Artificial Intelligence (AI) is everywhere. The possibility is that you are using it in one way or the other and you don't even know about it. One of the popular applications of AI in custom software development is Machine Learning (ML), in which computers, software, and devices perform via cognition (very similar to the human brain). Herein, we share few examples of machine learning that we use every day and perhaps have no idea that they are driven by ML.
Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. You can even instruct assistants for certain tasks like “Set an alarm for 6 AM next morning”, “Remind me to visit Visa Office the day after tomorrow”.
Image source: Make An App Like
Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.
Virtual Assistants are integrated to a variety of platforms. For example:
- Smart Speakers: Amazon Echo and Google Home
- Smartphones: Samsung Bixby on Samsung S8
- Mobile Apps: Google Allo
2. Predictions while Commuting
Traffic Predictions: We all have been using GPS navigation services. While we do that, our current locations and velocities are being saved at a central server for managing traffic. This data is then used to build a map of the current traffic. While this helps in preventing the traffic and does congestion analysis, the underlying problem is that there are fewer cars that are equipped with GPS. The machine learning models in such scenarios helps to estimate the regions where congestion can be found on the basis of daily experiences.
Image source: Uber Blog
Online Transportation Networks: When booking a cab, the app estimates the price of the ride. When sharing these services, how do they minimize the detours? The answer is machine learning. Jeff Schneider, the engineering lead at Uber ATC reveals in an interview that they use ML to define price surge hours by predicting the rider demand. In the entire cycle of the services, ML is playing a major role.
3. Videos Surveillance
Imagine a single person monitoring multiple video cameras! Certainly, a difficult job to do and boring as well. This is why the idea of training computers to do this job makes sense.
Image source: Bureau of Labor Statistics
The video surveillance system nowadays is powered by AI that makes it possible to detect crime before they happen. They track unusual behavior of people like standing motionless for a long time, stumbling, or napping on benches etc. The system can thus give an alert to human attendants, which can ultimately help to avoid mishaps. And when such activities are reported and counted to be true, they help to improve the surveillance services. This happens with machine learning models doing their job at the backend.
4. Social Media Services
From personalizing your news feed to better ads targeting, social media platforms are utilizing machine learning for their own and user benefits. Here are a few examples that you must be noticing, using, and loving in your social media accounts, without realizing that these wonderful features are nothing but the applications of ML.
- People You May Know: Machine learning works on a simple concept: understanding with experiences. Facebook continuously notices the friends that you connect with, the profiles that you visit very often, your interests, workplace, or a group that you share with someone etc. On the basis of continuous learning, a list of Facebook users are suggested that you can become friends with.
- Face Recognition: You upload a picture of yourself with a friend and Facebook instantly recognizes that friend. Facebook checks the poses and projections in the picture, notice the unique features, and then match them with the people in your friend list. The entire process at the backend is complicated and takes care of the precision factor but seems to be a simple application of ML at the front end.
Image source: Lowyat
- Similar Pins: Machine learning is the core element of Computer Vision, which is a technique to extract useful information from images and videos. Pinterest uses computer vision to identify the objects (or pins) in the images and recommend similar pins accordingly.
5. Email Spam and Malware Filtering
- There are a number of spam filtering approaches that email clients use. To ascertain that these spam filters are continuously updated, they are powered by machine learning. When rule-based spam filtering is done, it fails to track the latest tricks adopted by spammers. Multi Layer Perceptron, C 4.5 Decision Tree Induction are some of the spam filtering techniques that are powered by ML.
Image Source: Business Insider
- Over 325, 000 malwares are detected everyday and each piece of code is 90-98% similar to its previous versions. The system security programs that are powered by machine learning understand the coding pattern. Therefore, they detects new malware with 2-10% variation easily and offer protection against them.
6. Online Customer Support
A number of websites nowadays offer the option to chat with customer support representative while they are navigating within the site. However, not every website has a live executive to answer your queries. In most of the cases, you talk to a chatbot. These bots tend to extract information from the website and present it to the customers. Meanwhile, the chatbots advances with time. They tend to understand the user queries better and serve them with better answers, which is possible due to its machine learning algorithms.
Image source: Salesforce
7. Search Engine Result Refining
Google and other search engines use machine learning to improve the search results for you. Every time you execute a search, the algorithms at the backend keep a watch at how you respond to the results. If you open the top results and stay on the web page for long, the search engine assumes that the the results it displayed were in accordance to the query. Similarly, if you reach the second or third page of the search results but do not open any of the results, the search engine estimates that the results served did not match requirement. This way, the algorithms working at the backend improve the search results.
You shopped for a product online few days back and then you keep receiving emails for shopping suggestions. If not this, then you might have noticed that the shopping website or the app recommends you some items that somehow matches with your taste. Certainly, this refines the shopping experience but did you know that it’s machine learning doing the magic for you? On the basis of your behavior with the website/app, past purchases, items liked or added to cart, brand preferences etc., the product recommendations are made.
9. Online Fraud Detection
Machine learning is proving its potential to make cyberspace a secure place and tracking monetary frauds online is one of its examples. For example: Paypal is using ML for protection against money laundering. The company uses a set of tools that helps them to compare millions of transactions taking place and distinguish between legitimate or illegitimate transactions taking place between the buyers and sellers.
10. Intelligent Gaming
Some might remember the chess match between Gary Kasparov and IBM's Deep Blue, where Deep Blue came out victorious. Or a couple of years back in 2016 when Google DeepMind's AlphaGod defeated Lee Dedol the Go world champion.
Image source: The Keyword
This ancient Chinese game of Go is considered to be much more difficult for computers to learn and then to master than chess. However, the AI of AlphaGo was specifically trained to play Go and not by simply analyzing the moves of the world's best players but by practicing against itself millions of times.
11. Self-Driving Cars and Automated Transportation
Did you know that a Boeing 777 pilot spends only seven mins flying the plane manually? Flights today use FMS (Flight Management System) a combination of GPS, motion sensors, and computer systems to track its position during flight. However, when we try to apply the same concept to cars the dynamics change drastically. There are other cars on the road, obstacles to be avoided, and limitations to which are subject to the traffic rules. Even so, self-driving cars are a reality. These AI-powered cars can have better records than their human counterparts according to a study with 55 Google vehicles that the driven more than 1.3 million miles altogether. The navigation issues have already been solved by the use of Google Maps which sources location data from drivers smartphones.
Image source: Forbes
12. AI for Dangerous Jobs
Bomb disposal is one of the most dangerous jobs on the planet. This is another artificial intelligence example where the use of AI is very essential to save lives. Nowadays robots and drones are taking over these risky jobs. Presently drones require human control but as ML evolves, these very drones will be unmanned completely controlled by AI.
13. Environment Protection
Machines can access and store huge amounts of data using big data and AI could help in the identification of trends and use the information to devise solutions to previously untenable problems. For example, IBM's Green Horizon Project analyzes environmental data from multiple sensors and sources to produce accurate, evolving weather and pollution forecast. It helps city planners to understand the impact of the environment in their planning. Amazing environment-oriented innovations are emerging in the market regularly, from self-adjusting smart thermostats to distributed energy grids.
Image source: Korad
14. Improved ElderCare
For many elderly people, their daily task can be a daunting one. Many rely on help from outside for their elderly family members. Elderly care is a growing concern for families all around the globe. The solution is AI-powered in-home robots. These robots can help the elderly with everyday tasks, keeping them independent and in their home, thus improving their overall well-being. Medical and AI researchers have even piloted systems based on infrared cameras that can detect when the elderly falls, monitor food and alcohol consumption, restlessness, fevers, urinary frequency, chair and bed comfort, fluid intake, sleeping, eating, declining mobility and more.
15. Home Security and Smart Homes
AI-powered alarms and cameras are now at the forefront of cutting edge home security. These security systems use facial recognition software and machine learning models to build a catalogue of your home's frequent visitors. This allows the system to detect uninvited guests. There are other intriguing features such as tracking when you last walked your dog or notifying when your kids are back home from school. Some of the latest systems can automatically call emergency services, making it a beneficial alternative to subscription bases services of the same category.
16. Mood Analysis in Music Streaming
Music streaming platforms use machine learning for mood analysis to enhance user experiences. Algorithms analyze user listening history, song choices, and physiological responses to music to infer the user's mood. The platform can then curate playlists or recommend songs that align with the user's current emotional state, providing a more personalized and emotionally resonant music streaming experience.
Image source: RouteNote
17. Music Composition
Machine learning algorithms in music composition analyze vast datasets of musical compositions, identifying patterns and structures. AIVA (Artificial Intelligence Virtual Artist) is a prime example. Using deep learning, AIVA learns from a diverse range of musical genres, styles, and historical compositions. It leverages this knowledge to generate original compositions, demonstrating the capacity of machine learning to emulate and extend creative endeavors traditionally associated with human expression.
18. Predictive Text and Autocorrect
Predictive text and autocorrect functionalities on modern smartphones leverage machine learning algorithms to enhance typing experiences. These algorithms learn from users' typing habits, frequently used words, and contextual patterns. As users interact with their keyboards, the system predicts the next word in a sentence, offering suggestions that align with individual writing styles. Autocorrect features also utilize machine learning to identify and rectify typing errors, contributing to smoother and more accurate text input.
19. Automated Language Translation
Automated language translation powered by machine learning utilizes advanced neural machine translation models. These models, often based on deep learning architectures, learn from vast multilingual datasets to understand complex linguistic patterns and nuances. Platforms like Google Translate leverage these models to provide contextually accurate translations. The continuous learning and improvement from user interactions contribute to the system's ability to handle diverse language pairs and deliver more accurate translations over time.
Image source: Dribble
20. Medical Diagnosis and Imaging Analysis
Machine learning in medical imaging involves the use of algorithms to analyze complex visual data generated by various imaging modalities, such as X-rays, MRIs, and CT scans. Deep learning models, often based on convolutional neural networks (CNNs), are trained on extensive datasets of medical images to recognize patterns and anomalies. These algorithms can assist healthcare professionals in identifying subtle abnormalities, making more accurate diagnoses, and planning effective treatment strategies. The ability of machine learning to process vast amounts of medical imaging data quickly contributes to improved efficiency in healthcare workflows.
21. Automated Captioning for Images and Videos
Automated captioning in images and videos is achieved through machine learning-driven image recognition algorithms. These algorithms analyze visual content and generate descriptive captions, making multimedia content more accessible. This technology is particularly valuable for individuals with visual impairments, allowing them to understand the content of images and videos. The continuous learning from diverse datasets contributes to improved accuracy in generating relevant and meaningful captions.
Craft Your Own Powerful ML-Based Software Application
From personalized recommendations that enhance our entertainment experiences to predictive text that streamlines our communication, the influence of machine learning is pervasive. These real-world examples not only showcase the power of algorithms but also underscore their ability to adapt and evolve alongside us. As we look to the future, the horizon is filled with possibilities, and the impact of machine learning is bound to grow even more profound.