Have you ever wondered how products like Alexa and Siri made millions? Well, these smart products are the part of this trending digital technology which is standing at the verge of technologies like machine learning and artificial intelligence. Most of the companies are implementing these technologies to get the best from it with improved support, optimised services and increased revenue. Let’s explore how the impact of these technologies on the various sectors.
A few years ago, self-driving vehicles were just a myth. But now, advanced technologies like machine learning and artificial intelligence has converted this dream into reality. Big automotive giants like Tesla, BMW etc., are the one who leveraged this technology for creating autonomous vehicles. The implementation of ML algorithms like predictive analysis direct these vehicles on correct route and identify the malfunctioning in them. The autopilot feature of Tesla is the result of cameras and varieties of sensors. It shows the 360-degree view of the road to a range of 250 meters. Its autopilot can also identify the environmental conditions such as dust, fog or rain and provide response according to the situation.
Waymo- a Google’s self-driving vehicle project is one good example of an autonomous vehicle which is designed for reducing the chances of road accidents from drunk driving like scenarios. Gartner has predicted that the there will be more than 250 million smart vehicles available on the road by the end of 2020.
The financial sector is one of the major sector using the latest AI and ML technology for providing better services to their customers. The Fintech organisations are using voice recognition, pattern detection, predictive analysis, automated chatbots, recommendation engines, etc., to streamline their work in an effective way. The predictive analysis allows such companies to make a quick decision in critical scenarios.
Various banks are adopting the ML algorithms for fraud detection and prevention. They use it to estimate risks associated with the loan, fraudulent transaction identification etc., like tasks. These algorithms detect the anomalies in customer transaction and prevent frauds by recognising such patterns.
Various financial organisations use the ML to observe customer behaviour from their past data. The collected insights from this data used in generating qualified leads and converting them into the customers. Apart from the pattern, the voice and text-based automated chatbots assist customers by providing solutions to their queries. The development of such intelligent bots was also incomplete without the ML. If you are also interested in developing such applications, you can go through the Machine Learning Course and start sailing in this digital ocean. Here, you will encounter critical developments like voice and text recognition, pattern analysis
You must have heard about the IBM invented supercomputer named Watson. Currently, Watson is used in the healthcare industry for various purposes such as diagnosis, monitoring, drug discovery, imaging etc. It helps the doctor in finding the cure for dangerous diseases like cancer, tumour etc. This tool processes millions of provided historical data, patient and research data from the healthcare industry and provides effective results.
ML designed models are very fast for processing the data. They detect the symptoms and suggest for the possible diagnosis with the highest chance of the cure. New Robo-doctors lacks human touch but up to some extent they are the god. According to the IDC predictions, 30 percent of the healthcare service providers will start leveraging the cognitive analytics for patient treatment by the end of this year.
Manufacturing industries are transforming their operating procedures by implementing ML and AI like technologies in their premises. The manufacturing sector collects a vast amount of information through sensors used in the production and other units. Since the day IoT was introduced to the manufacturing industry, most of the processes are automated such as predictive maintenance, asset monitoring etc.
Since data is generated at every stage of this sector- analytics is necessary to collect valuable insights from this data. These insights assist organisations in critical decision making. The most important role machine learning play in this industry is anomaly recognition. Detecting these anomaly helps in predicting the health of machinery, throughput, safety procedures etc. ML is also leveraging the supply and distribution in such industries by forecasting market demand, stock maintenance, safe transportation and optimising price.
Well, you can see how technology is transforming almost all the sectors with its effective and efficient solutions.
By: Danish Wadhwa