Even though 2021 is not about robots walking the streets, the first two decades of the 21st century have witnessed dramatic technological advancement.
Kids today are growing up surrounded by applications of artificial intelligence (AI) and machine learning (ML). From virtual assistants to the recommendations on online shopping websites, ML is everywhere. What’s notable about this is how it is shaping the next-gen creators.
Let’s admit the fact that machine learning is often misunderstood to be a complicated topic. With this article, we want to make it easier for you to explain to your child how Machine Learning fits into our lives and what it does.
But first, what is Artificial Intelligence?
AI involves machines performing tasks that are attributes of human intelligence.
What is Machine Learning?
Machine learning is essentially a simple method of achieving artificial intelligence.
It provides computers with the ability to think like humans by learning from experience. Let me put it this way. The machine learns like kids.
Repetition is the key.
For example, How does a toddler learn to not eat the toys? And, how do you make them understand that toys are not edible?
By repeating it a thousand times.
Machines are no different, except for the fact that they learn it much quicker and self-correct the mistakes based on the examples we provide.
When was the last time you went on a race against a car and actually won?
Never. Because machines are fast. Blazing fast. And that’s why we train the machines to do tedious repetitive things, whereas, we focus on doing creative stuff.
Real-Life Examples of Machine Learning:
Music and Video Recommendations:
As online music and video streaming become the dominant medium for us to listen to our favourite songs or watch our favourite movies – recommender systems are one of the most successful and widespread applications of machine learning. If you are familiar with music apps like Spotify or Apple Music, you have probably wondered how these apps can suggest other songs you might enjoy. More often, the recommended songs are from the artists or the genre you previously listened to.
Same with YouTube—how does it recommend the next lesson(video) on playing the guitar, just after you finish the first guitar lesson on Youtube? All of this is made possible with machine learning.
The music and video streaming services collect a large amount of data on the listening habits of users. The algorithm is then trained with previously watched videos, and from that info, builds and improves an algorithm that defines the listener’s or viewer’s taste.
Video games are one of the major applications of Machine Learning. They are used to generate levels, character appearances, and even plot twists. The idea is that games can be created by providing a machine with a fixed set of rules and goals. The machine then iteratively proves if it can find paths to achieve these goals within the boundaries of those rules.
For example, Machine Learning algorithms can be used to automate core game tasks such as placing objects in games like Minecraft or detecting objects or characters in games like GTA.
What if someone could read your mind and show you what you wanna buy next?
That’d be awesome, isn’t it?
The machine learning algorithms used by online shopping giants like Amazon and Flipkart enable retailers to predict what their customers will purchase next and make those items available as soon as possible. These predictions are based on the customer’s past purchases, which makes it incredibly efficient for shoppers who have already made previous purchases with that retailer.
Let’s say, you purchased a pair of jeans using any online shopping app. The machine can predict what you can pair it up with according to your style and recommend the best items available.
Speech recognition technologies such as Alexa, Cortana, Google Assistant and Siri are changing the way people interact with their devices, homes and cars. This technology is designed to help us perform basic tasks and allow a computer or device to interpret what we’re saying in order to respond to our queries or commands.
These digital assistants can access information from vast databases and various digital sources to help us solve problems in real-time, enhancing the experience and productivity.
Real-world examples of speech recognition:
- Voice search
- Voice dialling
- Appliance control
Some of the most popular digital assistants include:
- Amazon’s Alexa
- Apple’s Siri
- Google’s Google Assistant
- Microsoft’s Cortana
Search Engine Result Refining:
Ever wondered – how does Google know that all the thousands of results listed are related to a topic you searched for?
Machine learning is employed at almost every part of the stack at major search engines like Google or Bing.
While you start typing in the search box it automatically anticipates what you are looking for. To then provide suggested search terms for the same. These suggestions are showcased because of past searches (recommendations), trends (what everyone is looking for), or from your present location.
Have you observed that if you make a typo while searching on Google, you get this message: “Did you mean _____”?
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.
Google uses machine learning algorithms to provide its customers with a valuable and personalized experience. Gmail, Google Search and Google Maps, already have machine learning embedded in services.
Example: Traffic alerts
How would Google Maps know that you are on the fastest route despite the traffic?
Google Maps captures and stores data like – Location, average travelling speed, day, time and the specific occasion on the day of commute.
By using this data, AI and machine learning algorithms make the right conclusions and give you the exact information
The updated feature of Google Maps also helps us to know how far is the upcoming bus from a specific stop and even make predictions on the bus delays. Further, the machines are so intelligent that they can even tell you how crowded the train is so that you can make a call beforehand!
Gone are those days where you need to drive cars. It reminds of those lift operators in the 19th century. Why did people employ lift operators when we can just press a button and it takes us to any floor?
Obviously, cars are much different. It needs to measure the distance and identify other vehicles, pedestrians, road signs, potholes, road edges, lane markings and many other factors. With Tesla leading the pack, self-driving cars are no longer a thing of sci-fi-movie-only kind of stuff. Innovative minds are shaping our future before our eyes, and it’s time we stop and take notice.
With autonomous cars, we don’t just finally say goodbye to the hassle of driving, but make the process of taking us from point A to B much more efficient. Self-driving cars use a complex system of sensors to identify and map their environment to make smart decisions. The car learns from its own experience by receiving data from sensors, cameras, and computers about traffic, road conditions, and obstacles in the environment, and chooses the most efficient route possible.
It is a fact that most accidents happen due to Distracted Driving, Drunk Driving, Reckless Driving/Speeding, Fatigue, Inadequate lighting, and a myriad of things. Machines, on the other hand, react more quickly than humans would be able to.
Do you unlock a phone simply by looking at it?
If yes, you are using machine learning. The camera of your phone can recognize 80 modal points on your face. The machine learning technologies measure the variable of a person’s face and unlock the phone.
Phone unlocking is now among the most common machine learning applications.
In addition, image recognition uses machine learning to train computers to recognize a brand logo or photos of certain products, without any accompanying text.
Today’s generation uses face recognition to make quick bank payments. Other image recognition uses are:
- Self-driving cars
- Military surveillance
- Forest activities
The prolonged period of forced closure has pushed schools towards extensive use of technology to grant students continuity in learning.
A lot of apps have popped up, helping kids learn math, science and languages in a fun way. While gamification is one thing, machine learning is another major thing that’s shaping the online education industry. Every kid has his/her own learning style, be it visual (by seeing pictures/videos), auditory (by listening to the lectures), or kinesthetic (learning by doing). Every kid also has his/her learning speed. Some kids grasp one thing at a time while there are also fast learners. Personalizing a kid’s learning path to make him/her understand the topic is a teacher’s job, and machines help the teachers achieve exactly that.
AI-powered tutors can give personalized lessons and develop a curriculum tailored to the student’s needs based on how they perform in previous tests.
Social Media Services:
Think of times when you just talk about or search for something and then you see an ad while scrolling through your social media account – well, that’s not a coincidence.
Though there is no one sitting with a pair of headphones listening to our conversations. Social media platforms like Facebook, Twitter, Instagram and YouTube use machine learning algorithms to surveil our online behaviours: i.e., the websites we visit, what terms we search for, what we purchase online, etc. – to go off of to learn our potential interests for advertising purposes.
Whereas, for businesses –
As social media channels have become one of the biggest platforms for business expansion and branding – machine learning is integrated into many aspects of it.
Social media monitoring helps measure the success of past posts, including the number of likes, comments, clicks on a link or views for a video. Machine learning tools integrated into platforms like Twitter and Instagram can also tell you a lot about your audiences including demographic information and the peak times when their followers are most active on the platform.
Apart from 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.
As machine learning continues to advance, the range of applications and use cases will certainly expand by 2030. With the new decade just getting underway, it’s worth keeping an eye on how machine learning use cases will be deployed to improve efficiency, reduce costs, and deliver better user experiences.
It’s an exciting time for the creators of tomorrow to know more about it and get in on the action! Introduce your child to the technologies of tomorrow with the LearnPanda Smart Tech program. Know more on www.learnpanda.school.