Experts predict that the the global AI market is expected to grow at a compound annual growth rate of 40.2% from 2021 to 2028 to reach USD 997.77 billion by 2028. These cutting-edge technologies are essential for keeping your business competitive regardless of the industry. As a result, it's critical that you understand their capabilities and how they relate to machine learning.
The AI vs. machine learning distinction is an interesting one, but it actually is fairly simple to understand. Here, we are going to define both of these terms and compare their benefits. Read on to learn which is right for your business.
Artificial Intelligence (AI) is a broad term referring to computer systems that mime human intelligence. These systems use algorithms and can work with their own intelligence. This means that they do not need to be pre-programmed to complete daily operations.
These intelligence systems are capable of logic and problem-solving skills. They use a series of mathematical algorithms and logical steps to mimic the ways that humans reason through problems and puzzles.
Essentially, ML is a subset of AI. AI is a specific category of technology; machine learning is an even more specific category that falls under AI's more broad umbrella definition. The two are therefore closely connected.
Machine learning is a process of using data models that let a computer learn with no direct commands. AI can use reasoning to perform tasks, but it does not extract knowledge or understanding from the data that it uses. Machine learning is an application of AI in which computers can learn from previous experience and apply this new knowledge to other tasks.
Because machine learning derives meaning from historical data, it will give predictions for future operations based on that data. It can create algorithms to discover your target audience, analyze trends in communications, recommend products and services, and detect certain trends to identify images or text patterns.
Machine learning may simply be a subset of AI, but it has very different goals. AI's objective is simply to solve complex problems, but machine learning is meant to allow machines the chance to predict future patterns. Their goal is to allow accurate data output based on past information.
AI systems are solely concerned about succeeding in their operations. They do not care about future endeavors. This makes these technologies goal-oriented.
On the flip side, machine learning is a process-oriented category of machine learning. Rather than focusing on the end result, these systems are more concerned with the processes and patterns that lead to success. They want to learn these patterns to propel and streamline future endeavors.
AI also has broader capabilities than machine learning does. When you use AI, you teach a system to perform any and all mundane tasks as a human would. Machine learning experts teach machines to use data and perform a particular individual task more accurately.
This limited functionality may sound like a downside at first brush, but it is actually beneficial. It lets the bot become an expert at that specific task so that it can perform it with a higher impact. Machine learning may have a limited scope, but it maximizes the quality of the output.
There are generally three types of data that businesses:
Because it is such a broad term, AI deals with all three forms of data. The subcategory of machine learning only looks at structured and semi-structured data. This makes sense when you consider that machine learning needs to analyze easy-to-interpret patterns to understand trends.
If your business plans to implement a machine learning system, it is definitionally implementing artificial intelligence. You can implement artificial intelligence without machine learning, but you will not get many of the specific benefits that you otherwise could reap.
To figure out what your business needs, consider the applications that you want machines to perform. Some major applications of AI (outside of machine learning) include:
Note that these are very repetitive tasks despite being necessary for the workplace.
Some core applications of machine learning include:
See more machine learning applications in our case studies.
As you can see, these tasks require a bit more high-level analysis. They are also necessary for professional success. Because both AI and machine learning are essential, you will need to incorporate both of them into your business.
While understanding machine learning can be a challenge at first, it's essential if you want to operate your business as effectively as possible. Now that you know the difference between AI vs. machine learning, it's time to get started.
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