What is Machine Learning in AI? #ArtificialIntelligence
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What is AI Machine Learning and How Does it Work?

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AI Dictionary Machine Learning header

Whether it is dystopian earth-shattering fear or the vision of a utopic imagined future that the term ‘AI’ provokes from you, the constant trending term of #ArtificialIntelligence has been a near inescapable topic in recent years. But what exactly is AI? And what do all these abbreviations and new tech vocabulary appearing with every explore page or news feed refresh mean?

Join The Beat Asia in demystifying the popular buzzwords in the artificial intelligence scene, so that you know exactly what you’re talking about without having to consult the binary Bard (now Gemini). Welcome to our AI dictionary series.

What is machine learning?

Under the umbrella of artificial intelligence (AI), machine learning is a computer science that replicates the process of taking in new information into algorithms. It focuses on using data and algorithms to help AI process new input and facts the way that humans learn and study. The computer system is fed with all sorts of data, which it then extrapolates predictions from, based on existing patterns the machine identifies.

How does machine learning work?

In the process of machine learning, data is iteratively fed through algorithms, allowing the system to learn and improve over time. This process is like practising a musical instrument for thousands of hours to master it. Three main components define the learning process: the decision process, and the error function, followed by model optimization.

Machine learning algorithms begin in the decision process by analysing input data, whether labelled or unlabelled, to make predictions or classifications. These predictions are based on identifying patterns within the data. An error function evaluates the accuracy of the model's predictions by comparing them to known examples. This helps measure the discrepancy between the model's predictions and the actual outcomes.

During optimization, the model adjusts its internal parameters (known as weights) to minimize the error between its predictions and the actual outcomes. This process involves continuously evaluating the model's performance and optimizing its parameters with each input and outcome, until greater accuracy is achieved.

What is machine learning useful for?

Machine learning has emerged as a powerful tool for tackling problems that were previously challenging or even impossible to solve using traditional programming methods. Tasks such as image recognition and language translation have greatly benefited from machine learning algorithms.

One key requirement for successful machine learning is a vast amount of data, which has only become more readily available in recent years due to the digitization of information. Additionally, advancements in computer hardware have made it faster, smaller, and more powerful, enabling more efficient processing of large datasets. This is why we now see large language models like ChatGPT and BingChat gaining popularity. This is all thanks to their ability to understand and generate human-like text, showcasing the potential of machine learning in various applications.

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