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Understanding
Machine-learning based systems also enable computers to show some sort of understanding. This learning is based on pure data and not on manually programmed codes. Just like humans, then, computers do not understand by what there are being told, but by what they see and observe on their own. Companies and websites such as Netflix, Amazon and eBay take advantage of such machine-learning abilities in order to suggest products to their users based on their preferences and previous choices. Another example is the Chinese website Baidu which uses a leading edge image recognition system. The system can understand and search for pictures, for instance, which are similar in many ways (like position, facial expression, contours, shapes, color, etc.) to the picture one has uploaded before that. This proves that computers and computer-based systems can comprehend what they see, can generate descriptions and can deliver results based on received data. It is not exactly human performance and understanding but it is analogues.
Brain/Database
Drawing parallels between human brain and computer database there comes up a variety of other similarities. Both of them have memory, both of them use electrical signals, both of them can retrieve and transmit data, both of them have partitions and both of them connect data in order to reach to conclusions which are logical and working. Being able to analyse and link scattered and proportionate data, computers, consequently, have the capabilities to create logical structures, allowing them to understand and learn.
Drawing assumptions from all this, people come to realize that human-like capabilities of computers grow exponentially and at rates faster than those of any person. No doubt that machines follow commands but it is the way in which they follow those commands that amazes us. What is more, computers succeed in exhibiting that they can function as separate agents, without the direct presence of a computer programmer or engineer. They learn, comprehend and respond in the most profound ways. They come up with images based on descriptions, produce answers based on questions and evoke meaning based on understanding. And just like people, computers adapt and evolve over time.
Humans and computers can be easily distinguished. There is no confusion about this. To put it in the simplest way possible, the most significant difference between them both rests with the principle that computers are machines and men are alive and breathing. This is not to say that computers are not alive. They are. But they are alive in a quite different way – in an artificial and non-breathing way which is not the way that will make them grow old. Computers’ liveliness can be better understood as mere existence. And while every object can exist, people do, indeed, live and experience.
We have pointed out various factors which, more or less, blur the boundaries between men and machines. We have discovered what the similarities between these two are and why sometimes computers are regarded as equal to humans. Some of the points we have underlined in the previous piece include examples of Artificial Intelligence (AI) beating Human Intelligence (HI) in chess games and quizzes. Moreover, we have also discussed how computers, like human beings, have evolved over time and are able to demonstrate a sort of thinking, understanding, perceiving and learning (think of Watson and Deep Blue). However parallels between such technological products and people come around to remind us that computers are incapable of going beyond some of the core and flexible human capabilities. At least not now.
In “Dancing with Robots: Human Skills for Computerized Work”, the authors Frank Levy and Richard J. Murnane argue that one of the most extraordinary strengths of humans is their ability to be flexible. Precisely, they indicate that flexibility represents “the ability to process and integrate many kinds of information to perform a complex task, such as solving problems for which standard operating procedures do not currently exist, and working with new information—acquiring it, making sense of it, communicating it to others.” Needless to say than, that computers lack this flexibility. It is true that different software developments can work to solve different problems but computers’ problem-solving skills are founded on established algorithms and rules-based logic. When trying to reach to a decision or a solution, machines lack the capability to define the impact. The aftermath is unclear. While humans can navigate their decisions and solutions through a number of processes which evaluate the possible outcomes and effects. People can reason, computers cannot. And this leads us to the conclusion that people’s functionalities are cognitive, while those of technologies are analytical. Respectively, the formal ones produce semantic results, while the latter – mathematical and literal.
Machines can think. Machines can learn. Machines can adopt. They make emotion-free decisions, but they are decisions nevertheless. They can read, talk, calculate and understand. Machines solve problems and occasionally create some. But most importantly, they are able to develop human-like characteristics, thanks to deep learning, data mining, pattern recognition and more. It seems like the purpose of technologies is slightly changing now. Computers move away from conventional uses that simplify everyday activities or business processes to reach to more complex operational levels. Those of becoming more intuitive like never before. We have reached a phase in our technological evolution in which people have existentialist conversations with Cleverbot and applications like Siri. And as if that’s not enough, the 21st century science has advanced so much that artificial intelligence (AI) is now challenging and beating biological intelligence at different games and face-to-face situations.
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