Machine-intelligence futurists are making enormous efforts that would require nothing more than Google-scaled data and computing power to culminate artificial intelligence into the neocortex of humans. We as a human being are thriving on to reinforce algorithms to simplify systems and solve different problems.
The key advancements made in the technological aspects of modern-day industries and end-user applications have made quantum leaps in the quality of lives. Deep learning, however, is still in its nascent stage and hasn’t reached a level of maturity. A hot topic for debating is interpretability that refers to inferring a machine human-like qualities and understanding why a system makes certain decisions. Advances made in understanding the theory and practicality behind image recognition have extended far beyond cool social apps. The technological developments made with respect to medical care aspects have been ascendingly claiming that they would be able to process X-ray images, MRIs and CT scan images more accurately and rapidly. The diagnostic centers are not becoming less invasive in terms of improving robotics and prototypes of self-programmed prosthetics. Better image recognition is unleashing improvements in automation and is the main essence in making self-driving cars a reality. Ford, Tesla, Uber, Baidu, and Google parent Alphabet are all testing prototypes of self-piloting vehicles on public roads today. The original academic designation of deep neural networks is making strides by training vehicles to figure out for itself how to recognize the desired objects, pedestrians, or other near-by hurdles. However, if we look back unexpected ramifications and unrealized dreams of automating different types of vehicles, a range of sectors will need to track these technological, sociological and psychological developments to address the complex sociologically complex problems of accidental standpoints.
“A little learning is a dangerous thing; Drink deep, or taste not the Pierian spring.”