Empowerment of Artificial Intelligence
The concept of artificial intelligence (AI) was first proposed by Jay McCartney in 1955. He believed that machines should be capable of imitating the complex functions of the human brain. They must also be programmed to use a general language. Furthermore, a machine should contain a hypothetical neuron that can be ordered to form a command, as well as to solve problems that humans are capable of solving.
AI must be capable of self-improvement and abstractions. There are two types of AI –strong and weak. Strong AI have behavior and intellectual capacity equivalent to that of a human brain. This type of AI is still in the process of development and can only be seen in movies and TV shows. One particular example is David, the android in Prometheus. On the other hand, weak AI is limited only to one narrow task. It moves based on rules and just follows an algorithm programmed into it.
Most commonly-used AI applications today, such as Siri, Alexa, and Tesla, are considered to be weak. Another perfect example is IBM’s deep blue, a chess playing AI, which was fed with millions of chess moves to ensure that it will process the right moves in an actual chess game. Well, despite being categorized as weak, it actually defeated the world chess champion Gary Kasparov.
Process of Technological Evolution
In recent years, programmers have developed solutions best described as a combination of strong and weak AI. These imitate human reasoning but still follow a set of algorithm. Google’s deep learning is an example. The neural network of Google’s deep learning is similar to that of the human brain. It uses nodes to act as a hypothetical neuron to collect and connect information. So basically, as the machine obtains more data, it improves over time. If programmers want the machine to recognize a handwritten digit, they could input numerous images of numerical digits and develop an algorithm to look into the data provided.
Most algorithms used by programmers follow the expert system. It mimics the if-then rules of decision making to solve complex problems. Its architecture is divided into three – knowledge base, inference engine, and user interface. Knowledge base contains factual and heuristic knowledge. The knowledge engineer would organize the information from the knowledge base in the form of if-then rule and will be later on used by the inference engine. The knowledge obtained will be used to come up with a solution – a process in which either forward chaining or backward chaining will be employed. As for the user interface, it is a natural language processor and provides interaction between the user and the system.
Increasing Significance and Demand
Artificial intelligence started as a study research of a group of doctoral students and grew later on, as a branch of science with wide application in business, health, and agriculture. It’s what the AI is capable of that creates a high demand in certain markets. The development of IBM’s Watson, for example, paved the way for a revolution in cancer treatment plan design. The program identifies potential treatment plans based on clinical notes, reports, and studies. Another is IBM’s Medical Sieve, a cognitive health assistant that can analyze radiology images to identify the problem faster. In agriculture, the creation of remote sensors, satellites, and UAVs enables farmers to track plant health, soil condition, and temperature. The goal here is to optimize crop yield, which in part will help solve the problem of food security.
Moving Towards the Future with AI
The development of artificial intelligence has created a path that will enable humanity to be healthier, more productive, and more creative. AI would improve further and certainly make a major impact in many areas of society – and it is only to be expected that in the future, it will become a necessity.