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|Artificial Intelligence #66: How can we transform the learning of AI for Engineering and Life Sciences ?
From LinkedIn post
|From LinkedIn post|
In this edition, I will discuss something I have been planning for some time: “How can we transform the learning of AI for Engineering and Life Sciences”
I am speaking of traditional engineers ex: mechanical engineers, chemical engineers etc- not data engineers
I am grateful to many collaborators such as francesco ciriello, with whom I have bounced ideas off for years
I have been working on similar ideas in my teaching and fellowship, especially at Oxford, but I wanted to make this more global. I hope this will be a personal legacy. This is not associated with any educational institution I am associated with
I want to set this up as a social enterprise (UK based) based on open source. I am still figuring out the exact logistics of this - but in any case, I do not see it as a conventional start-up model - because I want to create a structure for longevity.
There are three parts to this (longish) post -
In Part one, I will elaborate the reason why AI for Engineering and Life Sciences is different; In Part two, we discuss why the problem is best modelled as a digital twin and in Part three, I will discuss more about the structure creating a global educational institute based on open source.
There are a number of reasons why AI for Engineering and life sciences is fundamentally different from other domains (such as financial services or insurance).
Here are five reasons:
1) Significance of domain knowledge
2) Significance of Physics based modelling
3) Importance of Small data
4) The need for complex simulations
5) The need for expert knowledge to complement algorithms
Each of these areas are complex and its not possible to explain in brief, but the below gives an idea. I have tried to link papers which can provide you with more information
For the Complete article click below
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