Imagine sitting at the doctor’s office when a robot walks in, asks a series of questions, and then says, “The doctor will be with you in a moment.”
The doctor finally arrives with another robot trailing beside him. The doctor asks questions and performs a few tests, while inputting symptoms and details into his iPad. The robot actively processes all the information and disappears with the doctor to obtain “the results.” Upon return, the doctor provides a detailed packet that lists several health concerns and numerous home remedies.
What happened behind the scenes? What was the robot’s role? While the doctor had formed an initial hypothesis, the robot was able to confirm his thoughts and list all possible treatment options. How did it do that?
The robot was fed thousands of medical textbooks, countless research articles, and millions of web searches. As the doctor was asking questions, the robot was able to process all of this information almost instantaneously to come up with a diagnosis and recommendation equal to or possibly better than what the doctor could have provided.
Welcome to the world of artificial intelligence.
AI uses computers or machines to mimic human reasoning, learning, thought, and behavior. The backbone of AI is the concept of machine learning. Machine learning works by using algorithms (step-by-step mathematical rules) that tell the machine or computer how to behave. These algorithms are built so the machine or computer can understand concepts beyond what was programmed.
In essence, the algorithm sets the initial foundation of knowledge and the machine is able to take that knowledge and build upon it.
For example, an advanced AI machine was recently programmed to understand the basics of human behavior. Initially, it was taught simple things such as if someone leans in to someone else with their eyes closed and lips puckered, it usually indicates a kiss is about to happen. But, after watching hours of popular TV, that same machine now can predict more sophisticated human action and emotion before it occurs, according to an article in Popular Science magazine.
In this light, researchers studying AI often debate the definition and stages of the development of human cognition. At what point does a machine’s thinking become human-like? According to an article published by a computer science professor at Michigan State University, there are four sequential levels of AI:
Fixed purpose and task oriented
Capabilities: Predictions, analysis, consistent behavior
Example: Deep Blue—IBM's machine that beat the world chess champion in 1997
Tracks observed trends over time
Capabilities: Temporary memory, pattern recognition, programmed representation of the world
Example: Self-driving car that observes surroundings, signals, and traffic.
Awareness that others exist and everyone has unique thoughts and desires
Capabilities: Modeling human emotions, desires, and human-like thought processes
Example: ChatGPT
Same characteristics as Theory of Mind plus self-awareness and thoughts
Capabilities: Consciousness, understanding emotions, common sense, learning from experiences
Example: Female robot in the movie Ex Machina
Today’s AI capabilities put us in type three of the table above: Theory of Mind AI.
Does this mean jobs are at risk? How about for other industries?
Author Dennis Gunton sparked a heated dispute when he stated, “Anyone who can be replaced by a machine deserves to be.” Harsh, right?
Indeed, many critics of AI are such because of the fear robots will take jobs away from human beings. And while that has been the case for industries such as manufacturing and call centers, automation will also create jobs in the long run. Although they are often more efficient, machines still require humans to develop, operate, and maintain them. At least for now, machines cannot accurately, genuinely emulate complex human qualities like empathy, critical thinking, and emotional intelligence. Regardless of anyone’s feelings about AI, one thing is quite clear: technology will continue to learn, adapt, and advance.
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