Jun 16, 2017 10:00:27 PM
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. 1. Virtual Personal Assistants 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”. 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.
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.
1. Virtual Personal Assistants
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”.
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
Also Read: How Daffodil builds an AI-enabled mobile app to aid visually and hearing impaired to identify the denomination of Indian currency notes
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.
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.
The video surveillance system nowadays is powered by AI that makes it possible to detect crime before they happen. They track unusual behaviour 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..
- 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.
- 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.
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.
8. Product Recommendations
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 behaviour with the website/app, past purchases, items liked or added to cart, brand preferences etc., the product recommendations are made.
Also Read: How Daffodil helped India’s leading multi-brand online beauty retailer to leverage AI and achieve a 40% add-to-bag conversion rate
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. 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.
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.
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.
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.
How do you Use Machine Learning Daily?
Except for the examples shared above, there are a number of ways where machine learning has been proving its potential. Let us know how machine learning is changing your day-to-day life and share with us your experience with it in the comments below.
Topics: Artificial Intelligence
Written by Archna Oberoi
Content strategist by profession and blogger by passion, Archna is avid about updating herself with the freshest dose of technology and sharing them with the readers. Stay tuned here as she brings some trending stories from the tech-territory of mobile and web.
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One of the most common applications of Machine Learning is Automatic Friend Tagging Suggestions in Facebook or any other social media platform. Facebook uses face detection and Image recognition to automatically find the face of the person which matches it's Database and hence suggests us to tag that person based on ...What is machine learning give some examples? ›
Machine learning, however, is the part of AI that allows machines to learn from the hoards of data it receives without explicitly being programmed. ML, for example, can make predictions using statistical algorithms and perform tasks beyond what it was explicitly programmed for.Is Google an example of machine learning? ›
Google services, for example, the image search and translation tools use sophisticated machine learning. This allows the computer to see, listen and speak in much the same way as humans do. Much wow! Google uses machine learning algorithms to provide its customers with a valuable and personalized experience.Is Siri a machine learning? ›
Siri relies on natural language generation, natural language processing, and machine learning in order to effectively operate and improve its performance over time.What are the examples of AI in real life? ›
- Manufacturing robots.
- Self-driving cars.
- Smart assistants.
- Healthcare management.
- Automated financial investing.
- Virtual travel booking agent.
- Social media monitoring.
- Marketing chatbots.
Alexa is also a part of Amazon which uses Machine Learning to predict what the user will ask the information for and then based on that gives a rich user experience when the user asks the question.What are the five applications of machine learning? ›
2. What are the five applications of machine learning? The most common five machine learning applications are fraud detection, virtual personal assistants, product recommendations, speech recognition, and customer segmentation.What is AI with example? ›
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.What are applications of machine learning? ›
Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend tagging suggestion: Facebook provides us a feature of auto friend tagging suggestion.What is machine learning and its uses? ›
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Machine learning allows speech recognition systems to caption videos on Facebook, making them more accessible. It powers the translation of more than 2 billion stories every day, so people can connect in any language. It makes connections between people and local businesses.Is Alexa an AI? ›
Alexa and Siri, Amazon and Apple's digital voice assistants, are much more than a convenient tool—they are very real applications of artificial intelligence that is increasingly integral to our daily life.Is YouTube algorithm AI? ›
Like Netflix, YouTube uses AI to determine the “best” videos for viewers (or at least the person whose account is currently logged in).Is Alexa a robot or AI? ›
|Type||Intelligent personal assistant, cloud-based voice service|
If John McCarthy, the father of AI, were to coin a new phrase for "artificial intelligence" today, he would probably use "computational intelligence." McCarthy is not just the father of AI, he is also the inventor of the Lisp (list processing) language.Is Google is AI? ›
Google Search is a form of narrow AI, as is predictive analytics, or virtual assistants. Artificial general intelligence (AGI) would be the ability for a machine to “sense, think, and act” just like a human.Is Siri an example of artificial intelligence? ›
Siri is Apple's personal assistant for iOS, macOS, tvOS and watchOS devices that uses voice recognition and is powered by artificial intelligence (AI).How is AI used in education? ›
AI enhances the personalization of student learning programs and courses, promotes tutoring by helping students improve their weak spots and sharpen their skills, ensures quick responses between teachers and students, and enhances universal 24/7 learning access.Which is a good example of an AI application? ›
- Personalized Shopping. ...
- AI-powered Assistants. ...
- Fraud Prevention. ...
- Administrative Tasks Automated to Aid Educators. ...
- Creating Smart Content. ...
- Voice Assistants. ...
- Personalized Learning. ...
- Autonomous Vehicles.
Despite Amazon's secrecy, an extremely likely candidate source for the voice of Alexa has been revealed by author Brad Stone. According to Wired, Stone's research for his book, "Amazon Unbound: Jeff Bezos and the Invention of a Global Empire," pointed to Boulder, Colorado, voice actress Nina Rolle.
Google Assistant is a virtual assistant software application developed by Google that is primarily available on mobile and home automation devices. Based on artificial intelligence, Google Assistant can engage in two-way conversations, unlike the company's previous virtual assistant, Google Now.How do self driving cars use machine learning? ›
Machine learning algorithms make it possible for self-driving cars to exist. They allow a car to collect data on its surroundings from cameras and other sensors, interpret it, and decide what actions to take. Machine learning even allows cars to learn how to perform these tasks as good as (or even better than) humans.How is machine learning used in healthcare? ›
- Predicting and treating disease.
- Providing medical imaging and diagnostics.
- Discovering and developing new drugs.
- Organizing medical records.
These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.What is Elon Musk AI? ›
Tesla CEO Elon Musk unveiled the company's new humanoid robot dubbed “Optimus” at the 2022 AI Day on Friday. The prototype, which he says is a “rough development robot,” was put together in six months. It walked slowly onto the stage, waved to the crowd and danced.What are the 4 types of AI? ›
According to the current system of classification, there are four primary AI types: reactive, limited memory, theory of mind, and self-aware.How AI is used in social media? ›
An AI-powered social monitoring tool or social listening tool can deliver insights from your brand's social media profiles and audience. This often involves using the power of AI to analyze social data at scale, understand what's being said in them, then extracting insights based on that information.Does Alexa use machine learning? ›
Alexa is also a part of Amazon which uses Machine Learning to predict what the user will ask the information for and then based on that gives a rich user experience when the user asks the question.What are applications of machine learning? ›
Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend tagging suggestion: Facebook provides us a feature of auto friend tagging suggestion.Does Tesla use machine learning? ›
Tesla plans to build the auto-pilot model purely with computer vision accompanied by machine learning and video streams from the cameras. This raw footage is then processed through Convolutional Neural Networks (CNNs) for object tracking and detection.
What is machine learning? Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.Who is Alexa's voice? ›
Despite Amazon's secrecy, an extremely likely candidate source for the voice of Alexa has been revealed by author Brad Stone. According to Wired, Stone's research for his book, "Amazon Unbound: Jeff Bezos and the Invention of a Global Empire," pointed to Boulder, Colorado, voice actress Nina Rolle.Is Siri considered AI? ›
Siri is Apple's personal assistant for iOS, macOS, tvOS and watchOS devices that uses voice recognition and is powered by artificial intelligence (AI).Is Google Assistant an AI? ›
Google Assistant is a virtual assistant software application developed by Google that is primarily available on mobile and home automation devices. Based on artificial intelligence, Google Assistant can engage in two-way conversations, unlike the company's previous virtual assistant, Google Now.How is machine learning used in marketing? ›
Marketers use machine learning to find patterns in user activities on a website. This helps them predict the further behavior of users and quickly optimize advertising offers.How is machine learning used in healthcare? ›
- Predicting and treating disease.
- Providing medical imaging and diagnostics.
- Discovering and developing new drugs.
- Organizing medical records.
AI software in the car is connected to all the sensors and collects input from Google Street View and video cameras inside the car. The AI simulates human perceptual and decision-making processes using deep learning and controls actions in driver control systems, such as steering and brakes.Do self-driving cars use AI? ›
One of them includes using artificial intelligence to make cars self-driving. A self-driving car (also known as an autonomous car or driverless car) is a vehicle that uses a different number of sensors, radars, cameras, and artificial intelligence to travel to destinations without needing a human driver.Is autopilot an AI? ›
The autopilot is not Artificial Intelligence. Over 100 years ago, the first autopilot was invented in 1912. To demonstrate it working, the inventor climbed out onto the wing letting the airplane fly itself in front of an audience.What are the 3 types of machine learning? ›
The three machine learning types are supervised, unsupervised, and reinforcement learning.
There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.What is the best language for machine learning? ›
First, let's look at the overall popularity of machine learning languages. Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% prioritising it for development.