IoT is hugely helpful in predicting accidents and crimes. It finds massive application in healthcare where doctors can get valuable insights into information from pacemakers or biochips. The vision of a smart home filled with connected appliances is possible only through IoT. There is a play of huge amount of data in this regard. It is highly implausible for us to achieve these using traditional data processing methods. The problem will be to assess this overflow of performance data and the information which the IoT devices create. Therefore the speed and accuracy of big data analysis has to be improved to a great extent. That’s where AI is greatly helpful.
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Domains where AI is used with IoT
Heavy losses result from equipment breakdown. Among offshore oil and gas operators such losses account for $38 million. This kind of downtime also costs $50 billion per year for industrial manufacturing. Predictive maintenance is helpful here very much where the equipment failure can be predicted way ahead of time to schedule maintenance procedures. As per Deloitte, the time required to plan maintenance can be reduced to 20-50% through predictive maintenance in the manufacturing sector. The overall maintenance costs can be cut down by 5-10 percent.
Machine Learning is used to identify patterns and anomalies and make predictions based on huge data. Therefore they are useful in effecting predictive maintenance. SK Innovation which is a leading South Korean oil refiner saves huge money by using Machine Learning to predict failure of connected compressors. EDF group which is a French firm has already saved over $1 million with Machine Learning based predictive warning on equipment failure.
Enhanced products and services
The marriage of AI and IoT finds good application to improve new products and services of organizations. This can be found in drone and robot-based industrial inspection services of GE. The firm is opting AI to automate identification of defects from the data captured and also in navigation of inspecting devices. This results in safer, more accurate and upto 25% cheaper inspections for the client. Thomas Jefferson University Hospital uses natural language processing to improve patient experience. Patients can now control room environment and get a host of information using voice commands. Rolls-Royce has rolled out IoT enabled airplane engine maintenance services. The firm uses Machine Learning to help spot patterns and detect operational insights to sell it to airlines firms.
Increased operational efficiency
This is possible through Machine Learning’s ability to get accurate predictions and valuable insights along with AI’s ability to automate a variety of tasks. Hershey is a good example for this where the weight of their products have to be managed efficiently. Hershey saves about $500,000 for 14,000 gallon batch of Twizzlers for every 1 percent improvement in weight precision. The firm used Machine Learning and IoT to reduce weight variability in production to reduce making use of 240 process adjustments a day. Google has cut down 40 percent of its data center cooling costs through AI prediction. It leverages data from sensors in the facility, predicts pressure and temperature over the subsequent hour to reduce power consumption.
It would be rare to come up with an IoT implementation not using AI. The IDC estimates that by 2019 AI will be the support for major IoT deployments. Those deployments bereft of AI use will have low value. IoT vendors across the globe are increasingly including AI support in their applications. These two are among the foremost emerging technologies. They will be hugely relevant in the near future as they will be in the forefront of technological revolution along with Cloud Computing. You are in for a treat as Intellipaat provides training on either of these technologies. You’ll technically understand as to how these deployments really happen in the industry through the training.
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