Source: thenewstack.io
Can Self-Supervised Learning Teach AI Systems Common Sense?Category: Business, Data, Architecture, automation, artificial-intelligence
Imagine having an artificial intelligence (AI) system that is capable of mimicking human language and intelligence. Despite recent advancements in AI (especially in the fields of natural language processing (NLP) and computer vision applications), mastering the unique complexities of human language continues to be one of AI’s biggest challenges.
As companies continue to develop and deploy AI solutions to automate processes, solve complex problems and enhance customer experiences, many are realizing its shortcomings — including the amount of data required to train machine learning (ML) algorithms and the flexibility of these algorithms in understanding human language.
The self-supervised learning algorithm must then analyze visible data and enable it to predict the remaining hidden data.
While deep learning has made significant strides in recent years, it requires large amounts of data in order to have useful outputs.
As companies continue to develop and deploy AI solutions to automate processes, solve complex problems and enhance customer experiences, many are realizing its shortcomings — including the amount of data required to train machine learning (ML) algorithms and the flexibility of these algorithms in understanding human language.
The self-supervised learning algorithm must then analyze visible data and enable it to predict the remaining hidden data.
While deep learning has made significant strides in recent years, it requires large amounts of data in order to have useful outputs.
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