Machine Learning (ML) is a subset of Artificial Intelligence (AI), simply put, based on programmed algorithms Machine Learning can learn from data recorded in the past and improve over time. Machine Learning focuses mainly on developing programs that rely on accessing data and they use it for self-learning. The biggest benefit of Machine Learning is realizing tasks based on pre-accessed data without needing guidance and third-party intervention. For example, programmers could invent an application to control Covid-19 epidemic patients based on fingerprints by storing scanned images and similar disease diagnoses.
Here are some specific examples to help you have a clearer overview of the practical applications of Machine Learning in modern life.
- Recognize speech and convert it into text
This is one of the most common features on smartphones, computers or some other electronics. Google’s virtual assistant and iOs’s are good examples, we can use our own voice to make demand or ask question to the virtual assistant, it will display one or a series of information to respond to your order. In recent times, speech recognition is also used in chat conversations in some applications such as: Zalo, Facebook, Slack, Instagram,… to optimize user utility, attract customers when using the application.
- User face recognition
Under the development of technology, intelligent applications are constantly developing to serve human needs. Based on ML techniques, programming engineers have created facial recognition features that greatly assist humans in personnel management as well as enhance security in some private applications. For this feature, it is possible to identify an object as a digital image, based on the analysis of pixels in a black-and-white or color image. This function is present in smartphones, with face recognition (Face ID) applied to unlocking the screen, unlocking some applications to enhance user security. On the Facebook platform, they rely on the development of Machine Learning, the software developers have installed the automatic friend tagging recommendation task, this application uses face recognition and image recognition. Photos to automatically find faces of people that match their database has been updated and will suggest users to tag that person based on DeepFace. In addition, in the medical field, Machine Learning has now been applied to detect some diseases based on X-ray images, which helps the results become more accurate and saves the working time of the patients, doctors, and nurses.
- Fraud detection
Currently, banking sector is considered the place need ML’s features the most to ensure customer’s interests as well as prevent fraud in transactions. More specifically, in the environment of e-commerce transactions through e-wallets, debit cards,… has become a big challenge for leaders of credit institutions when the problem of fraud, fake profiles, and more. The application of Machine Learning to customer information management has helped the bank to screen and remove fake documents because any transaction that takes place, the ML model will carefully capture the user’s profile to search for suspicious points and return results to the server. This has helped the bank avoid a series of fraud, cheating, property seizure,…
- Predictions and warnings
For this function, we can see it most clearly in the Google Maps application. When using this platform, users will be able to predict the travel time when the destination is selected, in addition Google Maps can warn of routes that are in traffic jams or various other problems based on Machine Learning. Besides, it can predict fluid flows and mineral volumes in complex non-traditional reservoirs in geophysics based on available high-quality well log data. This provides a clearer understanding of mineral layers, allowing operators to exploit their full potential throughout the mine.
- Classification based on Machine Learning
Classification problem is the most common problem of ML when it has many tasks to mention such as: Bad debt classification, which is one of the highest-risk portfolios of many banks. To manage this risk, banking institutions need to rely on Machine Learning tools to analyze potentially risky loans, such as customers at risk of default, customers who are unable to pay, customers falsify records,…; Or apply it to work distinguishing diseases in medical term, computer vision models can make recommendations to doctors about the location, size and type of tumors or based on laboratory indicators such as red blood cells, white blood cells, platelets, blood pressure,… Machine Learning can help us classify different groups of diseases. To enable this feature, programmers need to solve the supervised ML problem of Machine Learning – data scientists provide algorithms with labeled training data and identify variables they want the algorithm to evaluate to find correlations. Both the input and output of the algorithm are specified.
Based on the learning style of algorithms, we can classify Machine Learning into 3 different types of learning: supervised learning, unsupervised learning and semi-supervised learning. At each attribute of different learning styles, software developers can build new features that meet the needs of mankind. In today’s life, we cannot deny that the explosive development of ML has solved many problems from simple to complex for humans, making everything gradually become simpler in the 4.0 era. In the future, under the breakthrough of the field of AI in general and ML in particular, the socio-economic life of people is constantly improving, creating a new world with the civilization of Artificial Intelligence.