By the rate at which technology has stormed into our lives, I don’t think there’s any going back! It’s a fact that machines have replaced essential roles in our lives and provided us with convenience throughout every stage, especially in the technological sector.
Now, through machine learning, it has become easy for computers to tackle the tasks previously only performed by people. Starting from speech translation to self-driving cars, machine learning has made the future of artificial intelligence quite promising.
Sometimes, I stop and wonder what exactly machine learning is. How has it brought a rise in the power of artificial intelligence, and how far could it go? So let’s ponder over the answers to these questions together.
- Machine Learning – The Powerful Algorithms
- Why Is Machine Learning Important?
- Supervised Machine Learning
- Semi-Supervised Machine Learning
- Real-World Examples Of Machine Learning
- Social Media Sites
Machine Learning – The Powerful Algorithms
In the simplest terms, machine learning is the process of feeding data to computer systems and watching them come up with accurate predictions. Thus, machine learning can be known as a branch of artificial intelligence, and its algorithms could come up with a proper response to anything.
In fact, you can use the machine learning algorithm to accurately carry out speech-to-text functions to generate captions for a video game. There are more advanced algorithms that include natural language processing, autonomous systems, and even predictive analytics.
How Does It Work?
Essentially, you can break down the learning system or machine learning algorithm into three main parts. The first part is the decision process, the second part is called the error function, and the third part is the model optimization process.
The algorithm will be used to develop a classification or a prediction through data given to it in the decision process. Data doesn’t need to have labels, but the machine learning system will estimate based on the patterns it learns.
The error function will evaluate the decision process’s answer through comparisons to determine how accurate the model is. Finally, the data fits a given set in the model optimization process, and an answer is confirmed.
Why Is Machine Learning Important?
There are many reasons why machine learning holds such importance in the lives of people. For example, through machine learning, a business can understand the trends in customer requirements and figure out the patterns in its operations. Not only this, but businesses can even use this feature to develop new products.
Many pioneers today have made machine learning an integral part of their operations, like Facebook, Google, Uber, and so many more. Staying on top of such technology provides a competitive edge to many businesses too. The use of machine learning can also help to identify ventures that are profitable and risky as well.
Any company that relies heavily on data would have to associate itself with machine learning, and if it doesn’t, it’s only missing out. The use of machine learning helps to make data efficient and accurately analyzed for proper business models and actions.
Method Of Machine Learning
Machine learning is categorized by understanding the way its algorithm becomes more accurate while presenting predictions. These approaches can be broken down into supervised, unsupervised, semi-supervised, and reinforcement learning.
The approach that scientists end up with would typically have something to do with the type of data they aim to predict. Let me discuss these with you in detail below!
Supervised Method Of Machine Learning
In this category of machine learning, the data scientists will provide the algorithms with labeled data. This data will be used to identify variables in order to figure out the correlations and assess them properly. The input and the output will be specified correctly in supervised learning.
Supervised learning algorithms can be used for binary classification, regression modeling, and multi-class classification.
Unsupervised Machine Learning
While supervised learning works with labeled data, unsupervised learning works with unlabeled data. The algorithm is trained by looking at data that is unlabeled, so it scans through sets to find correlations and connections.
In unsupervised learning, the data for predictions and the recommendations that are provided as the output are both specified. Thus, unsupervised learning will best suit tasks like clustering, anomaly detection, dimensionality reduction, and association mining.
Semi-Supervised Machine Learning
In this approach, machine learning works with the two preexisting types of algorithms it has. For example, there could be labeled data, and data scientists could be working with that, but there is no hard and fast rule.
The model has complete freedom of exploration to work with anything and understand data sets in any way. For example, data scientists could use semi-supervised learning for machine translation, fraud detection, and labeling data.
Reinforcement Learning
In other cases, data scientists can also use reinforcement learning to teach a computer how to complete a step-by-step process with predetermined rules. For example, they could provide the computer with a task and give it positive or negative hints to help it figure out how to complete it.
The data scientist cannot alter or manipulate the actions of the algorithm. The computer will decide on its own what it has to do and which steps it has to take to reach the endpoint.
Reinforcement learning is used in platforms such as robotics, video gameplay, and resource management.
Real-World Examples Of Machine Learning: Speech Recognition
Machine learning works all around us. It’s more common than you think it is! Nowadays, it has just been so normalized that we don’t consider the breakthroughs it has made. Nevertheless, there are many ways through which you and I have been encountering machine learning in our lives.
Known as automatic speech recognition (ASR) in technical terms, computer speech recognition or speech-to-text feature is where natural language processing (NLP) is used to turn speech into written format. So even if you pick up your mobile phone and call for Siri, you are using speech recognition – all thanks to machine learning!
This feature is incorporated in many devices to operate them through voice. It gives accessibility and convenience to us! At least I wouldn’t have to text and drive now because I can ask my phone to do it for me.
Customer Service
Companies don’t even need human agents to cater to their customers anymore. Online chatbots have become quite famous now, providing direct answers to frequently asked questions and prompt questions. These bots can cross-sell products, offer suggestions to size, and give advice too!
For me, this has completely revolutionized the way online shopping works. Many e-commerce websites use virtual agents on their messaging apps to make this process easy for customers and efficient for their business.
According to Forbes, 57% of the executives believe that the growth of AI and machine learning improves customer experiences and provides ease of use while shopping online.
Social Media Sites
The most evident benefit of machine learning comes straight to social media sites. These websites analyze clients’ search history and then tailor recommendations to them based on it. So, for example, if I have been looking for a new hairdryer, my feed would surprisingly include one!
It’s starting to become the norm for retail services in the future to depend heavily on AI and machine learning as they intend to understand and learn people’s behavior.
You and I can now get our shopping experiences customized based on our own likes and dislikes. Like in the example of the hairdryer, when you search for more products online, the machines will be able to learn and understand your preferences, which will help businesses effectively market specific products to you.
Governmental And Healthcare Services
Government officials need to have accurate data for them to predict future scenarios and drastically changing situations. Not only this, but the government can also use machine learning to work on cybersecurity and provide information to cyber intelligence. Machine learning can even help with counterterrorism and make logistics efficient within the management.
Moreover, medical equipment like pulse rates is now used to monitor a patient’s behavior, along with wearable sensors to detect changes. In seconds, you could have the oxygen, sugar, and data based on sleeping patterns generated for doctors to understand their patients better.
That’s just for the simple things, though. Machine learning aids in detecting tumors through mammograms, and it can be used to detect skin cancer and so much more. For this reason, machine learning plays an essential part in healthcare as well.
Conclusion
In short, machine learning and artificial intelligence are going nowhere. Therefore, it’s only essential to know how to use machine learning and apply it to meet the challenges and goals of your business.
However, machine learning doesn’t provide you with guaranteed success – it’s more about how efficiently you use it!