Machine learning: Top 10 machine learning applications in 2023
What is machine learning?
Machine learning is an artificial intelligence subfield that involves the creation of algorithms and statistical models that allow computers to learn from data without being explicitly programmed. AI can be achieved through machine learning. Machine learning can be classified into three types: supervised learning, unsupervised learning, and reinforcement learning.
Machine learning is a rapidly expanding field with the potential to transform the way we live and work. Machine learning has a wide range of applications, from self-driving cars to personalised healthcare. In this article, we'll look at the top ten machine learning applications that are making a big difference in today's world. But first, let's look at the different types of machine learning. We have simplified the concept, Enjoy!
Supervised learning: It’s a type of machine learning in which the algorithm is trained on a labelled dataset where the correct output or label for each input is previously known. By detecting patterns in the training data, the algorithm learns to map inputs to outputs. Once trained, the algorithm can be used to predict the output for new, previously unseen inputs. Linear regression, logistic regression, and support vector machines are common examples of supervised learning.
Unsupervised learning: It’s a type of machine learning in which the algorithm is trained on an unlabeled dataset where the correct output for each input is unknown. By grouping similar inputs together or reducing the dimensionality of the data, the algorithm learns to find patterns and structure in the data. Unsupervised learning techniques include k-means clustering, principal component analysis, and autoencoders.
Reinforcement learning: The algorithm learns in this type of machine learning by interacting with its environment and receiving feedback in the form of rewards or penalties. The algorithm's goal is to maximise the total reward over time. Q-learning, SARSA, and Deep-Q Networks are examples of reinforcement learning.
Now, here are the top 10 applications of machine learning this year.
1. Computer Vision: Machine learning is being used to create advanced computer vision systems that can recognise objects, facial expressions, and even handwritten text. Self-driving cars, facial recognition systems, and image search engines are all using this technology.
Sample applications
Self-driving cars: The development of self-driving cars is one of the most well-known uses of computer vision. Machine learning algorithms are used in these vehicles to analyse data from cameras and sensors to comprehend their surroundings and make safe driving judgments. Waymo, an Alphabet subsidiary, for example, utilises computer vision to detect and classify other vehicles, pedestrians, and road signs in real-time, allowing their self-driving cars to navigate roadways safely.
Facial recognition systems: The development of facial recognition systems is another real-world use of computer vision. These systems examine images of faces and compare them to a database of known individuals using machine learning methods. Clearview AI, for example, matches photos of a person's face against a database of over 3 billion images collected from the internet, allowing law enforcement organisations to identify suspects.
2. Natural Language Processing: Natural language processing systems that can interpret and respond to human speech are being developed using machine learning. This technology is utilised in applications including speech recognition, language translation, and virtual assistants.
Sample applications
Speech Recognition: Speech recognition is a popular application of natural language processing. This technology lets users engage with devices and software by speaking to them. Apple's Siri, Amazon's Alexa, and Google Assistant, for example, all use natural language processing to understand and respond to voice requests.
Language Translation: Machine translation is another use of natural language processing that allows for real-time translation of text or speech from one language to another. Google Translate, for example, employs natural language processing to translate text from more than 100 languages. It also supports conversation mode, which allows for real-time speech translation between multiple languages.
3. Recommender Systems: Machine learning is being utilised to create recommender systems that can provide recommendations to consumers based on their likes for items, movies, or music. This technology is utilised in services such as online shopping, video streaming, and music streaming.
Sample applications
Online Shopping: Many e-commerce websites employ recommender systems to suggest products to users based on their browsing and purchasing history. Amazon, for example, employs recommender systems to recommend things to customers based on their browsing and purchasing histories.
Music Streaming Services: Another use for recommender systems may be found in music streaming services like Spotify and Pandora, which utilise machine learning algorithms to propose songs to users based on their listening history.
4. Fraud Detection: Machine learning is being utilised in financial systems to detect fraudulent transactions. Credit card fraud detection, anti-money laundering, and cyber security are all applications of this technique.
Sample applications
Credit Card Fraud Detection: Machine learning algorithms are employed in financial systems to detect fraudulent transactions. Many credit card issuers, for example, utilise machine learning algorithms to detect fraudulent transactions by examining data trends such as purchasing history, geography, and merchant type.
Anti-Money Laundering: Another application of machine learning in fraud detection is anti-money laundering (AML) compliance in the financial sector. Many banks, for example, utilise machine learning algorithms to detect money laundering by examining data patterns such as wire transfers, account balances, and transaction histories.
5. Healthcare: By evaluating massive volumes of patient data, machine learning is being utilised to generate tailored treatment. This technology is employed in a variety of applications, including drug development, medical imaging, and individualised treatment programmes.
Sample applications
Drug Discovery: Pfizer, for example, employed machine learning to find possible COVID-19 medication candidates by evaluating data from millions of scholarly papers, clinical trials, and patents.
Medical Imaging: Machine learning algorithms are used to evaluate medical images such as X-rays and CT scans in order to detect tumours, identify diseases, and guide therapies.
6. Robotics: Machine learning is used to create robots that can execute jobs that were previously done solely by humans. This technology is employed in a variety of industries, including manufacturing, logistics, and healthcare.
Sample applications
Manufacturing: Fanuc, a Japanese robotics company, for example, utilises machine learning algorithms to teach its robots new tasks and improve their manufacturing floor performance.
Logistics: Logistics is another application of machine learning in robotics. Companies such as Amazon and DHL, for example, utilise machine learning algorithms to optimise the routes and scheduling of their warehouse robots to improve productivity and save costs.
7. Self-Driving Cars: Machine learning is being utilised to create self-driving cars capable of navigating roads and traffic safely. This technology is utilised in applications such as self-driving cars, ride-hailing services, and delivery drones.
Sample applications
Autonomous Vehicles: To understand their surroundings and make safe driving decisions, self-driving cars employ machine learning algorithms to evaluate data from cameras, lidar, and other sensors. Waymo, an Alphabet company, has, for example, been testing self-driving cars for over a decade and has logged millions of kilometres on public roads in numerous locations.
Ride-Sharing Services: Ride-sharing services are another application for self-driving cars. Uber, for example, has been developing self-driving cars and testing them on public roads in a few cities. By eliminating the need for human drivers, the idea is to minimise the cost of operating the ride-hailing service.
8. Predictive Maintenance: Machine learning is being used to forecast when equipment may break and schedule maintenance before it happens. This technology is employed in a variety of industries, including manufacturing, transportation, and energy.
Sample applications
Manufacturing: For example, GE Predix, an industrial IoT platform, analyses data from industrial equipment to forecast when maintenance is required, helping businesses to avoid equipment breakdowns and save downtime.
Transportation: Transportation is another example of predictive maintenance. Airlines, for example, utilise machine learning algorithms to forecast when aircraft components require maintenance, allowing them to arrange maintenance during scheduled downtime and prevent costly emergency repairs.
9. Energy: Machine learning is being used to improve energy efficiency and forecast energy consumption. This technology is employed in smart grids, energy storage, and renewable energy applications.
Sample applications
Smart Grids: Machine learning is used to improve energy efficiency and forecast energy consumption. Companies such as Siemens and GE, for example, utilise machine learning algorithms to evaluate data from smart metres and other devices to estimate energy demand, allowing them to maximise the usage of renewable energy sources while lowering costs.
Energy Storage: Companies such as Tesla and LG Chem, for example, utilise machine learning algorithms to enhance battery performance, allowing them to boost the efficiency and lifespan of their energy storage systems.
10. Agriculture: Machine learning is used to improve crop yields, forecast weather patterns, and detect pests. Precision farming, crop monitoring, and food security are all applications of this technology.
Sample applications
Precision Farming: Machine learning algorithms are used by companies such as John Deere and CropX to evaluate data from sensors and drones to maximise crop yields, reduce water and fertiliser usage, and detect pests and illnesses.
Crop Monitoring: Crop monitoring is another application of machine learning in agriculture. Companies such as Taranis and Descartes Labs, for example, utilise machine learning algorithms to evaluate data from satellite and aerial photos to monitor crop health, predict crop yields, and detect environmental changes.
Conclusion
Machine learning is transforming the way we live and work. With machine learning's continuing advancement, we should expect to see even more inventive and game-changing applications within 2023 and beyond.