Today, Artificial Intelligence and Machine Learning are seen as part of the everyday life of large organizations in various fields. The rapid pace of AI development, achievements in automated in aeronautical vehicles, the ability to overcome humans at chess and poker, automated customer service, and analytical systems, shows that AI is a revolutionary technology designed to change the way people use devices and conduct business.
The appearance of intelligent machines capable of solving difficult problems in a way that only humans could previously haven’t gone unnoticed. From the day of its invention until now, AI technology has been evolving and improving.
Due to its ability to learn, AI is widely used in many spheres. Particular applications of AI include expert systems, Speech Recognition, Machine Learning, and Machine Vision.
According to the 2019 Survey, the number of companies achieving AI technologies in some form has increased by 270 percent in the past four years, and by 37 percent in the past year alone.
It’s worth mentioning that AI in this context doesn’t relate to actual self-aware intelligence machines in a pure form. Rather, it can be considered a general designation for a range of applications used by the website and mobile app developers. They include image and Speech Recognition, Cognitive Computing, Automatic Analysis, and Machine Learning.
There are two main factors driving the fast appropriation of AI. The first is access to high-quality, versatile learning models. The second is the need to manage large amounts of data in a cheaper and more effective way instead of investing heavily in massive infrastructure.
Also Read: 5 Examples of Artificial Intelligence In Business Applications
How Machine Learning Works
According to the commonly accepted definition, ML is the ability for computers to learn and act without being explicitly programmed. To enable this self-learning function, ML uses two techniques: supervised learning and unsupervised learning.
Supervised learning
Supervised learning involves training design on a known set of input data and known replies to that data (outputs) so it can predict future responses to new data. In turn, supervised learning uses classification and regression techniques to develop predictive models.
Classification techniques predict discrete responses by analyzing input data into categories or classes. This type of ML is usually applied for image classification, diagnostics, identity fraud detection, and speech recognition. For instance, ML can say whether an email is genuine or spam or whether a tumor is malignant or benign.
Regression techniques predict continuous responses to constantly changing or developing data. They help a lot when it comes to weather forecasting, advertising popularity predictions, market predictions, and algorithmic trading.
Organizations usually choose supervised learning when their goal is to train a model to make a prediction regarding the future value of a continuous variable, such as temperature or a stock price, or to identify makes of cars from webcam video footage.
Unsupervised learning models
Unsupervised learning models are capable of finding hidden patterns and intrinsic groupings within input data and don’t require knowledge of the output for this.
The most typical unsupervised learning technique is clustering. Applications of cluster analysis include gene sequence analysis, recommender systems, targeted marketing, and object recognition.
In practice, clustering can be implemented like this: Let’s say a cell provider wants to figure out the best places to locate their cell phone towers.
For this, they need to acquire information about the number of users concentrated in each specific area around the company’s existing towers. Machine Learning can estimate this number.
As a phone can receive a signal only from one tower at a time, the telecom operative can use a clustering algorithm to determine the best placement of cell towers to optimize signal response for different groups of customers.
Organizations usually rely on unsupervised learning if they need to explore their data and train a model that will split data into groups.
Also Read: Top 46 Artificial Intelligence & Machine Learning Companies USA, India in 2020
Applications of ML
Machine Learning has made its way into a variety of human activities. The most common uses of Machine Learning are in the following four sectors:
Business
Almost every organization dealing with customer service has embraced AI technology in one way or another. Machine Learning algorithms are being widely integrated into analytics and CRM platforms to uncover information on how to better serve customers. Chatbots are being added to websites and mobile apps to provide immediate responses to visitors and users.
Adding ML to a mobile app has become a common request from business owners. Mobile app developers have learned fast how to integrate machine learning into Android and iOS apps. Apple offers developers their own Machine Learning frameworks: Core ML and the Create ML app.
Education
ML models can automate grading, giving teachers more time to concentrate on other aspects of education. Other programs are capable of assessing students in their study, adapting to their needs and level, and helping them work at their own pace. There are even AI tutors that provide support to students, ensuring they stay on track.
Healthcare
ML algorithms are widely integrated into medical applications. With the help of Machine Learning, doctors can make better and faster diagnoses and predict patient outcomes sooner.
Chatbots are also being actively applied by Healthcare service providers, as these online programs can assist patients in many ways such as scheduling appointments, answering common questions, aiding in the payment process, and even providing basic virtual diagnostics.
Manufacturing
Industrial companies have been at the forefront of incorporating robots into the workflow. Industrial robots have helped to automate and optimize certain processes and tasks previously performed by humans. This has helped to increase the volumes of manufactured products and reduce labor costs.
Conclusion
AI is capable of greatly facilitating the workflow of different organizations. That’s why the adoption of this technology is moving at a breathtaking pace. Seasoned companies along with fresh-baked startups are integrating Machine Learning and Deep Learning algorithms into their applications to offer advanced features to end-users.
Healthcare, Business, Education, and Manufacturing are only some of the sectors using ML applications. The continuous development of AI in general and Machine Learning, in particular, is leading to new opportunities for business owners and new markets for services.
