Although one would assume these two highly used key terms would be well known and well defined in their respective industries/departments, they are surprisingly not. Artificial intelligence and machine learning, although similar, are quite different in many aspects, and a clear definition of each seems to be… necessary.
What is Artificial Intelligence?
The official definition of Artificial Intelligence (AI) reads, the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
In simpler terms, Artificial Intelligence seeks to imitate human intelligence. Using a process called statistical learning, Artificial Intelligence is able to receive and process information. There are numerous types of artificial intelligence that include Narrow AI and Strong AI. Narrow AI is a specific type of AI that is used to perform a narrow task. They are also known as Weak AI. Programmed to perform a single task, they lack the self-awareness and consciousness to perform Intelligent tasks. Strong Artificial Intelligence are typically types of AI that can impersonate actual human intelligence. They can think and perform tasks on their own just like a human being. Strong AI is also known as Artificial General intelligence. Strong AI are distinctive in that they are self-aware and conscious to make decisions.
What is Machine Learning?
Machine Learning (ML) is the process of a computer reprogramming itself to perform more accurately and effectively based on statistical values that it picks up on. For example, if you wanted a computer that could tell the difference between dogs and cats, you could show it a few pictures of each and tell it whether the picture is a dog or a cat. The computer would pick up on details and differences that it notices, and the more pictures it sees, the more it will learn, and the better it will be at identifying the picture. Currently Machine Learning is being used for a wide variety of things in our everyday lives. Machine Learning is responsible for predictive texts on your phone, recommendations on music and movie streaming services, facial recognition, and spam filters on your email to name a few. Machine Learning is important because it makes it possible to quickly and automatically produce models that can analyze larger, more complex data and deliver faster and more accurate results even on a giant scale. Also, by building precise models, any organization has a better chance of identifying profitable opportunities and avoiding unknown risks.
How is Artificial Intelligence Being Used Today?
Artificial Intelligence is a growing technology that has found itself being used in many industries for many different purposes. Some popular examples of Artificial Intelligence are Apple’s Siri, Amazon Alexa, Google’s Assistant, Netflix’ recommendation algorithm, and Nest thermostats along with other companies incorporating AI in their products. AI is also being used in self-driving cars, like Tesla and Mercedes-Benz in addition to the automotive industry in its entirety whether that be in vehicles brake/crash avoidance detection.
Artificial Intelligence is being used in many ways in the workplace, often in ways that people don’t even realize. AI is also commonly used in customer support, security systems, and to automate many tasks that people take for granted.
What is the Future of Artificial Intelligence?
The future of Artificial Intelligence is extremely broad and could present many new and creative outlets to provide humans with a better quality of life and enabling humans to be able to multitask in ways we’ve never done before. This could include, but is not limited to, fully robotically-controlled assembly lines, Autonomous household appliances that could make meals and wash dishes without a user ever interfering with said AI. Other outlets of Artificial Intelligence could be automating hospitals and other first responder services like police or firefighters. Artificial Intelligence could reduce the risk of first responders getting injured and could potentially increase the ability for early detection on threats or natural disasters.
