Best Machine Learning Applications
Machine learning software can significantly expand our capabilities. It helps us make more informed decisions and process information faster. In this post, I have collected some examples of the best ML applications in 2020.
Practical business uses of machine learning
Computer vision is an interdisciplinary field of machine learning that teaches the machines to process, analyze, and recognize visual information.
The most common technologies used to build computer vision systems are artificial neural networks and deep learning. Other machine learning algorithms such as SVM, KNN, and Naive Bayes are also crucial in computer vision.
Computer vision is used for various tasks: object recognition, scene reconstruction, identification, image retrieval, motion analysis, and so on.
Computer vision can be used for face recognition in security systems. A special program recognizes the faces of the employees and lets them inside the building. It can also automatically check their name in the attendance register. This solution is much more convenient than traditional keys and identity cards and much more secure since these objects can easily get lost or stolen.
ML face recognition technology is used to identify terrorists in the crowd among the visitors of airports, congress centers, and other events.
Digitizing archives, exam papers, and documents by hand is a time-consuming and inaccurate process. Machine learning allows scanning and digitizing documents in minutes. This solution can be used in universities, exam centers, museums, police, and other organizations that have to work with hand-written documents.
Business applications of computer vision
FacePRO is a facial recognition system empowered with deep learning, created by Panasonic. FacePRO uses live or recorded video from Panasonic i-PRO cameras for face matching and performs notification and alerts.
Waymo is working on cars that can completely autonomously operate on highways and city roads. Their goal is to make driving safer and more accessible for more people. Waymo’s mission is to prevent crashes and injuries on the road caused by the human factor. You can learn more on their official website.
Speech recognition is the transformation of spoken words into text. It is also known as “automatic speech recognition” (ASR) or “speech-to-text” (STT).
This technology allows computers to recognize phonemes or words (depending on the system). Speech recognition provides a means for direct communication between a user and a machine.
Speech recognition is widely used in everyday life to create voice interfaces and voice assistants. STT can be found in in-car systems, healthcare for medical documentation, and the military. Not only is this convenient, but it also improves accessibility.
The ML techniques that help to build speech recognition systems are vector quantization, dynamic time warping, and artificial neural networks.
Speech recognition software allows you to perform operations on your device without touching it. Using a VUI (voice user interface) instead of pressing buttons is very convenient when you are driving or doing other activities that demand a contactless interface.
Voice assistants and chatbots are often used for streamlining customer service in retail and telecommunications. It allows reducing the number of personnel needed to perform routine tasks. Also, researchers report that many customers prefer to communicate with chatbots rather than humans if it saves time.
Business applications of speech recognition
When you think of voice assistants, Siri or Alexa are probably the ones that come to mind. These can be useful if you are a native English speaker, but their possibilities are limited, even in this case.
- Samsung has created a voice assistant for people with vision impairments. Voice Assistant can provide those users with maximum control over their phones. When the Voice Assistant is turned on, the phone offers voice feedback, helping blind and visually impaired users. For example, it describes what you touch, what you select and activate. You can also edit text and change various settings, such as volume or speech speed, with familiar gestures of touching and swiping the screen.
- C2 Solutions collaborates with Google on voice recognition and dictation solutions. They use these technologies to work with Gmail and text documents, which can be useful for people with disabilities and those who want to free their hands when checking the email box. In this video, they explain how it works.
Computers today can understand speech, images, and other types of information and also generate data.
Generative adversarial networks are used when you want to build ML-powered software that can draw, talk, or learn. Given a training set, the machine can learn to generate imitations.
Speech synthesis is used in devices for visually impaired people and students with reading difficulties. Image and music generation tools are widely used in entertainment and for research purposes. Programs that generate text exist as well, but the texts that they make do not make sense. Overall, this technology has not yet shown its full potential.
With a free Amazon account, you can try out text-to-speech generation yourself.
Business applications of text-to-speech
- CereProc is a Scottish company that specializes in text-to-speech technologies. They have collaborated on Sophia the Robot to create her unique voice and were in charge of her singing capabilities. CereProc hosts a voice shop where business owners can choose a unique digital voice for their corporate application. They also help you to clone your own voice. Developers can use CereProc voice tools to work on their projects.
- NaturalReader is a software that can read any text from platforms such as web pages, PDF files, and Gmail. It can convert printed documents and screenshots to digital text as well.
For more examples on how TTS technologies help people with disabilities, visit Tecla’s website.
Machine learning algorithms for anomaly detection allow discovering abnormalities in a continuous stream of unstructured data. Finding anomalies can be useful in many situations: for example, if there have been more than three attempts to log in to an email address, it can be a hacking attack. It becomes even more valuable when the amount of data is so enormous that humans cannot process it.
Algorithms that you use for anomaly detection are SVM, k-Nearest Neighbor, Isolations forests.
Banking and finance are the fields where machine learning can save hundreds and thousands of dollars. Using ML-powered software, the financial institution can uncover hidden patterns, detect suspicious operations, and block them before it is too late. A huge advantage of such systems is that everything happens in real-time.
If your network suddenly fails, it can negatively affect your business. Anomaly detection software can detect a sudden rise in the number of failed server requests before it is too late. Moreover, it can provide you with the necessary information about what caused this problem.
The ability of ML models to accurately detect anomalies was found useful in medical diagnosis. The research has proven that specialized software is able to diagnose people with higher accuracy than experienced doctors. The software can detect several parameters at the same time and process medical records in real-time. Another benefit of medical ML is that it can quickly process large volumes of medical records and provide reliable statistical information. That helps with diagnosis and treatment.
Real-life business cases of anomaly detection
- Amazon fraud detection software is a full-fledged system that efficiently identifies potential fraudulent activities on the Internet, for example, when making online payments or creating fake accounts. Companies and people all around the globe annually lose tens of billions of US dollars. Fraud Detector uses your data and applies machine learning technologies to prevent such problems.
- Anodot is a company that provides ML-powered monitoring services for businesses. Their solutions for anomaly detection and forecasting analyze user data in real-time. Then, they use the discovered patterns to enhance the performance and reliability of the business. Using Anodot software, it is possible to monitor revenue, customer experience, and the partner ecosystem.
Machine learning algorithms can make predictions based on historical data. They apply the knowledge they got from familiar data to new data to forecast the likelihood of this or that outcome.
ML is usually applied to stock pricing prediction, marketing campaigns, scientific research, and many other cases.
Random forest and artificial neural networks are among the most common algorithms used to make predictions.
Stock pricing prediction
It is tough to predict the stock prices since there are so many factors that affect the outcome. However, discovering some characteristic features like the current state of an organization, its revenue, and so on or applying deep learning techniques, you can find valuable patterns. Analytics Vidhya has broken down how to build a stock price prediction model based on your parameters. One of the companies that help investors make informed decisions based on AI insights is Nvidia.
Which of these applications has surprised you the most? Would you like to have a ride in a driverless car or have a chat with an android? If you have an exciting idea for an ML-powered application, contact us.
- Practical business uses of machine learning
- Computer vision
- Speech recognition
- Text-to-speech generation
- Anomaly detection