AI in Real-time Computation: Advantages and Challenges

3Nov - by aiuniverse - 0 - In Artificial Intelligence

Source- cioreview.com

Artificial Intelligence (AI) boosts the present technological world with its ability to think on its own and take necessary decisions. There may arise a question regarding its benefits in terms of analyzing business and economic aspects. The answer is yes—with its minimum time consumption and undergoing multiple complex tasks at a time. It is mainly focused on enhancing the performance and scaling down the human expertise by replicating human intelligence.

These days, people fear the deployment of artificial intelligence in real-world scenario due to its dynamic activities. This fear may be due to robots taking away the jobs that are otherwise done by people, uncontrollable behavior of the surveillance system, and intelligence machines turning against humans. Although deployment of AI has reduced the job requirements, it has provided career opportunities in other ways. With the implementation of AI in a real-time scenario, tasks can be completed easily, as Facebook is tagging user photos without human intervention.

At present, in the medical field, AI is enabling healthcare providers to offer better services. Even the highly experienced doctors are facing a tough time in analyzing the complex medical imagery reports in which the fine details can go unnoticed. Through AI, the difference amongst the benign cyst and malignant tumor can be easily distinguished along with the diagnosis of other heart and brain-related diseases. Furthermore, the day-to-day real-time health issues can be easily monitored by pairing the android devices with AI.

In energy saving tasks, AI systems are helping to minimize the overall energy consumption of the system. Google has recently developed DeepMind technology for its data center application in which 40 percent of the energy consumption is saved through AI. Furthermore, with revolutionizing the agriculture industry, drones are used to monitor the crops, and better yield can be achieved through predictive analytics.

In summary, though AI supports to achieve day-to-day activities of human beings, several practices should be ascertained to implement AI safely. Universally accepted guidelines should be developed to enforce the usage of AI and its limitations. The machine learning techniques or algorithms developed should be efficient enough to manage integrity amongst the collected data along with risk mitigation due to anomalies. The designed system should be verified, validated, and tested through probabilistic and logic test cases to ascertain better predictability, safety, and robust operations.

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