You probably have artificial intelligence (AI) on your radar as a business owner. A few years ago, AI was only accessible to large corporations with huge budgets. AI had a profound positive impact on the bottom line of early AI enterprise adopters. They have seen improvements in customer satisfaction, manufacturing downtime decreases, and overall worker productivity.
Now, AI is available to help small and medium businesses too. You don’t need a Ph.D. or an expensive team of data scientists to implement AI. AI is blending into features of IT solutions, including connectivity, cloud, UCaaS, cybersecurity, IoT, and more. To understand AI better, let’s look at the history, definition a use case of AI.
Early AI
AI has been around for decades. Engineers considered computers as logical machines because they were able to reproduce intelligent capabilities such as arithmetic and memory. Some people believed that the goal of computer development was to create mechanical brains.
Today, we see calculator functions as trivial, and our current concept of AI has evolved. As technology and the understanding of neuroscience have advanced, our expectations of AI have changed.
Divide the Definitions
AI can be split into narrow artificial intelligence (ANI) and general artificial intelligence (GAI).
Narrow AI (ANI)
Narrow AI technology relies on algorithms and programmatic responses to simulate intelligence. ANI capabilities focus on specific tasks. For example, when you tell Amazon Alexa to turn on the lights, Alexa does not have any advanced understanding of language. “She” cannot determine the meaning behind the words you speak. The program listens for key sounds in your speech and, when it detects them, follows its programming to execute specific actions to turn on lights. There is no actual “thinking” going on behind the scenes.
General Artificial Intelligence (GAI)
GAI is intended to “think” on its own. This is AI that learns in a manner that matches or surpasses human intelligence. GAI is designed to learn and adapt, to make a decision tomorrow that is better than the one it made today. GAI is new and complex. Most of the activity around GAI is still happening in research labs.
Therefore, the AI you will typically encounter is ANI. Alexa turns on the lights; it doesn’t actually learn anything. When the user tells Alexa to turn off the lights, a program is executed to carry out the command. This is an example of a rules-based approach, one of the simplest forms of AI where the system operates on rules coded through if-then-else statements.
Machine Learning(ML)
Machine learning is a specific type of ANI, where device and software access streaming data from which it can learn, but nowhere near GAI levels. For example, Alexa can improve at recognizing your voice over time.
Manufacturing and Machines
A good example of ML is in manufacturing. Machines can ingest a data feed of temperature and tolerance information from sensors on equipment. With ongoing and rapid analysis, ML can conclude patterns or anomalies that could produce and send relevant alerts to employees. This way, operators can take action before an issue arises. The operator’s job is easier and more efficient.
Are you interested to learn more about how AI can benefit your company? Give us a call today.
