Artificial Intelligence Today and Future of Machine Learning

Artificial Intelligence Today and Future of Machine Learning

The field of artificial intelligence is moving fast. Since the modern era of deep learning started at the 2012 ImageNet competition, only eight years past. Since then, progress in the field has been relentless and breathtaking.

Moreover, the breakneck pace only accelerates. The field of Artificial Intelligence will look quite different in five years from now than it does today. Nowadays, cutting-edge methods will become outdated. Ways that are on the fringes or nascent today will be mainstream.

Today, the dominant paradigm in the Artificial Intelligence world is supervised learning. AI models learn from datasets, in supervised learning, that humans have labeled and curated to predefined categories.

Over the past decade, supervised learning made remarkable progress in AI. Nevertheless, from autonomous vehicles to voice assistants, it has severe limitations.

The process of manually labeling thousands or even millions of data points might be cumbersome and enormously expensive. The humans must label data by hand before machine learning models can ingest it. Thus, it has become a significant bottleneck in Artificial Intelligence.

Artificial Intelligence

Supervised learning is a circumscribed and narrow form of learning at a deeper level. Supervised algorithms orient only to the categories and concepts that researchers have identified ahead of time, rather than absorb and explore all the latent information, implications, and relationships in each dataset.

Unsupervised learning is an approach to Artificial Intelligence in which algorithms learn from data without human-provided guidance or labels.

Many Artificial Intelligence leaders see unsupervised learning as the next great frontier in AI. Yann LeCun is the legend of AI. He said that the next AI revolution would not be supervised. Jitenda Malik is a UC Berkeley professor. He said that labels are the opium of the research of machine learning.

The system learns about some parts of the world based on other parts of the world, in a nutshell.

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