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Machine learning is a technique for teaching artificial intelligence (AI) systems to carry out tasks by exposing them to data and permitting them to learn from it. It entails training a model on a dataset and then using the trained model for predicting or make a decision based on emerging inputs. Machine learning can be monitored, where the model is trained with labelled data and the desired output is supplied, or unmonitored, where the model is given no labelled data and must discover patterns and relationships in the data on its own.

 

Generative AI is a type of AI that focuses on creating unique content, such as text, images, or audio. This is accomplished by learning the trends and characteristics of a specific type of data and then utilizing that knowledge to generate new, clear examples.

 

One significant distinction between machine learning and generative AI is that the latter has been specifically intended to generate unique new content, whilst machine learning can be used for a variety of tasks such as prediction, categorization, and optimization. Furthermore, because it is producing content that may not have a clear “correct” output, generative AI frequently relies on unsupervised learning.

Why should we be involved with generative AI? Generative AI may be of interest or significance for a range of reasons, including:
Ultimately, generative AI has the potential to deliver significant advantages and advancements in a variety of industries and businesses. However, it is also critical to consider the technology’s potential risks and ethical implications, as well as to guarantee that it is developed and used appropriately.