These days, artificial intelligence has become a central part of our lives. If you’ve ever used Siri on your iPhone, asked Alexa a question or leveraged the live chat feature of a website, you’ve interacted with AI. From a business perspective, the rise of AI can be both exciting and challenging. Furthermore, it’s a concept that isn’t always easy for everyone to grasp. The most common question from people who aren’t deeply involved with tech is, “What, exactly, is artificial intelligence?” Perhaps the easiest way to understand AI is to compare it to something that is already widely understood – human intelligence.
How Does Human Intelligence Work?
For the most part, human intelligence follows a straightforward and relatively predictable pattern. We gather information from our external environment. We process that information. And then we use that processed information to make a decision on what to do next. These three basic steps can be summed up as follows:
Input —> Processing —> Output
Input occurs through sensing and perceiving the things around us. Our senses – that is, the eyes, ears, nose, mouth, etc. – gather raw input, such as the sight of light or the scent of a flower. The brain then processes that information and uses it to determine what, if any, action to take. In the processing stage, knowledge is formed, memories are retrieved, and inferences and decisions are made. Output then occurs via an action taken, based on the information processed.
As an example, you might hear a siren, see an ambulance in your rear view mirror and subsequently decide to pull your vehicle over to let it pass.
In order to safely navigate the world in which we live, we must effectively process all of the various inputs we are constantly encountering. This basic concept is the crux of human intelligence, which can further be broken down into three specific segments:
People gain knowledge through the ingestion of facts (i.e. the Pilgrims landed in Plymouth in 1620) as well as social norms (i.e. saying “Please” or “Thank you”). From there, our memory allows us to recall information from the past and apply it to present situations.
Inferences and decisions are made based on the raw input our brains receive, combined with our memories and/or reservoir of knowledge. For instance, let’s say you tried a new food a few months ago that turned out to be way too spicy for your taste. The next time you’re offered that food, you would automatically decline it.
There are a number of ways human beings can learn, including observation, example and algorithm. With observation, we determine the outcome on our own. With example, we are advised of the outcome. Learning by algorithm, on the other hand, allows us to carry out a task by following a series of steps. A good example of this would be solving a math problem using long division.
Human Learning vs. Machine Learning
The main aspects of human intelligence are actually quite similar to those of artificial intelligence. In the same way that humans gather information, process it and determine an output, machines are capable of doing this as well.
Of course, because machines don’t possess physical senses like people do, the way they gather input differs. For example, rather than using sight or smell, artificial intelligence gathers information through things like speech recognition, visual recognition and other data sources. Think about how a self-driving vehicle can sense obstacles in the roadway or how your Amazon Echo hears and recognizes your voice.
The processing piece of the formula is also similar to how human intelligence works. Just as people accrue memories and build knowledge, machines can create representations of knowledge and databases where information is stored. And, just as people draw inferences and make decisions, machines are capable of predicting, optimizing and determining what the ideal ‘next steps’ should be in order to accomplish a particular goal.
Similarly, just as humans learn by either observation, example or algorithm, machines can also be “taught.” For instance, supervised machine learning is akin to learning by example: the computer is provided with a data set containing labels that act as answers. Over time the machine can essentially “learn” to differentiate between those labels to produce the correct outcome.
Unsupervised machine learning is similar to learning by observation. The computer recognizes and identifies certain patterns and subsequently learns how to distinguish groups and patterns on its own. Lastly, learning by algorithm is the process by which a programmer “instructs” the computer what to do, line by line, using a software program. Ideally, the most effective form of artificial intelligence will utilize a combination of all of the above learning methods.
The output that results sums up how machines interact with the world around them, whether it’s speech generation, navigation, robotics, etc.
Take, for example, the business use case of cybersecurity threat detection. Artificial intelligence is capable of scanning enormous amounts of data and monitoring an entire infrastructure in real-time. It can then, through a combination of unsupervised and algorithmic learning, identify anomalies that could potentially represent data breaches. Finally, it can use that information to investigate and test, automatically determining what the next steps should be, whether it’s escalation to a human agent or performing automatic remediation.
The Future is Now
We have, undoubtedly, only seen the tip of the iceberg as it relates to artificial intelligence and its potential impact on our lives – both personal and professional. As technology continues to evolve and improve at a breakneck speed, AI and machine learning capabilities will also continue to evolve. Why wait? Position yourself ahead of the curve and experience the next generation of automation and AI by taking Ayehu NG for a test drive today.