Calctech Innovations

What Is Machine Learning, And Where Do We Use It?

What exactly is machine learning? We can define machine learning as the branch of AI and computer science that focuses on using algorithms and data to emulate the way humans learn. Machine learning algorithms can use data mining and statistical methods to analyze, classify, predict, and come up with insights into big data.

If you’ve been hanging out with the Remotasks Community, chances are you’ve heard that our work in Remotasks involves helping teams and companies make better artificial intelligence (AI). That way, we can help create new real-world technologies such as the next self-driving car, better chatbots, or even “smarter” smart assistants. However, if you’re curious about the technical side of our Remotasks projects, it helps to know that a lot of our work has to do with machine learning

If you’ve been reading articles in the tech space, you might remember that machine learning involves some very technical engineering or computer science concepts. We’ll try to dissect some of these concepts here for you to get a comprehensive understanding of the basics of machine learning. And more importantly, why is it so vital for us to help facilitate machine learning in our AI projects. 

What is Machine Learning?

What exactly is machine learning? We can define machine learning as the branch of AI and computer science that focuses on using algorithms and data to emulate the way humans learn. Machine learning algorithms can use data mining and statistical methods to analyze, classify, predict, and come up with insights into big data. 

How does Machine Learning work?

At its core, folks from UC Berkeley haselaborated the overall machine learning processinto three distinct parts:

  • The Decision Element. A machine learning algorithm can create an estimate based on the kind of input data it receives. This input data can come in the form of both labeled and unlabeled data. Machine learning works this way because algorithms are almost always used to create a classification or a prediction. In Remotasks, our labeling tasks create labeled data that machine learning algorithms of our customers can use.
  • The Error Function. A machine learning algorithm has an error function that assesses the model’s accuracy. This function determines whether the decision process follows the algorithm’s purpose correctly or not.
  • The Model Optimization Process. A machine learning algorithm has a process that allows it to evaluate and optimize its current operations constantly. The algorithm can adjust its elements to ensure there’s only the slightest discrepancy between their estimates.

What are some Machine Learning methods?

Machine learning algorithms can accomplish their tasks in a multitude of ways. These methods differ in the kind of data they use and how they interpret these data sets. Here are the standard machine learning methods:

  • Supervised Machine Learning. Also known as supervised learning, Supervised Machine Learning uses labeled data to train its algorithms. Its primary purpose is to predict outcomes accurately, depending on the trends shown in the labeled data. 
  • Upon receiving input data, a supervised learning model will adjust its parameters to arrive at a model appropriate for the data. This cross-validation process ensures that the data won’t overfit or underfit the model.
  • As the name implies, data scientists usually help Supervised Machine Learning models analyze and assess the data points they receive. 
  • Specific methods used in supervised learning include neural networks, random forest, and logistic regression. 
  • Thanks to supervised learning, organizations in the real world can solve problems from a larger standpoint. These include separating spam in emails or identifying vehicles on the road for self-driving vehicles. 
  • Unsupervised Machine Learning. Also known as unsupervised learning, Unsupervised Machine Learning uses unlabeled data. Unlike Supervised Machine Learning that needs human assistance, algorithms that use Unsupervised Machine Learning don’t need human intervention.
  • Since unsupervised learning uses unlabeled data, the algorithm used can compare and contrast the information it receives. This process makes unsupervised learning ideal to identify data groupings and patterns. 
  • Specific methods used in unsupervised learning include neural networks and probabilistic clustering methods, among others. 
  • Companies can use unlabeled data for customer segmentation, cross-selling strategies, pattern recognition, and image recognition, thanks to unsupervised learning.
  • Semi-Supervised Machine Learning. Also known as semi-supervised learning, Semi-Supervised Machine Learning applies principles from both supervised and unsupervised learning to its algorithms. 
  • A semi-supervised learning algorithm uses a small set of labeled data to help classify a larger group of unlabeled data.
  • Thanks to semi-supervised learning, teams, and companies can solve various problems even if they don’t have enough labeled data. 
  • Reinforcement Machine Learning. Also known as reinforcement learning, Reinforcement Machine Learning has similarities to supervised learning. However, a Reinforcement Machine Learning algorithm doesn’t use sample data to receive training. Instead, the algorithm can learn through trial and error. 
  • As the name implies, successful outcomes in the trial and error will receive reinforcement from the algorithm. That way, the algorithm can create new policies or recommendations based on the reinforced results. 

Where do we use Machine Learning?

So basically, machine learning uses data to “train” itself and learn how to interpret new data all by itself. But with that in mind, why is machine learning relevant in real life? Perhaps the best way to explain the importance of machine learning is to learn about its many uses in our lives today. Here are some of the most important ways we’re relying on machine learning:

  • Self-Driving Vehicles. Specifically for us in Remotasks, our submissions can help advance the field of data science and its application in self-driving vehicles. Thanks to our tasks, we can help the AI in self-driving vehicles use machine learning to “remember” the way our Remotaskers identified objects on the road. With enough examples, AI can use machine learning to make their own assessments about new objects they encounter on the road. With this technology, we may be able to see self-driving cars in the future.  
  • Image Recognition. Have you ever posted a picture on a social media site and get shocked at how it can recognize you and your friends almost instantly? Thanks to machine learning and computer vision, devices and software can have recognition algorithms and image detection technology in order to identify various objects in a scene. 
  • Speech Recognition. Have you ever had a smart assistant understand something you’ve said over the microphone and get surprised with extremely helpful suggestions? We can thank machine learning for this, as its training data can also help it facilitate computer speech recognition. Also called “speech to text,” this is the kind of algorithm and programming that devices use to help us tell smart assistants what to do without typing them. And thanks to AI, these smart assistants can use their training data to find the best responses and suggestions to our queries. 
  • Spam and Malware Filtration. Have you ever wondered how your email gets to identify whether new messages are important or spam? Thanks to deep learning, email companies can use AI to properly sort and filter through our emails to identify spam and malware. Explicitly programmed protocols can help email AI filter according to headers and content, as well as permissions, general blacklists, and special rules. 
  • Product Recommendations. Have you ever freaked out when something you and your friends were talking about in chat suddenly appears as product recommendations in your timeline? This isn’t your social media websites doing tricks on you. Rather, this is deep learning in action. Courtesy of algorithms and our online browsing habits, various companies can provide meaningful suggestions for products and services that we might find interesting or adequate for our needs.
  • Stock Market Trading. Have you ever wondered how stock trading platforms can make “automatic” recommendations on how we should move our stocks? Thanks to linear regression and machine learning, a stock trading platform’s AI can use neural networks to predict stock market trends. That way, the software can assess the stock market’s movements and make “predictions” based on these ascertained patterns. 
  • Translation. Have you ever jotted down words in an online translator and wonder just how grammatically accurate its translations are? Thanks to machine learning, an online translator can make use of natural language processing in order to provide the most accurate translations of words, phrases, and sentences put together in software. This software can use things such as chunking, named entity recognition, and POS tagging in order to make its translations more accurate and semantically sensible. 
  • Chatbots. Have you ever stumbled upon a website and immediately notice a chatbot ready to converse with you regarding your queries? Thanks to machine learning, an AI can help chatbots retrieve information from parts of a website in order to answer and respond to queries that users might have. With the right programming, a chatbot can even learn how to retrieve information faster or assess queries in order to provide better answers to assist customers. 

Machine Learning in Remotasks: Human Insight Is Key

Wait, if our work in Remotasks involves “technical” machine learning, wouldn’t we all need advanced degrees and take advanced courses to work on them? Not necessarily! In Remotasks, we provide a machine learning model what is called training data

Notice how our tasks and projects tend to be “repetitive” in nature, where we follow a set of instructions but to different images and videos? Thanks to Remotaskers, who provide highly accurate submissions, our vast amounts of data can teach machine learning algorithms to become more efficient in their work. 

Think of it as providing an algorithm with many examples of “the right way” to do something - say, the correct label of a car. Thanks to hundreds of these examples, a machine learning algorithm knows how to properly label a vehicle and apply its new learnings to other examples. 

Join The Machine Learning Revolution In Remotasks!

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