For a very long time, humans has been trying to design a machine that have complex capabilities like how human brain does. When artificial intelligence first came into existence, people thought that’s making a model that imitates human will be easy. But it is took more than five decades for scientists to turn the concept successful. we are running after machines that carry the cognitive capabilities of human brain in it.
Why is designing a mechanism that it is similar to human brain complex?
To be honest, researchers are still working on finding how human brain functions. Even though our daily work & action are just a part of simple routine, the brain has cognitive capabilities that power our body to cooperate with it. While researchers have explored some of the ends of brain, some others still remain an unsolved riddle. Maybe that’s why researchers took the complex human brain as an inspiration to formulate a machine brain. Unfortunately, recreating cognitive capabilities in a deep neural network is not easy & it is the elusive goal of artificial intelligence. But neural network emerge as a source to emulate human and machine connections. As neural network evolve, other human cognitive skills are also becoming increasingly relevant. However, despite the challenges, more AI researchers are engaged in implementing algorithms that are inspired by specific cognitive mechanisms in the human brain that have been producing incredibly promising results.
Human-machine interaction is already in place, thanks to Watson, Siri, Cortana, etc. But for artificial intelligence to have a truly transformational impact, artificial neural networks need to be further reinforced by human native intelligence. But why is it hard for scientists to replicate human brain with machine capabilities? It is because humans did not reach the place they are today easily. They have been through a lot. The human brain has advanced over time in responding to survival instincts, harnessing intellectual curiosity, and managing the demands of nature. Starting from the time when Neanderthals hunted animals for food to today, humans realizing that light-weighted wings could make a helicopter fly on Mars, everything was a slow evolution. While the human brain finds ways to exceed our physical capabilities, the combination of mathematics, algorithms, computational methods, and statistical methods is accelerating our scientific pursuit.
Have you ever thought that’s machines can’t focus on something or get distracted the other when it hear a sound or sees something out of the box? We humans do that all the time. Furthermore, we can do both. We can focus and give our attention to the certain thing for a long time by forgetting what is around us, & on the other hand, we can also divert our attention when necessary. Researchers are taking inspiration from the human brain’s attention mechanism. They are powering convolutional neural networks (CNN) or deep generative models by the deep learning methods.
Thinking is a routine activity of humans. We don’t put too much effort to come up with solutions. The reason behind this is we has memory, information process, speech or object recognition capabilities etc. that’s are simultaneously working along with us. This can be replicated in artificial intelligence models by a creating theoretical models of the human brain for inter-disciplinary studies on it is functions, including vision, motion, sensory control, and learning.
Recognizing certain things is comparatively tough for machines. In order to recognize in object or a voice, artificial intelligence models has to be fed with similar data. Only after showing thousands of images of a monkey, the model will be able to recognize it correctly. Henceforth, the mechanism behind a machine brain is reproducing brain data. It helps machines detect an object in various positions under different lighting conditions.