AI can have simple forms of intelligence, such as recognizing speech or analyzing visual patterns in images. Or it can be more complex, such as learning from past mistakes and problem-solving.
What Is Artificial Intelligence?
To understand what artificial intelligence means, think about what you observe in nature which makes you convinced something has intelligence. Something as simple as a lab rat learning the correct path through a maze represents a simple form of intelligence (there are four types of AI). It involves memory and learning, similar to human intelligence. In 1950, Alan Turing described “thinking machines” as recognizable because they could use reason to solve puzzles. In the 1950s, John McCarthy said computers could “do things, which, when done by people, are said to involve intelligence.” These ideas boil down to three characteristics used to identify a machine or computer as having “artificial intelligence.” They can:
Use inputs, such as sensors or data, to analyze information.Process vast amounts of data to identify patterns, trends, or correlations.Adapt their decisions and actions based on learnings derived from inputs and data.
It’s precisely how human intelligence helps humans learn and adapt in our daily lives.
Components Making Up Artificial Intelligence
An “intelligent” machine is made up of many different components. These all work together to help a machine take input from the real world and make decisions.
AI Sensors
If you think about how a human collects data from the real world, intelligent machines need sensors to collect the same information. These sensors can include:
Cameras: Visual cues to do things like facial recognition, avoiding obstacles, or infrared cameras to detect when objects are hot.Microphones: Interact with humans via voice, detect activity in a room, or respond to music.Tactile sensors: Lets robots adjust their grip or game consoles’ strength to respond to how hard you’re moving a game controller.Position, temperature, or flow sensors: Provides information about gas or liquid flowing through pipes, temperatures of chemicals or metals, and even liquids’ chemical makeup.
In fact, with modern-day sensor technology, machines can detect things about the world that even humans can’t.
AI Data and Machine Learning
An essential component of AI is machine learning. It’s the ability to collect vast amounts of information from multiple sources and analyze it for meaningful patterns and correlations. For example, during vehicle crash tests, a computer can analyze pressures and temperatures. The computer can analyze the data and tell vehicle manufacturers where to place airbags to provide the highest safety level. Machine learning also helps with troubleshooting problems. By collecting manufacturing data across hundreds of sensors, computers can identify anomalies that result in faulty products. Then, by correlating other sensor data, the computer can tell technicians which components in a process are flawed. Since machine learning can do this in a fraction of the time a human can, companies can identify and fix problems faster, improve the quality of products, and boost overall production.
Deep Learning
A more advanced form of machine learning is “deep learning,” when a machine identifies failures and learns the most efficient way to accomplish a task. For example, a self-driving car will use machine learning to drive a car by watching road markings, looking for pedestrians, and identifying traffic lights. But a deep-learning, self-driving car would also learn how steering adjustments keep the car more in the center of the lanes. Over time, this car could teach itself how to become a better driver.
What’s the Purpose of Artificial Intelligence?
Scientists are developing artificial intelligence so we can use machines to improve the quality of life for humans. It lets machines do repetitive tasks which might injure or be dangerous for humans. Artificial intelligence can improve the safety of cars and airplanes. Ultimately, their purpose is to supplement humans with insights from vast amounts of data only computers can process. Dan Prince, CEO and Founder of Illumisoft, says that the starting point for understanding AI is to understand our own intelligence. “Humans have the capacity to learn, to solve problems, to recognize patterns, and to explain and predict natural phenomena (which are) all attributes commonly associated with intelligence,” he says. “Perhaps most importantly, we’re able to act in ways that shape and transform our environment for our benefit. AI, understood most generally, refers to a system or group of systems that is able to simulate that kind of human intelligence. An intelligent system would be one that exhibits human-like capacities for reasoning, problem-solving, or even creativity. “The ultimate goal for many researchers is to generate an artificial general intelligence (AGI), something analysts acknowledge has not yet been achieved. As technology currently stands, a particular AI might be good at simulating one aspect of human intelligence, but not others. There are AI systems, for example, that are proficient at understanding language, while others are good at fine motor control. There are very few that can do both.” Philosophers often question whether we can take AI too far. What if artificial intelligence surpasses human intelligence to the point where robots become superior? Then there’s the question of whether machines will ever be able to understand emotion. Currently, there’s no sensor capable of emotion. However, most machines with AI are only capable of focused areas of learning. We can’t apply it to the multitude of decisions an average human makes daily. Because of that, the idea of machines overtaking humans any time soon is not something anyone needs to worry about now.
