Artificial Intelligence Examples in Various Fields of Industrial Health -
29 May 2023

Latest posts by Media ( Aldy ) (see all)

Artificial Intelligence Examples in Various Fields of Industrial Health

Now more and more companies are implementing AI technology as part of their information technology (IT) systems. According to IDC, as many as forty percent of all digital transformation initiatives in 2019 will be powered by AI capabilities. The increased use of AI technology can also be seen from the list of fifty companies selected by the editors of the MIT Technology Review in the 50 Smartest Companies in 2016.

For the first time, out of the 50 companies selected, 13 have relied on AI to support their business. From several surveys conducted by research companies, it is increasingly visible that the application of AI is increasingly widespread. The results of a poll published recently by Tech Pro Research stated that as many as 38% of respondents said they were selecting AI and machine learning (ML) vendors. From the survey results it was also revealed that as many as fifty percent of companies would immediately implement the technology in the next few years. There are various reasons why companies need to implement AI technology. Increasing business productivity and efficiency which results in increasing company competitiveness can be the reason for implementing AI. For other companies, AI can create new business opportunities.

AI Handled Work

There are many fields that can be tackled by AI or more specifically machine learning (ML). From the results of the Tech Pro Research survey, it was revealed that respondents plan to apply AI or ML for research, consumer behavior analysis, fraud detection, market projections/sales forecasts, Internet and IT security monitoring, and office automation. Meanwhile, from a survey conducted by Narrative Science, a company that developed the Advanced Natural-Language-Generation (NLG) system, it was revealed that AI/ML technology is widely used for data analysis. Of the 235 respondents successfully recruited by NLG, all of whom were business executives, it was revealed that predictive analytics was the most widely used, namely 58% of respondents used data mining, statistics, modeling and machine learning to perform data analysis and predictions. The analysis of large amounts of data can no longer be handled and understood by humans.

With the help of AI, the important information behind the data can be understood and even what will happen in the future can be predicted. An online fashion outlet,, as disclosed by TechRepublic, uses deep learning algorithms to find out and adjust customer needs on its website. There are millions of products and images that belong to these stores that are impossible for humans to handle. But by using algorithms that mimic what humans do and how they think and understand things, can provide better customer service and predict future customer needs. Many companies have now realized the ability of AI to make predictions based on available data. Predictions in business are not only useful for knowing future customer needs, but can also be used to predict other things. For example a manufacturing company can use this capability to predict machine maintenance. According to a 2015 McKinsey report, the use of predictive maintenance by manufacturers can provide savings of between US$240 billion and US$630 billion by 2025.

One of the aircraft and car engine vendors, Rolls Royce, uses an AI service from Microsoft, Azure Machine Learning, to predict fuel consumption and maintenance required by its aircraft engines. Another manufacturing company, Bosch, also utilizes predictive maintenance applications to save on maintenance costs for their machines. The results of the NLG survey also revealed that apart from data analysis, in second place, as many as 25% of users apply AI for applications related to automated written reporting, communication, and voice recognition/response. This is in line with Gartner’s forecast that by 2018 as much as twenty percent of business content (such as shareholder reports, legal documents, and press releases) will be automatically generated by machine. IDC also hopes that at least twenty percent of all employees will be assisted by automation technology next year.

Where to start?

AI is in sight. From companies that have implemented it get real benefits. Other companies have already started. So it’s time for your company to implement it. But, where can you start? For those who don’t know where to start to start carrying out AI transformation, it is recommended to do research on various approaches to implementing AI or ML solutions. Solutions offered by major vendors such as Microsoft, IBM, Amazon, Google, or others, can be taken into consideration, to decide which approach is most suitable for your company.

These vendors can provide infrastructure and platforms for developing AI applications. They can also provide data for modeling their algorithms. In short, you don’t have to start from scratch. Apart from the major vendors mentioned above, startup companies are now starting to emerge offering various AI implementation solutions. Some even offer solutions for small and medium enterprises (SMEs). NoHold, for example, which has an office in Milpitas, California, offers virtual assistant solutions for SMEs. In the next stage, you are advised to look at other companies that have implemented AI, at least companies that have the same target or goal. After that, you can choose the various platforms that the vendor offers. Vendors such as Amazon, Google, Microsoft, IBM, and Baidu, offer AI platform solutions for enterprises. Some of the options are priced the same among those vendors and are aimed at beginners.

Companies that Rely on AI

Below are some examples of companies that rely on AI for their business operations including those that provide AI solutions.

These online taxi companies generate large amounts of data all the time. Uber management utilizes machine learning to “understand” the data it obtains. From there, it is hoped that managers can find ways to retain customers, how to provide better service to customers, and how to prevent a decline in business. It is reported that Uber is also planning to invest in self-driving cars. This makes the use of ML will develop for these new services.

As an electric car manufacturer, Tesla relies on ML to collect data from its cars on the streets. The data is then used to improve the performance of the Autopilot feature on its products. AI helps cars to better understand various objects encountered on the road, including their behavior. Elon Musk (CEO of Tesla) stated that by using the Autopilot feature, the accident rate can be reduced by up to fifty percent.

This German manufacturing company that has been operating for a long time uses AI to increase its business growth. By using machine learning or predictive maintenance and self-monitoring applications for its machines, Bosch estimates it will be able to increase its business revenue by up to one billion dollars and make cost savings in its modern manufacturing.

Nvidia is already very popular with its GPU (graphics processing unit) chip products. Now this company has invested in making chips with AI features. In April 2016, Nvidia launched the Tesla P100 chip for deep-learning applications. Then, in September too last year, Nvidia unveiled the Tesla P4 and Tesla P40 chips which are designed to recognize speech, images or text in response to a user or device. With this chip, machines such as drones or robots can react to events around them in real time.

Enlitic is a startup company that uses AI to detect health problems in X-ray irradiation results. The system it created – which is still in the testing phase – promises to be more accurate and faster than specialist doctors in detecting problems such as lung cancer.

Leave a Reply

Your email address will not be published. Required fields are marked *