Understanding Artificial Intelligence Workloads
Some of the common AI-related workloads are as follows:
- Machine learning: Machine learning is the process by which we “teach” a computer model to make predictions and draw conclusions based on data. This process is often the foundation for an artificial intelligence (AI) system. Machine learning models are a type of AI-related computer program used to detect trends in data and extrapolate future outcomes. To build these models, developers use machine learning algorithms.
- Anomaly detection: The term “anomaly detection” is used to describe any method that seeks out and identifies data that doesn’t fit the norm. These outliers may indicate abnormal network activity, the presence of a faulty sensor, or the need to clean the data before further analysis.
- Computer vision: With the help of cameras, videos, and still images, computer vision allows software to perceive its surroundings.
- Natural language processing: Processing of natural language refers to the ability of a computer to understand spoken or written language.
- Knowledge mining: Knowledge mining is the process of extracting information from various data sources in order to build a knowledge repository that can be searched.
Now, let us understand them in a little bit more detail.
Machine Learning
The question then is how do machines learn?
The answer can be gleaned from the data. As we go about our lives in today’s world, we generate enormous amounts of data. This data can be used in a variety of ways. We produce a massive amount of information every day, from the texts, emails, and social media posts that we send to the photographs and videos that we take on our phones. Millions of sensors built into our homes, cars, cities, public transportation systems, and factories continue to produce more data.
Machine learning relies heavily on algorithms. These huge amounts of data are fed into machine learning algorithms so that they can learn from them. In general, the more data that is provided to a machine learning algorithm, the higher the level of accuracy that it achieves.