Let’s admit that we cannot live without machines. We need them for nuclear physics, we need them to toast sandwiches. And now with Machine Learning (ML), we are stepping into the age of machines that are smart enough to collect data for themselves and reshape automation like never before. The acceptance for Machine Learning, to achieve industrial automation, has always been high as efficiency leaks due to improper coordination has always been a challenging affair. And with Machine Learning, these challenges are being solved with a better flow of data and efficient channelization of information into machineries and the system.
At present, technologies like Artificial Intelligence, IoT are working effectively in sync with Machine Learning development companies, facilitating the collection and real-time analysis of data to amplify machine learning algorithms even more. Machine Learning algorithms are designed in a way that they can collect data from various sources in a consistent manner for analysis and improvement. What reduces effort on the operators’ side is the automated, organized way in which data comes and leaves the system. And among all these data streams pouring in from all directions, ML algorithms can be programmed to distinguish between data streams and utilize only the data relevant to its functions.
Now let’s take a look at various applications of Machine Learning in various industries:
Robotic surgery is yet one of the most sensitive aspects of applications of artificial intelligence in medicine and healthcare. Although a form of robotic surgery i.e., robotic-assisted surgery, has already been practiced earlier in the field of medicine, machine learning algorithms are still being tested for fully-independent surgical robots that can, in turn, assist the surgeon at the job. So far where we have reached utilizing machine learning in healthcare is measured incisions and even use of augmented reality to help the robot learn more about the human body and required surgical techniques.
Although the use of machines in retail businesses is quite limited or say negligible, applications of machine learning algorithms have proved to be quite productive in the retail sector. For efficient inventory management, Machine Learning is already being used in maintaining stocks intelligently by integrating with the ERP software in big retail chains like Walmart, Kroger, Costco, etc. Walmart even went a step further to use machine learning algorithms to define a protocol that can identify unsatisfied customers through facial expressions and send customer associates to help them with they need. Taking the advantage of recent advancements in machine learning technologies, Amazon recently launched ‘Amazon Go Store’, which is just like a retail supermarket. The only difference is that the customers can just pick whatever they want and leave, and this won’t be considered theft because of the cameras, powered by machine learning, installed at every corner of the market, which capture what a customer is picking off the shelves and charge you an equivalent amount on your Amazon Wallet.
Sales and Marketing
Sales and marketing is quite a vast field, especially when you have a large enterprise/huge customer base. Machine Learning provides an efficient analytics engine to create customer-specific advertisements to streamline target marketing. Data-driven techniques are the newest trend in marketing, as with the help of ML algorithms, enterprises can reach out to customers on the basis of their preferences. The difference Machine Learning creates is huge by spending precisely on offering to only the prospective customers looking for a particular commodity. For companies, it not only has saved costs, it also saved time and effort to be utilized in other areas.
Logistics and Transportation
The two sister-sectors have greatly benefited from machine learning by route optimization. Talking specifically about logistics, machine learning has reduced transportation costs and introduced better inventory management techniques for faster movement of goods. For public transportation systems like Uber and Ola that heavily rely on Google Maps and other navigation tools, machine learning acts as a backbone. Anticipating traffic jams, broken routes, etc., is one of the big uses of machine learning to reduce ETA. Besides, drive partner ratings, which are representative of how professional a driver acts, is also calculated from the user’s behavioral data. To serve the customers better, customer ratings from drive partner are also collected to serve the customer in a more personalized way.
The core aim of machine learning is to grease up or even perform a task without human intervention. However, in coming years, engineers and data science companies will have to focus extensively on eliminating the deviations from the ML protocols, in order to facilitate widespread acceptance among the general public.
In the following five years, expect to see much more Machine Learning in action to change the way we live and get everything done.
If you want to get knowledge of Machine Learning or want to develop a mobile app for any platform, you can contact FuGenX Technologies- The top mobile app development company in Dubai, Whose team is highly professional in developing iOS, Android, Windows apps as well as games. And our Machine Learning experts and data scientists can help you better understand the potential of Machine Learning for your business.