Artificial intelligence differs from human sight in that computer vision cannot deal with a variance of data in the real world ( Russell & Norvig2016). The social visual system can recognize objects from different angles, against different backgrounds, and under different lighting conditions. Even when objects are partially obscured by other objects or colored in unconventional ways, our vision system uses pieces of knowledge and cues to fill the missing information and reason about what we are seeing. Artificial intelligence lacks these object recognition capabilities. The human vision system can easily generalize its knowledge such that objects can be seen from a few angles, and the image is created under a new position and different visual conditions (Russell & Norvig, 2016).
The convolutional neural network develops an object very different from that internal representation of a biological neural network of the human brain (Lu, Li, Chen, Kim, & Serikawa, 2018). It recognizes an image completely different, and no changes can be seen, which shows that the convolutional neural network uses different information from humans to identify pictures. The world around humans has been fully processed by the human visual system, which makes it predictable and reliable, unlike the computer vision system (Lu et al., 2018).
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According to Lu et al. (2018), a convolutional neural network cannot understand images in terms of objects and their parts. The human vision system helps us to understand the composition of objects that gives a sense of things that have not been seen before. Convolutional neural lacks coordinate frames which is present in human vision.
Both Artificial intelligence and human experience can recognize objects. Object recognition by our visual eyes from different perception, whereas convolutional neural nets have one percept which does not depend on imposed coordinate frames. Both of them can cope with translations where an image can be identified regardless of changes in viewpoints.4D, or 6D maps can use to train artificial intelligence and objects from different angles and under different lighting conditions can be recognized (Lu et al., 2018). The human vision system and artificial intelligence can generalize its knowledge. Data augmentation is used in the convolutional neural network, which helps in better generation over variations of the same objects. Despite differences in object development, biological neural brain networks, and Artificial intelligence have an internal representation of an object (Lu et al., 2018).
Artificial intelligence can be used to solve crimes and uncover criminal activity due to facial recognition technology. That can be effectively utilized in the justice system without interfering with the privacy of an individual (Gunning, 2017). Artificial intelligence can be used in traffic to reduce congestion issues and save humans from stressful commutes, which in turn will increase job productivity. Artificial intelligence can improve healthcare facilities operations and medical organizations, which can help in cost reduction and saves money. Patient care will be significantly influenced. Drug protocols, treatment plans of a person, and information access to providers across medical facilities will be enhanced. (Gunning, 2017) It can be used in repetitive or dangerous tasks which allow humans to engage in work they are better equipped involving empathy and creativity. This artificial intelligence influence in the workplace improves efficiency.
Artificial intelligence has caused the loss of jobs and the evolvement of the workforce. It will replace existing jobs, and humans are faced with the challenge of changing how to make a living. There is social oppression due to making decisions based on the intelligence by businesses and government. The data collected from every person compromises our privacy. Use of artificial intelligence in the introduction of autonomous vehicles, pedestrians are hurt, and there is no determination of who is at fault (Li & Du, 2007). There are no defined measures to manage the global independent arms race.
References
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach . Malaysia; Pearson Education Limited,.
Li, D., & Du, Y. (2007). Artificial intelligence with uncertainty . CRC press.
Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications , 23 (2), 368-375.
Gunning, D. (2017). Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web , 2 .
