When you hear the term "machine learning," what comes to your mind at once?
Does it remind you of computers that handle our issues independently, without the need for any human assistance and taking over your jobs and replacing you?
A majority would agree with this, However this is a false fallacy, and machine learning is far from taking over our lives and our jobs..
What is machine learning?
Understanding machine learning is easier than you think. It's all about finding patterns in data, constructing models to explain those patterns, and then utilizing those models to make predictions.
As an example, if you feed a machine learning algorithm with a set of images of cats and dogs, it would learn from the labeled data. Then it will be able to decide whether the next image it processes is a cat or a dog. The more it is used, it gets better because each new piece of data is a "learning" opportunity for the machine.
However, there can be instances where objects that are seemingly alike can cause confusions to a machine learning algorithm. As an example, consider the set of images below. It is along the lines of training a computer to identify certain types of dogs. Even though as a human you would be able to distinguish fried chicken from a fluffy dog, a machine learning algorithm would require far more samples of negative and positive data sets to arrive at a considerable level of accuracy.
Let’s have a look at some approaches to machine learning
- Supervised learning In this type of learning, an algorithm learns the connection between the input and output using training data and human feedback. For example, if the goal is to forecast future land prices, the inputs could include "time of year" and "interest rates."
Supervised learning works in three steps:
- A person categorizes every element of the input (e.g. "time of year," "interest rates," etc.) and defines the relevant output (e.g. "land prices").
- The algorithm is practiced on the data to find the relationship between the input and the output.
- Once training is complete – Generally when the algorithm is sufficiently accurate in finding output from input – then the algorithm is applied to new data.
- Unsupervised learning
With unsupervised learning, an algorithm obtains input data without being given any explicit output. For example, the input data might be "customer buying data". Then the algorithm is used to explore and identify patterns.
- Reinforcement learning
Reinforcement learning can be used when there is not a lot of training data available. It can either be used when the ideal outcome cannot be clearly defined or when there is no other way to learn about the environment but to interact with it. Algorithms learn to perform a task by maximizing the rewards it receives for the actions it does. In 2017 with the help of reinforcement learning, AI system AlphaGo defeated world champion Ke Jie in an ancient Chinese board game!
A few instances of application of machine learning from real life
Image recognition
Machine learning is used to teach computers how to recognize the contents of an image. Have you seen those internet challenges where you have to find all the buses, crosswalks, or traffic signals in a series of nine pictures? With your replies, you're helping to train a machine learning system on picture recognition, proving that you aren't a robot.
Speech recognition
Machine learning techniques are also being used to improve speech recognition. There is a wide variety of applications that require speech input. Whether you're typing on a computer or using a smart speaker, voice recognition is essential. It eases the burden on consumers and even broadens the range of people who can utilize the service.
Reps for customer service
While buying online, do not be surprised if a person answers the small chat box that pops up. A growing number of businesses are turning to chatbots that use conversational AI to respond to client queries.They employ machine learning to understand and respond to consumers' questions and answers better.
Virtual personal assistants (VPA)
Siri, Alexa, and Google all have virtual assistants that employ machine learning to improve their replies. They also employ speech recognition, which we mentioned earlier. Other than that, when you're asking something, they use machine learning to collect data on what you're asking for and how often they get it correct.
Social media customization
Your social media profile has a wealth of information about your interests and likes. It knows the times of day and places where your activity is highest. Your social media feeds will be personalized and better targeted to you, thanks to machine learning algorithms!
What’s popular in this space and what to look forward to
Isn't it interesting how Netflix always knows what to recommend? For the same reason that Netflix uses machine learning to guide your next binge-watch. Based on what you like to listen to and what you've recently watched, Spotify proposes playlists, and YouTube recommends similar videos. Even though most of it may be marketing, it still helps personalize the customer experience, which benefits everyone!
Car development companies such as Tesla, Waymo and Honda are exploring the possibility of deploying self-driving cars with the help of machine learning. So it is no secret that machine learning is currently at work in a wide range of technologies, from Netflix's recommendation system to self-driving automobiles. Machine learning algorithms are becoming more effective as technology evolves. There is no doubt that tremendous opportunities for business will be opened up by these algorithms. There is unlimited potential to unlock in this space which businesses can utilize to take the next step in their technology journey!