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How is the line blurring between AI and Humans?

CIOInsider Team

Interestingly, when we compare a human brain with a supercomputer, there are millions of facts that make machines diverse from humans and the most significant one will be the processing speed. Human brains can perform up to 1,000,000 trillion operations per second, whereas a supercomputer can do 93,000 trillion operations per second only. Human intelligence is mostly the quality of the mind that is made up of a set of capabilities learned and acquired during the course of the life of a human. The individual’s cognitive skills help them to learn from the environment and shape them to respond to each situation accordingly. Machines, particularly AI feed on a set of data and operates on its own algorithm. AI is appreciable for its accuracy and efficiency that perfectly translates to improve the performance resembling human action. As a machine that integrates with robotic control, geospatial systems, and vision based sensing, AI embraces automation purposes too in order to advance associated machines.

Since the advancements in the AI domain has been touting different applications coupled with it, more

industries and enterprises has started recognizing its effective usability. As mentioned above, there are various things that can be taken into account when comparing the intelligence system infrastructure of AI and humans. In fact, business enterprises has started depending majorly on machines for decision-making processes across fraud detection, maintenance etc. Concierge services are now powered using artificial intelligence and deep learning capabilities to assist customer in ticket booking services. By migrating human capabilities to AI backed services, it has not only resulted in improving business productivity, but has managed to handle a huge amount of data and operations in seconds.

Human nervous system has inspired technicians in designing the structure of artificial neural networks, thereby paving way for machines to recognize and identify speech, images, and patterns. This can be observed in the facial recognition services offered by many smartphones, and smart devices. For instance, DeepFace, Facebook’s facial recognition system, was trained to identify human’s faces from images can be considered as a significant step in this category. The much looked up discipline of Computational Neuroscience is a giant leap in the road for bridging the gap between AI and human intelligence. This study uses mathematical tools and theories that can be used to investigate how brain functions. A combination of diverse approaches from electrical engineering, physics, and computer science, is what makes it work. The development in machine learning is a prime example of how the thin line between AI and humans disappear. The advances in deep learning, a branch of machine learning, is significant to a business only if it generate humongous amount of data for evaluation. Machine brain network does not function the way human brain does, but differs in the data it beholds to perform its task. In case of humans, the data is not stored as in bytes in the brain therefore the chance of data loss is high. A deep learning system consumes much energy, 10 million watts, when compared to that of humans 20 watts.

Deep neural networks using more data for self-learning can produce immense advantages and contribute to different fields of study. Being a force multiplier to industries and transform human activities into a much effortless error-free operations, AI is leveraged for more powerful activities having human-like interactions with the real world.

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