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Google AI Develops a Language Model to Solve Quantitative Reasoning Problems: Minerva

 

Google AI Develops a Language Model to Solve Quantitative Reasoning Problems: Minerva

 

Go Digital

What happened? Google AI has developed a new deep learning language model called Minerva which could solve mathematical quantitative problems using step-by-step reasoning. In the recently published paper related to Minerva, researchers explained the development of this deep learning Model. They achieved a state-of-the-art solution by training a deep learning model on a large training dataset that contains quantitative reasoning with symbolic expressions. The final model, Minerva, could solve quantitative mathematical problems on STEM reasoning tasks.

 

Stay Human

Why is this important? And what will be the impact on people and our society? As digitalization advances, the demand for machines that can think and reason like humans increases. This is where artificial intelligence (AI) comes in – googling “How to do X” will soon become a thing of the past. With Minerva, Google has created an AI model that can not only read and comprehend text but also solve quantitative problems by breaking them down into simpler steps, just like humans. While this is still in its early stages, the potential applications of Minerva are far-reaching and can have a significant impact on society, especially in the field of education. For example, Minerva can be used to develop personalized learning programs for students or to create intelligent tutoring systems.

 

What’s next?

The next step for Google is to test Minerva on a wider range of problem types and domains. They also plan to open-source the model so that other researchers can build on their work. We can expect to see more developments in the field of AI-powered education in the near future – so watch this space!

 

Do you think Minerva will be able to replace human teachers? Let us know in the comments below!


Author: Christian Kromme

First Appeared On: Disruptive Inspiration Daily

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