In the earliest parts of the slow evolutionary process of human beings, the first people on Earth, often called the proto-men, originated in Africa and had black or dark-colored skin. After millions of years of further evolution, the proto-men eventually became true-men, which are considered as the Homo sapiens, the final evolution of humans. These true-men had become animal hunters, and a number of groups started to migrate towards Asia, Europe, and later America in pursuit of other animals to hunt. In these distant lands, the climate was different from Africa, and the unforeseen changes in climates started bringing about the differences in the color of the skin of these people. This phenomenon started about 35,000 years ago, and interestingly, it is still going on until today. Although they are descendants of the same ancestors from proto-men, the appearance of these migrated humans has changed so much that the similarities are no longer visible. The accompanying map below shows the distribution of skin color among human beings, and these colors are fully suited and adapted to the climatic conditions of the areas where they live.
Artificial intelligence and robotics are emerging technologies that try to simulate human intelligence in AI systems. It was American scientist John McCarthy who came up with the name “artificial intelligence” in 1955.
Artificial intelligence (AI) and robotics are general terms that imply the use of computer and cyber technology to model and replicate human behavior. Research on AI and robotics focuses on the analysis and development of algorithms that exhibit and perform intelligent behavior with the slightest human intervention.
These techniques have been applied to a wide range of problems that emerge in robotics, e-commerce, mathematics, medical diagnosis, gaming, and military planning and logistics, just to mention a few. A lot of research groups fall under the umbrella term of AI in the department. However, they are also disciples in their own right, which include: robotics, computer vision, natural language processing, computational biology, and e-commerce.
Generally speaking, the term “artificial intelligence” refers to a program that simulates human cognition or thought process. At least, some of the things that we associate with other minds, like learning and problem solving, can be replicated by computers, although not precisely the same way we do. Professors and AI researchers Andreas Kaplan and Michael Haenlein describe AI as a system capable of correctly interpreting external data, learning from such data, and using what it has learned to accomplish specific tasks and goals by way of flexible adaptation.
The ideal “artificial intelligence” machine should be a flexible agent that perceives its surrounding environment and then takes action to maximize its chances of achieving a goal or objective. As machines are evolving and advancing their capabilities, some of the mental faculties that were once believed to require intelligence are now eliminated from this definition. For instance, optical character recognition (OCR) is no longer thought of as a part of “artificial intelligence,” but a mere kind of technology.
Currently, people use the term “artificial intelligence” for understanding human speech successfully, competing in strategic game systems (such as chess and Go, an Asian board game similar to checkers) at a high level, interpreting complex data, and self-driving cars. However, some people also think of AI as a danger to humanity – even a threat and menace – if it continues its rapid growth and powerful capabilities. It has made a lot of people uncomfortable and paranoid about the possible inevitability of the AI takeover.
The ultimate goal of AI research is to develop and create computer programs that can learn, think logically and reasonably, and solve problems. But that’s only on paper. But in reality and practical situations, most of the applications have picked on problems that computers can do well. In fact, there are areas that computers do much better than humans do, such as searching databases and performing calculations. On the other hand, the “perceiving its environment” matter, in a real-life world, is way beyond computing.
AI also affects fields such as computer science, mathematics, psychology, linguistics, philosophy, and neuroscience. Researchers are trying to create a “general artificial intelligence” that can solve a variety of problems, not just concentrating on one type of situation. Researchers are hoping to develop an “emotional” and “creative” artificial intelligence, which can be capable of empathizing with and displaying artful skills and talents.
Deriving concepts from management literature, Kaplan and Haenlein classify artificial intelligence systems into three groups:
- Analytical artificial intelligence – It has characteristics consistent with cognitive intelligence. It involves the generation of cognitive representation of the world, as well as the use of learning based on past experiences to inform decisions in the future. Machine learning is a prime example of this system.
- Human-inspired intelligence – It combines the elements of cognitive intelligence, plus emotional intelligence. Aside from the cognitive elements, it also analyzes human emotions, which can be useful in decision-making.
- Humanized artificial intelligence – It combines the elements of cognitive intelligence and emotional intelligence, plus self-consciousness and self-awareness in interactions with others.