WHAT IS MACHINE LEARNING?
Machine Learning has been defined as an application of Artificial Intelligence. The technology enables the system to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and learn from it themselves. In simpler terms, Machine Learning involves the science of getting computers to perform tasks that human beings do naturally on a daily basis. Over a while, they are fed data and information in the form of observations and real-world interactions. The predictions that the system predicts could be as simple as determining if a piece of fruit in a photo is an orange or pineapple, spotting people crossing the road in front of a self-driven car or recognizing speech accurately enough to generate captions for a Netflix show. The fact that differentiates Machine Learning from traditional computer software is that a human developer hasn’t written any form of code that instructs the system to figure out a difference between an orange or pineapple. Instead, a machine-learning model has been taught how to seamlessly distinguish between fruits by being trained on a large amount of data.
Machine learning involves characteristics that replicate the pattern and behaviour that matches the way the human brain functions. The algorithms teach computers to identify the features of an object. For example when a computer is shown orange and determined that it is orange. The computer then further uses that information to classify the several characteristics of an orange. In the beginning, a computer might classify an orange with a round characteristic. It will then build a model that says that if something has a round shape then it is orange. But when a different fruit like an apple is introduced, the computer learns that if something is round and also red, it’s an apple.
Further, when a tomato is introduced, the computer will automatically modify its model based on new information. It will then assign a predictive value to each model, indicating the degree of confidence that an object is one thing over another just like how yellow is a more predictive value for a banana than red is for an apple.
ARE ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING THE SAME?
In the introductory phase of Artificial Intelligence, it was defined as a mechanism capable of performing a task that would typically require human intelligence. These systems will generate traits such as planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. Besides machine learning, there are various other programs used to build AI systems, including evolutionary computation, where algorithms undergo random deviations and combinations between generations in an attempt to develop optimal solutions, and expert systems. Computers are programmed with rules that allow them to mimic the behaviour of a human expert in a particular domain.
HOW DO MACHINES LEARN?
There are various approaches through which a machine can learn. This process involves using basic decision trees to clustering to layers of artificial neural networks. All of this depends on the task you’re trying to accomplish and the type and amount of data that you have available. This dynamic sees itself played out in applications as extreme as medical diagnostics or self-driving cars.
Research that has been conducted while working on real applications often drives progress in the field. This includes the trend to discover boundaries and limitations of existing methods. Researchers and developers working with domain experts and leveraging time and expertise to improve system performance.
Sometimes this also occurs by chance. We might consider model ensembles, or combinations of many learning algorithms to improve accuracy. In 2009 it was found that when researchers combined their learners with other team’s learners, it resulted in an improved recommendation algorithm.
Another point that was highlighted, in terms of application within business and elsewhere, is that machine learning is not just about automation. It has often been a misunderstood concept. If a person thinks this way then they are bound to miss the valuable insights that machines can provide that also come with great opportunities.
Machines that learn are useful to humans because, with all of their processing power, they’re able to quickly highlight or find patterns in big data that would have otherwise been skipped unknowingly by human beings. Machine learning is a tool that can be used to enhance the abilities of humans to solve problems and make informed inferences on a wide range of problems, from helping diagnose diseases to coming up with solutions for global climate change.
TYPES OF MACHINE LEARNING
Machine learning algorithms are often categorized as supervised or unsupervised.
- Supervised machine learning algorithms can be used on the past things learned to new data by applying labelled examples to predict future events. Beginning from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system will be able to provide targets for any new input only after sufficient training. The learning algorithm is also capable of comparing its output with the correct, intended output through which it detects errors and modifies the model accordingly.
- The unsupervised machine learning algorithms are utilised when the information that is used to train is neither classified nor labelled. Unsupervised learning studies describe how systems can produce a function to describe a hidden composition from unlabeled data. Though the system doesn’t comprehend the right output, it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
- Semi-supervised machine learning algorithms are considered as an amalgamation of both supervised and unsupervised learning. The technology uses both labelled and unlabeled data for training. It comprises a small aspect of labelled data and a large amount of the unlabeled data. The systems that use the Semi-supervised method have considerably improved the learning accuracy. This form of learning is chosen when the obtained data requires skilled and relevant resources to acquire.
- Reinforcement machine learning algorithms is a method that interacts with its surroundings by producing actions and discovers errors or rewards. The trial and error search and delayed reward are the most relevant features of reinforcement learning. This method enables machines and software agents to automatically ascertain the ideal behaviour within a particular context to maximize its performance. Simple reward feedback or a reinforcement signal is required for the agent to determine which action is the best.
USES OF MACHINE LEARNING
Machine learning systems have impacted our surroundings for years making it a foundation of the modern internet.
Machine-learning systems play an impactful role in helping you recommend products you may wish to buy on Amazon or probably help you decide a video that you may like watching on Netflix or Prime.
Every Google search involves multiple machine-learning systems, to understand the language in your query and personalizing results accordingly. For eg, a person looking for a bass for fishing may not be happy to view guitar results. This system also helps in keeping your email accounts spam and inbox sections clear of rogue messages by using phishing-recognition systems that use machine-learning trained models.
One of the most primitive successful examples of machine learning is its famous virtual assistants, such as Apple’s Siri, Amazon’s Alexa, the Google Assistant, and Microsoft Cortana.
Each of them relies majorly on machine learning to support their voice recognition and ability from understanding natural language to needing an immense corpus to draw upon to answer queries that humans ask.
But beyond these major technologies of machine learning that have a significant appearance in everyday lives, these systems are beginning to make use in just about every industry. This includes computer vision for driverless cars, drones and delivery robots, speech and language recognition and synthesis for chatbots and service robots, facial recognition for surveillance in countries like China, helping radiologists to pick out tumours in x-rays, aiding researchers in spotting genetic sequences related to diseases and identifying molecules that could lead to more effective drugs in healthcare, allowing for predictive maintenance on infrastructure by analyzing IoT sensor data, underpinning the computer vision that makes the cashier-less Amazon Go supermarket possible, offering reasonably accurate transcription and translation of speech for business meetings and much more.