how to create artificial intelligence
First we have to clarify what is the difference between forms of artificial intelligence?
The story begins in the form of science fiction: machines that can speak, machines that can think, and machines that can feel. Although the last part (feeling) may be impossible without sparking widespread debate about the existence of consciousness, scientists have recently been able to make strides with the first two parts.
?How is artificial intelligence made
The story begins in the form of science fiction: machines that can speak, machines that can think, and machines that can feel. Although the last part (feeling) may be impossible without sparking widespread debate about the existence of consciousness, scientists have recently been able to make strides with the first two parts.
Over the past years, we have been hearing a lot about artificial intelligence, machine learning, and deep learning. However, how do we distinguish these three somewhat vague terms, and how do they relate to each other?
Artificial Intelligence (AI) is the public domain that covers everything related to the acquisition of machines as "intelligence", with the aim of mimicking human unique logical thinking capabilities. Machine learning is a class within the broader field of artificial intelligence, and it specializes in giving machines the ability to "learn". This is achieved by using algorithms that can detect patterns, and generate ideas from the data presented to them, to apply them to decision-making processes and future forecasts, a process that avoids the need to program the steps in a way that is specific to each possible action alone.
On the other hand, deep learning represents a subset of machine learning: it is the most sophisticated branch of artificial intelligence, which brings artificial intelligence more than ever before to the goal of enabling machines to learn and think as human as possible.
Here are some of the general historical information to better explain the differences between the three forms, and how each discovery and every development has been made, has paved the way for what was achieved in the next stage:
Artificial intelligence
Philosophers tried to understand human atonement within a system-shaped context, and this idea gave rise to the term “artificial intelligence” in 1956. Philosophy is still believed to have an important role in the development of artificial intelligence to this day. David Deutch, a physicist at Oxford University, wrote in an article about his belief that philosophy still holds the key to achieving General Artificial Intelligence (AGI), the level of machine intelligence that is analogous to that of the human brain, despite the fact that "there is no brain on earth even Now, it's close to knowing what our brains do to fulfill any of their functions. "
The advances in artificial intelligence have fueled the intensity of the discussions, especially as they pose a threat to humanity, both physically and economically (and the idea of comprehensive primary income due to artificial intelligence has also been brought up and is being tested in certain countries).
Learn the machine
Machine learning is nothing but an approach to embodying artificial intelligence, eventually eliminating (or greatly reducing) the need to write a code for a program that faces a list of possibilities, and how machine intelligence has to deal with each of them. Throughout the period from 1949 to the 1960s, American electrical engineer Arthur Samuel worked hard on developing AI from pattern recognition only, to learning from experience, which made him a leader in this field. He used checkers in his research while working with IBM, and this later affected the programming of the first IBM computers.
Current applications are developing more and more as they head towards complex medical applications.
Examples of these applications include analyzing large genome groups in an attempt to prevent diseases, diagnosing depression based on speech patterns, and identifying people with suicidal tendencies.
Deep learning
When we delve into higher and even more sophisticated levels of machine learning, here comes the role of deep learning. Deep learning requires a complex structure that simulates the neuronal networks of the human brain, in order to understand patterns, even with noise, missing details, and other sources of disruption. Although the possibilities for deep learning are very broad, but the requirements are many as well, you need a large amount of data, and huge mathematical capabilities.
This means not needing to program a future artificial intelligence with much effort, enjoying this bewildering type of "intelligence", but rather, it can be said that all the capabilities we seek in terms of intelligence and logical thinking capabilities lie in the program itself, it is very similar to the mind of a child Small is incomplete, but its flexibility is limitless.