One of the most profound risks to the global job market is artificial intelligence (AI) and robotics. Economists fear that global corporations will increasingly turn to AI to lower expenses.
In fact, some estimates project that AI will replace nearly half of all existing jobs. The benefit to corporations is that robots do not call out sick or need long-term benefits.
A confusing element to these conversations is distinguishing AI from machine learning. While often used interchangeably, they are two very different things.
Read on to learn more about the difference between machine learning vs artificial intelligence. Exploring what makes them different will help prepare your business for the future.
What Is Artificial Intelligence?
Let's start off with a definition of AI. In short, AI is the development of machines or robots to replace human tasking.
Another way to look at AI is the creation of devices that act intelligently. Contrary to popular belief, AI has been around for generations.
Perhaps one of the simplest examples is the calculator. In this example, humans created a device which performs mathematical calculations for the user.
AI belongs in 2 different groups; applied or general. In everyday life, the average person is most familiar with applied AI.
There are many examples of applied AI that the average person is aware of. For instance, you have likely seen news reports about self-driving cars.
This is an opportunity to note that AI is not a system. Instead, engineers integrate AI into a system. In the car example, self-driving software and capability is integrated into the vehicle itself.
General AI is the second grouping and is far less common in everyday life. Conceptually, general AI can autonomously handle any task that it is given.
What Is Machine Learning?
Now that we have covered AI from a top-level perspective, it is time to cover machine learning. In machine learning, the robotic entity can self-acquire knowledge or skill.
Scientists and software engineers realized that it was inefficient to teach machines how to do each task. Instead, the optimal solution was to code machines to acquire knowledge and skill as humans do.
This includes writing self-learning algorithms to facilitate this.
The knowledge acquisition process is initiated when the machine is given data. The machine uses the data to improve performance and learn new tasks.
What Is the Big Picture?
Both AI and machine learning pose a risk to the global job market. Applied AI allows machines to replicate tasks that are normally performed by humans.
On the other hand, machine learning gives robots the ability to exceed human performance and provide limitless capability. As technology improves, it is inevitable that robots will consume more human labor.
Machine Learning Vs Artificial Intelligence Difference Recap
The debate over the future influence of robots in the global economy is fascinating. Improvements to AI and machine learning have the ability to rewrite the world order.
Productivity will undoubtedly improve, yet many humans may lose their jobs. Knowing how to prepare your business with the changing times can help you stay ahead of the game.
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