“ In recent news, we found that there is an AI-based tool for recommending new recipes, with new flavors after crunching thousands of existing recipes.

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Using AI in Advance science
venkat vajradhar
Nov 5, 2019 · 3 min read
“ In recent news, we found that there is an AI-based tool for recommending new
recipes, with new flavors after crunching thousands of existing recipes.
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.
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.
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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.
fter 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.
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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
“A

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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.
Arti cial Intelligence Science Machine Learning
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