction with another to provide strong technological advancements to improve the quality of life, business, and personal enhancements.