Using AI in Advance science

Using AI in Advance science On a recent trip to the Accenture Dock facility in Dublin, they demonstrated an AI services -based tool for recommending new recipes, with new flavors after crunching thousands of existing recipes. Although the work is interesting, such an approach is particularly interesting when applied to innovation.

Rombombination is what is today growing inventions and looking at existing ideas and concepts in fresh and creative ways. Researchers are increasingly using AI-based tools to create the most accessible roads to explore.

For example, a recent paper from researchers at Carnegie Mellon University and the Hebrew University of Jerusalem highlights an AI solutions driven approach to patent mine databases and research mine databases, for reusable ideas in new problem-solving.

The key to this approach is to find similarities that integrate disparate methods and problems. Before training the deep learning algorithm, they used

crowdsourcing to understand how people create new similarities and my intellectual databases for potential innovations.

"After decades of effort, this is the first time anyone has gained computational traction on a problem like this," the authors say. "If you can search for similarities, you can accelerate innovation. If you can accelerate the rate of innovation, it will solve many other problems."

Suffice it to say that finding similarities is not an easy thing for a computer to do, because it is one of the things we humans do without always understanding how to do it. Previous attempts to automate the job required first creating a craftsmanship data structure, which is very time consuming and therefore not scalable.

The team turned to Mechanical Turk to recruit a large number of volunteers for similar products through the Product Innovation Website Quirky. They did this by looking at a range of products with similar purposes or ways of doing their job.

Each participant was asked to explain how they came to their conclusions. For example, they can describe the product in terms of both its purpose and its approach. This helps to provide connectors that can be used to create similarities.

These insights are provided in the algorithm before being loosely set in an additional dataset of product descriptions. Researchers have developed the algorithm with some similarities of its own. Interestingly, this proves to be good at work and goes beyond just surface similarities to find similarities between

different products. When tested, new product suggestions are rated as the most innovative ideas.

The team believes that this process can be easily used as part of the innovation process by allowing companies to uncover previously hidden connections between patents or research papers. Both datasets are enormous and growing exponentially. This is a problem that is well suited to autonomy practices, so it will be interesting to see what comes next for the team and their system.

Researchers applied a related approach to derive relationships between words and subjects before learning about 3.3 million scientific materials from papers published between 1922 and 2018 on Materials Science.

This approach was able to capture the basic knowledge of the nature and structure of chemicals and their properties, as well as some new chemical compounds similar to thermoelectric materials. Not examined before.

The team believes that this approach is an effective and effective way to explore new avenues for scientific research and therefore accelerates the advancement of knowledge in various fields.

P.VenkatVajradhar

Marketing Team,SEO Executive

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